Journal articles on the topic 'Global optimisation solution'
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Khurana, M., and H. Winarto. "Development and validation of an efficient direct numerical optimisation approach for aerofoil shape design." Aeronautical Journal 114, no. 1160 (October 2010): 611–28. http://dx.doi.org/10.1017/s0001924000004097.
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Abstract Intelligent shape optimisation architecture is developed, validated and applied in the design of high-altitude long endurance aerofoil (HALE). The direct numeric optimisation (DNO) approach integrating a geometrical shape parameterisation model coupled to a validated flow solver and a population based search algorithm are applied in the design process. The merit of the DNO methodology is measured by computational time efficiency and feasibility of the optimal solution. Gradient based optimisers are not suitable for multi-modal solution topologies. Thus, a novel particle swarm optimiser with adaptive mutation (AM-PSO) is developed. The effect of applying the PARSEC and a modified variant of the original function, as a shape parameterisation model on the global optimal is verified. Optimisation efficiency is addressed by mapping the solution topology for HALE aerofoil designs and by computing the sensitivity of aerofoil shape variables on the objective function. Variables with minimal influence are identified and eliminated from shape optimisation simulations. Variable elimination has a negligible effect on the aerodynamics of the global optima, with a significant reduction in design iterations to convergence. A novel data-mining technique is further applied to verify the accuracy of the AM-PSO solutions. The post-processing analysis, to swarm optimisation solutions, indicates a hybrid optimisation methodology with the integration of global and local gradient based search methods, yields a true optima. The findings are consistent for single and multi-point designs.
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Kianifar, Mohammed Reza, and Felician Campean. "Global Optimisation of Car Front-End Geometry to Minimise Pedestrian Head Injury Levels." Proceedings of the Design Society: International Conference on Engineering Design 1, no. 1 (July 2019): 2873–82. http://dx.doi.org/10.1017/dsi.2019.294.
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AbstractThe paper presents a multidisciplinary design optimisation strategy for car front-end profile to minimise head injury criteria across pedestrian groups. A hybrid modelling strategy was used to simulate the car- pedestrian impact events, combining parametric modelling of front-car geometry with pedestrian models for the kinematics of crash impact. A space filling response surface modelling strategy was deployed to study the head injury response, with Optimal Latin Hypercube (OLH) Design of Experiments sampling and Kriging technique to fit response models. The study argues that the optimisation of the front-end car geometry for each of the individual pedestrian models, using evolutionary optimisation algorithms is not an effective global optimization strategy as the solutions are not acceptable for other pedestrian groups. Collaborative Optimisation (CO) multidisciplinary design optimisation architecture is introduced instead as a global optimisation strategy, and proven that it can enable simultaneous minimisation of head injury levels for all the pedestrian groups, delivering a global optimum solution which meets the safety requirements across the pedestrian groups.
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Shankland, K., T. Griffin, A. Markvardsen, W. David, and J. van de Streek. "Rapid structure solution using global optimisation and distributed computing." Acta Crystallographica Section A Foundations of Crystallography 61, a1 (August 23, 2005): c37. http://dx.doi.org/10.1107/s0108767305098417.
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Tunay, Mustafa, and Rahib Abiyev. "Improved Hypercube Optimisation Search Algorithm for Optimisation of High Dimensional Functions." Mathematical Problems in Engineering 2022 (April 22, 2022): 1–13. http://dx.doi.org/10.1155/2022/6872162.
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This paper proposes a stochastic search algorithm called improved hypercube optimisation search (HOS+) to find a better solution for optimisation problems. This algorithm is an improvement of the hypercube optimisation algorithm that includes initialization, displacement-shrink and searching area modules. The proposed algorithm has a new random parameters (RP) module that uses two control parameters in order to prevent premature convergence and slow finishing and improve the search accuracy considerable. Many optimisation problems can sometimes cause getting stuck into an interior local optimal solution. HOS+ algorithm that uses a random module can solve this problem and find the global optimal solution. A set of experiments were done in order to test the performance of the algorithm. At first, the performance of the proposed algorithm is tested using low and high dimensional benchmark functions. The simulation results indicated good convergence and much better performance at the lowest of iterations. The HOS+ algorithm is compared with other meta heuristic algorithms using the same benchmark functions on different dimensions. The comparative results indicated the superiority of the HOS+ algorithm in terms of obtaining the best optimal value and accelerating convergence solutions.
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Oppong, Stephen Opoku, Benjamin Ghansah, Evans Baidoo, Wilson Osafo Apeanti, and Daniel Danso Essel. "Experimental Study of Swarm Migration Algorithms on Stochastic and Global Optimisation Problem." International Journal of Distributed Artificial Intelligence 14, no. 1 (January 2022): 1–26. http://dx.doi.org/10.4018/ijdai.296389.
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Complex computational problems are occurrences in our daily lives that needs to be analysed effectively in order to make meaningful and informed decision. This study performs empirical analysis into the performance of six optimisation algorithms based on swarm intelligence on nine well known stochastic and global optimisation problems, with the aim of identifying a technique that returns an optimum output on some selected benchmark techniques. Extensive experiments show that, Multi-Swarm and Pigeon inspired optimisation algorithm outperformed Particle Swarm, Firefly and Evolutionary optimizations in both convergence speed and global solution. The algorithms adopted in this paper gives an indication of which algorithmic solution presents optimal results for a problem in terms of quality of performance, precision and efficiency.
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Purchina, Olga, Anna Poluyan, and Dmitry Fugarov. "The algorithm development based on the immune search for solving unclear problems to detect the optical flow with minimal cost." E3S Web of Conferences 258 (2021): 06052. http://dx.doi.org/10.1051/e3sconf/202125806052.
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The main aim of the research is the development of effective methods and algorithms based on the hybrid principles functioning of the immune system and evolutionary search to determine a global optimal solution to optimisation problems. Artificial immune algorithms are characterised as diverse ones, extremely reliable and implicitly parallel. The integration of modified evolutionary algorithms and immune algorithms is proposed to be used for the solution of above problem. There is no exact method for the efficient solving unclear optimisation problems within the polynomial time. However, by determining close to optimal solutions within the reasonable time, the hybrid immune algorithm (HIA) is capable to offer multiple solutions, which provide compromise between several goals. Quite few researches have been focused on the optimisation of more than one goal and even fewer used to have distinctly considered diversity of solutions that plays fundamental role in good performance of any evolutionary calculation method.
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Amine, Khalil. "Multiobjective Simulated Annealing: Principles and Algorithm Variants." Advances in Operations Research 2019 (May 23, 2019): 1–13. http://dx.doi.org/10.1155/2019/8134674.
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Simulated annealing is a stochastic local search method, initially introduced for global combinatorial mono-objective optimisation problems, allowing gradual convergence to a near-optimal solution. An extended version for multiobjective optimisation has been introduced to allow a construction of near-Pareto optimal solutions by means of an archive that catches nondominated solutions while exploring the feasible domain. Although simulated annealing provides a balance between the exploration and the exploitation, multiobjective optimisation problems require a special design to achieve this balance due to many factors including the number of objective functions. Accordingly, many variants of multiobjective simulated annealing have been introduced in the literature. This paper reviews the state of the art of simulated annealing algorithm with a focus upon multiobjective optimisation field.
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Azmi, Azralmukmin, Samila Mat Zali, Mohd Noor Abdullah, Mohammad Faridun Naim Tajuddin, and Siti Rafidah Abdul Rahim. "The performance of COR optimisation using different constraint handling strategies to solve ELD." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 2 (February 1, 2020): 680. http://dx.doi.org/10.11591/ijeecs.v17.i2.pp680-688.
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This research compares the performance of Competitive Over Resources (COR) optimisation method using a different type of constraint handling strategy to solve the economic load dispatch (ELD) problem. Previously, most research focused on proposing various optimisation techniques using the Penalty Factor Strategy (PFS) to search for a better global optimum. The issue using the penalty factor is that it is difficult to find the correct tune of constant value that influences the algorithm to find the solution. The other technique is using Feasible Solution Strategy (FSS), the idea of which is to locate the infeasible particle to the feasible solution and avoid being trapped by the unsuccessful condition of constraint. This paper investigates the performance of PFS and FSS on the COR optimisation method for solving ELD. Both strategies have been tested on two standard test systems to compare the performance in terms of a global solution, robustness and convergence. The simulation shows that FSS is a better solution compared to PFS.
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Ali, Ahmed F., and Mohamed A. Tawhid. "Direct Gravitational Search Algorithm for Global Optimisation Problems." East Asian Journal on Applied Mathematics 6, no. 3 (July 20, 2016): 290–313. http://dx.doi.org/10.4208/eajam.030915.210416a.
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AbstractA gravitational search algorithm (GSA) is a meta-heuristic development that is modelled on the Newtonian law of gravity and mass interaction. Here we propose a new hybrid algorithm called the Direct Gravitational Search Algorithm (DGSA), which combines a GSA that can perform a wide exploration and deep exploitation with the Nelder-Mead method, as a promising direct method capable of an intensification search. The main drawback of a meta-heuristic algorithm is slow convergence, but in our DGSA the standard GSA is run for a number of iterations before the best solution obtained is passed to the Nelder-Mead method to refine it and avoid running iterations that provide negligible further improvement. We test the DGSA on 7 benchmark integer functions and 10 benchmark minimax functions to compare the performance against 9 other algorithms, and the numerical results show the optimal or near optimal solution is obtained faster.
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Makki, Mohammed, Milad Showkatbakhsh, Aiman Tabony, and Michael Weinstock. "Evolutionary algorithms for generating urban morphology: Variations and multiple objectives." International Journal of Architectural Computing 17, no. 1 (May 29, 2018): 5–35. http://dx.doi.org/10.1177/1478077118777236.
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Morphological variation of urban tissues, which evolve through the optimisation of multiple conflicting objectives, benefit significantly from the application of robust metaheuristic search processes that utilise search and optimisation mechanisms for design problems that have no clear single optimal solution, as well as a solution search space that is too large for a ‘brute-force’ manual approach. As such, and within the context of the experiments presented within this article, the rapidly changing environmental, climatic and demographic global conditions necessitates the utilisation of stochastic search processes for generating design solutions that optimise for multiple conflicting objectives by means of controlled and directed morphological variation within the urban fabric.
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Figueiredo, António, Romeu Vicente, Rui Oliveira, Fernanda Rodrigues, and António Samagaio. "Multiscale Modelling Approach Targeting Optimisation of PCM into Constructive Solutions for Overheating Mitigation in Buildings." Applied Sciences 10, no. 22 (November 12, 2020): 8009. http://dx.doi.org/10.3390/app10228009.
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Nowadays, the rising gap between the global energy supply and demand is a well-known circumstance in society. Exploring the solution to invert this tendency leads to several different scenarios of energy demand saving strategies that can be improved using phase change materials (PCM), especially in cold-formed steel-framed buildings. The present research reports the overheating (indoor air temperature above 26 °C expressed as an annualized percentage rate) reduction in south-oriented compartments and energy performance of a detached house located in the Aveiro region, in Portugal. An optimisation study was performed incorporating different phase change materials (PCMs) solutions and their position in the exterior envelope focusing overheating rate reduction and heating demand. The optimisations were managed by using a hybrid evolutionary algorithm coupled with EnergyPlus® simulation software. The overheating risk was reduced by up to 24% in the cooling season, for the case of the building compartments with south orientation. Thus, the use of construction solutions using PCMs with different melting temperatures revealed to be a good strategy to maximise PCM efficiency as a passive solution.
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Tesch, Krzysztof, and Katarzyna Kaczorowska-Ditrich. "The Discrete-Continuous, Global Optimisation of an Axial Flow Blood Pump." Flow, Turbulence and Combustion 104, no. 4 (November 20, 2019): 777–93. http://dx.doi.org/10.1007/s10494-019-00100-5.
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AbstractThis paper presents the results of the discrete-continuous optimisation of an axial flow blood pump. Differential evolution (DE) is used as a global optimisation method in order to localise the optimal solution in a relatively short time. The whole optimisation process is fully automated. This also applies to geometry modelling. Numerical simulations of the flow inside the pump are performed by means of the Reynolds-Average Navier-Stokes approach. All equations are discretised by means of the finite volume method, and the corresponding algebraic equation systems are solved by the open source software for CFD, namely OpenFOAM. Finally, the optimisation results are presented and discussed. The objective function to be maximised is simply pressure increase. The higher pressure increase the lower angular velocities required. This makes it possible to minimise the effect of haemolysis because it is mainly caused by high shear stresses which are related, among others, to angular velocities.
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Jang, Ilsik, Seeun Oh, Yumi Kim, Changhyup Park, and Hyunjeong Kang. "Well-placement optimisation using sequential artificial neural networks." Energy Exploration & Exploitation 36, no. 3 (September 6, 2017): 433–49. http://dx.doi.org/10.1177/0144598717729490.
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In this study, a new algorithm is proposed by employing artificial neural networks in a sequential manner, termed the sequential artificial neural network, to obtain a global solution for optimizing the drilling location of oil or gas reservoirs. The developed sequential artificial neural network is used to successively narrow the search space to efficiently obtain the global solution. When training each artificial neural network, pre-defined amount of data within the new search space are added to the training dataset to improve the estimation performance. When the size of the search space meets a stopping criterion, reservoir simulations are performed for data in the search space, and a global solution is determined among the simulation results. The proposed method was applied to optimise a horizontal well placement in a coalbed methane reservoir. The results show a superior performance in optimisation while significantly reducing the number of simulations compared to the particle-swarm optimisation algorithm.
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Basińska, Małgorzata, Dobrosława Kaczorek, and Halina Koczyk. "Building Thermo-Modernisation Solution Based on the Multi-Objective Optimisation Method." Energies 13, no. 6 (March 19, 2020): 1433. http://dx.doi.org/10.3390/en13061433.
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This study presents a multi-objective optimisation of building thermo-modernisation for multi-family buildings. The applied model has considered alternative solutions for insulation materials, with different thicknesses and different types of windows. The weighted sum method was applied to find a solution considering the minimisation of global cost, primary energy ratio and CO2 emissions. The solutions were compared for a building equipped with natural ventilation, and with mechanical supply—exhaust ventilation. In reference to the two considered types of ventilation, we analysed how the modification of an insulation thickness, its type and the type of installed windows, can be converted into individual evaluation criteria. The weights of the considered criteria were changed; however, this had no influence on the optimal solution. If the aim is to achieve the standards of zero-energy buildings, natural ventilation cannot be applied, despite the high value of thermal insulation of the building envelopes. Alternative solutions exist for buildings with natural ventilation and mechanical ventilation with heat recovery, where the primary energy ratio is the same for both, but the global costs are different. The additional energy and environmental input for the production of materials and elements to be replaced are insignificant in comparison to the savings brought about by thermo-modernisation.
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Velásquez H., Juan David. "R-chaosoptimiser: an optimiser for unconstrained global nonlinear optimisation written in R language for statistical computing." Ingeniería e Investigación 31, no. 3 (September 1, 2011): 50–55. http://dx.doi.org/10.15446/ing.investig.v31n3.26383.
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This paper discusses using R-chaosoptimiser, an R language package for nonlinear optimisation based on gradient techniques and chaos optimisation algorithms. Its implementation was based on three building blocks which could be executed alone or in combination: the first carrier wave algorithm, the chaos-based cyclical coordinate search method and the second wave carrier algorithm. Using chaos optimisation algorithms allows the tool to break away from local optimal points and converge towards an overall optimum inside a predefined search domain. Within the previous components, a user would be specifying the BFGS algorithm for refining the current best solution. Using the BFGS algorithm is not mandatory, so that its implementation was able to optimise problems having objective function discontinuities. However, the BFGS algorithm is a powerful local search method, meaning that it is used to exploit current knowledge about an objective function for improving a current solution; an explanatory example is presented.
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Young, Brian. "Analysis and optimisation of looped water distribution networks." Journal of the Australian Mathematical Society. Series B. Applied Mathematics 41, no. 4 (April 2000): 508–26. http://dx.doi.org/10.1017/s0334270000011796.
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AbstractA three stage procedure for the analysis and least-cost design of looped water distribution networks is considered in this paper. The first stage detects spanning trees and identifies the true global optimum for the system. The second stage determines hydraulically feasible pipe flows for the network by the numerical solution of a set of non-linear simultaneous equations and shows that these solutions are contained within closed convex polygonal regions in the solution space bounded by singularities resulting from zero flows in individual pipes. Ideal pipe diameters, consistent with the pipe flows and the constant velocity constraint adopted to prevent the system degenerating into a branched network, are selected and costed. It is found that the most favourable optimum is in the vicinity of a vertex in the solution space corresponding to the minimum spanning tree. In the third stage, commercial pipes are specified and the design finalised. Upper bound formulae for the number of spanning trees and hydraulically feasible solutions in a network have also been proposed. The treatment of large networks by a heuristic procedure is described which is shown to result in significant economies compared with designs obtained by non-linear programming.
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Sun, J., J. M. Garibaldi, N. Krasnogor, and Q. Zhang. "An Intelligent Multi-Restart Memetic Algorithm for Box Constrained Global Optimisation." Evolutionary Computation 21, no. 1 (March 2013): 107–47. http://dx.doi.org/10.1162/evco_a_00068.
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In this paper, we propose a multi-restart memetic algorithm framework for box constrained global continuous optimisation. In this framework, an evolutionary algorithm (EA) and a local optimizer are employed as separated building blocks. The EA is used to explore the search space for very promising solutions (e.g., solutions in the attraction basin of the global optimum) through its exploration capability and previous EA search history, and local search is used to improve these promising solutions to local optima. An estimation of distribution algorithm (EDA) combined with a derivative free local optimizer, called NEWUOA (M. Powell, Developments of NEWUOA for minimization without derivatives. Journal of Numerical Analysis, 28:649–664, 2008 ), is developed based on this framework and empirically compared with several well-known EAs on a set of 40 commonly used test functions. The main components of the specific algorithm include: (1) an adaptive multivariate probability model, (2) a multiple sampling strategy, (3) decoupling of the hybridisation strategy, and (4) a restart mechanism. The adaptive multivariate probability model and multiple sampling strategy are designed to enhance the exploration capability. The restart mechanism attempts to make the search escape from local optima, resorting to previous search history. Comparison results show that the algorithm is comparable with the best known EAs, including the winner of the 2005 IEEE Congress on Evolutionary Computation (CEC2005), and significantly better than the others in terms of both the solution quality and computational cost.
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Panagant, Natee, and Sujin Bureerat. "Solving Partial Differential Equations Using a New Differential Evolution Algorithm." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/747490.
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This paper proposes an alternative meshless approach to solve partial differential equations (PDEs). With a global approximate function being defined, a partial differential equation problem is converted into an optimisation problem with equality constraints from PDE boundary conditions. An evolutionary algorithm (EA) is employed to search for the optimum solution. For this approach, the most difficult task is the low convergence rate of EA which consequently results in poor PDE solution approximation. However, its attractiveness remains due to the nature of a soft computing technique in EA. The algorithm can be used to tackle almost any kind of optimisation problem with simple evolutionary operation, which means it is mathematically simpler to use. A new efficient differential evolution (DE) is presented and used to solve a number of the partial differential equations. The results obtained are illustrated and compared with exact solutions. It is shown that the proposed method has a potential to be a future meshless tool provided that the search performance of EA is greatly enhanced.
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Boulekchour, Mohammed, Nabil Aouf, and Mark Richardson. "Robust L∞ convex optimisation for UAVs cooperative motion estimation." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 231, no. 11 (November 13, 2016): 2006–31. http://dx.doi.org/10.1177/0954410016675889.
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In this paper, a system for real-time cooperative monocular visual motion estimation with multiple unmanned aerial vehicles is proposed. Distributing the system across a network of vehicles allows for efficient processing in terms of both computational time and estimation accuracy. The resulting global cooperative motion estimation employs state-of-the-art approaches for optimisation, individual motion estimation and registration. Three-view geometry algorithms are developed within a convex optimisation framework on-board the monocular vision systems of each vehicle. In the presented novel distributed cooperative strategy a visual loop-closure module is deployed to detect any simultaneously overlapping fields of view of two or more of the vehicles. A positive feedback from the latter module triggers the collaborative motion estimation algorithm between any vehicles involved in this loop-closure. This scenario creates a flexible stereo set-up which jointly optimises the motion estimates of all vehicles in the cooperative scheme. Prior to that, vehicle-to-vehicle relative pose estimates are recovered with a novel robust registration solution in a global optimisation framework. Furthermore, as a complementary solution, a robust non-linear H∞filter is designed to fuse measurements from the vehicles’ on-board inertial sensors with the visual estimates. The proposed cooperative navigation solution has been validated on real-world data, using two unmanned aerial vehicles equipped with monocular vision systems.
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Paszek, S. "Use of Pareto optimisation for tuning power system stabilizers." Bulletin of the Polish Academy of Sciences: Technical Sciences 60, no. 1 (March 1, 2012): 125–31. http://dx.doi.org/10.2478/v10175-012-0018-5.
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Use of Pareto optimisation for tuning power system stabilizers The paper presents a method for determining sets of Pareto optimal solutions (compromise sets) - parameter values of PSS3B system stabilizers working in a multi-machine power system - when optimising different multidimensional criteria. These criteria are determined for concrete disturbances when taking into account transient waveforms of the instantaneous power, angular speed and terminal voltage of generators in one, chosen generating unit or in all units of the system analysed. The application of multi-criteria methods allows taking into account the optimisation process of power system stabilizer (PSS) parameters, many sometimes contradictory requirements (criteria) without losing ability to reach the optimal solution. A choice of the compromise solution can be made by assuming the values of the weighting coefficients associated with particular components of the vector criterion and determining the equivalent, global criterion. A change of the values of those weighting coefficients in the equivalent criterion does not require, in the case of the Pareto optimization, carrying out repeated calculations.
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Xie, Hailun, Li Zhang, Chee Peng Lim, Yonghong Yu, and Han Liu. "Feature Selection Using Enhanced Particle Swarm Optimisation for Classification Models." Sensors 21, no. 5 (March 5, 2021): 1816. http://dx.doi.org/10.3390/s21051816.
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In this research, we propose two Particle Swarm Optimisation (PSO) variants to undertake feature selection tasks. The aim is to overcome two major shortcomings of the original PSO model, i.e., premature convergence and weak exploitation around the near optimal solutions. The first proposed PSO variant incorporates four key operations, including a modified PSO operation with rectified personal and global best signals, spiral search based local exploitation, Gaussian distribution-based swarm leader enhancement, and mirroring and mutation operations for worst solution improvement. The second proposed PSO model enhances the first one through four new strategies, i.e., an adaptive exemplar breeding mechanism incorporating multiple optimal signals, nonlinear function oriented search coefficients, exponential and scattering schemes for swarm leader, and worst solution enhancement, respectively. In comparison with a set of 15 classical and advanced search methods, the proposed models illustrate statistical superiority for discriminative feature selection for a total of 13 data sets.
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Skalna, Iwona, and Andrzej Pownuk. "A global optimisation method for computing interval hull solution for parametric linear systems." International Journal of Reliability and Safety 3, no. 1/2/3 (2009): 235. http://dx.doi.org/10.1504/ijrs.2009.026843.
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Kaidassov, Zhetkerbay, and Zhailan S. Tutkusheva. "Algorithm for Calculating the Global Minimum of a Smooth Function of Several Variables." Mathematical Modelling of Engineering Problems 8, no. 4 (August 31, 2021): 591–96. http://dx.doi.org/10.18280/mmep.080412.
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Every year the interest of theorists and practitioners in optimisation problems is growing, and extreme problems are found in all branches of science. Local optimisation problems are well studied and there are constructive methods for their solution. However, global optimisation problems do not meet the requirements in practice; therefore, the search for the global minimum remains one of the major challenges for computational and applied mathematics. This study discusses the search for the global minimum of multidimensional and multiextremal problems with high precision. Mechanical quadrature formulas, that is, the formulas for approximate integration were applied to calculate the integrals. Of all the approximate integration formulas, the Sobolev lattice cubature formulas with a regular boundary layer were chosen. In multidimensional examples, the Sobolev formulas are optimal. Computational experiments were carried out in the most popular C++ programming language. Based on the computational experiments, a new algorithm was proposed. In three-dimensional space, the calculations of the global minimum have been described using specific examples. Computational experiments show that the proposed algorithm works for multiextremal problems with the same amount of time as for convex ones.
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R., Gohilai, and Prashanth K. "Artificial Intelligence Based MPPT Techniques of Photo Voltaic System." International Journal of Innovative Research in Advanced Engineering 10, no. 07 (July 31, 2023): 518–22. http://dx.doi.org/10.26562/ijirae.2023.v1007.13.
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In this paper, Particle Swarm Optimisation (PSO) investigates the global optimal solution by taking advantage of the memory of the particle and the swarm. PSO has evolved into one of the most significant Swam Intelligence techniques and Evolutionary Computation algorithms due to its characteristics of low constraint on the continuity of goal function and joint of search space, and capacity to adapt to dynamic environments. The development of algorithms over the years is then discussed, along with applications in multi-objective optimisation, neural networks, electronics, etc. The remaining issues and potential prospects for PSO research are then examined. One of the concepts of swarm intelligence introduced in the field of computing and artificial intelligence is particle swarm optimisation (PSO). PSO is a novel collective and distributed intelligent paradigm for problem solving, primarily in the field of optimisation, without centralized control or the provision of a global model. In this work, the basic PSO, its improvements, its applications to various systems, including electric power systems, and its premature convergence as well as its combination with other intelligent algorithms to enhance search capacity and shorten the time required to exit local optimums are all thoroughly reviewed.
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Afanasyev, Andrey, Anna Andreeva, and Anna Chernova. "Numerical optimisation of CO<sub>2</sub> flooding using a hierarchy of reservoir models." Advances in Geosciences 56 (September 16, 2021): 19–31. http://dx.doi.org/10.5194/adgeo-56-19-2021.
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Abstract. We present a method for accelerated optimisation of CO2 injection into petroleum reservoirs. The optimisation assumes maximisation of the net present value by coupling reservoir models with the calculation of cash flows. The proposed method is based on the construction of a hierarchy of compositional reservoir models of increasing complexity. We show that in dimensionless volumes, the optimal water and gas slugs are very close for the 1-D and 2-D areal reservoir models of the water-alternating-gas (WAG) process. Therefore, the solution to the 1-D optimisation problem gives a good approximation of the solution to the 2-D problem. The proposed method is designed by using this observation. It employs a larger number of less computationally expensive 1-D compositional simulations to obtain a good initial guess for the injection volumes in much more expensive 2-D simulations. We suggest using the non-gradient optimisation algorithms for the coarse models on low levels of the hierarchy to guarantee convergence to the global maximum of the net present value. Then, we switch to the gradient methods only on the upper levels. We give examples of the algorithm application for optimisation of different WAG strategies and discuss its performance. We propose that 1-D compositional simulations can be efficient for optimising areal CO2 flooding patterns.
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Batilović, Mehmed, Radovan Đurović, Zoran Sušić, Željko Kanović, and Zoran Cekić. "Robust Estimation of Deformation from Observation Differences Using Some Evolutionary Optimisation Algorithms." Sensors 22, no. 1 (December 27, 2021): 159. http://dx.doi.org/10.3390/s22010159.
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In this paper, an original modification of the generalised robust estimation of deformation from observation differences (GREDOD) method is presented with the application of two evolutionary optimisation algorithms, the genetic algorithm (GA) and generalised particle swarm optimisation (GPSO), in the procedure of robust estimation of the displacement vector. The iterative reweighted least-squares (IRLS) method is traditionally used to perform robust estimation of the displacement vector, i.e., to determine the optimal datum solution of the displacement vector. In order to overcome the main flaw of the IRLS method, namely, the inability to determine the global optimal datum solution of the displacement vector if displaced points appear in the set of datum network points, the application of the GA and GPSO algorithms, which are powerful global optimisation techniques, is proposed for the robust estimation of the displacement vector. A thorough and comprehensive experimental analysis of the proposed modification of the GREDOD method was conducted based on Monte Carlo simulations with the application of the mean success rate (MSR). A comparative analysis of the traditional approach using IRLS, the proposed modification based on the GA and GPSO algorithms and one recent modification of the iterative weighted similarity transformation (IWST) method based on evolutionary optimisation techniques is also presented. The obtained results confirmed the quality and practical usefulness of the presented modification of the GREDOD method, since it increased the overall efficiency by about 18% and can provide more reliable results for projects dealing with the deformation analysis of engineering facilities and parts of the Earth’s crust surface.
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Duarte, Grasiele Regina, Afonso Celso de Castro Lemonge, and Leonardo Goliatt da Fonseca. "An algorithm inspired by social spiders for truss optimisation problems." Engineering Computations 34, no. 8 (November 6, 2017): 2767–92. http://dx.doi.org/10.1108/ec-12-2016-0447.
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Purpose The purpose of this paper is to evaluate the performance of social spider algorithm (SSA) to solve constrained structural optimisation problems and to compare its results with others algorithms such as genetic algorithm, particle swarm optimisation, differential evolution and artificial bee colony. Design/methodology/approach To handle the constraints of the problems, this paper couples to the SSA an efficient selection criteria proposed in the literature that promotes a tournament between two solutions in which the feasible or less infeasible solution wins. The discussion is conducted on the competitiveness of the SSA with other algorithms as well as its performance in constrained problems. Findings SSA is a population algorithm proposed for global optimisation inspired by the foraging of social spiders. A spider moves on the web towards the position of the prey, guided by vibrations that occur around it in different frequencies. The SSA was proposed to solve problems without constraints, but these are present in most of practical problems. This paper evaluates the performance of SSA to solve constrained structural optimisation problems and compares its results with other algorithms such as genetic algorithm, particle swarm optimisation, differential evolution and artificial bee colony. Research limitations/implications The proposed algorithm has no limitations, and it can be applied in other classes of constrained optimisation problems. Practical implications This paper evaluated the proposed algorithm with a benchmark of constrained structural optimisation problems intensely used in the literature, but it can be applied to solve real constrained optimisation problems in engineering and others areas. Originality/value This is the first paper to evaluate the performance of SSA in constrained problems and to compare its results with other algorithms traditional in the literature.
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Feng, Kailun, Weizhuo Lu, Shiwei Chen, and Yaowu Wang. "An Integrated Environment–Cost–Time Optimisation Method for Construction Contractors Considering Global Warming." Sustainability 10, no. 11 (November 14, 2018): 4207. http://dx.doi.org/10.3390/su10114207.
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Construction contractors play a vital role in reducing the environmental impacts during the construction phase. To mitigate these impacts, contractors need to develop environmentally friendly plans that have optimal equipment, materials and labour configurations. However, construction plans with optimal environment may negatively affect the project cost and duration, resulting in dilemma for contractors on adopting low impacts plans. Moreover, the enumeration method that is usually used needs to assess and compare the performances of a great deal of scenarios, which seems to be time consuming for complicated projects with numerous scenarios. This study therefore developed an integrated method to efficiently provide contractors with plans having optimal environment–cost–time performances. Discrete-event simulation (DES) and particle swarm optimisation algorithms (PSO) are integrated through an iterative loop, which remarkably reduces the efforts on optimal scenarios searching. In the integrated method, the simulation module can model the construction equipment and materials consumption; the assessment module can evaluate multi-objective performances; and the optimisation module fast converges on optimal solutions. A prototype is developed and implemented in a hotel building construction. Results show that the proposed method greatly reduced the times of simulation compared with enumeration method. It provides the contractor with a trade-off solution that can average reduce 26.9% of environmental impact, 19.7% of construction cost, and 10.2% of project duration. The method provides contractors with an efficient and practical decision support tool for environmentally friendly planning.
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Paradis, Gregory, Mathieu Bouchard, Luc LeBel, and Sophie D’Amours. "A bi-level model formulation for the distributed wood supply planning problem." Canadian Journal of Forest Research 48, no. 2 (February 2018): 160–71. http://dx.doi.org/10.1139/cjfr-2017-0240.
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The classic wood supply optimisation model maximises even-flow harvest levels and implicitly assumes infinite fibre demand. In many jurisdictions, this modelling assumption is a poor fit for actual fibre consumption, which is typically a subset of total fibre allocation. Failure of the model to anticipate this bias in industrial wood fibre consumption has been linked to increased risk of wood supply failure. In particular, we examine the distributed wood supply planning problem where the roles of forest owner and fibre consumer are played by independent agents. We use game theory to frame interactions between public forest land managers and industrial fibre consumers. We show that the distributed wood supply planning problem can be modelled more accurately using a bi-level formulation and present an extension of the classic wood supply optimisation model that explicitly anticipates industrial fibre consumption behaviour. We present a solution methodology that can solve a convex special case of the problem to global optimality and compare output and solution times of classic and bi-level model formulations using a computational experiment on a realistic dataset. Experimental results show that the bi-level formulation can mitigate risk of wood supply failure.
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Wang, Bangjia, Xuanyu Jie, and Jiayu Chen. "Research On Replenishment Strategy Of Vegetable Commodities Based On Linear Programming And Multi-Objective Particle Swarm Optimisation Algorithm." Journal of Education, Humanities and Social Sciences 25 (January 26, 2024): 198–205. http://dx.doi.org/10.54097/7rkc4637.
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Because vegetable commodities are characterised by shorter freshness periods and diminishing character, they need to be replenished and priced on a daily basis based on historical sales and demand. In order to determine the optimal order quantity and sales price of vegetable commodities, this paper uses the least squares method to establish a regression model to obtain the relationship between sales volume and cost-plus pricing equation and verify the fitting effect, and then the total sales volume of the category obtained from the regression model and the cost-plus pricing equation combined with the derivation of the profit, the establishment of the optimisation model, the solution to obtain the replenishment volume and pricing strategy that will maximise the profitability of the superstore in the coming week. Considering the restricted situation of commodity categories, this paper chooses 0-1 integer planning,and adopts the multi-objective particle swarm optimisation algorithm to solve the optimisation model, and then iteratively calculates to get the global optimal solution, so as to find out the daily replenishment quantity of a single product and pricing strategy which will make the supercommercial supermarket have the largest revenue on 1 July.
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Zhang, Yu, Qing He, Liu Yang, and Chenghan Liu. "An Improved Tunicate Swarm Algorithm for Solving the MultiObjective Optimisation Problem of Airport Gate Assignments." Applied Sciences 12, no. 16 (August 17, 2022): 8203. http://dx.doi.org/10.3390/app12168203.
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Airport gate assignment is a critical issue in airport operations management. However, limited airport parking spaces and rising fuel costs have caused serious issues with gate assignment. In this paper, an effective multiobjective optimisation model for gate assignment is proposed, with the optimisation objectives of minimising real-time flight conflicts, maximising the boarding bridge rate, and minimising aircraft taxiing fuel consumption. An improved tunicate swarm algorithm based on cosine mutation and adaptive grouping (CG-TSA) is proposed to solve the airport gate assignment problem. First, the Halton sequence is used to initialise the agent positions to improve the initial traversal and allocation efficiency of the algorithm. Second, the population as a whole is adaptively divided into dominant and inferior groups based on fitness values. To improve the searchability of the TSA for the dominant group, an arithmetic optimisation strategy based on ideas related to the arithmetic optimisation algorithm (AOA) is proposed. For the inferior group, the global optimal solution is used to guide the update to improve the convergence speed of the algorithm. Finally, the cosine mutation strategy is introduced to perturb the optimal solution and prevent the target from falling into the local extrema as a way to efficiently and reasonably allocate airport gates. The CG-TSA is validated using benchmark test functions, Wilcoxon rank-sum detection, and CEC2017 complex test functions and the results show that the improved algorithm has good optimality-seeking ability and shows high robustness in the multiobjective optimisation problem of airport gate assignment.
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Purnomo, Muhammad Ridwan Andi. "Optimisation-in-the-loop simulation of multi products single vendor-multi buyers supply chain systems with reactive lateral transhipment." Jurnal Sistem dan Manajemen Industri 7, no. 2 (December 1, 2023): 116–26. http://dx.doi.org/10.30656/jsmi.v7i2.6495.
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Considering that batik is one of the most popular products in Indonesia, it is important to analyse the supply chain system for batik products. In reality, the supply chain system for batik products enables orders between buyers to receive products more rapidly, allowing them to anticipate stock outs and obtain lower ordering costs than when ordering from vendors. It is referred to as reactive lateral transshipment. This paper discusses the development of a simulation-based stochastic optimisation model for a batik product supply chain system with multiproducts and single vendor-multi buyers. The utilised solution searching algorithm is a modified Genetic Algorithms (GA) executed in-loop with the developed simulation-based stochastic model. The results demonstrate that the proposed modified GA is able to provide a global optimum solution, allowing the proposed simulation-based stochastic model to reduce the joint total cost (JTC) of the investigated supply chain system by up to 19% when compared to the local optimisation model in each supply chain party.
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Dahl, Nicolai J., Pere L. Muntal, and Michael A. E. Andersen. "Systematic Design of a Pseudodifferential VCO Using Monomial Fitting." Elektronika ir Elektrotechnika 29, no. 5 (October 31, 2023): 36–43. http://dx.doi.org/10.5755/j02.eie.35279.
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Digital integrated electronics benefits from its higher abstraction level, allowing optimisation methods and automated workflows. However, analogue integrated circuit design is still predominantly done manually, leading to lengthy design cycles. This paper proposes a new systematic design approach for the sizing of analogue integrated circuits to address this issue. The method utilises a surrogate optimisation technique that approximates a simple monomial function based on few simulation results. These monomials are convex and can be optimised using a simple linear optimisation routine, resulting in a single global optimal solution. We show that monomial functions, in many cases, have an analytic relation to integrated circuits, making them well suited for the application. The method is demonstrated by designing a 14 MHz pseudodifferential voltage-controlled oscillator (VCO) with minimised current consumption and is manufactured in a 180 nm process. The measured total current matches the predicted and is lower than that for other similar state-of-the-art VCOs.
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Chen, Lian-Meng, Kai-Yu Huang, Yi-Jie Liu, Yi-Hong Zeng, Ze-Bin Li, Yi-Yi Zhou, and Shi-Lin Dong. "Optimisation of Cable Dome Structure Design for Progressive Collapse Resistance." Applied Sciences 13, no. 4 (February 6, 2023): 2086. http://dx.doi.org/10.3390/app13042086.
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Since the literature lacks an effective analysis method of collapse mechanisms and optimisation design theory for progressive collapse resistance of cable dome structure, a structural performance-based optimisation approach was proposed to improve the progressive collapse resistance for cable dome structures in this study. First, the dynamic response and collapse model of a cable dome structure were analysed after its members were removed using Ansys LS-DYNA and the full dynamic equivalent load-based instantaneous unloading method. Second, the importance coefficients of the members were calculated to determine the contribution of each member to the progressive collapse resistance of the structure. Finally, a stepwise optimisation solution was proposed by integrating a global optimisation model, which uses the mean of the importance coefficients of all members as the optimisation index, with a local optimisation model, which minimises the maximum member importance coefficient. The results indicated that different members exhibited varying levels of importance in the progressive collapse resistance of the structure, with the inner and outer hoop cables demonstrating the highest levels of importance, followed by the inner upper string of the tension hoop. The other members had low levels of importance. Compared with the cable dome structure based on the Geiger topology, the cable dome structure based on the Levy topology was more resistant to progressive collapse; such resistance decreased as the number of cable-truss frames decreased. Additionally, the local optimisation approach based on the genetic algorithm reduced the maximum member importance coefficient (i.e., that of the outer hoop cable) by 60.26%.
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Lenarčič, J., and L. Žlajpah. "Comparison of local and global solution in optimisation of joint torques of n-R planar manipulator." IFAC Proceedings Volumes 27, no. 14 (September 1994): 447–52. http://dx.doi.org/10.1016/s1474-6670(17)47351-4.
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Chia, C. M., Jem A. Rongong, and Keith Worden. "Structural Optimisation using a Hybrid Cellular Automata (HCA) Algorithm." Applied Mechanics and Materials 5-6 (October 2006): 93–100. http://dx.doi.org/10.4028/www.scientific.net/amm.5-6.93.
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The Hybrid Cellular Automata (HCA) algorithm has been used by several researchers to optimise structures during the last decade. Close observation of their work shows that the proposed optimisation algorithms are sensitive to the controller (local rule), the design variable and the field variable used. The aim of this work is to identify and understand the important parameters when using the HCA algorithm to optimise structures. For static loading, it is shown that the most important parameters are the design variable, the constraints on the design variable, the local rule, and the mesh density of the structure. The choice of the design variable affects the selection of the target value and the homogeneity of the resulting optimum structure. With constraints on the design variable, it is shown that the algorithm cannot always drive the structure to an optimum solution, as stresses in the resulting structure can be significantly higher than expected. Besides, the choice of the local rule and the mesh density of the structure can affect the convergence rate and may cause the algorithm to arrive at a local optimum rather than the global optimum solution.
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De Ath, George, Richard M. Everson, Alma A. M. Rahat, and Jonathan E. Fieldsend. "Greed Is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisation." ACM Transactions on Evolutionary Learning and Optimization 1, no. 1 (May 20, 2021): 1–22. http://dx.doi.org/10.1145/3425501.
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The performance of acquisition functions for Bayesian optimisation to locate the global optimum of continuous functions is investigated in terms of the Pareto front between exploration and exploitation. We show that Expected Improvement (EI) and the Upper Confidence Bound (UCB) always select solutions to be expensively evaluated on the Pareto front, but Probability of Improvement is not guaranteed to do so and Weighted Expected Improvement does so only for a restricted range of weights. We introduce two novel -greedy acquisition functions. Extensive empirical evaluation of these together with random search, purely exploratory, and purely exploitative search on 10 benchmark problems in 1 to 10 dimensions shows that -greedy algorithms are generally at least as effective as conventional acquisition functions (e.g., EI and UCB), particularly with a limited budget. In higher dimensions, -greedy approaches are shown to have improved performance over conventional approaches. These results are borne out on a real-world computational fluid dynamics optimisation problem and a robotics active learning problem. Our analysis and experiments suggest that the most effective strategy, particularly in higher dimensions, is to be mostly greedy, occasionally selecting a random exploratory solution.
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GHOSH, RANADHIR, JOHN YEARWOOD, MOUMITA GHOSH, and ADIL BAGIROV. "A HYBRID NEURAL LEARNING ALGORITHM USING EVOLUTIONARY LEARNING AND DERIVATIVE FREE LOCAL SEARCH METHOD." International Journal of Neural Systems 16, no. 03 (June 2006): 201–13. http://dx.doi.org/10.1142/s0129065706000615.
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In this paper we investigate a hybrid model based on the Discrete Gradient method and an evolutionary strategy for determining the weights in a feed forward artificial neural network. Also we discuss different variants for hybrid models using the Discrete Gradient method and an evolutionary strategy for determining the weights in a feed forward artificial neural network. The Discrete Gradient method has the advantage of being able to jump over many local minima and find very deep local minima. However, earlier research has shown that a good starting point for the discrete gradient method can improve the quality of the solution point. Evolutionary algorithms are best suited for global optimisation problems. Nevertheless they are cursed with longer training times and often unsuitable for real world application. For optimisation problems such as weight optimisation for ANNs in real world applications the dimensions are large and time complexity is critical. Hence the idea of a hybrid model can be a suitable option. In this paper we propose different fusion strategies for hybrid models combining the evolutionary strategy with the discrete gradient method to obtain an optimal solution much quicker. Three different fusion strategies are discussed: a linear hybrid model, an iterative hybrid model and a restricted local search hybrid model. Comparative results on a range of standard datasets are provided for different fusion hybrid models.
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Beckers, Jarl, Diederik Coppitters, Ward De Paepe, Francesco Contino, Joeri Van Mierlo, and Björn Verrelst. "Multi-Fidelity Design Optimisation of a Solenoid-Driven Linear Compressor." Actuators 9, no. 2 (May 11, 2020): 38. http://dx.doi.org/10.3390/act9020038.
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Improved management and impermeability of refrigerants is a leading solution to reverse global warming. Therefore, crank-driven reciprocating refrigerator compressors are gradually replaced by more efficient, oil-free and hermetic linear compressors. However, the design and operation of an electromagnetic actuator, fitted on the compression requirements of a reciprocating linear compressor, received limited attention. Current research mainly focuses on the optimisation of short stroke linear compressors, while long stroke compressors benefit from higher isentropic and volumetric efficiencies. Moreover, designing such a system focuses mainly on the trade-off between number of copper windings and the current required, due to the large computational cost of performing a full geometric design optimisation based on a Finite Element Method. Therefore, in this paper, a computationally-efficient, multi-objective design optimisation for six geometric design parameters has been applied on a solenoid driven linear compressor with a stroke of 44.2 mm. The proposed multi-fidelity optimisation approach takes advantage of established models for actuator optimisation in mechatronic applications, combined with analytical equations established for a solenoid actuator to increase the overall computational efficiency. This paper consists of the multi-fidelity optimisation algorithm, the analytic model and Finite Element Method of a solenoid and the optimised designs obtained for optimised power and copper volume, which dominates the actuator cost. The optimisation results illustrate a trade-off between minimising the peak power and minimising the volume of copper windings. Considering this trade-off, an intermediate design is highlighted, which requires 33.3% less power, at the expense of an increased copper volume by 5.3% as opposed to the design achieving the minimum copper volume. Despite that the effect of the number of windings on the input current remains a dominant design characteristic, adapting the geometric parameters reduces the actuator power requirements significantly as well. Finally, the multi-fidelity optimisation algorithm achieves a 74% reduction in computational cost as opposed to an entire Finite Element Method optimisation. Future work focuses on a similar optimisation approach for a permanent magnet linear actuator.
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D., OSTRENKO, and KOLLAROV O. "Genetic algorithms in the problems of solar power-station optimisation." Journal of Electrical and power engineering 29, no. 2 (December 19, 2023): 43–49. http://dx.doi.org/10.31474/2074-2630-2023-2-43-49.
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Solving the tasks of finding the maximum in the field of renewable energy sources. The scientific work is devoted to the application of genetic algorithms to solve the problems of optimizing the efficiency of solar energy in renewable energy sources. The main focus of the work is on finding the maximum parameter values in solar power plants. The relevance of the study is due to the constant growth in the popularity of the use of solar energy and the need to increase its conversion efficiency. The application of genetic algorithms in solving the tasks of finding the maximum in solar energy is an important step in achieving optimal configurations of FES and ensuring the stable functioning of this type of power plants in general. The work includes the analysis of the influence of various parameters on the efficiency of photovoltaic panels and the development of optimal strategies for the use of genetic algorithms to improve their performance. The obtained results open up new opportunities for increasing the competitiveness of FES in the field of renewable energy sources. The genetic algorithm is recognized for its ability to provide quality results and work faster than the selection method. This method is widely used in world practice [6]. Modern algorithms for tasks where the size of the search space is so large that the exact finding of the optimal solution becomes impossible, then in such cases heuristic solutions meet the requirements, have also been studied. One of the goals of the research is the analysis of optimization algorithms and their applicability for solving the optimization tasks of solar energy. Genetic algorithms, although effective, have their limitations - in many cases, they tend to converge to a local optimum (or even an arbitrary point), instead of a global one. This indicates their inability to decide how to maintain high fitness in the short term. Additionally, the complication is related to how to protect evolutionarily formed parts from destructive mutations [7, 9]. In the process of research, the specified limitations were taken into account and mechanisms were developed to reduce their negative impact. The algorithm considered in the work is not only resistant to local minima, but also, due to the internal parallelism expressed in working with individual solutions, rather than whole classes of solutions, provides a relatively fast search for the optimal solution. Research methods basically use the iterative technique of improving results
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Sánchez, Cristian, Lionel Bloch, Jordan Holweger, Christophe Ballif, and Nicolas Wyrsch. "Optimised Heat Pump Management for Increasing Photovoltaic Penetration into the Electricity Grid." Energies 12, no. 8 (April 25, 2019): 1571. http://dx.doi.org/10.3390/en12081571.
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Advanced control of heat pumps with thermal storage and photovoltaics has recently been promoted as a promising solution to help decarbonise the residential sector. Heat pumps and thermal storage offer a valuable flexibilisation mean to integrate stochastic renewable energy sources into the electricity grid. Heat pump energy conversion is nonlinear, leading to a challenging nonlinear optimisation problem. However, issues like global optimum uncertainty and the time-consuming methods of current nonlinear programming solvers draw researchers to linearise heat pump models that are then implemented in faster and globally convergent linear programming solvers. Nevertheless, these linearisations generate some inaccuracies, especially in the calculation of the heat pump’s coefficient of performance ( C O P ). In order to solve all of these issues, this paper presents a heuristic control algorithm (HCA) to provide a fast, accurate and near-optimal solution to the original nonlinear optimisation problem for a single-family house with a photovoltaic system, using real consumption data from a typical Swiss house. Results highlight that the HCA solves this optimisation problem up to 1000 times faster, yielding an operation that is up to 49% cheaper and self-consumption rates that are 5% greater than other nonlinear solvers. Comparing the performance of the HCA and the linear solver intlinprog, it is shown that the HCA provides more accurate heat pump control with an increase of up to 9% in system Operating Expense OPEX and a decrease of 8% in self-consumption values.
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Hu, Binwen, Zonghui Xiong, Aihong Sun, and Yiping Yuan. "Scheduling of Container Transportation Vehicles in Surface Coal Mines Based on the GA–GWO Hybrid Algorithm." Applied Sciences 14, no. 10 (May 8, 2024): 3986. http://dx.doi.org/10.3390/app14103986.
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The coal loading operation of the coal preparation plant of an open pit coal mine causes chaos in coal mine vehicle scheduling due to the unreasonable arrival times of outgoing and container transportation vehicles. To further reduce the length of time that vehicle transportation equipment waits for each other and to reduce the total cost of container transportation, the optimisation model of container transportation vehicle scheduling in an open pit coal mine is constructed to minimise the minimum sum of the shortest time of container reversal and the lowest cost of container transportation. To accurately measure the total cost of container backward transportation, waiting time and unit waiting time cost parameters are introduced, and the total cost of container transportation is measured using the transportation cost and the waiting time cost transformation method. An improved grey wolf algorithm is proposed to speed up the convergence of the algorithm and improve the quality of the solution. When employing the genetic algorithm (GA) and grey wolf optimisation algorithm (GWO) for optimising the scheduling of container transport vehicles in coal mines, it is noted that while the GA can achieve the global optimum, its convergence speed is relatively slow. Conversely, the GWO converges more quickly, but it tends to be trapped in local optima. To accelerate the convergence speed of the algorithm and improve the solution quality, a hybrid GA−GWO algorithm is proposed, which introduces three genetic operations of selection, crossover, and mutation of GA into the GWO algorithm to prevent the algorithm from falling into the local optimum due to the fall; at the same time, it introduces hunting and attacking operations into the elite retention strategy of GA, which improves the stability of the algorithm’s global convergence. Analysis indicates that, compared to SA, GWO, and GA, the hybrid algorithm enhances optimisation speed by 43.1%, 46.2%, and 43.7%, increases optimisation accuracy by 4.12%, 6.1%, and 3.2%, respectively, and reduces the total container reversal time by 35.46, 22, and 31 h. The total cost of container transportation is reduced by 2437 RMB, 3512 RMB, and 1334 RMB, respectively.
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Gheraibia, Youcef, Abdelouahab Moussaoui, Sohag Kabir, and Smaine Mazouzi. "Pe-DFA." International Journal of Applied Metaheuristic Computing 7, no. 2 (April 2016): 58–70. http://dx.doi.org/10.4018/ijamc.2016040104.
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DNA Fragment Assembly (DFA) is a process of finding the best order and orientation of a set of DNA fragments to reconstruct the original DNA sequence from them. As it has to consider all possible combinations among the DNA fragments, it is considered as a combinatorial optimisation problem. This paper presents a method showing the use of Penguins Search Optimisation Algorithm (PeSOA) for DNA fragment assembly problem. Penguins search optimisation is a nature inspired metaheuristic algorithm based on the collaborative hunting strategy of penguins. The approach starts its operation by generating a set of random population. After that, the population is divided into several groups, and each group contains a set of active fragments in which the penguins concentrate on the search process. The search process of the penguin optimisation algorithm is controlled by the oxygen reserve of penguins. During the search process each penguin shares its best found solution with other penguins to quickly converge to the global optimum. In this paper, the authors adapted the original PeSOA algorithm to obtain a new algorithm structure for DNA assembly problem. The effectiveness of the proposed approach has been verified by applying it on the well-known benchmarks for the DNA assembly problem. The results show that the proposed method performed well compared to the most used DNA fragment assembly methods.
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Singh, Pushpendra, Supriya Tripathi, and Raunak Kumar Tamrakar. "Fluence map optimisation for prostate cancer intensity modulated radiotherapy planning using iterative solution method." Polish Journal of Medical Physics and Engineering 26, no. 4 (December 1, 2020): 201–9. http://dx.doi.org/10.2478/pjmpe-2020-0024.
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Abstract Here we projected a model-based IMRT treatment plan to produce the optimal radiation dosage by considering that the maximum amount of prescribed dose should be delivered to the target without affecting the surrounding healthy tissues especially the OARs. Fluence mapping is used for inverse planning. This suggested method can generate global minima for IMRT plans with reliable plan quality among diverse treatment planners and to provide better safety for significant parallel OARs in an effective way. The whole methodology is having the capability to handles various objectives and to generate effective treatment procedures as validated with illustrations on the CORT dataset. For the validation of our methodology, we have compared our result with the two other approaches for calculating the objectives based on dose-volume bounds and found that in our methodology dose across the prostate and lymph nodes is maximum and the time required for the convergence is minimum.
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Ioannidis, Evangelos, Takis Merkouris, Li-Chun Zhang, Martin Karlberg, Michalis Petrakos, Fernando Reis, and Photis Stavropoulos. "On a Modular Approach to the Design of Integrated Social Surveys." Journal of Official Statistics 32, no. 2 (June 1, 2016): 259–86. http://dx.doi.org/10.1515/jos-2016-0013.
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Abstract This article considers a modular approach to the design of integrated social surveys. The approach consists of grouping variables into ‘modules’, each of which is then allocated to one or more ‘instruments’. Each instrument is then administered to a random sample of population units, and each sample unit responds to all modules of the instrument. This approach offers a way of designing a system of integrated social surveys that balances the need to limit the cost and the need to obtain sufficient information. The allocation of the modules to instruments draws on the methodology of split questionnaire designs. The composition of the instruments, that is, how the modules are allocated to instruments, and the corresponding sample sizes are obtained as a solution to an optimisation problem. This optimisation involves minimisation of respondent burden and data collection cost, while respecting certain design constraints usually encountered in practice. These constraints may include, for example, the level of precision required and dependencies between the variables. We propose using a random search algorithm to find approximate optimal solutions to this problem. The algorithm is proved to fulfil conditions that ensure convergence to the global optimum and can also produce an efficient design for a split questionnaire.
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Mizgan, H., and M. Ganea. "Optimization of aluminium die-casting process through predictive maintenance and parameter traceability systems." IOP Conference Series: Materials Science and Engineering 1256, no. 1 (October 1, 2022): 012028. http://dx.doi.org/10.1088/1757-899x/1256/1/012028.
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Abstract The purpose of this paper is to present the optimisation potential for aluminium die casting process through predictive maintenance and parameter traceability systems. Aluminium considered the metal of the future due to its physical and chemical properties, and this paper is specifically focused on the die-casting process, its potential failures and proposed solutions for reduction of defects. The methodology for optimisation is based is focused on the Total Traceability Management (TTM) software and its technical solutions for predictive maintenance. As a key function of the TTM, the predictive maintenance module is based on conditioning monitoring systems. Temperature, colour, vibration, force, chemical, ultrasound, light, laser, dimensional sensors, all these are developing on the global market as part of the 4th industrial revolution, Industry 4.0. The TTM is combining the factory floor technologies with the informatics systems as ERP, Customer Portals, and MES, through a specific algorithm and based on PLC and sensorial hardware. The TTM is becoming a mandatory requirement for automotive and not only industry as stated in the new norms of AIAG (American Industrial Automotive Group), VDA (German Association of the Automotive Industry), and JAMA - Japan Automobile Manufacturers Association. The approach of this paper is a theoretical presentation of the practical experiments presenting the most modern solution in terms of software, sensorial installations, monitored equipment and the realized outputs. The TTM concept are not yet fully mature, various solutions being deployed on the market with specificities for diverse industries.
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Sakiyama, Tomoko, Kotaro Uneme, and Ikuo Arizono. "Rank-Based Ant System via the Relative Position in a Local Hierarchy." Complexity 2021 (September 7, 2021): 1–6. http://dx.doi.org/10.1155/2021/8372318.
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ASrank has been proposed as an improved version of the ant colony optimisation (ACO) model. However, ASrank includes behaviours that do not exist in the actual biological system and fall into a local solution. To address this issue, we developed ASmulti, a new type of ASrank, in which each agent contributes to pheromone depositions by estimating its rank by interacting with the encountered agents. In this paper, we attempt further improvements in the performance of ASmulti by allowing agents to consider their position in a local hierarchy. Agents in the proposed model (AShierarchy) contribute to pheromone depositions by estimating the consistency between a local hierarchy and global (system) hierarchy. We show that, by using several TSP datasets, the proposed model can find a better solution than ASmulti.
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Zimmermann, R., and S. Görtz. "Improved extrapolation of steady turbulent aerodynamics using a non-linear POD-based reduced order model." Aeronautical Journal 116, no. 1184 (October 2012): 1079–100. http://dx.doi.org/10.1017/s0001924000007491.
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AbstractA reduced-order modelling (ROM) approach for predicting steady, turbulent aerodynamic flows based on computational fluid dynamics (CFD) and proper orthogonal decomposition (POD) is presented. Model-order reduction is achieved by parameter space sampling, solution space representation via POD and restriction of a CFD solver to the POD subspace. Solving the governing equations of fluid dynamics is replaced by solving a non-linear least-squares optimisation problem. The method will be referred to as LSQ-ROM method. Two approaches of extracting POD basis information from CFD snapshot data are discussed: POD of the full state vector (global POD) and POD of each of the partial states separately (variable-by-variable POD). The method at hand is demonstrated for a 2D aerofoil (NACA 64A010) as well as for a complete industrial aircraft configuration (NASA Common Research Model) in the transonic flow regime by computing ROMs of the compressible Reynolds-averaged Navier-Stokes equations, pursuing both the global and the variable-by-variable POD approach. The LSQ-ROM approach is tried for extrapolatory flow conditions. Results are juxtaposed with those obtained by POD-based extrapolation using Kriging and the radial basis functions spline method. As a reference, the full-order CFD solutions are considered. For the industrial aircraft configuration, the cost of computing the reduced-order solution is shown to be two orders of magnitude lower than that of computing the reference CFD solution.
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Basińska, M. "The use of multi-criteria optimization to choose solutions for energy-efficient buildings." Bulletin of the Polish Academy of Sciences Technical Sciences 65, no. 6 (December 1, 2017): 815–26. http://dx.doi.org/10.1515/bpasts-2017-0084.
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AbstractThe goal of this paper was to optimize the building envelope and technical equipment in the building through the mitigation of the global cost value, and then to evaluate the influence of the chosen assumptions on the primary energy index. The analyses carried out using global cost method allow for finding the cost optimal solution but only for the some range of primary energy index variability. In order to find the optimal solutions it was proposed to use the multi-criteria optimisation, assuming the following as basic criteria: a global cost value and investment prices increase (economic criteria), a primary energy index (energy-related criterion), an emission of carbon dioxide (environmental criterion). The analysed case study refers to the technical solutions for the residential buildings with the usable energy demand at the level of 40 and 15 kWh/m2/a. The presented method might be applied to different types of buildings: those being designed and those being the subject of the thermo-modernisation. The results demonstrate that the proposed model allows for classification of the alternative technical solutions regarding the designing process and the building’s technical equipment. The carried out analyses indicate the economic possibility to achieve the low energy building standard and show the need to concentrate the activities related to the installation technology and used energy source.
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Alli, Kolapo S., and Haniph A. Latchman. "A Semidefinite Programming Method Weighted Sum Based for Solving a Multi-area Emission (Environmental) Economic Dispatch Problem." West Indian Journal of Engineering 46, no. 2 (January 2024): 21–33. http://dx.doi.org/10.47412/kabn8939.
Full textAbstract:
This paper presents a semi-definite programming (SDP) optimisation approach for solving multi area, emission/economic dispatch (MAEED) problems in thermal power systems. The multi-objective problem was transformed into matrix form as an SDP relaxation problem and subsequently solved with a MATLAB programming suite. The system inequality and equality constraints were entered into a Self-Dual Minimisation (SeDuMi) parser. Simulations were tested on four (4) IEEE benchmark models to validate the efficiency of the proposed SDP method. However, in the generation of the Pareto-fr ont solutions, ideal minimum points were used in the determination of the maximum spread out of the Pareto solutions by the algorithm. This involves the use of a standard weighted sum method in generating the Pareto-optimal solution between two objective functions. Twenty-one (21) runs were carried out for the parameter value to explore the effect of control weight selection k1. A comparative study demonstrated the effectiveness of tie-line constraint and without tie-line constraint, respectively on the optimi sation model for the power dispatch problems solved by using the proposed SDP approach. The optimisation results for the minimum fuel cost using the SDP approach for the case when a tie-line constraint is included was US$ 2,151.7 /hr with minimum emission of 3.4687 Ton/hr. This is lower than US$ 2,162.2 /hr with minimum emission of 3.6662 Ton/hr obtained when the tie-line constraint is not included in the optimisation model on IEEE four-area, sixteen network. Additionally, the comparative results with all other methods reported in the literature for different test cases were also presented in this paper. Lower operational costs were obtained for both tie-lines and no tie-lines power transfer cases, showing that the SDP method performed well in accuracy compared to other methods. The SDP method was employed in this research to deal with the multi-area emission economic power dispatch problem due to its inherent advantages in achieving a global optimal solution and, this approach would also assist the power administrator to come up with an improved decision making in attaining an optimum power dispatch of power generation.