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Journal articles on the topic 'Evolutionary Optimiser'

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Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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1

Ab. Rashid, M. F. F., N. M. Z. Nik Mohamed, and A. N. Mohd Rose. "A modified artificial bee colony algorithm to optimise integrated assembly sequence planning and assembly line balancing." Journal of Mechanical Engineering and Sciences 13, no. 4 (December 30, 2019): 5905–21. http://dx.doi.org/10.15282/jmes.13.4.2019.13.0469.

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Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) are traditionally optimised independently. However recently, integrated ASP and ALB optimisation has become more relevant to obtain better quality solution and to reduce time to market. Despite many optimisation algorithms that were proposed to optimise this problem, the existing researches on this problem were limited to Evolutionary Algorithm (EA), Ant Colony Optimisation (ACO), and Particle Swarm Optimisation (PSO). This paper proposed a modified Artificial Bee Colony algorithm (MABC) to optimise the integrated ASP and ALB problem. The proposed algorithm adopts beewolves predatory concept from Grey Wolf Optimiser to improve the exploitation ability in Artificial Bee Colony (ABC) algorithm. The proposed MABC was tested with a set of benchmark problems. The results indicated that the MABC outperformed the comparison algorithms in 91% of the benchmark problems. Furthermore, a statistical test reported that the MABC had significant performances in 80% of the cases.

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al-Rifaie, Mohammad Majid. "Exploration and Exploitation Zones in a Minimalist Swarm Optimiser." Entropy 23, no. 8 (July 29, 2021): 977. http://dx.doi.org/10.3390/e23080977.

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The trade off between exploration and exploitation is one of the key challenges in evolutionary and swarm optimisers which are led by guided and stochastic search. This work investigates the exploration and exploitation balance in a minimalist swarm optimiser in order to offer insights into the population’s behaviour. The minimalist and vector-stripped nature of the algorithm—dispersive flies optimisation or DFO—reduces the challenges of understanding particles’ oscillation around constantly changing centres, their influence on one another, and their trajectory. The aim is to examine the population’s dimensional behaviour in each iteration and each defined exploration-exploitation zone, and to subsequently offer improvements to the working of the optimiser. The derived variants, titled unified DFO or uDFO, are successfully applied to an extensive set of test functions, as well as high-dimensional tomographic reconstruction, which is an important inverse problem in medical and industrial imaging.

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Kunakote, Tawatchai, and Sujin Bureerat. "Surrogate-Assisted Multiobjective Evolutionary Algorithms for Structural Shape and Sizing Optimisation." Mathematical Problems in Engineering 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/695172.

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The work in this paper proposes the hybridisation of the well-established strength Pareto evolutionary algorithm (SPEA2) and some commonly used surrogate models. The surrogate models are introduced to an evolutionary optimisation process to enhance the performance of the optimiser when solving design problems with expensive function evaluation. Several surrogate models including quadratic function, radial basis function, neural network, and Kriging models are employed in combination with SPEA2 using real codes. The various hybrid optimisation strategies are implemented on eight simultaneous shape and sizing design problems of structures taking into account of structural weight, lateral bucking, natural frequency, and stress. Structural analysis is carried out by using a finite element procedure. The optimum results obtained are compared and discussed. The performance assessment is based on the hypervolume indicator. The performance of the surrogate models for estimating design constraints is investigated. It has been found that, by using a quadratic function surrogate model, the optimiser searching performance is greatly improved.

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Giel, Oliver, and Per Kristian Lehre. "On the Effect of Populations in Evolutionary Multi-Objective Optimisation." Evolutionary Computation 18, no. 3 (September 2010): 335–56. http://dx.doi.org/10.1162/evco_a_00013.

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Multi-objective evolutionary algorithms (MOEAs) have become increasingly popular as multi-objective problem solving techniques. An important open problem is to understand the role of populations in MOEAs. We present two simple bi-objective problems which emphasise when populations are needed. Rigorous runtime analysis points out an exponential runtime gap between the population-based algorithm simple evolutionary multi-objective optimiser (SEMO) and several single individual-based algorithms on this problem. This means that among the algorithms considered, only the population-based MOEA is successful and all other algorithms fail.

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Delelegn, S. W., A. Pathirana, B. Gersonius, A. G. Adeogun, and K. Vairavamoorthy. "Multi-objective optimisation of cost–benefit of urban flood management using a 1D2D coupled model." Water Science and Technology 63, no. 5 (March 1, 2011): 1053–59. http://dx.doi.org/10.2166/wst.2011.290.

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This paper presents a multi-objective optimisation (MOO) tool for urban drainage management that is based on a 1D2D coupled model of SWMM5 (1D sub-surface flow model) and BreZo (2D surface flow model). This coupled model is linked with NSGA-II, which is an Evolutionary Algorithm-based optimiser. Previously the combination of a surface/sub-surface flow model and evolutionary optimisation has been considered to be infeasible due to the computational demands. The 1D2D coupled model used here shows a computational efficiency that is acceptable for optimisation. This technological advance is the result of the application of a triangular irregular discretisation process and an explicit finite volume solver in the 2D surface flow model. Besides that, OpenMP based parallelisation was employed at optimiser level to further improve the computational speed of the MOO tool. The MOO tool has been applied to an existing sewer network in West Garforth, UK. This application demonstrates the advantages of using multi-objective optimisation by providing an easy-to-comprehend Pareto-optimal front (relating investment cost to expected flood damage) that could be used for decision making processes, without repeatedly going through the modelling–optimisation stage.

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Sartakhti, Moein Salimi, Ahmad Yoosofan, Ali Asghar Fatehi, and Ali Rahimi. "Single Document Summarization Based on Grey Wolf Optimization." Global Journal of Computer Sciences: Theory and Research 10, no. 2 (October 30, 2020): 48–56. http://dx.doi.org/10.18844/gjcs.v10i2.5807.

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The amazing growth of online services has caused an information explosion issue. Text summarisation is condensing the text into a small version and preserving its overall concept. Text summarisation is an important way to extract significant information from documents and offer that information to the user in an abbreviated form while preserving its major content. For human beings, it is very difficult to summarise large documents. To do this, this paper uses some sentence features and word features. These features assign scores to all the sentences. In this paper, we combine these features by Grey Wolf Optimiser (GWO). Optimisation of features gives better results than using individual features. This is the first attempt to show the performance of GWO for Persian text summarisation. The proposed method is compared with the genetic algorithm and the evolutionary strategy. The results show that our model will be useful in this research area. Keywords: Text summarisation, genetic algorithm, sentence, score function, evolutionary strategy.

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Marrero, Alejandro, Eduardo Segredo, Coromoto León, and Carlos Segura. "A Memetic Decomposition-Based Multi-Objective Evolutionary Algorithm Applied to a Constrained Menu Planning Problem." Mathematics 8, no. 11 (November 5, 2020): 1960. http://dx.doi.org/10.3390/math8111960.

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Encouraging healthy and balanced diet plans is one of the most important action points for governments around the world. Generating healthy, balanced and inexpensive menu plans that fulfil all the recommendations given by nutritionists is a complex and time-consuming task; because of this, computer science has an important role in this area. This paper deals with a novel constrained multi-objective formulation of the menu planning problem specially designed for school canteens that considers the minimisation of the cost and the minimisation of the level of repetition of the specific courses and food groups contained in the plans. Particularly, this paper proposes a multi-objective memetic approach based on the well-known multi-objective evolutionary algorithm based on decomposition (MOEA/D). A crossover operator specifically designed for this problem is included in the approach. Moreover, an ad-hoc iterated local search (ILS) is considered for the improvement phase. As a result, our proposal is referred to as ILS-MOEA/D. A wide experimental comparison against a recently proposed single-objective memetic scheme, which includes explicit mechanisms to promote diversity in the decision variable space, is provided. The experimental assessment shows that, even though the single-objective approach yields menu plans with lower costs, our multi-objective proposal offers menu plans with a significantly lower level of repetition of courses and food groups, with only a minor increase in cost. Furthermore, our studies demonstrate that the application of multi-objective optimisers can be used to implicitly promote diversity not only in the objective function space, but also in the decision variable space. Consequently, in contrast to the single-objective optimiser, there was no need to include an explicit strategy to manage the diversity in the decision space in the case of the multi-objective approach.

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Ashrafian, Ali, Naser Safaeian Hamzehkolaei, Ngakan Ketut Acwin Dwijendra, and Maziar Yazdani. "An Evolutionary Neuro-Fuzzy-Based Approach to Estimate the Compressive Strength of Eco-Friendly Concrete Containing Recycled Construction Wastes." Buildings 12, no. 8 (August 21, 2022): 1280. http://dx.doi.org/10.3390/buildings12081280.

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There has been a significant increase in construction and demolition (C&D) waste due to the growth of cities and the need for new construction, raising concerns about the impact on the environment of these wastes. By utilising recycled C&D waste, especially in concretes used in construction, further environmental damage can be prevented. By using these concretes, energy consumption and environmental impacts of concrete production can be reduced. The behaviour of these types of concrete in laboratories has been extensively studied, but reliable methods for estimating their behaviour based on the available data are required. Consequently, this research proposes a hybrid intelligent system, Fuzzy Group Method of Data Handling (GMDH)–Horse herd Optimisation Algorithm (HOA), for predicting one of the most important parameters in concrete structure design, compressive strength. In order to avoid uncertainty in the modelling process, crisp input values were converted to Fuzzy values (Fuzzification). Next, using Fuzzy input variables, the group method of data handling is used to predict the compressive strength of recycled aggregate concrete. The HOA algorithm is one of the newest metaheuristic algorithms being used to optimise the Fuzzy GMDH structure. Several databases containing experimental mix design records containing mixture components are gathered from published documents for compressive strength to assess the accuracy and reliability of the proposed hybrid Fuzzy-based model. Compared to other original approaches, the proposed Fuzzy GMDH model with the HOA optimiser outperformed them in terms of accuracy. A Monte Carlo simulation is also employed for uncertainty analysis of the empirical, standalone, and hybridised models in order to demonstrate that the evolutionary Fuzzy-based approach has less uncertainty than the standalone methods when simulating compressive strength.

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Gonzalez, L. F., D. S. Lee, K. Srinivas, and K. C. Wong. "Single and multi–objective UAV aerofoil optimisation via hierarchical asynchronous parallel evolutionary algorithm." Aeronautical Journal 110, no. 1112 (October 2006): 659–72. http://dx.doi.org/10.1017/s0001924000001524.

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Abstract Unmanned aerial vehicle (UAV) design tends to focus on sensors, payload and navigation systems, as these are the most expensive components. One area that is often overlooked in UAV design is airframe and aerodynamic shape optimisation. As for manned aircraft, optimisation is important in order to extend the operational envelope and efficiency of these vehicles. A traditional approach to optimisation is to use gradient-based techniques. These techniques are effective when applied to specific problems and within a specified range. These methods are efficient for finding optimal global solutions if the objective functions and constraints are differentiable. If a broader application of the optimiser is desired, or when the complexity of the problem arises because it is multi-modal, involves approximation, is non-differentiable, or involves multiple objectives and physics, as it is often the case in aerodynamic optimisation, more robust and alternative numerical tools are required. Emerging techniques such as evolutionary algorithms (EAs) have been shown to be robust as they require no derivatives or gradients of the objective function, have the capability of finding globally optimum solutions among many local optima, are easily executed in parallel, and can be adapted to arbitrary solver codes without major modifications. In this paper, the formulation and application of a evolutionary technique for aerofoil shape optimisation is described. Initially, the paper presents an introduction to the features of the method and a short discussion on multi-objective optimisation. The method is first illustrated on its application to mathematical test cases. Then it is applied to representative test cases related to aerofoil design. Results indicate the ability of the method for finding optimal solutions and capturing Pareto optimal fronts.

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Saravanan, R., S. Ramabalan, and C. Balamurugan. "Multiobjective trajectory planner for industrial robots with payload constraints." Robotica 26, no. 6 (November 2008): 753–65. http://dx.doi.org/10.1017/s0263574708004359.

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SUMMARYA general new methodology using evolutionary algorithms viz., Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Differential Evolution (MODE), for obtaining optimal trajectory planning of an industrial robot manipulator (PUMA 560 robot) in the presence of fixed and moving obstacles with payload constraint is presented. The problem has a multi-criterion character in which six objective functions, 32 constraints and 288 variables are considered. A cubic NURBS curve is used to define the trajectory. The average fuzzy membership function method is used to select the best optimal solution from Pareto optimal fronts. Two multi-objective performance measures namely solution spread measure and ratio of non-dominated individuals are used to evaluate the strength of Pareto optimal fronts. Two more multi-objective performance measures namely optimiser overhead and algorithm effort are used to find computational effort of the NSGA-II and MODE algorithms. The Pareto optimal fronts and results obtained from various techniques are compared and analysed. Both NSGA-II and MODE are best for this problem.

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Barham, Reham Shawqi, Ahmad Sharieh, and Azzam Sleit. "A meta-heuristic framework based on clustering and preprocessed datasets for solving the link prediction problem." Journal of Information Science 45, no. 6 (December 17, 2018): 794–817. http://dx.doi.org/10.1177/0165551518816296.

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This study presents a solution to a problem commonly known as link prediction problem. Link prediction problem interests in predicting the possibility of appearing a connection between two nodes of a network, while there is no connection between these nodes in the present state of the network. Finding a solution to link prediction problem attracts variety of computer science fields such as data mining and machine learning. This attraction is due to its importance for many applications such as social networks, bioinformatics and co-authorship networks. Towards solving this problem, Evolutionary Link Prediction (EVO-LP) framework is proposed, presented, analysed and tested. EVO-LP is a framework that includes dataset preprocessing approach and a meta-heuristic algorithm based on clustering for prediction. EVO-LP is divided into preprocessing stage and link prediction stage. Feature extraction, data under-sampling and feature selection are utilised in the preprocessing stage, while in the prediction stage, a meta-heuristic algorithm based on clustering is used as an optimiser. Experimental results on a number of real networks show that EVO-LP improves the prediction quality with low time complexity.

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Sepulveda Palacios, Eduardo, and Howard Smith. "Impact of mission requirements on the design of low observable UCAV configurations." Aircraft Engineering and Aerospace Technology 91, no. 10 (November 4, 2019): 1295–307. http://dx.doi.org/10.1108/aeat-09-2018-0249.

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Purpose The purpose of this paper is to characterise the effects of mission and performance parameters on the design space of low observable subsonic unmanned combat aerial vehicles (UCAVs) operating in typical Hi-Lo-Hi ground strike missions. Design/methodology/approach Conceptual design methodologies appropriate to low observable, tailless UCAVs have been integrated into a multidisciplinary aircraft design environment, GENUS, developed at Cranfield University’s aircraft design group. A basic Hi-Lo-Hi mission is designed and a baseline configuration is established through the GENUS framework. Subsequently, an evolutionary optimiser and a robust gradient-based optimiser are used to obtain convergent design solutions for various leading edge sweep angles, mission ranges, cruise Mach numbers and other operational constraints. Findings The results indicate that performance constraints, specifically in the form of specific excess power (SEP), have a large influence on the overall sizing of subsonic tailless UCAVs. This requirement drives the engine sizing, which represents a considerable proportion of the empty and gross mass of the vehicle. Cruise Mach number studies show that no significant advantages exist for operating at low speeds while maintaining performance requirements consistent with combat missions. There is a drastic increase in the vehicle’s mass and thrust requirements for flight speeds above Mach 0.8, with low sweep configurations showing a more pronounced effect. Increases in the range are not overly dependent on the leading edge sweep angle. Top-level radar cross section (RCS) results also favour configurations with higher leading edge sweep angles, especially from the nose-on aspect. Finally, research and development costs are shown to be directly linked to engine size. Originality/value This research shows the use of an integrated aircraft design environment that incorporates aerodynamics, performance, packaging and low observability aspects into the optimisation loop. Through this methodology, this study supports the efforts towards characterising and establishing alternate visions of the future of aerial warfare through the use of low cost, survivable unmanned platforms in network-centric cooperative tasks.

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Neshat, Mehdi, Nataliia Sergiienko, Seyedali Mirjalili, Meysam Majidi Nezhad, Giuseppe Piras, and Davide Astiaso Garcia. "Multi-Mode Wave Energy Converter Design Optimisation Using an Improved Moth Flame Optimisation Algorithm." Energies 14, no. 13 (June 22, 2021): 3737. http://dx.doi.org/10.3390/en14133737.

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Ocean renewable wave power is one of the more encouraging inexhaustible energy sources, with the potential to be exploited for nearly 337 GW worldwide. However, compared with other sources of renewables, wave energy technologies have not been fully developed, and the produced energy price is not as competitive as that of wind or solar renewable technologies. In order to commercialise ocean wave technologies, a wide range of optimisation methodologies have been proposed in the last decade. However, evaluations and comparisons of the performance of state-of-the-art bio-inspired optimisation algorithms have not been contemplated for wave energy converters’ optimisation. In this work, we conduct a comprehensive investigation, evaluation and comparison of the optimisation of the geometry, tether angles and power take-off (PTO) settings of a wave energy converter (WEC) using bio-inspired swarm-evolutionary optimisation algorithms based on a sample wave regime at a site in the Mediterranean Sea, in the west of Sicily, Italy. An improved version of a recent optimisation algorithm, called the Moth–Flame Optimiser (MFO), is also proposed for this application area. The results demonstrated that the proposed MFO can outperform other optimisation methods in maximising the total power harnessed from a WEC.

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Bhat, Shantanu S., Jisheng Zhao, John Sheridan, Kerry Hourigan, and Mark C. Thompson. "Evolutionary shape optimisation enhances the lift coefficient of rotating wing geometries." Journal of Fluid Mechanics 868 (April 11, 2019): 369–84. http://dx.doi.org/10.1017/jfm.2019.183.

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Wing shape is an important factor affecting the aerodynamic performance of wings of monocopters and flapping-wing micro air vehicles. Here, an evolutionary structural optimisation method is adapted to optimise wing shape to enhance the lift force due to aerodynamic pressure on the wing surfaces. The pressure distribution is observed to vary with the span-based Reynolds number over a range covering most insects and samaras. Accordingly, the optimised wing shapes derived using this evolutionary approach are shown to adjust with Reynolds number. Moreover, these optimised shapes exhibit significantly higher lift coefficients (${\sim}50\,\%$) than the initial rectangular wing forebear. Interestingly, the optimised shapes are found to have a large area outboard, broadly in line with the features of high-lift forewings of multi-winged insects. According to specific aerodynamic performance requirements, this novel method could be employed in the optimisation of improved wing shapes for micro air vehicles.

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Williams, Jonathan. "Multiple timescales of evolution." Behavioral and Brain Sciences 29, no. 4 (August 2006): 426–27. http://dx.doi.org/10.1017/s0140525x06439091.

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Keller & Miller's (K&M's) treatment of disorders usefully avoids diagnostic minutiae; but it needs more real-world constraints. Classifying processes by their evolutionary age helps to clarify both evolution and current function. Evolutionarily old, optimised, normative processes deserve special recognition, because they can be studied in animals and computers, and because they provide the machinery through which disorder-related polymorphisms act.

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McGerty, Sean, and Frank Moisiadis. "Optimised Random Mutations for Evolutionary Algorithms." International Journal of Artificial Intelligence & Applications 5, no. 4 (July 31, 2014): 15–34. http://dx.doi.org/10.5121/ijaia.2014.5402.

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Horvath, Dragos, J. Brown, Gilles Marcou, and Alexandre Varnek. "An Evolutionary Optimizer of libsvm Models." Challenges 5, no. 2 (November 24, 2014): 450–72. http://dx.doi.org/10.3390/challe5020450.

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Yamamoto, Toshihiko, Hiroshi Sato, and Akira Namatame. "Evolutionary optimised consensus and synchronisation networks." International Journal of Bio-Inspired Computation 3, no. 3 (2011): 187. http://dx.doi.org/10.1504/ijbic.2011.040317.

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Komatsu, Takanori, and Akira Namatame. "Dynamic diffusion in evolutionary optimised networks." International Journal of Bio-Inspired Computation 3, no. 6 (2011): 384. http://dx.doi.org/10.1504/ijbic.2011.043608.

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Ohenoja, Markku, Aki Sorsa, and Kauko Leiviskä. "Model Structure Optimization for Fuel Cell Polarization Curves." Computers 7, no. 4 (November 9, 2018): 60. http://dx.doi.org/10.3390/computers7040060.

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The applications of evolutionary optimizers such as genetic algorithms, differential evolution, and various swarm optimizers to the parameter estimation of the fuel cell polarization curve models have increased. This study takes a novel approach on utilizing evolutionary optimization in fuel cell modeling. Model structure identification is performed with genetic algorithms in order to determine an optimized representation of a polarization curve model with linear model parameters. The optimization is repeated with a different set of input variables and varying model complexity. The resulted model can successfully be generalized for different fuel cells and varying operating conditions, and therefore be readily applicable to fuel cell system simulations.

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Malaranbal, C., and G. Sumalatha. "Evolutionary Energy Hole Alleviation by handling Inconsistency in Cluster Head Selection for Optimized Routing in WSN." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (October 31, 2019): 549–58. http://dx.doi.org/10.5373/jardcs/v11sp10/20192841.

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Wong, Yuwa. "Optimism, Pessimism, and Evolutionary Thinking." Politics and the Life Sciences 13, no. 1 (February 1994): 35–37. http://dx.doi.org/10.1017/s073093840002219x.

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Abdel-Basset, Mohamed, Reda Mohamed, Karam M. Sallam, and Ripon K. Chakrabortty. "Light Spectrum Optimizer: A Novel Physics-Inspired Metaheuristic Optimization Algorithm." Mathematics 10, no. 19 (September 23, 2022): 3466. http://dx.doi.org/10.3390/math10193466.

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This paper introduces a novel physical-inspired metaheuristic algorithm called “Light Spectrum Optimizer (LSO)” for continuous optimization problems. The inspiration for the proposed algorithm is the light dispersions with different angles while passing through rain droplets, causing the meteorological phenomenon of the colorful rainbow spectrum. In order to validate the proposed algorithm, three different experiments are conducted. First, LSO is tested on solving CEC 2005, and the obtained results are compared with a wide range of well-regarded metaheuristics. In the second experiment, LSO is used for solving four CEC competitions in single objective optimization benchmarks (CEC2014, CEC2017, CEC2020, and CEC2022), and its results are compared with eleven well-established and recently-published optimizers, named grey wolf optimizer (GWO), whale optimization algorithm (WOA), and salp swarm algorithm (SSA), evolutionary algorithms like differential evolution (DE), and recently-published optimizers including gradient-based optimizer (GBO), artificial gorilla troops optimizer (GTO), Runge–Kutta method (RUN) beyond the metaphor, African vultures optimization algorithm (AVOA), equilibrium optimizer (EO), grey wolf optimizer (GWO), Reptile Search Algorithm (RSA), and slime mold algorithm (SMA). In addition, several engineering design problems are solved, and the results are compared with many algorithms from the literature. The experimental results with the statistical analysis demonstrate the merits and highly superior performance of the proposed LSO algorithm.

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Moen, H. J. F., T. Sparr, and S. Kristoffersen. "Improved radar detection using evolutionary optimised filter." IET Radar, Sonar & Navigation 6, no. 9 (December 1, 2012): 803–12. http://dx.doi.org/10.1049/iet-rsn.2012.0099.

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van Bommel, Patrick. "Experiences with EDO: An evolutionary database optimizer." Data & Knowledge Engineering 13, no. 3 (October 1994): 243–63. http://dx.doi.org/10.1016/0169-023x(94)00017-4.

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Bandyopadhyay, Sanghamitra, and Ujjwal Maulik. "An Improved Evolutionary Algorithm as Function Optimizer." IETE Journal of Research 46, no. 1-2 (January 2000): 47–56. http://dx.doi.org/10.1080/03772063.2000.11416134.

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Saremi, Shahrzad, Seyedeh Zahra Mirjalili, and Seyed Mohammad Mirjalili. "Evolutionary population dynamics and grey wolf optimizer." Neural Computing and Applications 26, no. 5 (December 31, 2014): 1257–63. http://dx.doi.org/10.1007/s00521-014-1806-7.

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Pošík, Petr, Waltraud Huyer, and László Pál. "A Comparison of Global Search Algorithms for Continuous Black Box Optimization." Evolutionary Computation 20, no. 4 (December 2012): 509–41. http://dx.doi.org/10.1162/evco_a_00084.

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Four methods for global numerical black box optimization with origins in the mathematical programming community are described and experimentally compared with the state of the art evolutionary method, BIPOP-CMA-ES. The methods chosen for the comparison exhibit various features that are potentially interesting for the evolutionary computation community: systematic sampling of the search space (DIRECT, MCS) possibly combined with a local search method (MCS), or a multi-start approach (NEWUOA, GLOBAL) possibly equipped with a careful selection of points to run a local optimizer from (GLOBAL). The recently proposed “comparing continuous optimizers” (COCO) methodology was adopted as the basis for the comparison. Based on the results, we draw suggestions about which algorithm should be used depending on the available budget of function evaluations, and we propose several possibilities for hybridizing evolutionary algorithms (EAs) with features of the other compared algorithms.

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Moravec, Jerry. "Hand contour classification using evolutionary algorithm." Information Technology And Control 49, no. 1 (March 25, 2020): 55–79. http://dx.doi.org/10.5755/j01.itc.49.1.24140.

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A biometric identification of persons wchich utilize contour of a human hand belogs to still very interesting and still not totally explored areas and its accuracy and effectiveness depends on technical capabilities to some extent. Presented paper solves given problem using combination of different algorithms. A hand contour is used, topological description of the hand, evolutionary algorithm, algorithm linear regression to estimate the knuckles positions and for contours comparison is used an algorithm Iterative Closest Point (ICP) in its genuine shape. All 5 fingers is at computer classification fully moveable, thumb has 2 knuckles. Modern evolutionary optimizers enable markedly to cut down computational demands of the algorithm ICP. Experimental verification of proposed recipes were performed with use of two different databases named THID and GPDS with persons of both gender and different age (cca 20-65let) with total number of oeprons in individual database 104 and 94. Experimental results checked succesfuly suitability of use combination of methods ICP and evolutionary optimizer which is named as EPSDE for solving of the given task with algorithmic complexity O(N) and success rate give by coefficient THID:EER=0.38% and GPDS:EER=0.35% on real images.

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Parent, Benjamin, Annemarie Kökösy, and Dragos Horvath. "Optimized Evolutionary Strategies in Conformational Sampling." Soft Computing 11, no. 1 (March 15, 2006): 63–79. http://dx.doi.org/10.1007/s00500-006-0053-y.

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Zhou, Tong, J. M. Carlson, and John Doyle. "Evolutionary dynamics and highly optimized tolerance." Journal of Theoretical Biology 236, no. 4 (October 2005): 438–47. http://dx.doi.org/10.1016/j.jtbi.2005.03.023.

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Gao, Cong, Zhongbo Hu, and Wangyu Tong. "Linear prediction evolution algorithm: a simplest evolutionary optimizer." Memetic Computing 13, no. 3 (July 28, 2021): 319–39. http://dx.doi.org/10.1007/s12293-021-00340-x.

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Szczepanik, M., and T. Burczyński. "Swarm optimization of stiffeners locations in 2-D structures." Bulletin of the Polish Academy of Sciences: Technical Sciences 60, no. 2 (October 1, 2012): 241–46. http://dx.doi.org/10.2478/v10175-012-0032-7.

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Abstract. The paper is devoted to the application of the swarm methods and the finite element method to optimization of the stiffeners location in the 2-D structures (plane stress, bending plates and shells). The structures are optimized for the stress and displacement criteria. The numerical examples demonstrate that the method based on the swarm computation is an effective technique for solving the computer aided optimal design. The additional comparisons of the effectiveness of the particle swarm optimizer (PSO) and evolutionary algorithms (EA) are presented.

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FATHIANPOUR, A., and S. SEYEDTABAII. "EVOLUTIONARY SEARCH FOR OPTIMIZED LNA COMPONENTS GEOMETRY." Journal of Circuits, Systems and Computers 23, no. 01 (January 2014): 1450011. http://dx.doi.org/10.1142/s021812661450011x.

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In this paper, an optimized design procedure based on genetic algorithm (GA) for automatic synthesis of dual-band concurrent fully integrated low-noise amplifiers (LNA) targeted to 802.16d @ 3.5 GHz and 802.11b, g @ 2.4 GHz standards is discussed. The algorithm delivers the circuit elements geometry, rather than their values, and bias levels to secure the best level of LNA gain, input matching, output matching and power consumption. Working on the components geometry level aims at considering the elements parasitic effects. The basic cascode and a current reuse folded cascode LNA's are tried. GA as an optimization engine is programmed in MATLAB and performance evaluation in 0.18 μm RF CMOS TSMC technology is ceded to HSPICE. Results indicate that the automated scheme well computes the desired circuit in an acceptable time span; otherwise, it may be explored by either tremendous manual trial and error or astronomical cycles of an exhaustive search. This is not accomplished without imposing certain approximate search space constraints.

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Szécsi, Dorottya, Poojan Agrawal, Richard Wünsch, and Norbert Langer. "Bonn Optimized Stellar Tracks (BoOST)." Astronomy & Astrophysics 658 (February 2022): A125. http://dx.doi.org/10.1051/0004-6361/202141536.

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Massive and very massive stars can play important roles in stellar populations by ejecting strong stellar winds and exploding in energetic phenomena. It is therefore imperative that their behavior be properly accounted for in synthetic model populations. We present nine grids of stellar evolutionary model sequences, together with finely resolved interpolated sequences and synthetic populations, of stars with 9–500 M⊙ and with metallicities ranging from Galactic metallicity down to 1/250 Z⊙. The stellar models were computed with the Bonn evolutionary code with consistent physical ingredients, and covering core hydrogen- and core helium-burning phases. The interpolation and population synthesis were performed with our newly developed routine SYNSTARS. Eight of the grids represent slowly rotating massive stars with a normal or classical evolutionary path, while one grid represents fast-rotating, chemically homogeneously evolving models. The grids contain data on stellar wind properties such as estimated wind velocity and kinetic energy of the wind, as well as common stellar parameters such as mass, radius, surface temperature, luminosity, mass-loss rate, and surface abundances of 34 isotopes. We also provide estimates of the helium and carbon-oxygen core mass for calculating the mass of stellar remnants. The Bonn Optimized Stellar Tracks (BoOST) project is published as simple tables that include stellar models, interpolated tracks, and synthetic populations. Covering the broadest mass and metallicity range of any published massive star evolutionary model sets to date, BoOST is ideal for further scientific applications such as star formation studies in both low- and high-redshift galaxies.

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Li, Kangshun, Zhichao Wen, Zhaopeng Wang, and Shen Li. "Optimised placement of wireless sensor networks by evolutionary algorithm." International Journal of Computational Science and Engineering 15, no. 1/2 (2017): 74. http://dx.doi.org/10.1504/ijcse.2017.085995.

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Wen, Zhichao, Zhaopeng Wang, Kangshun Li, and Shen Li. "Optimised placement of wireless sensor networks by evolutionary algorithm." International Journal of Computational Science and Engineering 15, no. 1/2 (2017): 74. http://dx.doi.org/10.1504/ijcse.2017.10006999.

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Zaman, Fawad, and Ijaz Mansoor Qureshi. "5D Parameter Estimation of Near-Field Sources Using Hybrid Evolutionary Computational Techniques." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/310875.

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Hybrid evolutionary computational technique is developed to jointly estimate the amplitude, frequency, range, and 2D direction of arrival (elevation and azimuth angles) of near-field sources impinging on centrosymmetric cross array. Specifically, genetic algorithm is used as a global optimizer, whereas pattern search and interior point algorithms are employed as rapid local search optimizers. For this, a new multiobjective fitness function is constructed, which is the combination of mean square error and correlation between the normalized desired and estimated vectors. The performance of the proposed hybrid scheme is compared not only with the individual responses of genetic algorithm, interior point algorithm, and pattern search, but also with the existing traditional techniques. The proposed schemes produced fairly good results in terms of estimation accuracy, convergence rate, and robustness against noise. A large number of Monte-Carlo simulations are carried out to test out the validity and reliability of each scheme.

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De Falco, Ivanoe, Umberto Scafuri, and Ernesto Tarantino. "Optimizing Personalized Touristic Itineraries by a Multiobjective Evolutionary Algorithm." International Journal of Information Technology & Decision Making 15, no. 06 (November 2016): 1269–312. http://dx.doi.org/10.1142/s0219622016500413.

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The paper presents an electronic tourist guide, relying on an evolutionary optimizer, able to plan personalized multiple-day itineraries by considering several contrasting objectives. Since the itinerary planning can be modeled as an extension of the NP-complete team orienteering problem with time windows, a multiobjective evolutionary optimizer is proposed to find in reasonable times near-optimal solutions to such an extension. This optimizer automatically designs the itinerary by aiming at maximizing the tourists’ satisfaction as a function of their personal preferences and environmental constraints, such as operating hours, visiting times and accessibility of the points of interests, and weather forecasting. Experimental evaluations have demonstrated that the proposed optimizer is effective in different simulated operating conditions.

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Sanchez-Montero, Rocio, Sancho Salcedo-Sanz, J. A. Portilla-Figueras, and Richard Langley. "HYBRID PIFA-PATCH ANTENNA OPTIMIZED BY EVOLUTIONARY PROGRAMMING." Progress In Electromagnetics Research 108 (2010): 221–34. http://dx.doi.org/10.2528/pier10072804.

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Dees, Nathan D., and Sonya Bahar. "Mutation Size Optimizes Speciation in an Evolutionary Model." PLoS ONE 5, no. 8 (August 3, 2010): e11952. http://dx.doi.org/10.1371/journal.pone.0011952.

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Shiroie, Masoud, and Karim Mohammadi. "Optimized Asynchronous Circuit Design based on Evolutionary Algorithm." International Journal of Computer Applications 40, no. 4 (February 29, 2012): 1–6. http://dx.doi.org/10.5120/5029-7177.

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Dang, Duc-Cuong, Anton Eremeev, and Per Kristian Lehre. "Escaping Local Optima with Non-Elitist Evolutionary Algorithms." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (May 18, 2021): 12275–83. http://dx.doi.org/10.1609/aaai.v35i14.17457.

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Most discrete evolutionary algorithms (EAs) implement elitism, meaning that they make the biologically implausible assumption that the fittest individuals never die. While elitism favours exploitation and ensures that the best seen solutions are not lost, it has been widely conjectured that non-elitism is necessary to explore promising fitness valleys without getting stuck in local optima. Determining when non-elitist EAs outperform elitist EAs has been one of the most fundamental open problems in evolutionary computation. A recent analysis of a non-elitist EA shows that this algorithm does not outperform its elitist counterparts on the benchmark problem JUMP. We solve this open problem through rigorous runtime analysis of elitist and non-elitist population-based EAs on a class of multi-modal problems. We show that with 3-tournament selection and appropriate mutation rates, the non-elitist EA optimises the multi-modal problem in expected polynomial time, while an elitist EA requires exponential time with overwhelmingly high probability. A key insight in our analysis is the non-linear selection profile of the tournament selection mechanism which, with appropriate mutation rates, allows a small sub-population to reside on the local optimum while the rest of the population explores the fitness valley. In contrast, we show that the comma-selection mechanism which does not have this non-linear profile, fails to optimise this problem in polynomial time. The theoretical analysis is complemented with an empirical investigation on instances of the set cover problem, showing that non-elitist EAs can perform better than the elitist ones. We also provide examples where usage of mutation rates close to the error thresholds is beneficial when employing non-elitist population-based EAs.

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Abudhahir, A., and S. Baskar. "Evolutionary optimised nonlinear function for linearisation of constant temperature anemometer." IET Science, Measurement & Technology 2, no. 4 (July 1, 2008): 208–18. http://dx.doi.org/10.1049/iet-smt:20070048.

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Zhang, Hong, and Masumi Ishikawa. "The performance verification of an evolutionary canonical particle swarm optimizer." Neural Networks 23, no. 4 (May 2010): 510–16. http://dx.doi.org/10.1016/j.neunet.2009.12.002.

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Shakya, S., M. Kern, G. Owusu, and C. M. Chin. "Neural network demand models and evolutionary optimisers for dynamic pricing." Knowledge-Based Systems 29 (May 2012): 44–53. http://dx.doi.org/10.1016/j.knosys.2011.06.023.

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CHIBA, Kazuhisa. "Evolutionary-Based Hybrid Optimizer Applicable to Large-Scale Design Problems." Journal of Computational Science and Technology 7, no. 1 (2013): 28–37. http://dx.doi.org/10.1299/jcst.7.28.

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Xiaohong, Qiu, and Qiu Xiaohui. "An Evolutionary Particle Swarm Optimizer Based on Fractal Brownian Motion." Journal of Computational Intelligence and Electronic Systems 1, no. 1 (June 1, 2012): 138–43. http://dx.doi.org/10.1166/jcies.2012.1016.

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Gómez-Iglesias, Antonio, Miguel A. Vega-Rodríguez, Francisco Castejón-Magaña, Miguel Cárdenas-Montes, and Enrique Morales-Ramos. "Evolutionary computation and grid computing to optimise nuclear fusion devices." Cluster Computing 12, no. 4 (August 26, 2009): 439–48. http://dx.doi.org/10.1007/s10586-009-0101-3.

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Hu, Yabao, Hanning Chen, Maowei He, Liling Sun, Rui Liu, and Hai Shen. "Multi-Swarm Multi-Objective Optimizer Based on p-Optimality Criteria for Multi-Objective Portfolio Management." Mathematical Problems in Engineering 2019 (January 21, 2019): 1–22. http://dx.doi.org/10.1155/2019/8418369.

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Portfolio management is an important technology for reasonable investment, fund management, optimal asset allocation, and effective investment. Portfolio optimization problem (POP) has been recognized as an NP-hard problem involving numerous objectives as well as constraints. Applications of evolutionary algorithms and swarm intelligence optimizers for resolving multi-objective POP (MOPOP) have attracted considerable attention of researchers, yet their solutions usually convert MOPOP to POP by means of weighted coefficient method. In this paper, a multi-swarm multi-objective optimizer based on p-optimality criteria called p-MSMOEAs is proposed that tries to find all the Pareto optimal solutions by optimizing all objectives at the same time, rather than through the above transforming method. The proposed p-MSMOEAs extended original multiple objective evolutionary algorithms (MOEAs) to cooperative mode through combining p-optimality criteria and multi-swarm strategy. Comparative experiments of p-MSMOEAs and several MOEAs have been performed on six mathematical benchmark functions and two portfolio instances. Simulation results indicate that p-MSMOEAs are superior for portfolio optimization problem to MOEAs when it comes to optimization accuracy as well as computation robustness.

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