Artykuły w czasopismach: „Multi-Task Optimisation”

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Pearce, Michael, and Juergen Branke. "Continuous multi-task Bayesian Optimisation with correlation." European Journal of Operational Research 270, no. 3 (2018): 1074–85. http://dx.doi.org/10.1016/j.ejor.2018.03.017.

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Li, Feng, Lin Zhang, T. W. Liao, and Yongkui Liu. "Multi-objective optimisation of multi-task scheduling in cloud manufacturing." International Journal of Production Research 57, no. 12 (2018): 3847–63. http://dx.doi.org/10.1080/00207543.2018.1538579.

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Panchu K., Padmanabhan, M. Rajmohan, R. Sundar, and R. Baskaran. "Multi-objective Optimisation of Multi-robot Task Allocation with Precedence Constraints." Defence Science Journal 68, no. 2 (2018): 175. http://dx.doi.org/10.14429/dsj.68.11187.

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Efficacy of the multi-robot systems depends on proper sequencing and optimal allocation of robots to the tasks. Focuses on deciding the optimal allocation of set-of-robots to a set-of-tasks with precedence constraints considering multiple objectives. Taguchi’s design of experiments based parameter tuned genetic algorithm (GA) is developed for generalised task allocation of single-task robots to multi-robot tasks. The developed methodology is tested for 16 scenarios by varying the number of robots and number of tasks. The scenarios were tested in a simulated environment with a maximum of 20 robots and 40 multi-robot foraging tasks. The tradeoff between performance measures for the allocations obtained through GA for different task levels was used to decide the optimal number of robots. It is evident that the tradeoffs occur at 20 per cent of performance measures and the optimal number of robot varies between 10 and 15 for almost all the task levels. This method shows good convergence and found that the precedence constraints affect the optimal number of robots required for a particular task level.

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Bellotti, Renato, Romana Boiger, and Andreas Adelmann. "Fast, Efficient and Flexible Particle Accelerator Optimisation Using Densely Connected and Invertible Neural Networks." Information 12, no. 9 (2021): 351. http://dx.doi.org/10.3390/info12090351.

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Particle accelerators are enabling tools for scientific exploration and discovery in various disciplines. However, finding optimised operation points for these complex machines is a challenging task due to the large number of parameters involved and the underlying non-linear dynamics. Here, we introduce two families of data-driven surrogate models, based on deep and invertible neural networks, that can replace the expensive physics computer models. These models are employed in multi-objective optimisations to find Pareto optimal operation points for two fundamentally different types of particle accelerators. Our approach reduces the time-to-solution for a multi-objective accelerator optimisation up to a factor of 640 and the computational cost up to 98%. The framework established here should pave the way for future online and real-time multi-objective optimisation of particle accelerators.

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Cvetkovski, Goga, and Lidija Petkovska. "Design Improvement of Permanent Magnet Motor Using Single- and Multi-Objective Approaches." Power Electronics and Drives 9, no. 1 (2024): 34–49. http://dx.doi.org/10.2478/pead-2024-0003.

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Abstract Optimisation, or optimal design, has become a fundamental aspect of engineering across various domains, including power devices, power systems, and industrial systems. Engineers and academics have been actively involved in optimising these systems to achieve better performance, efficiency, and cost-effectiveness. Optimising electrical machines, including permanent magnet motors, is a complex task. It often involves solving intricate problems with various parameters and constraints. Engineers use different optimisation methods to tackle these challenges. Depending on the specific requirements and goals of a design project, engineers may employ either single-objective or multi-objective optimisation approaches. Single-objective optimisation focuses on optimising a single objective, while multi-objective optimisation considers multiple conflicting objectives. In optimisation, objective functions are mathematical representations of what needs to be optimised. In this case, optimising the efficiency of the motor, reducing cogging torque, and minimising the total weight of active materials are defined as possible objective functions. Genetic algorithms are nature based algorithms that are commonly used in engineering to find optimal solutions to complex problems, including those with multiple objectives. In this paper, after conducting optimisations using different objective functions and methods, a comparative analysis of the results is performed. This helps in understanding the trade-offs and benefits of different design choices. Finite element analysis (FEA) is a computational method used to analyse the physical properties and behaviours of complex structures and systems. In this case, FEA is used to validate and analyse selected optimisation solutions to ensure they meet the desired characteristics and parameters. Overall, this work demonstrates the interdisciplinary nature of engineering, where mathematics, computer science (for optimisation algorithms), and physics (for FEA) converge to improve the performance and efficiency of electrical machines. It also underscores the importance of considering multiple objectives in design processes to find optimal solutions that strike a balance between competing goals.

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Trianni, Vito, and Manuel López-Ibáñez. "Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics." PLOS ONE 10, no. 8 (2015): e0136406. http://dx.doi.org/10.1371/journal.pone.0136406.

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Ramachandram, S., and Prashant Balkrishna Jawade. "Task scheduling in multi-cloud environment via improved optimisation theory." International Journal of Wireless and Mobile Computing 27, no. 1 (2024): 64–77. http://dx.doi.org/10.1504/ijwmc.2024.10064647.

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Jawade, Prashant Balkrishna, and S. Ramachandram. "Task scheduling in multi-cloud environment via improved optimisation theory." International Journal of Wireless and Mobile Computing 27, no. 1 (2024): 64–77. http://dx.doi.org/10.1504/ijwmc.2024.139671.

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Lisowski, Józef. "Multi-Criteria Optimisation of Multi-Stage Positional Game of Vessels." Polish Maritime Research 27, no. 1 (2020): 46–52. http://dx.doi.org/10.2478/pomr-2020-0005.

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AbstractThe paper presents a mathematical model of a positional game of the safe control of a vessel in collision situations at sea, containing a description of control, state variables and state constraints as well as sets of acceptable ship strategies, as a multi-criteria optimisation task. The three possible tasks of multi-criteria optimisation were formulated in the form of non-cooperative and cooperative multi-stage positional games as well as optimal non-game controls. The multi-criteria control algorithms corresponding to these tasks were subjected to computer simulation in Matlab/Simulink software based on the example of the real navigational situation of the passing of one’s own vessel with eighteen objects encountered in the North Sea.

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Goddanti, N. S. S. L. Venkata Jwala, Pooja Ponakampalli, Shiny Sharon Neela, Reashma Sulthana Shaik, and V. Suresh Chintalapudi. "An OptiAssign-PSO based optimisation for multi-objective multi-level multi-task scheduling in cloud computing environment." i-manager’s Journal on Cloud Computing 11, no. 1 (2024): 1. http://dx.doi.org/10.26634/jcc.11.1.20484.

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Cloud computing is a prominent and evolving distributed computing paradigm that provides users with on-demand services through a network of diverse autonomous systems with flexible computational structures. The significance of task scheduling becomes evident, serving as a vital component to elevating cloud computing's overall performance. Streamlining cost-effective execution and optimizing resource utilization is a key objective, given the NP-hard nature of the task scheduling problem. Although numerous meta-heuristic techniques have been explored to address task allocation challenges, ample opportunities remain for the development of optimal strategies. This paper presents a state-of-the-art task assignment model that revolves around OptiAssign particle swarm optimization (PSO), with a strong emphasis on the crucial role played by efficient dependency handling and multi-level task scheduling. The primary aim of this model is to optimize the utilization of virtual machine capacities, simultaneously minimizing execution time, makespan, wait time, and overall execution costs within a variety of distributed computing systems. This novel algorithm showcases outstanding performance when compared to traditional approaches in task scheduling, highlighting the importance of skillful dependency management and the implementation of multi-level task scheduling strategies. The results of this study further affirm the effectiveness of the model in addressing the inherent complexities of scenarios involving intricate task dependencies and diverse scheduling priorities.

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Faroqi, Hamed, and Mohammad saadi Mesgari. "Performance Comparison between the Multi-Colony and Multi-Pheromone ACO Algorithms for Solving the Multi-objective Routing Problem in a Public Transportation Network." Journal of Navigation 69, no. 1 (2015): 197–210. http://dx.doi.org/10.1017/s0373463315000594.

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Routing in a multimodal urban public transportation network, according to the user's preferences, can be considered as a multi-objective optimisation problem. Solving this problem is a complicated task due to the different and incompatible objective functions, various modes in the network, and the large size of the network. In this research, two optimisation algorithms are considered for solving this problem. The multi-colony and multi-pheromone Ant Colony Optimisation (ACO) algorithms are two different modes of the Multi-Objective ACO (MOACO) algorithm. Moreover, according to the acquired information, the algorithms implemented in the public transportation network of Tehran consist of four modes. In addition, three objective functions have been simultaneously considered as the problem's objectives. The algorithms are run with different initial parameters and afterwards, the results are compared and evaluated based on the different obtained routes and with the aid of the convergence and repeatability tests, diversity and convergence metrics.

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Trianni, Vito, and Manuel López-Ibáñez. "Correction: Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics." PLOS ONE 10, no. 10 (2015): e0140056. http://dx.doi.org/10.1371/journal.pone.0140056.

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Kandasamy, Kirthevasan, Gautam Dasarathy, Junier Oliva, Jeff Schneider, and Barnabás Póczos. "Multi-fidelity Gaussian Process Bandit Optimisation." Journal of Artificial Intelligence Research 66 (September 15, 2019): 151–96. http://dx.doi.org/10.1613/jair.1.11288.

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In many scientific and engineering applications, we are tasked with the maximisation of an expensive to evaluate black box function f. Traditional settings for this problem assume just the availability of this single function. However, in many cases, cheap approximations to f may be obtainable. For example, the expensive real world behaviour of a robot can be approximated by a cheap computer simulation. We can use these approximations to eliminate low function value regions cheaply and use the expensive evaluations of f in a small but promising region and speedily identify the optimum. We formalise this task as a multi-fidelity bandit problem where the target function and its approximations are sampled from a Gaussian process. We develop MF-GP-UCB, a novel method based on upper confidence bound techniques. In our theoretical analysis we demonstrate that it exhibits precisely the above behaviour and achieves better bounds on the regret than strategies which ignore multi-fidelity information. Empirically, MF-GP-UCB outperforms such naive strategies and other multi-fidelity methods on several synthetic and real experiments.

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Robinson, G. M., and A. J. Keane. "A case for multi-level optimisation in aeronautical design." Aeronautical Journal 103, no. 1028 (1999): 481–85. http://dx.doi.org/10.1017/s0001924000064435.

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Abstract This paper discusses how the inevitable limitations of computing power available to designers has restricted adoption of optimisation as an essential design tool. It is argued that this situation will continue until optimisation algorithms are developed which utilise the range of available analysis methods in a manner more like human designers. The concept of multi-level algorithms is introduced and a case made for their adoption as the way forward. The issues to be addressed in the development of multi-level algorithms are highlighted. The paper goes on to discuss a system developed at Southampton University to act as a test bed for multi-level algorithms deployed on a realistic design task. The Southampton University multi-level wing design environment integrates drag estimation algorithms ranging from an empirical code to an Euler CFD code, covering a 150,000 fold difference in computational cost. A simple multi-level optimisation of a civil transport aircraft wing is presented.

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Dong, Xueshi, Wenyong Dong, and Yongle Cai. "Ant colony optimisation for coloured travelling salesman problem by multi-task learning." IET Intelligent Transport Systems 12, no. 8 (2018): 774–82. http://dx.doi.org/10.1049/iet-its.2016.0282.

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Liu, Weining, Bo Liu, Dihua Sun, Yiming Li, and Gang Ma. "Study on multi-task oriented services composition and optimisation with the ‘Multi-Composition for Each Task’ pattern in cloud manufacturing systems." International Journal of Computer Integrated Manufacturing 26, no. 8 (2013): 786–805. http://dx.doi.org/10.1080/0951192x.2013.766939.

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Sarathambekai, S., and K. Umamaheswari. "Task scheduling using multi-objective hamming discrete particle swarm optimisation in distributed systems." International Journal of Swarm Intelligence 2, no. 2/3/4 (2016): 100. http://dx.doi.org/10.1504/ijsi.2016.081132.

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Sarathambekai, S., and K. Umamaheswari. "Task scheduling using multi-objective hamming discrete particle swarm optimisation in distributed systems." International Journal of Swarm Intelligence 2, no. 2/3/4 (2016): 100. http://dx.doi.org/10.1504/ijsi.2016.10002174.

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Nagalakshmi, Bantupalli, and Sumathy Subramanian. "An efficient multi-objective task scheduling in edge computing using adaptive honey badger optimisation." International Journal of Web Engineering and Technology 19, no. 2 (2024): 110–26. http://dx.doi.org/10.1504/ijwet.2024.139866.

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Karczewski, Artur, and Janusz Kozak. "A Generative Approach to Hull Design for a Small Watercraft." Polish Maritime Research 30, no. 1 (2023): 4–12. http://dx.doi.org/10.2478/pomr-2023-0001.

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Abstract In the field of ocean engineering, the task of spatial hull modelling is one of the most complicated problems in ship design. This study presents a procedure applied as a generative approach to the design problems for the hull geometry of small vessels using elements of concurrent design with multi-criteria optimisation processes. Based upon widely available commercial software, an algorithm for the mathematical formulation of the boundary conditions, the data flow during processing and formulae for the optimisation processes are developed. As an example of the application of this novel approach, the results for the hull design of a sailing yacht are presented.

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Bonisoli, Elvio, Francesco Di Monaco, Stefano Tornincasa, Fabio Freschi, Luca Giaccone, and Maurizio Repetto. "Multi-physics optimisation of an energy harvester device for automotive application." COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 33, no. 3 (2014): 846–55. http://dx.doi.org/10.1108/compel-10-2012-0208.

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Purpose – Supplying remote wireless sensors is not an easy task if the site where the device is located is not easily accessible. In order to obtain direct measurements of the road-vehicle interactions, sensors must be placed inside the tyre environment thus a power supply must be available for their working there without any wire connection with the car main power. The paper aims to discuss these issues. Design/methodology/approach – An electro-mechanical energy harvester has thus been developed for supplying an automotive wireless sensor of pressure, temperature and acceleration to be placed on the inner line of a tyre. The primary energy source is the vibrations or variable accelerations imposed to the device and induced in the tyre by the wheeling. Findings – The harvester has been designed by means of a multi-physics optimisation based on an integrated electromagnetic-mechanical circuit simulator. Thus an automated optimisation of the device with respect to volume constraints, magnets dimensions, induction coils placement and size have been performed to increase the average power extracted from the device at different wheeling speeds. Originality/value – The use of the multi-physics environment together with automated optimisation technique has been tested for the first time on the electromagnetic harvester structure.

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Benkhelifa, Elhadj, Ashutosh Tiwari, and Mohamed Abdel-Maguid. "Advanced Design Optimisation by Means of Multiobjective Evolutionary Algorithms: The Case of Two Real World Applications." Key Engineering Materials 572 (September 2013): 589–92. http://dx.doi.org/10.4028/www.scientific.net/kem.572.589.

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The Design Optimisation (DO) of Complex Systems is often a multidisciplinary task and involves multiple conflicting objectives and design constraints, where conventional methods cannot solve efficiently. This paper presents Advanced DO by Means of Evolutional Algorithms in two Real World Applications Electronics and Micro-Electro-Mechanical-Systems (MEMS). The former is presented in the context of multi-objective evolutionary synthesis and optimisation of analogue systems. As for the latter, DO of MEMS bio-mimetically is a very novel area of research, Which addresses the compelling change in the traditional landscape of the associated research disciplines by seeking to provide a novel biologically inspired computational platform for DO of micro-scale designs. This paper presents the latest advancements in the application of EAs in the DO of MEMS and analogue electronic systems and the emergence of the new area of Multidisciplinary Optimisation'.

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Chander, Satish, P. Vijaya, and Praveen Dhyani. "ADOFL: Multi-Kernel-Based Adaptive Directive Operative Fractional Lion Optimisation Algorithm for Data Clustering." Journal of Intelligent Systems 27, no. 3 (2018): 317–29. http://dx.doi.org/10.1515/jisys-2016-0175.

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Abstract The progress of databases in fields such as medical, business, education, marketing, etc., is colossal because of the developments in information technology. Knowledge discovery from such concealed bulk databases is a tedious task. For this, data mining is one of the promising solutions and clustering is one of its applications. The clustering process groups the data objects related to each other in a similar cluster and diverse objects in another cluster. The literature presents many clustering algorithms for data clustering. Optimisation-based clustering algorithm is one of the recently developed algorithms for the clustering process to discover the optimal cluster based on the objective function. In our previous method, direct operative fractional lion optimisation algorithm was proposed for data clustering. In this paper, we designed a new clustering algorithm called adaptive decisive operative fractional lion (ADOFL) optimisation algorithm based on multi-kernel function. Moreover, a new fitness function called multi-kernel WL index is proposed for the selection of the best centroid point for clustering. The experimentation of the proposed ADOFL algorithm is carried out over two benchmarked datasets, Iris and Wine. The performance of the proposed ADOFL algorithm is validated over existing clustering algorithms such as particle swarm clustering (PSC) algorithm, modified PSC algorithm, lion algorithm, fractional lion algorithm, and DOFL. The result shows that the maximum clustering accuracy of 79.51 is obtained by the proposed method in data clustering.

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Guze, Sambor, Tomasz Neumann, and Przemysław Wilczyński. "Multi-Criteria Optimisation of Liquid Cargo Transport According to Linguistic Approach to the Route Selection Task." Polish Maritime Research 24, s1 (2017): 89–96. http://dx.doi.org/10.1515/pomr-2017-0026.

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AbstractThe main aim of the paper is to present the possibility of use of the multi-criteria optimization method Analytical Hierarchy Process (AHP) to liquid cargo transportation by sea. Finding the optimal solution is not simple. There are many factors influencing the shipping process. In the case of liquid cargo, the most important thing is the safety of the crew, ship, and environment. Therefore, the Mathematical Theory of Evidence is introduced and used to determine the optimal path in terms of time and safety of transport. Moreover, the details of liquid cargo transport process are described with particular attention to ship to ship operations. Besides, the basic concept of the AHP method, steps of the algorithm are introduced. Finally, the multicriteria optimization of the transport of the liquid cargo from the Persian Gulf to Port of Gdansk is done. It is based on the experts′ opinions.

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López Ruiz, José L., Ángeles Verdejo Espinosa, Alicia Montoro Lendínez, and Macarena Espinilla Estévez. "OBLEA: A New Methodology to Optimise Bluetooth Low Energy Anchors in Multi-occupancy Location Systems." JUCS - Journal of Universal Computer Science 29, no. 6 (2023): 627–46. http://dx.doi.org/10.3897/jucs.96878.

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Nowadays, it is becoming increasingly important to understand the multiple configuration factors of BLE anchors in indoor location systems. This task becomes particularly crucial in the context of activity recognition in multi-occupancy smart environments. Knowing the impact of the configuration of BLE anchors in an indoor location system allows us to distinguish the interactions performed by each inhabitant in a smart environment according to their proximity to each sensor. This paper proposes a new methodology, OBLEA, that determines the optimisation of Bluetooth Low Energy (BLE) anchors in indoor location systems, considering multiple BLE variables to increase flexibility and facilitate transferability to other environments. Concretely, we present a model based on a data-driven approach that considers configurations to obtain the best performing configuration with a minimum number of anchors. This methodology includes a flexible framework for the indoor space, the architecture to be deployed, which considers the RSSI value of the BLE anchors, and finally, optimisation and inference for indoor location. As a case study, OBLEA is applied to determine the location of ageing inhabitants in a nursing home in Alcaudete, Ja&eacute;n (Spain). Results show the extracted knowledge related to the optimisation of BLE anchors involved in the case study.

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Minaeva, Yu V. "ATHEMATICAL MODEL FOR THE OPTIMISATION OF HIERARCHICAL MULTI-LEVEL PRODUCTION SYSTEMS." Herald of Dagestan State Technical University. Technical Sciences 45, no. 2 (2018): 140–48. http://dx.doi.org/10.21822/2073-6185-2018-45-2-140-148.

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Objectives The aim of the study is to develop a mathematical model for the complex solution of various problems in designing and reconstructing the technological system of a production workshop of a machine-building enterprise.Methods Complex system theory and an aggregative decomposition approach are used as the methodological basis for modelling complex hierarchical productions, making it possible to represent a complex system in the form of a set of interconnected subsystems.Results A mathematical model designed for a complex solution of problems associated with the formation of an optimal production programme and selection ofequipment was developed. Operative parameters for processing machines according to a single optimisation criterion for the workshop were established. Distinctive features of the optimisation model proposed in the article are the possibility of its application both for design and for the reconstruction of a technological system, as well as the possibility of simple scaling to the required level (i.e. the workshop as a whole or a separate section).Conclusion The article presents a complex mathematical model for optimising the technological system based on a single optimisation criterion for the workshop, combining the solution of the main tasks of design and reconstruction of the workshop. The use of a single integral optimisation criterion for several problems allows the strong interrelationships between individual tasks to be taken into account. The model is based on the principle of arranging a model from a set of typical elements, easing the construction of models for any sub-task combinations, as well as their respective options. It is possible to use the model’s multi-level unification and scalability to increase modelling efficiency and thus optimise complex multinomenclature productions.

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Zhang, Qinglei, Ning Li, Jianguo Duan, Jiyun Qin, and Ying Zhou. "Resource Scheduling Optimisation Study Considering Both Supply and Demand Sides of Services under Cloud Manufacturing." Systems 12, no. 4 (2024): 133. http://dx.doi.org/10.3390/systems12040133.

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In cloud manufacturing environments, the scheduling of multi-user manufacturing tasks often fails to consider the impact of service supply on resource allocation. This study addresses this gap by proposing a bi-objective multi-user multi-task scheduling model aimed at simultaneously minimising workload and maximising customer satisfaction. To accurately capture customer satisfaction, a novel comprehensive rating index is introduced, integrating the actual completion cost, time, and processing quality against customer expectations. Furthermore, vehicle constraints are incorporated into the model to accommodate potential delays in transport vehicle availability, thereby enhancing its alignment with real-world manufacturing settings. The proposed mathematical model is solved using an improved three-stage genetic algorithm, which integrates the k-means algorithm and a real-time sequence scheduling strategy to optimise solution quality. Validation against alternative algorithms across various case scales demonstrates the efficacy of the approach in providing practical scheduling solutions for real-case scenarios.

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Bawane, Madhuri N., and K. M. Bhurchandi. "Multi-objective particle swarm optimisation for mental task classification using hybrid features and hierarchical neural network classifier." International Journal of Biomedical Engineering and Technology 32, no. 2 (2020): 177. http://dx.doi.org/10.1504/ijbet.2020.10027366.

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Bawane, Madhuri N., and K. M. Bhurchandi. "Multi-objective particle swarm optimisation for mental task classification using hybrid features and hierarchical neural network classifier." International Journal of Biomedical Engineering and Technology 32, no. 2 (2020): 177. http://dx.doi.org/10.1504/ijbet.2020.105653.

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Albowarab, Mustafa Hasan, Nurul Azma Zakaria, and Zaheera Zainal Abidin. "Directionally-Enhanced Binary Multi-Objective Particle Swarm Optimisation for Load Balancing in Software Defined Networks." Sensors 21, no. 10 (2021): 3356. http://dx.doi.org/10.3390/s21103356.

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Various aspects of task execution load balancing of Internet of Things (IoTs) networks can be optimised using intelligent algorithms provided by software-defined networking (SDN). These load balancing aspects include makespan, energy consumption, and execution cost. While past studies have evaluated load balancing from one or two aspects, none has explored the possibility of simultaneously optimising all aspects, namely, reliability, energy, cost, and execution time. For the purposes of load balancing, implementing multi-objective optimisation (MOO) based on meta-heuristic searching algorithms requires assurances that the solution space will be thoroughly explored. Optimising load balancing provides not only decision makers with optimised solutions but a rich set of candidate solutions to choose from. Therefore, the purposes of this study were (1) to propose a joint mathematical formulation to solve load balancing challenges in cloud computing and (2) to propose two multi-objective particle swarm optimisation (MP) models; distance angle multi-objective particle swarm optimization (DAMP) and angle multi-objective particle swarm optimization (AMP). Unlike existing models that only use crowding distance as a criterion for solution selection, our MP models probabilistically combine both crowding distance and crowding angle. More specifically, we only selected solutions that had more than a 0.5 probability of higher crowding distance and higher angular distribution. In addition, binary variants of the approaches were generated based on transfer function, and they were denoted by binary DAMP (BDAMP) and binary AMP (BAMP). After using MOO mathematical functions to compare our models, BDAMP and BAMP, with state of the standard models, BMP, BDMP and BPSO, they were tested using the proposed load balancing model. Both tests proved that our DAMP and AMP models were far superior to the state of the art standard models, MP, crowding distance multi-objective particle swarm optimisation (DMP), and PSO. Therefore, this study enables the incorporation of meta-heuristic in the management layer of cloud networks.

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Dharani Pragada, Venkata Aditya, Akanistha Banerjee, and Srinivasan Venkataraman. "OPTIMISATION OF NAVAL SHIP COMPARTMENT LAYOUT DESIGN USING GENETIC ALGORITHM." Proceedings of the Design Society 1 (July 27, 2021): 2339–48. http://dx.doi.org/10.1017/pds.2021.495.

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AbstractAn efficient general arrangement is a cornerstone of a good ship design. A big part of the whole general arrangement process is finding an optimized compartment layout. This task is especially tricky since the multiple needs are often conflicting, and it becomes a serious challenge for the ship designers. To aid the ship designers, improved and reliable statistical and computation methods have come to the fore. Genetic algorithms are one of the most widely used methods. Islier's algorithm for the multi-facility layout problem and an improved genetic algorithm for the ship layout design problem are discussed. A new, hybrid genetic algorithm incorporating local search technique to further the improved genetic algorithm's practicality is proposed. Further comparisons are drawn between these algorithms based on a test case layout. Finally, the developed hybrid algorithm is implemented on a section of an actual ship, and the findings are presented.

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Agyemang, Brighter, Fenghui Ren, and Jun Yan. "Proactive Agent Behaviour in Dynamic Distributed Constraint Optimisation Problems." Information 15, no. 5 (2024): 255. http://dx.doi.org/10.3390/info15050255.

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In multi-agent systems, the Dynamic Distributed Constraint Optimisation Problem (D-DCOP) framework is pivotal, allowing for the decomposition of global objectives into agent constraints. Proactive agent behaviour is crucial in such systems, enabling agents to anticipate future changes and adapt accordingly. Existing approaches, like Proactive Dynamic DCOP (PD-DCOP) algorithms, often necessitate a predefined environment model. We address the problem of enabling proactive agent behaviour in D-DCOPs where the dynamics model of the environment is unknown. Specifically, we propose an approach where agents learn local autoregressive models from observations, predicting future states to inform decision-making. To achieve this, we present a temporal experience-sharing message-passing algorithm that leverages dynamic agent connections and a distance metric to collate training data. Our approach outperformed baseline methods in a search-and-extinguish task using the RoboCup Rescue Simulator, achieving better total building damage. The experimental results align with prior work on the significance of decision-switching costs and demonstrate improved performance when the switching cost is combined with a learned model.

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Hameed, Khurram, Douglas Chai, and Alexander Rassau. "A Sample Weight and AdaBoost CNN-Based Coarse to Fine Classification of Fruit and Vegetables at a Supermarket Self-Checkout." Applied Sciences 10, no. 23 (2020): 8667. http://dx.doi.org/10.3390/app10238667.

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The physical features of fruit and vegetables make the task of vision-based classification of fruit and vegetables challenging. The classification of fruit and vegetables at a supermarket self-checkout poses even more challenges due to variable lighting conditions and human factors arising from customer interactions with the system along with the challenges associated with the colour, texture, shape, and size of a fruit or vegetable. Considering this complex application, we have proposed a progressive coarse to fine classification technique to classify fruit and vegetables at supermarket checkouts. The image and weight of fruit and vegetables have been obtained using a prototype designed to simulate the supermarket environment, including the lighting conditions. The weight information is used to change the coarse classification of 15 classes down to three, which are further used in AdaBoost-based Convolutional Neural Network (CNN) optimisation for fine classification. The training samples for each coarse class are weighted based on AdaBoost optimisation, which are updated on each iteration of a training phase. Multi-class likelihood distribution obtained by the fine classification stage is used to estimate a final classification with a softmax classifier. GoogleNet, MobileNet, and a custom CNN have been used for AdaBoost optimisation, with promising classification results.

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Elsedimy, Elsayed, and Fahad Algarni. "MOTS‐ACO: An improved ant colony optimiser for multi‐objective task scheduling optimisation problem in cloud data centres." IET Networks 11, no. 2 (2022): 43–57. http://dx.doi.org/10.1049/ntw2.12033.

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Saravana Balaji, B., Gomathi B, and Karthikeyan Krishnasamy. "Epsilon-Fuzzy Dominance Sort Based Composite Discrete Artificial Bee Colony optimisation for Multi-Objective Cloud Task Scheduling Problem." International Journal of Business Intelligence and Data Mining 12, no. 3/4 (2017): 1. http://dx.doi.org/10.1504/ijbidm.2017.10004803.

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Gomathi, B., Karthikeyan Krishnasamy, and B. Saravana Balaji. "Epsilon-fuzzy dominance sort-based composite discrete artificial bee colony optimisation for multi-objective cloud task scheduling problem." International Journal of Business Intelligence and Data Mining 13, no. 1/2/3 (2018): 247. http://dx.doi.org/10.1504/ijbidm.2018.088435.

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Veneri, Giacomo, Francesca Rosini, Pamela Federighi, Antonio Federico, and Alessandra Rufa. "Evaluating gaze control on a multi-target sequencing task: The distribution of fixations is evidence of exploration optimisation." Computers in Biology and Medicine 42, no. 2 (2012): 235–44. http://dx.doi.org/10.1016/j.compbiomed.2011.11.013.

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Lomazzi, L., F. Cadini, M. Giglio, et al. "Integrated multi-objective optimisation of the support structure of heliostats in concentrated solar power plants using a genetic algorithm." IOP Conference Series: Materials Science and Engineering 1214, no. 1 (2022): 012027. http://dx.doi.org/10.1088/1757-899x/1214/1/012027.

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Abstract The optimisation of the support structure of heliostats in concentrating solar power plants is a fundamental task aimed at attempting to reduce the high levelised cost of energy (LCOE) of current configurations. In this work, an integrated multi-objective optimisation framework is presented, which relies on the combination of a lean and fast structural model with a genetic algorithm to simultaneously minimise both the overall mass of the support structure and the mean angle of rotation of the mirror surface, which directly affects the optical efficiency of the component. A particular feature of the proposed framework is that it represents an integrated solution, i.e., it allows to simultaneously optimise the main components of the heliostat support structure, i.e., the pedestal, the truss and the back support structure, assuming they are off-the-shelf components easily available on the market. The optimisation problem is set up selecting as design variables (i) the number of elements in the back support structure and (ii) the relevant characteristics of all the components considered, i.e., section shape and dimensions, according to the components commercial datasheets. At each iteration of the optimisation process, the structural model is fed with the current design variables values and, according to some computed aerodynamic loads, it allows evaluating the displacement and rotation of the points of interest within the mirror surface. An aerodynamic model present in the literature based on experimental wind tunnel tests is used to estimate the wind forces acting on the heliostat as a function both of the mirror inclination angle with respect to the ground and of the wind direction with respect to the mirror orientation. In this work, the proposed methodology is demonstrated on a realistic case study and the results commented in detail, highlighting possible future developments and the limitations of the framework.

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SUN, YUEXIN, and YU CHEN. "Fast textile pattern generation combining MRF-based and Gram-based methods." Industria Textila 74, no. 04 (2023): 439–45. http://dx.doi.org/10.35530/it.074.04.202254.

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Textile pattern design is a tedious and challenging task for designers. This paper proposes a fast textile pattern generation algorithm that combines MRF-based and Gram-based methods. First, the reconstruction method based on image optimisation is determined after analysing the specific requirements of textile pattern design. The pre-trained VGG19 is selected as the style feature extraction neural network. Then, we compare the generation results of various combinations of style loss functions and propose a multi-resolution image optimisation method. Finally, the smoothing loss and colour histogram matching are added to improve the generation quality further, thus constructing an image generation algorithm for textile pattern design. Experimental results demonstrate that our algorithm can effectively generate complex textile patterns with global style and local detail features. The average image generation time is 575s, over 84.3% faster than traditional algorithms. At the same time, this algorithm is convenient for switching styles and requires lower computational capability. It can improve pattern design efficiency and promote the application of image generation technology in textile design.

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Garus, Jerzy, and Bogdan Żak. "The practical aspects of implementation of the thrust allocation procedure for a multi-propulsor underwater robot." Polish Hyperbaric Research 65, no. 4 (2018): 39–48. http://dx.doi.org/10.2478/phr-2018-0022.

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Abstract This article addresses the practical aspects of the synthesis of an automatic control system for the thrust allocation strategy in the propulsion system of an unmanned underwater vehicle. The vehicle under consideration is a robot submarine equipped with a multi-propulsion system providing four degrees of freedom of movement. The power distribution algorithms are based on limited optimisation methods that allow the determination, on the basis of generalised torques and forces, of how much thrust is required to be produced by individual propulsors. Considering the issue of power distribution as a task of square and linear programming, two algorithms of thrust allocation were proposed and compared. The conducted model tests made it possible to evaluate their quality and efficiency in relation to speed and computational complexity.

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Andreu Casas, Enric, ALBERTO GARCIA VILLORIA, and RAFAEL PASTOR MORENO. "SOLVING THE MULTI-MANNED ASSEMBLY LINE BALANCING PROBLEM WITH DEPENDENT TASK TIMES BY MEANS OF EMPIRICALLY ADJUSTED GREEDY HEURISTICS (EAGH)." DYNA MANAGEMENT 11, no. 1 (2023): [9P.]. http://dx.doi.org/10.6036/mn10905.

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ABSTRACT: Multi-manned assembly lines are widely used in manufacturing industries that process a high-volume of large-sized workpieces. At each workstation there are multiple workers simultaneously performing different tasks with the possibility of interfering with each other, leading to an increase in the processing task times. This paper studies this type of problem: the multi-manned assembly line balancing problem with dependent task times (MALBP-DTT). As discussed in recent literature, the HEUR_PART procedure presents the best behaviour for solving MALBP-DTT. In this paper, HEUR_PART is improved by using the Empirically Adjusted Greedy Heuristics (EAGH) procedure along with a new procedure (named “EAGH-CKTL”) that is presented in this paper and is based on using EAGH combined with the cocktail of heuristics concept. EAGH and EAGH-CKTL are used to design new priority rules for solving MALBP-DTT through the HEUR_PART steps. In particular, EAGH-CKTL is applied for building new priority rules that have good performance as part of a cocktail of heuristics. The computational experiments show the efficiency of using both EAGH and EAGH-CKTL in the process of designing efficient priority rules: one of the priority rules designed with EAGH presents better performance than any other rule proposed in the literature for HEUR_PART, while another rule designed with EAGH-CKTL evidences a remarkable improvement in the HEUR_PART results when added to its original cocktail of heuristics. Keywords: combinatorial optimisation; assembly line balancing; multi-manned workstations; dependent task times; EAGH; cocktail of heuristics.

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Cheng Seong, Khor. "A Model-Based Optimisation Approach for Process Synthesis of Olefins from Petroleum with Application to the Malaysian Petrochemical Industry." ASM Science Journal 12 (December 30, 2019): 1–15. http://dx.doi.org/10.32802/asmscj.2019.393.

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The shale gas revolution has rekindled interest in olefins production due to the abundance of ethane as a raw material resource. However, the main technology still revolves around the cost-intensive distillation operation. Hence this work aims to investigate the economic optimisation of olefins synthesis from petroleum in the light of recent developments. A model-based approach is applied to determine the optimal sequencing of separation and reaction processes for a multi-component hydrocarbon mixture feed to produce mainly ethylene and propylene. a mixed-integer linear program (MILP) is formulated based on a superstructure that captures numerous plausible synthesis alternatives. The model comprises linear mass balance reactor representation and simple sharp distillation based on split fractions for product recovery. Integer binary variablesis used for selecting the task for equipment and continuous variables for representing the flowrate of each task. To expedite converging to an optimal solution of a least total annualised cost configuration, the formulation is appended with logical constraints on the design and structural specifications derived from heuristics based on practical knowledge and experience. The modelling approach on actual case studies based on two such petrochemical facilities operating in Malaysia is implemented. Additionally, the solution analysis is enriched with the investigation on a second- and third-best (suboptimal) configurations obtained through appropriate integer cuts as constraints to the model. The results show good agreement with existing plant configurations, thus substantiating the value and verification of the proposed model-based optimisation approach.

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Nurhafizah Anual, Siti, Mohd Faisal Ibrahim, Nurhana Ibrahim, et al. "GA-based Optimisation of a LiDAR Feedback Autonomous Mobile Robot Navigation System." Bulletin of Electrical Engineering and Informatics 7, no. 3 (2018): 433–41. http://dx.doi.org/10.11591/eei.v7i3.1275.

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Autonomous mobile robots require an efficient navigation system in order to navigate from one location to another location fast and safe without hitting static or dynamic obstacles. A light-detection-and-ranging (LiDAR) based autonomous robot navigation is a multi-component navigation system consists of various parameters to be configured. With such structure and sometimes involving conflicting parameters, the process of determining the best configuration for the system is a non-trivial task. This work presents an optimisation method using Genetic algorithm (GA) to configure such navigation system with tuned parameters automatically. The proposed method can optimise parameters of a few components in a navigation system concurrently. The representation of chromosome and fitness function of GA for this specific robotic problem are discussed. The experimental results from simulation and real hardware show that the optimised navigation system outperforms a manually-tuned navigation system of an indoor mobile robot in terms of navigation time.

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Doll, U., I. Röhle, and M. Dues. "Unsteady Multi-Parameter Flow Diagnostics By Filtered Rayleigh Scattering: System Design By Multi-Objective Optimisation." Proceedings of the International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics 20 (July 11, 2022): 1–18. http://dx.doi.org/10.55037/lxlaser.20th.23.

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The measurement of the time-resolved three-component (3C) velocity field together with scalar flow quantities such as temperature or pressure by laser-optical diagnostics is a challenging task. Current approaches typically employ combinations of different methods relying on tracer particles or molecules, which requires elaborate calibration procedures of the tracer's photo-physical properties and extensive instrumentation. In contrast to this, the tracer-free filtered Rayleigh scattering (FRS) technique has been proven to obtain combined time-averaged velocity and scalar fields and might offer a viable alternative for unsteady flow diagnostics. By applying multiple perspective views, two detection system variants are presented, combining 1) six observation branches with one camera/molecular filter and 2) four camera views with two cameras and molecular filters of differing vapour densities. Both configurations in principle allow for the simultaneous measurement of instantaneous 3C velocity, temperature and pressure fields. Multi-objective optimisation is used to enhance the detection setups for different sets of experimental configurations. It is shown that a higher number of observation positions and the associated dynamics of the FRS signal prove to be advantageous compared to the use of less views in combination with two acquisition channels equipped with different molecular filters. It is also demonstrated that the use of linearly polarised laser light is preferred over circular polarisation. Future work will focus on the realisation of the multiple-view FRS concept for the combined measurement of 3C velocity and scalar fields.

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Smith, Dale, Robbie Glachan, Scott Tranter, and Robert Potter. "Optimisation of System Configuration Using Machine Learning as a Surrogate Model." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 268, no. 6 (2023): 2096–107. http://dx.doi.org/10.3397/in_2023_0308.

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Complex mechanical systems provide a degree of reliability through redundancy. That is, they have duplicate systems, either similar or dissimilar, performing the same function. These duplicate systems will vary in their dynamic properties and may have different vibration transmission paths due to their connections within the overall system. Therefore, the use of redundancy gives rise to different vibrational behaviour of the overall system depending on the machines selected to deliver the intended system output. In some circumstances, it is necessary to minimise vibration levels by selecting a particular system configuration. In large complex systems consisting of multiple sources, this is not a trivial task, and virtually impossible to investigate all possible machinery combinations leading to the lowest vibration levels, whilst delivering the desired output. To address this, a machine learning model has been trained to provide predictions of machinery vibration levels. Data are obtained using an experimental vibration rig, emulating a complex, multi-source mechanical system. The machine learning model is subsequently utilised within a genetic algorithm optimisation routine in order to obtain the system configuration producing the lowest vibration levels at a number of observer locations for a specified system output.

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Cubukcuoglu, Cemre, Arzu Cilasun Kunduraci, and Sahar Asadollahi Asl Zarkhah. "Indoor Environmental Quality Optimisation Model for Institutional Care Rooms of Elderly People." Buildings 13, no. 10 (2023): 2625. http://dx.doi.org/10.3390/buildings13102625.

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It is known that the elderly usually spend the last years of their lives indoors, with little contact with others and the outside environment. Indoor environmental quality (IEQ) conditions related to lighting, air quality, thermal comfort, and acoustics directly affect their quality of life. In this study, the main focus is on the design of institutional care rooms for elderly people to create an indoor comfort. However, considering all four factors of IEQ in one model is a challenging task. A multi-objective problem is formulated based on a weighted sum of IEQ components in a parametric modelling environment using computational design methods. Several simulation tools are utilised, and a Self-Adaptive Ensemble Differential Evolution Algorithm is proposed to tackle this complex problem. The results show that optimal ranges for each IEQ component are achieved, with average values reaching 72% of the ideal benchmarks after the algorithm is converged. Results reveal strong correlations between IEQ components. This significant improvement in indoor environmental quality (IEQ) demonstrates the efficacy of the optimisation algorithm used. This study emphasises the flexibility and relevance of these findings for wider implementation in similar settings.

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Jusop, Masitah, and Mohd Fadzil Faisae Ab Rashid. "Optimisation of Assembly Line Balancing Type-E with Resource Constraints Using NSGA-II." Key Engineering Materials 701 (July 2016): 195–99. http://dx.doi.org/10.4028/www.scientific.net/kem.701.195.

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Assembly line balancing of Type-E problem (ALB-E) is an attempt to assign the tasks to the various workstations along the line so that the precedence relations are satisfied and some performance measures are optimised. A majority of the recent studies in ALB-E assume that any assembly task can be assigned to any workstation. This assumption lead to higher usage of resource required in assembly line. This research studies assembly line balancing of Type-E problem with resource constraint (ALBE-RC) for a single-model. In this work, three objective functions are considered, i.e. minimise number of workstation, cycle time and number of resources. In this paper, an Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) has been proposed to optimise the problem. Six benchmark problems have been used to test the optimisation algorithm and the results are compared to multi-objective genetic algorithm (MOGA) and hybrid genetic algorithm (HGA). From the computational test, it was found NSGA-II has the ability to explore search space, has better accuracy of solution and also has a uniformly spaced solution. In future, a research to improve the solution accuracy is proposed to enhance the performance of the algorithm.

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Wang, Qi, Dragan A. Savić, and Zoran Kapelan. "Hybrid metaheuristics for multi-objective design of water distribution systems." Journal of Hydroinformatics 16, no. 1 (2013): 165–77. http://dx.doi.org/10.2166/hydro.2013.009.

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Multi-objective design of Water Distribution Systems (WDSs) has received considerable attention in the past. Multi-objective evolutionary algorithms (MOEAs) are popular in tackling this problem due to their ability to approach the true Pareto-optimal front (PF) in a single run. Recently, several hybrid metaheuristics based on MOEAs have been proposed and validated on test problems. Among these algorithms, AMALGAM and MOHO are two noteworthy representatives which mix their constituent algorithms in contrasting fashion. In this paper, they are employed to solve a wide range of benchmark design problems against another state-of-the-art algorithm, namely NSGA-II. The design task is formulated as a bi-objective optimisation problem taking cost and network resilience into account. The performance of three algorithms is assessed via normalised hypervolume indicator. The results demonstrate that AMALGAM is superior to MOHO and NSGA-II in terms of convergence and diversity on the networks of small-to-medium size; however, for larger networks, the performance of hybrid algorithms deteriorates as they lose their adaptive capabilities. Future improvement and/or redesign on hybrid algorithms should not only adopt the strategies of adaptive portfolios of sub-algorithms and global information sharing, but also prevent the deterioration mainly caused by imbalance of constituent algorithms.

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Abdulrauf Sharifai, Garba, and Zurinahni Zainol. "Feature Selection for High-Dimensional and Imbalanced Biomedical Data Based on Robust Correlation Based Redundancy and Binary Grasshopper Optimization Algorithm." Genes 11, no. 7 (2020): 717. http://dx.doi.org/10.3390/genes11070717.

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The training machine learning algorithm from an imbalanced data set is an inherently challenging task. It becomes more demanding with limited samples but with a massive number of features (high dimensionality). The high dimensional and imbalanced data set has posed severe challenges in many real-world applications, such as biomedical data sets. Numerous researchers investigated either imbalanced class or high dimensional data sets and came up with various methods. Nonetheless, few approaches reported in the literature have addressed the intersection of the high dimensional and imbalanced class problem due to their complicated interactions. Lately, feature selection has become a well-known technique that has been used to overcome this problem by selecting discriminative features that represent minority and majority class. This paper proposes a new method called Robust Correlation Based Redundancy and Binary Grasshopper Optimization Algorithm (rCBR-BGOA); rCBR-BGOA has employed an ensemble of multi-filters coupled with the Correlation-Based Redundancy method to select optimal feature subsets. A binary Grasshopper optimisation algorithm (BGOA) is used to construct the feature selection process as an optimisation problem to select the best (near-optimal) combination of features from the majority and minority class. The obtained results, supported by the proper statistical analysis, indicate that rCBR-BGOA can improve the classification performance for high dimensional and imbalanced datasets in terms of G-mean and the Area Under the Curve (AUC) performance metrics.

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Droandi, G., and G. Gibertini. "Aerodynamic shape optimisation of a proprotor and its validation by means of CFD and experiments." Aeronautical Journal 119, no. 1220 (2015): 1223–51. http://dx.doi.org/10.1017/s0001924000011222.

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AbstractThe aerodynamic shape design of a proprotor for a tiltrotor aircraft is a very complex and demanding task because it has to combine good hovering capabilities with high propeller efficiency. The aim of the present work is to describe a two-level procedure and its results for the aerodynamic shape design of a new rotor blade for a high-performance tiltwing tiltrotor aircraft taking into account the most important flight conditions in which the aircraft can operate. Span-wise distributions of twist, chord and aerofoil were chosen making use of a multi-objective genetic optimiser that worked on three objectives simultaneously. A non-linear sweep angle distribution along the blade was designed to reduce the power losses due to compressibility effects during axial flight at high speed. During the optimisation process, the aerodynamic performance of the blade was evaluated with a classical two-dimensional strip theory solver. The optimised blade was than analysed by means of a compressible Navier-Stokes solver and calculations were validated comparing numerical results with experimental data obtained from wind-tunnel tests of a scaled model of the proprotor.

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