Results 11 to 20 of about 75,350 (240)
Improved NSGA-III Based on Kriging Model for Expensive Many-objective Optimization Problems [PDF]
Jisuanji kexue, 2023 In many real world multi-objective optimization problems(MOP),the cost of physical experiments or numerical simulations for fitness evaluation is very expensive,which poses a great challenge to most existing multi-objective evolutionary algorithmEAs ...
GENG Huantong, SONG Feifei, ZHOU Zhengli, XU Xiaohan
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Many-objective evolutionary algorithm based on three-way decision
Egyptian Informatics Journal, 2023 In recent years, many-objective optimization problems have been widely used. however, with the increase of the number of objectives, the difficulty of solving increases exponentially, and the imbalance between convergence and diversity becomes more ...
Zhihua Cui +3 more
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A chaotic-based improved many-objective Jaya algorithm for many-objective optimization problems
International Journal of Industrial Engineering Computations, 2021 The Jaya algorithm is a recently developed novel population-based algorithm. The proposed work presents the modifications in the existing many-objective Jaya (MaOJaya) algorithm by integrating the chaotic sequence to improve the performance to optimize many-objective benchmark optimization problems.
Sandeep U. Mane, M. R. Narsingrao
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Applied Sciences, 2022 Dynamic Multi-Objective Optimization Problems (DMOPs) and Many-Objective Optimization Problems (MaOPs) are two classes of the optimization field that have potential applications in engineering.
Ahlem Aboud +5 more
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A Visualizable Test Problem Generator for Many-Objective Optimization [PDF]
IEEE Transactions on Evolutionary Computation, 2022 Visualizing the search behavior of a series of points or populations in their native domain is critical in understanding biases and attractors in an optimization process. Distance-based many-objective optimization test problems have been developed to facilitate visualization of search behavior in a two-dimensional design space with arbitrarily many ...
Fieldsend, Jonathan E. +4 more
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A Survey of Decomposition Based Evolutionary Algorithms for Many-Objective Optimization Problems
IEEE Access, 2022 The framework of decomposition-based multi-objective evolutionary algorithms(MOEA/D) has evolved for more than ten years, and it has become irreplaceable tool for solving multi-objective optimization problems.
Xiaofang Guo
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Adaptive Global WASF-GA to handle many-objective optimization problems
Swarm and Evolutionary Computation, 2020 This work has been supported by the Spanish Ministry of Econ- omy and Competitiveness (project ECO2017-88883-R) co-financed by FEDER funds, and by the Regional Government of Andalucía (PAI group SEJ-532), This research has also been partially supported by grant num- bers TIN2017-88213-R (http://6city.lcc.uma.es) and RTC-2017-6714- 5 (http://ecoiot.lcc ...
Mariano Luque +3 more
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Novel PIO Algorithm with Multiple Selection Strategies for Many-Objective Optimization Problems
Complex System Modeling and Simulation, 2021 With the increase of problem dimensions, most solutions of existing many-objective optimization algorithms are non-dominant. Therefore, the selection of individuals and the retention of elite individuals are important.
Zhihua Cui +5 more
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Adaptive neighborhood selection for many-objective optimization problems [PDF]
Applied Soft Computing, 2018 Abstract It is generally accepted that conflicts between convergence and distribution deteriorate with an increase in the number of objectives. Furthermore, Pareto dominance loses its effectiveness in many-objectives optimization problems (MaOPs), which have more than three objectives.
Juan Zou +4 more
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Journal of King Saud University: Computer and Information Sciences, 2023 Pareto dominance-based many-objective evolutionary algorithms (PDMaOEAs) are challenging in dealing with many-objective problems (MaOPs) encountering many incomparable nondominated solutions.
Li-sen Wei, Er-chao Li
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