Results 31 to 40 of about 75,350 (240)
NSGA-II With Simple Modification Works Well on a Wide Variety of Many-Objective Problems
IEEE Access, 2020 In the last two decades, the non-dominated sorting genetic algorithm II (NSGA-II) has been the most widely-used evolutionary multi-objective optimization (EMO) algorithm.
Lie Meng Pang, Hisao Ishibuchi, Ke Shang
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A comparative study of many-objective optimizers on large-scale many-objective software clustering problems [PDF]
Complex & Intelligent Systems, 2021 AbstractOver the past 2 decades, several multi-objective optimizers (MOOs) have been proposed to address the different aspects of multi-objective optimization problems (MOPs). Unfortunately, it has been observed that many of MOOs experiences performance degradation when applied over MOPs having a large number of decision variables and objective ...
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Mathematics, 2023 Decomposition-based many-objective evolutionary algorithms (D-MaOEAs) are brilliant at keeping population diversity for predefined reference vectors or points.
Lining Xing +3 more
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Comparative evaluation of large-scale many objective algorithms on complex optimization problems [PDF]
Iranian Journal of Numerical Analysis and OptimizationIn the field of optimization, there has been an enormous surge in interest in addressing large-scale many-objective problems. Numerous academicians and practitioners have contributed to evolutionary computation by developing a variety of optimization ...
R. Chaudhary, A. Prajapati
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IEEE Access, 2021 Multi-objective optimization problems exist widely in scientific research and engineering applications. With the number of objectives increasing, the proportion of non-dominated individuals in the population of many-objective optimization problems ...
Lingfeng Hu, Jingxuan Wei, Yang Liu
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A New Evolutionary Algorithm for Solving Many-Objective Optimization Problems [PDF]
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2008 In this paper, we focus on the study of evolutionary algorithms for solving multiobjective optimization problems with a large number of objectives. First, a comparative study of a newly developed dynamical multiobjective evolutionary algorithm (DMOEA) and some modern algorithms, such as the indicator-based evolutionary algorithm, multiple single ...
Xiufen, Zou +3 more
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A Novel Angular-Guided Particle Swarm Optimizer for Many-Objective Optimization Problems
Complexity, 2020 Most multiobjective particle swarm optimizers (MOPSOs) often face the challenges of keeping diversity and achieving convergence on tackling many-objective optimization problems (MaOPs), as they usually use the nondominated sorting method or decomposition-
Fei Chen +4 more
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IEEE Access, 2021 Surrogate-assisted evolutionary algorithms have received a surge of attentions for their promising ability of solving expensive optimization problems. Existing surrogate-assisted evolutionary algorithms usually adopt the regression models and the binary ...
Ruoyu Wang +4 more
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Computational Complexity Measures for Many-objective Optimization Problems
Procedia Computer Science, 2014 AbstractMulti-objectiveOptimization Problems (MOPs) are commonly encountered in the study and design of complex systems. Pareto dominance is the most common relationship used to compare solutions in MOPs, however as the number of objectives grows beyond three, Pareto dominance alone is no longer satisfactory.
Curry, David M., Dagli, Cihan H.
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Bi-goal evolution for many-objective optimization problems
Artificial Intelligence, 2015 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Li, Miqing +2 more
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