micturition
发表于 2025-3-23 11:05:25
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采纳
发表于 2025-3-23 16:01:52
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使满足
发表于 2025-3-23 19:15:16
https://doi.org/10.1007/978-3-319-32083-0applying the modification only in the particle best population.We show through a number of experiments how, by keeping the selection pressure on a decreasing fraction of the population, COPSO can consistently solve a benchmark of constrained optimization problems.
激怒某人
发表于 2025-3-24 02:09:04
Continuous Constrained Optimization with Dynamic Tolerance Using the COPSO Algorithm,applying the modification only in the particle best population.We show through a number of experiments how, by keeping the selection pressure on a decreasing fraction of the population, COPSO can consistently solve a benchmark of constrained optimization problems.
ERUPT
发表于 2025-3-24 04:47:17
1860-949X rithms in constrained search spaces and the adaptation of novel nature-inspired algorithms for numerical optimization with constraints. .."Constraint-Handling in Evolutionary Optimization" is an important reference for researchers, practitioners and students in978-3-642-10155-7978-3-642-00619-7Series ISSN 1860-949X Series E-ISSN 1860-9503
patella
发表于 2025-3-24 07:14:52
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Supplement
发表于 2025-3-24 13:14:05
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ANA
发表于 2025-3-24 16:14:56
An Adaptive Penalty Function for Handling Constraint in Multi-objective Evolutionary Optimization, solutions. The proposed method is simple to implement and does not need any parameter tuning. The constraint handling technique is tested on several constrained multi-objective optimization problems and has shown superior results compared to some chosen state-of-the-art designs.
泥土谦卑
发表于 2025-3-24 21:24:20
Infeasibility Driven Evolutionary Algorithm for Constrained Optimization,the population allows the algorithm to approach the constraint boundary from the infeasible side of the search space in addition to its approach from the feasible side of the search space via evolution of feasible solutions. Furthermore, “good” infeasible solutions are ranked higher than the feasibl
monopoly
发表于 2025-3-25 00:12:18
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