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首页> 外文期刊>International journal of applied evolutionary computation >Progressive-Stepping-Based Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization
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Progressive-Stepping-Based Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization

机译：多目标优化的基于渐进式非支配排序遗传算法

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摘要

This paper demonstrates two approaches to achieve faster convergence and a better spread of Pareto solutions in fewer numbers of generations, compared to a few existing algorithms, including NSGA-Ⅱ and SPEA2 to solve multi-objective optimization problems (MOP's). Two algorithms are proposed based on progressive stepping mechanism, which is obtained by the hybridization of existing Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-Ⅱ) with novel guided search schemes, and modified chromosome selection and replacement mechanisms. Progressive Stepping Non-dominated Sorting based on Local search (PSNS-L) controls the step size, and Progressive Stepping Non-dominated Sorting based on Utopia point (PSNS-U) method controls the number of divisions to generate better chromosomes in each generation to achieve faster convergence. Four multi-objective evolutionary algorithms (EA's) are compared for different benchmark functions and PSNS outperforms them in most cases based on various performance metric values. Finally a mechanical design problem has been solved with PSNS algorithms.
机译：与少数现有的解决多目标优化问题的算法（包括NSGA-Ⅱ和SPEA2）相比，本文演示了两种方法，可以更快地收敛并在更少的世代中更好地扩展Pareto解决方案。基于渐进式步进机制，提出了两种算法，分别是将现有的非支配排序遗传算法Ⅱ（NSGA-Ⅱ）与新颖的导引搜索方案进行杂交，并改进了染色体的选择和替换机制。基于局部搜索的渐进式非支配排序（PSNS-L）控制步长，基于乌托邦点的渐进式非支配排序（PSNS-U）方法控制子代的数量，以在每一代中生成更好的染色体实现更快的收敛。比较了四种针对不同基准功能的多目标进化算法（EA's），并且在大多数情况下，PSNS根据各种性能指标值均优于其。最终，通过PSNS算法解决了机械设计问题。

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