Many-hard-objective optimization using differential evolution based on two-stage constraint-handling
Many-hard-objective optimization using differential evolution based on two-stage constraint-handling
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DOI：
10.1145/2463372.2463446
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发表时间：
2013-07
期刊：
影响因子：
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通讯作者：
K. Tagawa;Akihiro Imamura
中科院分区：
文献类型：
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作者：
K. Tagawa;Akihiro Imamura
本文研究了多硬目标优化问题（MHOP），其中多个目标受一个目标点的约束。为了获得MHOP问题Pareto最优可行解集的近似解，提出了一种新的多硬目标差分进化算法（DEMHO）。对于排序非支配的解决方案，DEMHO使用Pairwise Exclusive Hypervolume（PEH）与新提出的快速计算算法。此外，对于MHOP不可行解的处理，采用了一种新的两阶段截断方法。通过数值实验和对MHOP的统计检验，对DEMHO的性能进行了评估。作为一个案例研究，DEMHO的有用性也证明了对SAW滤波器的优化设计。
This paper focus on the Many-Hard-objective Optimization Problem (MHOP) in which a lot of objectives are limited by a goal point. In order to obtain an approximation of Pareto-optimal feasible solution set for MHOP, a new algorithm called Differential Evolution for Many-Hard-objective Optimization (DEMHO) is proposed. For sorting non dominated solutions, DEMHO uses Pairwise Exclusive Hypervolume (PEH) with a newly proposed fast calculation algorithm. Besides, for handing the infeasible solutions of MHOP, a new two-stage truncation method is employed. Through the numerical experiment and the statistical test conducted on some instances of MHOP, the performance of DEMHO is assessed. As a case study, the usefulness of DEMHO is also demonstrated on an optimum design of SAW duplexer.