Differential evolution with dynamic stochastic selection for constrained optimization
Differential evolution with dynamic stochastic selection for constrained optimization
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DOI：
10.1016/j.ins.2008.02.014
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发表时间：
2008-08
期刊：
影响因子：
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通讯作者：
Min Zhang;Wenjian Luo;Xufa Wang
中科院分区：
文献类型：
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作者：
Min Zhang;Wenjian Luo;Xufa Wang
本文研究了在进化过程中对有希望的不可行解应给予多大的关注。随机排序已被证明是求解约束优化问题的一种有效方法。在随机排序中，比较概率会影响排序后可行解的位置以及最终解的质量。本文在多元差异进化的框架下，提出了动态随机选择问题。首先，给出了一个比较概率呈线性下降的简单版本DSS-MDE。在13个常见的基准函数上，将DSS-MDE算法与两种最新的进化策略和三种竞争的差分进化算法进行了比较。DSS-MDE还在四个经过充分研究的工程设计实例上进行了评估，实验结果明显好于现有的结果。其次，对DSS-MDE中比较概率的其他动态设置也进行了设计和测试。实验结果表明，DSS-MDE算法对求解约束优化问题是有效的。最后，对CEC‘06中的22个基准函数进行了平方根调整比较概率的DSS-MDE测试，大多数函数的测试结果是具有竞争力的。
How much attention should be paid to the promising infeasible solutions during the evolution process is investigated in this paper. Stochastic ranking has been demonstrated as an effective technique for constrained optimization. In stochastic ranking, the comparison probability will affect the position of feasible solution after ranking, and the quality of the final solutions. In this paper, the dynamic stochastic selection (DSS) is put forward within the framework of multimember differential evolution. Firstly, a simple version named DSS-MDE is given, where the comparison probability decreases linearly. The algorithm DSS-MDE has been compared with two state-of-the-art evolution strategies and three competitive differential evolution algorithms for constrained optimization on 13 common benchmark functions. DSS-MDE is also evaluated on four well-studied engineering design examples, and the experimental results are significantly better than current available results. Secondly, other dynamic settings of the comparison probability for DSS-MDE are also designed and tested. From the experimental results, DSS-MDE is effective for constrained optimization. Finally, DSS-MDE with a square root adjusted comparison probability is evaluated on the 22 benchmark functions in CEC’06, and the experimental results on most functions are competitive.