Constrained optimisation by solving equivalent dynamic loosely-constrained multiobjective optimisation problem
Constrained optimisation by solving equivalent dynamic loosely-constrained multiobjective optimisation problem
复制标题
通过求解等效动态松约束多目标优化问题进行约束优化
DOI：
10.1504/ijbic.2019.098406
复制
发表时间：
2019-03
影响因子：
3.5
通讯作者：
Wang Rui
中科院分区：
文献类型：
--
作者：
Zeng Sanyou;Jiao Ruwang;Li Changhe;Wang Rui
本文通过求解一个等价的动态松约束多目标优化问题来求解约束优化问题。两种策略被认为是。1)一个额外的目标（约束违反目标），以获得一个双目标优化问题。这为采用多目标技术求解COP提供了一个框架。2）引入动态约束边界，由于宽边界逐渐略微减小到原始约束边界，因此得到等价的动态松约束多目标优化问题。这表明，动态约束多目标进化算法（DCMOEA）可以执行有效的多目标进化算法（MOEA）在解决无约束多目标优化问题。这一想法被实施到三种主要类型的MOEA中，即，基于Pareto排序的方法、基于分解的方法、偏好启发的协同进化方法。这三个实例进行了测试的两套基准问题。实验结果表明，它们优于或竞争的两个国家的最先进的约束优化，特别是对高维问题。
A constrained optimisation problem (COP) is solved by solving an equivalent dynamic loosely-constrained multiobjective optimisation problem in this paper. Two strategies are considered. 1) An additional objective (constrained-violation objective) is introduced to obtain a two-objective optimisation problem. This provides a framework for adopting multi-objective techniques to solve the COP, 2) A dynamic constraint boundary is introduced to obtain an equivalent dynamic loosely-constrained multiobjective optimisation problem since a broad boundary is gradually slightly reduced to the original constraint boundary. This suggests that an dynamic constrained multiobjective evolutionary algorithm (DCMOEA) can performs as effective as that of a multiobjective evolutionary algorithm (MOEA) in solving an unconstrained multiobjective optimisation problem. The idea is implemented into three major types of MOEAs, i.e., Pareto ranking based method, decomposition based method, preference-inspired co-evolutionary method. These three instantiations are tested on two sets of benchmark problems. Experimental results show that they are better than or competitive to two state-of-the-art constraint optimisers, especially for the problems with high dimensions.