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论文类型：期刊论文

发表时间：2014-11-01

发表刊物：Zidonghua Xuebao/Acta Automatica Sinica

收录刊物：EI、PKU、ISTIC

卷号：40

期号：11

页面范围：2469-2479

ISSN号：02544156

摘要：Evolutionary algorithms (EAs) were shown to be effective for complex constrained optimization problems. However, inflexible exploration in general EAs would lead to losing the global optimum nearby the ill-convergence regions. In this paper, we propose an iterative dynamic diversity evolutionary algorithm (IDDEA) with contractive subregions guiding exploitation through local extrema to the global optimum in suitable steps. In IDDEA, a novel optimum estimation strategy with multi-agents evolving diversely is suggested to efficiently compute dominance trend and establish a subregion. In addition, a subregion converging iteration is designed to redistrict a smaller subregion in current subregion for next iteration, which is based on a special dominance estimation scheme. Meanwhile, an infimum penalty function is embedded into IDDEA to judge agents and penalize adaptively the unfeasible agents with the lowest fitness of feasible agents. Furthermore, several engineering design optimization problems taken from the specialized literature are successfully solved by the present algorithm with high reliable solutions.