Enhancing MOEA/D with guided mutation and priority update for multi-objective optimization

Chih Ming Chen*, Ying-Ping Chen, Qingfu Zhang

*Corresponding author for this work

研究成果: Conference contribution

59 引文 斯高帕斯(Scopus)

摘要

Multi-objective optimization is an essential and challenging topic in the domains of engineering and computation because real-world problems usually include several conflicting objectives. Current trends in the research of solving multiobjective problems (MOPs) require that the adopted optimization method provides an approximation of the Pareto set such that the user can understand the tradeoff between objectives and therefore make the final decision. Recently, an efficient framework, called MOEA/D, combining decomposition techniques in mathematics and optimization methods in evolutionary computation was proposed. MOEA/D decomposes a MOP to a set of singleobjective problems (SOPs) with neighborhood relationship and approximates the Pareto set by solving these SOPs. In this paper, we attempt to enhance MOEA/D by proposing two mechanisms. To fully employ the information obtained from neighbors, we introduce a guided mutation operator to replace the differential evolution operator. Moreover, a update mechanism utilizing a priority queue is proposed for performance improvement when the SOPs obtained by decomposition are not uniformly distributed on the Pareto font. Different combinations of these approaches are compared based on the test problem instances proposed for the CEC 2009 competition. The set of problem instances include unconstrained and constrained MOPs with variable linkages. Experimental results are presented in the paper, and observations and discussion are also provided.

原文English
主出版物標題2009 IEEE Congress on Evolutionary Computation, CEC 2009
頁面209-216
頁數8
DOIs
出版狀態Published - 25 十一月 2009
事件2009 IEEE Congress on Evolutionary Computation, CEC 2009 - Trondheim, Norway
持續時間: 18 五月 200921 五月 2009

出版系列

名字2009 IEEE Congress on Evolutionary Computation, CEC 2009

Conference

Conference2009 IEEE Congress on Evolutionary Computation, CEC 2009
國家Norway
城市Trondheim
期間18/05/0921/05/09

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  • 引用此

    Chen, C. M., Chen, Y-P., & Zhang, Q. (2009). Enhancing MOEA/D with guided mutation and priority update for multi-objective optimization. 於 2009 IEEE Congress on Evolutionary Computation, CEC 2009 (頁 209-216). [4982950] (2009 IEEE Congress on Evolutionary Computation, CEC 2009). https://doi.org/10.1109/CEC.2009.4982950