標題: Enhancing MOEA/D with Guided Mutation and Priority Update for Multi-objective Optimization
作者: Chen, Chih-Ming
Chen, Ying-ping
Zhang, Qingfu
資訊工程學系
Department of Computer Science
公開日期: 2009
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 multi-objective 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 single-objective 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 SON 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.
URI: http://hdl.handle.net/11536/14832
ISBN: 978-1-4244-2958-5
期刊: 2009 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-5
起始頁: 209
結束頁: 216
顯示於類別:會議論文