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

Chih-Ming Chen, and Ying-ping Chen, and Qingfu Zhang, (2009) Enhancing MOEA/D with guided mutation and priority update for multi-objective optimization. In: 2009 IEEE Congress on Evolutionary Computation (CEC), 2009-05-18 - 2009-05-21.

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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. © 2009 IEEE.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 2009 IEEE Congress on Evolutionary Computation, CEC 2009
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 15 Aug 2012 14:16
Last Modified: 15 Jan 2022 00:47

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