Authors: Shengxiang Yang Shouyong Jiang Yong Jiang
Publish Date: 2016/02/18
Volume: 21, Issue: 16, Pages: 4677-4691
Abstract
It has been increasingly reported that the multiobjective optimization evolutionary algorithm based on decomposition MOEA/D is promising for handling multiobjective optimization problems MOPs MOEA/D employs scalarizing functions to convert an MOP into a number of singleobjective subproblems Among them penalty boundary intersection PBI is one of the most popular decomposition approaches and has been widely adopted for dealing with MOPs However the original PBI uses a constant penalty value for all subproblems and has difficulties in achieving a good distribution and coverage of the Pareto front for some problems In this paper we investigate the influence of the penalty factor on PBI and suggest two new penalty schemes ie adaptive penalty scheme and subproblembased penalty scheme SPS to enhance the spread of Paretooptimal solutions The new penalty schemes are examined on several complex MOPs showing that PBI with the use of them is able to provide a better approximation of the Pareto front than the original one The SPS is further integrated into two recently developed MOEA/D variants to help balance the population diversity and convergence Experimental results show that it can significantly enhance the algorithm’s performance
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