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Soft Computing

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18.02.2016 | Methodologies and Application

Improving the multiobjective evolutionary algorithm based on decomposition with new penalty schemes

verfasst von: Shengxiang Yang, Shouyong Jiang, Yong Jiang

Erschienen in: Soft Computing | Ausgabe 16/2017

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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 single-objective 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, i.e., adaptive penalty scheme and subproblem-based penalty scheme (SPS), to enhance the spread of Pareto-optimal 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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Source code available from http://dces.essex.ac.uk/staff/qzhang/.

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Titel: Improving the multiobjective evolutionary algorithm based on decomposition with new penalty schemes
verfasst von: Shengxiang Yang
Shouyong Jiang
Yong Jiang
Publikationsdatum: 18.02.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 16/2017
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-016-2076-3

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