Application of Differential Evolutionary Algorithm to Compare the Performance of Continuous Multi-Objective Problem Optimization

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ตรัยรัตน์ เกิดโภคทรัพย์


Decomposition is a basic strategy in traditional multi-objective optimization. At present, it has been interested and widely used in multi-objective evolutionary optimization. This article proposes an adaptive MOEA/D hybridized with differential evolution (AMOEA/D-DE). The concept is based on a multi-objective evolutionary algorithm based on decomposition (MOEA/D) and adaptive differential evolution algorithm (ADE). It decomposes a multi-objective problems (MOPs) into a number of scalar optimization subproblems (decomposes a MOPs into several SOPs) and optimizes them simultaneously with adjustment of control parameters and strategies to achieve a diversity of approximated true non-dominated solutions. Each subproblem is optimized by using information from its several neighboring subproblems, which makes AMOEA/D-DE had lower computational complexity and adapt itself to the current search requirements. In experiments, AMOEA/D-DE is compared with a MOEA/D and a multi-objective differential evolution algorithm based on decomposition (MODE/D) in order to solve continuous MOPs. The result from experiments show that AMOEA/D-DE outperforms the others in terms of convergence rate but it takes more time consumption when compared to the equivalent number of function evaluations.

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เกิดโภคทรัพย์ ต., “Application of Differential Evolutionary Algorithm to Compare the Performance of Continuous Multi-Objective Problem Optimization”, sej, vol. 13, no. 2, pp. 52–71, Aug. 2018.
Research Articles


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