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Multi-objective optimization using self-adaptive differential evolution algorithm

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4 Author(s)
V. L. Huang ; School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Ave., 639798 Singapore ; S. Z. Zhao ; R. Mallipeddi ; P. N. Suganthan

In this paper, we propose a multiobjective self-adaptive differential evolution algorithm with objective-wise learning strategies (OW-MOSaDE) to solve numerical optimization problems with multiple conflicting objectives. The proposed approach learns suitable crossover parameter values and mutation strategies for each objective separately in a multi-objective optimization problem. The performance of the proposed OW-MOSaDE algorithm is evaluated on a suit of 13 benchmark problems provided for the CEC2009 MOEA Special Session and Competition ( on Performance Assessment of Constrained/Bound Constrained Multi-Objective Optimization Algorithms.

Published in:

2009 IEEE Congress on Evolutionary Computation

Date of Conference:

18-21 May 2009