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2014 An Enhanced Differential Evolution Based Algorithm with Simulated Annealing for Solving Multiobjective Optimization Problems
Bili Chen, Wenhua Zeng, Yangbin Lin, Qi Zhong
J. Appl. Math. 2014: 1-13 (2014). DOI: 10.1155/2014/931630

Abstract

An enhanced differential evolution based algorithm, named multi-objective differential evolution with simulated annealing algorithm (MODESA), is presented for solving multiobjective optimization problems (MOPs). The proposed algorithm utilizes the advantage of simulated annealing for guiding the algorithm to explore more regions of the search space for a better convergence to the true Pareto-optimal front. In the proposed simulated annealing approach, a new acceptance probability computation function based on domination is proposed and some potential solutions are assigned a life cycle to have a priority to be selected entering the next generation. Moreover, it incorporates an efficient diversity maintenance approach, which is used to prune the obtained nondominated solutions for a good distributed Pareto front. The feasibility of the proposed algorithm is investigated on a set of five biobjective and two triobjective optimization problems and the results are compared with three other algorithms. The experimental results illustrate the effectiveness of the proposed algorithm.

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Bili Chen. Wenhua Zeng. Yangbin Lin. Qi Zhong. "An Enhanced Differential Evolution Based Algorithm with Simulated Annealing for Solving Multiobjective Optimization Problems." J. Appl. Math. 2014 1 - 13, 2014. https://doi.org/10.1155/2014/931630

Information

Published: 2014
First available in Project Euclid: 2 March 2015

zbMATH: 07131985
MathSciNet: MR3208641
Digital Object Identifier: 10.1155/2014/931630

Rights: Copyright © 2014 Hindawi

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