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Journal of Applied Mathematics
Volume 2014 (2014), Article ID 931630, 13 pages
http://dx.doi.org/10.1155/2014/931630
Research Article

An Enhanced Differential Evolution Based Algorithm with Simulated Annealing for Solving Multiobjective Optimization Problems

1School of Software, Xiamen University, Xiamen 361005, China
2School of Information Science and Engineering, Xiamen University, Xiamen 361005, China

Received 1 December 2013; Revised 27 March 2014; Accepted 7 April 2014; Published 7 May 2014

Academic Editor: Frank Werner

Copyright © 2014 Bili Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.