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Journal of Zhejiang University SCIENCE C 2010 Vol.11 No.7 P.538-543


Multi-objective differential evolution with diversity enhancement

Author(s):  Bo-yang Qu, Ponnuthurai-Nagaratnam Suganthan

Affiliation(s):  School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore

Corresponding email(s):   E070088@ntu.edu.sg, epnsugan@ntu.edu.sg

Key Words:  Multi-objective evolutionary algorithm (MOEA), Multi-objective differential evolution (MODE), Diversity enhancement

Bo-yang Qu, Ponnuthurai-Nagaratnam Suganthan. Multi-objective differential evolution with diversity enhancement[J]. Journal of Zhejiang University Science C, 2010, 11(7): 538-543.

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publisher="Zhejiang University Press & Springer",

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%T Multi-objective differential evolution with diversity enhancement
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%A Ponnuthurai-Nagaratnam Suganthan
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%DOI 10.1631/jzus.C0910481

T1 - Multi-objective differential evolution with diversity enhancement
A1 - Bo-yang Qu
A1 - Ponnuthurai-Nagaratnam Suganthan
J0 - Journal of Zhejiang University Science C
VL - 11
IS - 7
SP - 538
EP - 543
%@ 1869-1951
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C0910481

multi-objective differential evolution (MODE) is a powerful and efficient population-based stochastic search technique for solving multi-objective optimization problems in many scientific and engineering fields. However, premature convergence is the major drawback of MODE, especially when there are numerous local Pareto optimal solutions. To overcome this problem, we propose a MODE with a diversity enhancement (MODE-DE) mechanism to prevent the algorithm becoming trapped in a locally optimal Pareto front. The proposed algorithm combines the current population with a number of randomly generated parameter vectors to increase the diversity of the differential vectors and thereby the diversity of the newly generated offspring. The performance of the MODE-DE algorithm was evaluated on a set of 19 benchmark problem codes available from http://www3.ntu.edu.sg/home/epnsugan/. With the proposed method, the performances were either better than or equal to those of the MODE without the diversity enhancement.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Babu, B.V., Jehan, M.M.L., 2003. Differential Evolution for Multi-Objective Optimization. Congress on Evolutionary Computation, 4:2696-2703.

[2]Chen, X.Q., Hou, Z.X., Liu, J.X., 2008. Multi-Objective Optimization with Modified Pareto Differential Evolution. Int. Conf. on Intelligent Computation Technology and Automation, p.90-95.

[3]Coello, C.A., Lamont, G.B., 2004. Application of Multi-Objective Evolutionary Algorithms. World Scientific, Singapore.

[4]Corne, D.W., Knowles, J.D., Oates, M.J., 2000. The Pareto Envelope-Based Selection Algorithm for Multiobjective Optimization. 6th Int. Conf. on Parallel Problem Solving from Nature, p.839-848.

[5]Das, S., Abraham, A., Chakraborty, U.K., Konar, A., 2009. Differential evolution using a neighbourhood based mutation operator. IEEE Trans. Evol. Comput., 13(3):526-553.

[6]Deb, K., Pratap, A., Agarwal, S., Meyarivan, T., 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput., 6(2):182-197.

[7]Du, J., Cai, Z.H., 2007. A Sorting Based Algorithm for Finding Non-dominated Set in Multi-Objective Optimization. 3rd Int. Conf. on Natural Computation, p.417-421.

[8]Fan, H.Y., Lampinen, J., Levy, Y., 2006. An easy-to-implement differential evolution approach for multi-objective optimizations. Eng. Comput., 23(2):124-138.

[9]Fan, J., Xiong, S., Wang, J., Gong, C., 2008. IMODE: Improving Multi-Objective Differential Evolution Algorithm. 4th Int. Conf. on Natural Computation, p.212-216.

[10]Ghosh, A., Das, M.K., 2008. Non-dominated rank based sorting genetic algorithms. Fundam. Inform., 83(3):231-252.

[11]Huang, V.L., Qin, A.K., Deb, K., Zitzler, E., Suganthan, P.N., Liang, J.J., Preuss, M., Huband, S., 2007. Problem Definitions for Performance Assessment on Multi-Objective Optimization Algorithms. Technical Report, Nanyang Technological University, Singapore.

[12]Huang, V.L., Zhao, S.Z., Mallipeddi, R., Suganthan, P.N., 2009. Multi-Objective Optimization Using Self-Adaptive Differential Evolution Algorithm. IEEE Congress on Evolutionary Computation, p.190-194.

[13]Kim, M., Hiroyasu, T., Miki, M., Watanabe, S., 2004. SPEA2+: Improving the Performance of the Strength Pareto Evolutionary Algorithm 2. Proc. 8th Int. Conf. on Parallel Problem Solving from Nature, p.742-751.

[14]Knowles, J., Thiele, L., Zitzler, E., 2006. A Tutorial on the Performance Assessment of Stochastic Multiobjective Optimizers. Computer Engineering and Networks Laboratory, ETH Zurich, Switzerland.

[15]Kumar, A., Sharma, D., Deb, K., 2007. A Hybrid Multi-Objective Optimization Procedure Using PCX Based NSGA-II and Sequential Quadratic Programming. Proc. IEEE Congress on Evolutionary Computation, p.3011-3018.

[16]Qin, A.K., Huang, V.L., Suganthan, P.N., 2009. Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput., 13(2):398-417.

[17]Qu, B.Y., Suganthan, P.N., 2009. Multi-Objective, Evolutionary Programming without Non-domination Sorting Is up to Twenty Times Faster. IEEE Congress on Evolutionary Computation, p.2934-2939.

[18]Storn, R., Price, K.V., 1995. Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim., 11(4):341-359.

[19]Zhang, J.Q., Sanderson, A.C., 2008. Self-Adaptive Multi-Objective Differential Evolution with Direction Information Provided by Archived Inferior Solutions. IEEE Congress on Evolutionary Computation, p.2801-2810.

[20]Zhang, L., Zhou, C., Ma, M., Sun, C., 2007. Multi-objective differential evolution algorithm based on max-min distance density. J. Comput. Res. Dev., 44(1):177-184.

[21]Zhao, S.Z., Suganthan, P.N., 2010. Multi-objective evolutionary algorithm with ensemble of external archives. Int. J. Innov. Comput. Inform. Control, 6:1713-1726.

[22]Zhao, S.Z., Suganthan P.N., accepted. Two-lbests based multi-objective particle swarm optimizer. Eng. Optim.

[23]Zielinski, K., Laur, R., 2007a. Adaptive Parameter Setting for a Multi-Objective Particle Swarm Optimization Algorithm. IEEE Congress on Evolutionary Computation, p.3019-3026.

[24]Zielinski, K., Laur, R., 2007b. Differential Evolution with Adaptive Parameter Setting for Multi-Objective Optimization. IEEE Congress on Evolutionary Computation, p.3585-3592.

[25]Zitzler, E., Thiele, L., 1999. Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evol. Comput., 3(4):257-271.

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