Full Text:   <3023>

CLC number: TP18

On-line Access: 

Received: 2007-06-20

Revision Accepted: 2007-10-08

Crosschecked: 0000-00-00

Cited: 12

Clicked: 5676

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
1. Reference List
Open peer comments

Journal of Zhejiang University SCIENCE A 2007 Vol.8 No.12 P.1905-1911


Multiobjective extremal optimization with applications to engineering design

Author(s):  CHEN Min-rong, LU Yong-zai, YANG Gen-ke

Affiliation(s):  Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China; more

Corresponding email(s):   optmrchen@gmail.com

Key Words:  Multiobjective optimization, Extremal optimization (EO), Engineering design

CHEN Min-rong, LU Yong-zai, YANG Gen-ke. Multiobjective extremal optimization with applications to engineering design[J]. Journal of Zhejiang University Science A, 2007, 8(12): 1905-1911.

@article{title="Multiobjective extremal optimization with applications to engineering design",
author="CHEN Min-rong, LU Yong-zai, YANG Gen-ke",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Multiobjective extremal optimization with applications to engineering design
%A CHEN Min-rong
%A LU Yong-zai
%A YANG Gen-ke
%J Journal of Zhejiang University SCIENCE A
%V 8
%N 12
%P 1905-1911
%@ 1673-565X
%D 2007
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2007.A1905

T1 - Multiobjective extremal optimization with applications to engineering design
A1 - CHEN Min-rong
A1 - LU Yong-zai
A1 - YANG Gen-ke
J0 - Journal of Zhejiang University Science A
VL - 8
IS - 12
SP - 1905
EP - 1911
%@ 1673-565X
Y1 - 2007
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2007.A1905

In this paper, we extend a novel unconstrained multiobjective optimization algorithm, so-called multiobjective extremal optimization (MOEO), to solve the constrained multiobjective optimization problems (MOPs). The proposed approach is validated by three constrained benchmark problems and successfully applied to handling three multiobjective engineering design problems reported in literature. Simulation results indicate that the proposed approach is highly competitive with three state-of-the-art multiobjective evolutionary algorithms, i.e., NSGA-II, SPEA2 and PAES. Thus MOEO can be considered a good alternative to solve constrained multiobjective optimization problems.

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


[1] Bak, P., Tang, C., Wiesenfeld, K., 1987. Self-organized criticality. Phys. Rev. Lett., 59:381-384.

[2] Bak, P., Sneppen, K., 1993. Punctuated equilibrium and criticality in a simple model of evolution. Phys. Rev. Lett., 71(24):4083-4086.

[3] Baykasoglu, A., 2006. Applying multiple objective tabu search to continuous optimization problems with a simple neighborhood strategy. Int. J. Numer. Methods Eng., 65:406-424.

[4] Boettcher, S., Percus, A.G., 2000. Nature’s way of optimizing. Artif. Intell., 119:275-286.

[5] Bosman, P.A.N., Thierens, D., 2003. The balance between proximity and diversity in multiobjective evolutionary algorithms. IEEE Trans. on Evol. Comput., 7(2):174-188.

[6] Chen, M.R., Lu, Y.Z., Yang, G., 2006. Population-based Extremal Optimization with Adaptive Lévy Mutation for Constrained Optimization. Proc. Int. Conf. on Computational Intelligence and Security, p.258-261.

[7] Chen, M.R., Lu, Y.Z., 2007. A novel elitist multiobjective optimization algorithm: multiobjective extremal optimization. Eur. J. Operat. Res., in press.

[8] Coello Coello, C.A., 1996. An Empirical Study of Evolutionary Techniques for Multiobjective Optimization in Engineering Design. Ph.D Thesis, Department of Computer Science, Tulane University, New Orleans, LA.

[9] Coello Coello, C.A., 2006. Evolutionary multiobjective optimization: a historical view of the field. IEEE Comput. Intell. Mag., 1(1):28-36.

[10] Deb, K., Patrap, A., Moitra, S., 2000. Mechanical Component Design for Multi-objective Using Elitist Non-dominated Sorting GA. KanGAL Report No. 200002. Indian Institute of Technology Kanpur, India.

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

[12] Fonseca, C.M., Fleming, P.J., 1993. Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization. Proc. 5th Int. Conf. on Genetic Algorithms. Morgan Kauffman, San Mateo, CA, p.416-423.

[13] Knowles, J., Corne, D., 1999. The Pareto Archived Evolution Strategy: A New Baseline Algorithm for Multiobjective Optimization. Proc. Congress on Evolutionary Computation, p.98-105.

[14] Lu, Y.Z., Chen, M.R., Chen, Y.W., 2007. Studies on Extremal Optimization and its Applications in Solving Real World Optimization Problems. Proc. IEEE Symp. on Foundations of Computational Intelligence, p.162-168.

[15] Moser, I., Hendtlass, T., 2006. Solving Problems with Hidden Dynamics-Comparison of Extremal Optimization and Ant Colony System. Proc. IEEE Conf. on Evolutionary Computation, p.1248-1255.

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

[17] Zitzler, E., Laumanns, M., Thiele, L., 2001. SPEA2: Improving the Performance of the Strength Pareto Evolutionary Algorithm. Technical Report 103, Computer Engineering and Communication Networks Lab (TIK), Swiss Federal Institute of Technology (ETH) Zurich.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE