Neural Computing and Applications

, Volume 17, Issue 2, pp 101–109 | Cite as

Multiobjective optimization using population-based extremal optimization

  • Min-Rong ChenEmail author
  • Yong-Zai Lu
  • Genke Yang
BIC-TA 2006


In recent years, a general-purpose local-search heuristic method called Extremal Optimization (EO) has been successfully applied in some NP-hard combinatorial optimization problems. In this paper, we present a novel Pareto-based algorithm, which can be regarded as an extension of EO, to solve multiobjective optimization problems. The proposed method, called Multiobjective Population-based Extremal Optimization (MOPEO), is validated by using five benchmark functions and metrics taken from the standard literature on multiobjective evolutionary optimization. The experimental results demonstrate that MOPEO is competitive with the state-of-the-art multiobjective evolutionary algorithms. Thus MOPEO can be considered as a viable alternative to solve multiobjective optimization problems.


Multiobjective optimization Extremal optimization Self-organized criticality Pareto front 



The authors would like to thank the anonymous referees for their many useful comments and constructive suggestions. This work is supported by the National Natural Science Foundation of China under Grant No. 60574063.


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Copyright information

© Springer-Verlag London Limited 2007

Authors and Affiliations

  1. 1.Department of AutomationShanghai Jiao Tong UniversityShanghaiChina

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