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Computational Optimization and Applications

, Volume 48, Issue 1, pp 109–138 | Cite as

A fast steady-state ε-dominance multi-objective evolutionary algorithm

  • Minqiang Li
  • Liu Liu
  • Dan Lin
Article

Abstract

Multi-objective evolutionary algorithms (MOEAs) have become an increasingly popular tool for design and optimization tasks in real-world applications. Most of the popular baseline algorithms are pivoted on the use of Pareto-ranking (that is empirically inefficient) to improve the convergence to the Pareto front of a multi-objective optimization problem. This paper proposes a new ε-dominance MOEA (EDMOEA) which adopts pair-comparison selection and steady-state replacement instead of the Pareto-ranking. The proposed algorithm is an elitist algorithm with a new preservation technique of population diversity based on the ε-dominance relation. It is demonstrated that superior results could be obtained by the EDMOEA compared with other algorithms: NSGA-II, SPEA2, IBEA, ε-MOEA, PESA and PESA-II on test problems. The EDMOEA is able to converge to the Pareto optimal set much faster especially on the ZDT test functions with a large number of decision variables.

Keywords

Multi-objective optimization ε-dominance Steady-state EAs Diversity preservation 

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.School of ManagementTianjin UniversityTianjinPeople’s Republic of China
  2. 2.School of ScienceTianjin UniversityTianjinPeople’s Republic of China

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