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

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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.

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Authors and Affiliations

  1. School of Management, Tianjin University, Tianjin, 300072, People’s Republic of China

    Minqiang Li & Liu Liu

  2. School of Science, Tianjin University, Tianjin, 300072, People’s Republic of China

    Dan Lin

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  1. Minqiang Li
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  2. Liu Liu
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Correspondence to Minqiang Li.

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Li, M., Liu, L. & Lin, D. A fast steady-state ε-dominance multi-objective evolutionary algorithm. Comput Optim Appl 48, 109–138 (2011). https://doi.org/10.1007/s10589-009-9241-x

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