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A hybrid evolutionary algorithm for finding pareto optimal set in multi-objective optimization

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4 Author(s)
Yun Yang ; Dept. of Hydrosciences, Nanjing Univ., Nanjing, China ; Jian-feng Wu ; Xiao-bin Zhu ; Ji-chun Wu

The two primary goals of a multi-objective evolutionary algorithm (MOEA) for solving multi-objective optimization problems are to search as much non-dominated solutions as possible towards the true Pareto front and to maintain diversity of Pareto optimal solutions along tradeoff curves. This study presents a new hybrid MOEA, the niched Pareto tabu search combined with genetic algorithm (NPTSGA), to find Pareto-optimal solutions to multi-objective optimization problems. The NPTSGA is developed on the thoughts of integrating genetic algorithm (GA) with the improved tabu search (TS) based MOEA, niched Pareto tabu search (NPTS). The proposed NPTSGA is then tested through a simple test example and compared with other two techniques, NPTS and niched Pareto genetic algorithm (NPGA). Computational results indicate that the proposed NPTSGA is an efficient and effective method for solving multi-objective problems, while keeping the balance between the intensification of non-domination to the true Pareto-optimal solutions (TPS) and the diversification of the near Pareto-optimal solutions (NPS) along the tradeoff curves.

Published in:

Natural Computation (ICNC), 2011 Seventh International Conference on  (Volume:3 )

Date of Conference:

26-28 July 2011

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