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Zbl 1148.65042
Qian, Weiyi; Li, Ajun
Adaptive differential evolution algorithm for multiobjective optimization problems. (English)
[J] Appl. Math. Comput. 201, No. 1-2, 431-440 (2008). ISSN 0096-3003

Summary: A new adaptive differential evolution algorithm (ADEA) is proposed for multiobjective optimization problems. In ADEA, the variable parameter $F$ based on the number of the current Pareto-front and the diversity of the current solutions is given for adjusting search size in every generation to find Pareto solutions in mutation operator, and the select operator combines the advantages of the differential evolution algorithm with the mechanisms of Pareto-based ranking and crowding distance sorting. ADEA is implemented on five classical multiobjective problems, the results illustrate that ADEA efficiently achieves two goals of multiobjective optimization problems: find the solutions converge to the true Pareto-front and uniform spread along the front.

MSC 2000:: *65K05 Mathematical programming (numerical methods)
90C29 Multi-objective programming, etc.

Keywords: multiobjective optimization; select operator; numerical examples

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