2010•Unpublished venueRequires access

A pareto-based differential evolution algorithm for multi-objective optimization problems

Ruhai Lei, Yuhu Cheng

Abstract

A new Pareto-based differential evolution (PDE) algorithm for solving multi-objective optimization problems was proposed by applying the nondominated sorting and ranking selection procedure developed in NSGA-II to select nondominated individuals to constitute a nondominated solution set. The PDE algorithm was validated using eight benchmark cases. The experimental results show that PDE, compared with NSGA-II algorithm, can find many Pareto optimal solutions distributed onto the Pareto front uniformly, which is an effective method to solve multi-objective optimization problems.

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Key concepts: Sorting, Mathematical optimization, Benchmark (surveying), Multi-objective optimization, Differential evolution, Pareto principle, Selection (genetic algorithm), Set (abstract data type)