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Improved multi-objective hybrid differential evolution algorithm

Xiaozhe Wang

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Abstract

By using the differential evolution algorithm(DE) to solve multi-objective optimization problems,this paper proposed a Pareto optimal solution migration based differential evolution for multi-objective optimization(PSDEMO) to guarantee the diversity of Pareto optimal solution.It adopted the elitist strategy in the algorithm,and archived Pareto non-dominance solutions found in the evolution operation dynamically with the evolution process.In addition,it used all the non-dominance solutions in the archive to do migration operation after mutation and crossover operation of DE to increase the number and quality of non-dominated solutions.Compared with standard DE,simulation results show that the PSDEMO not only helps to improve the quantity of the Pareto non-dominance solution,but also has good balance keeping ability between the diversity and convergence.

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What this paper is about

By using the differential evolution algorithm(DE) to solve multi-objective optimization problems,this paper proposed a Pareto optimal solution migration based differential evolution for multi-objective optimization(PSDEMO) to guarantee the diversity of Pareto optimal solution.It adopted the elitist strategy in the algorithm,and archived Pareto non-dominance solutions found in the evolution operation dynamically with the evolution process.In addition,it used all the non-dominance solutions in the archive to do migration operation after mutation and crossover operation of DE to increase the number and quality of non-dominated solutions.Compared with standard DE,simulation results show that the PSDEMO not only helps to improve the quantity of the Pareto non-dominance solution,but also has good balance keeping ability between the diversity and convergence.

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Available abstract

By using the differential evolution algorithm(DE) to solve multi-objective optimization problems,this paper proposed a Pareto optimal solution migration based differential evolution for multi-objective optimization(PSDEMO) to guarantee the diversity of Pareto optimal solution.It adopted the elitist strategy in the algorithm,and archived Pareto non-dominance solutions found in the evolution operation dynamically with the evolution process.In addition,it used all the non-dominance solutions in the archive to do migration operation after mutation and crossover operation of DE to increase the number and quality of non-dominated solutions.Compared with standard DE,simulation results show that the PSDEMO not only helps to improve the quantity of the Pareto non-dominance solution,but also has good balance keeping ability between the diversity and convergence.

Key concepts: Differential evolution, Crossover, Computer science, Mathematical optimization, Pareto principle, Convergence (economics), Dominance (genetics), Multi-objective optimization

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