Adaptive plan system of swarm intelligent using differential evolution with genetic algorithm

Hieu Pham, Hiroshi Hasegawa

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

This paper describes a new proposed strategy for Adaptive Plan System of Swarm Intelligent - Particle Swarm Optimization (PSO) using Differential Evolution (DE) with Genetic Algorithm (GA) called DE/PSOGA to solve large scale optimization problems, to reduce calculation cost, and to improve convergence towards the optimal solution. This is an approach that combines the global search ability of DE, GA and the local search ability of Adaptive plan (AP). The proposed strategy incorporates concepts from DE and PSO, updating particles not only by DE operators but also by mechanism of PSO for Adaptive System (AS) with GA. To evaluate its performance, the DE/PSOGA is applied to various benchmark tests with multi-dimensions. It is shown to be statistically significantly superior to other Evolutionary Algorithms (EAs), and Memetic Algorithms (MAs). We confirmed satisfactory performance through various benchmark tests.

Original languageEnglish
Pages (from-to)458-473
Number of pages16
JournalJournal of Advanced Mechanical Design, Systems and Manufacturing
Volume7
Issue number3
DOIs
Publication statusPublished - 2013

Fingerprint

Particle swarm optimization (PSO)
Genetic algorithms
Adaptive systems
Evolutionary algorithms
Mathematical operators
Costs
Local search (optimization)

Keywords

  • Adaptive Plan
  • Differential Evolution
  • Genetic Algorithm
  • Multi-Peak Problems
  • Particle Swarm Optimization

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Mechanical Engineering

Cite this

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