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.
|ジャーナル||Journal of Advanced Mechanical Design, Systems and Manufacturing|
|出版ステータス||Published - 2013|
ASJC Scopus subject areas
- Mechanical Engineering
- Industrial and Manufacturing Engineering