Authors:
Pham Ngoc Hieu
and
Hiroshi Hasegawa
Affiliation:
Shibaura Institute of Technology, Japan
Keyword(s):
Adaptive System, Differential Evolution (DE), Genetic Algorithm (GA), Multi-peak Problems, Particle Swarm Optimization (PSO).
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolution Strategies
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Memetic Algorithms
;
Soft Computing
;
Swarm/Collective Intelligence
Abstract:
A new strategy using Differential Evolution (DE) for Adaptive Plan System of Particle Swarm Optimization (PSO) with Genetic Algorithm (GA) called DE-PSO-APGA is proposed to solve a huge scale optimization problem, and to improve the convergence towards the optimal solution. This is an approach that combines the global search ability of GA and Adaptive plan (AP) for local search ability. 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). The DE-PSO-APGA is applied to several benchmark functions with multi-dimensions to evaluate its performance. We confirmed satisfactory performance through various benchmark tests.