An Effective Hybrid Optimization Algorithm Based on Self-Adaptive Particle Swarm Optimization Algorithm and Artificial Immune Clone Algorithm

Author

Chen, Ai-ling ; Guo, Qiang

Author_Institution

Sch. of Inf. Manage., Shandong Econ. Univ., Jinan

Volume

7

fYear

2008

fDate

18-20 Oct. 2008

Firstpage

129

Lastpage

132

Abstract

To balance the exploration and exploitation abilities of particle swarm optimization (PSO), self-adaptive inertia weight factor is introduced in PSO. To improve the ability of each algorithm to escape from a local optimum, a hybrid optimization algorithm (PAHA) based on self-adaptive PSO and artificial immune clone algorithm (AICA) is developed. Simulation results have shown that PAHA is effective and efficient for the optimization problems.

Keywords

artificial immune systems; evolutionary computation; particle swarm optimisation; artificial immune clone algorithm; hybrid optimization algorithm; self-adaptive inertia weight factor; self-adaptive particle swarm optimization algorithm; Benchmark testing; Cloning; Computational modeling; Design engineering; Design optimization; Immune system; Information management; Manufacturing systems; Particle swarm optimization; Process control; Artificial immune clone algorithm; Hybrid optimization algorithm; Particle swarm optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation, 2008. ICNC '08. Fourth International Conference on

Conference_Location

Jinan

Print_ISBN

978-0-7695-3304-9

Type

conf

DOI

10.1109/ICNC.2008.678

Filename

4667958