An Improved Immune-Based Multi-modal Function Optimization Algorithm

Author

Zhang Xu ; Xu, Zhang

Author_Institution

Sch. of Mech. Eng., Dalian Jiaotong Univ., Dalian, China

Volume

1

fYear

2009

fDate

11-14 Dec. 2009

Firstpage

15

Lastpage

19

Abstract

The aim of this paper is to design an adaptive artificial immune algorithm for solving multi-modal optimization problems effectively and speedily. Based on analyzing the characteristics and disadvantages of CLONALG, an improved immune-based algorithm is proposed, which combines memory cells producing, network suppression and valley searching method. Testing benchmark functions show that it can fast find out all optimal solutions and local optimal solutions as many as possible without any prior knowledge.

Keywords

adaptive systems; artificial immune systems; CLONALG; adaptive artificial immune algorithm; immune-based multi-modal function optimization algorithm; memory cells producing; network suppression; valley searching method; Algorithm design and analysis; Cloning; Computational intelligence; Design optimization; Genetic algorithms; Immune system; Mechanical engineering; Optimization methods; Random number generation; Testing; CLONALG; adaptive; immune algorithm; multi-modal function optimization; valley searching method;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Security, 2009. CIS '09. International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-5411-2

Type

conf

DOI

10.1109/CIS.2009.241

Filename

5376754