An Improved Multimodal Artificial Immune Algorithm and its Convergence Analysis

Author

Tang, Tieying ; Qiu, Jiaju

Author_Institution

Dept. of Electr. Eng., Zhejiang Univ., Hangzhou

Volume

1

fYear

0

fDate

0-0 0

Firstpage

3335

Lastpage

3339

Abstract

A dynamic population immune algorithm (DPIA) for multimodal function optimization is proposed based on clone selection principle and immune network theory. This algorithm can search in the global-space and local-space simultaneously with mutation to low-bit genes and selection in subpopulation. Then the transition probability of the immune operators and the conception of multimodal algorithm convergence are given. It is proved that the DPIA is completely convergent based on the use of Markov chain. The experiment results verified the steady convergence of DPIA by optimizing the typical multi-modal functions and comparing with the similar algorithms

Keywords

Markov processes; artificial immune systems; convergence; dynamic programming; genetic algorithms; Markov chain; clone selection principle; convergence analysis; dynamic population immune algorithm; immune network theory; immune operators; multimodal algorithm convergence; multimodal artificial immune algorithm; multimodal function optimization; multimodal optimization algorithm; transition probability; Algorithm design and analysis; Cloning; Convergence; Diversity reception; Emulation; Flowcharts; Genetic mutations; Heuristic algorithms; Immune system; Robustness; Immune algorithm; Markov chain; complete convergence; multimodal optimization algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1712985

Filename

1712985