Title :
An Improved Multimodal Artificial Immune Algorithm and its Convergence Analysis
Author :
Tang, Tieying ; Qiu, Jiaju
Author_Institution :
Dept. of Electr. Eng., Zhejiang Univ., Hangzhou
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;
Conference_Titel :
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location :
Dalian
Print_ISBN :
1-4244-0332-4
DOI :
10.1109/WCICA.2006.1712985
