A steepest descent evolution immune algorithm for multimodal function optimization

Authors:
Li Zhu;Zhishu Li;Bin Sun
Affiliations:
School of Computer, Sichuan University and Network Center, Chengdu Sport University;School of Computer, Sichuan University;Engineering Development Center, Chengdu Aircraft Corporation, Chengdu, P.R. China
Venue:
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
Year:
2007

Citing 3
Cited 0

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Abstract

This paper presents an novel evolution and immune hybrid algorithm for multimodal function optimization. The algorithm constructs a multi-dimensional shape-space based on immune theory and approaches optima by steepest descent evolution strategy along each dimension, adjusts steps adaptively based on fitness in each iteration, as a result, gets steepest and surefooted ability approaching the optima. By suppressing close individuals in immune shape-space within a restraint radius and supplying new individuals to exploit new searching space, the algorithm obtains very good diversity. Experiments for multimodal functions show that the algorithm achieved global searching effect, obtained all the optima in shorter iterations and with lesser size of population compared with the GA, CSA and op-aiNet.