A hybrid dynamic multi-objective immune optimization algorithm using prediction strategy and improved differential evolution crossover operator

Authors:
Yajuan Ma;Ruochen Liu;Ronghua Shang
Affiliations:
Key Laboratory of Intelligent Perception and Image Understanding, of Ministry of Education of China, Xidian University, Xi'an, China;Key Laboratory of Intelligent Perception and Image Understanding, of Ministry of Education of China, Xidian University, Xi'an, China;Key Laboratory of Intelligent Perception and Image Understanding, of Ministry of Education of China, Xidian University, Xi'an, China
Venue:
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Year:
2011

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Abstract

In this paper, a hybrid dynamic multi-objective immune optimization algorithm is proposed. In the algorithm, when a change in the objective space is detected, aiming to improve the ability of responding to the environment change, a forecasting model, which is established by the non-dominated antibodies in previous optimum locations, is used to generate the initial antibodies population. Moreover, in order to speed up convergence, an improved differential evolution crossover with two selection strategies is proposed. Experimental results indicate that the proposed algorithm is promising for dynamic multi-objective optimization problems.