期刊
COMPUTERS & OPERATIONS RESEARCH
卷 40, 期 6, 页码 1590-1601出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cor.2011.11.011
关键词
Multiobjective optimization; Artificial immune system; Fine-grained selection; Adaptive mutation; Micro-population
类别
资金
- National Natural Science Foundation of China [61170283]
- Shenzhen Fundamental Research Plan [JC201005250045A]
In this paper, we present a novel immune multiobjective optimization algorithm based on micro-population, which adopts a novel adaptive mutation operator for local search and an efficient fine-grained selection operator for archive update. With the external archive for storing nondominated individuals, the population diversity can be well preserved using an efficient fine-grained selection procedure performed on the micro-population. The adaptive mutation operator is executed according to the fitness values, which promotes to use relatively large steps for boundary and less-crowded individuals in high probability. Therefore, the exploratory capabilities are enhanced. When comparing the proposed algorithm with a recently proposed immune multiobjective algorithm and a scatter search multiobjective algorithm in various benchmark functions, simulations show that the proposed algorithm not only improves convergence ability but also preserves population diversity adequately in most cases. (C) 2011 Elsevier Ltd. All rights reserved.
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