期刊
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
卷 204, 期 2, 页码 294-302出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.ejor.2009.10.010
关键词
Multiple objective programming; Artificial immune systems; Clonal selection principle; Hybrid mutation; Artificial intelligence
资金
- National Natural Science Foundation of China [60703112]
In this paper, we develop a hybrid immune multiobjective optimization algorithm (HIMO) based on clonal selection principle. In HIMO, a hybrid mutation operator is proposed with the combination of Gaussian and polynomial mutations (GP-HM operator). The GP-HM operator adopts an adaptive switching parameter to control the mutation process, which uses relative large steps in high probability for boundary individuals and less-crowded individuals. With the generation running, the probability to perform relative large steps is reduced gradually. By this means, the exploratory capabilities are enhanced by keeping a desirable balance between global search and local search, so as to accelerate the convergence speed to the true Pareto-optimal front in the global space with many local Pareto-optimal fronts. When comparing HIMO with various state-of-the-art multiobjective optimization algorithms developed recently, simulation results show that HIMO performs better evidently. (C) 2009 Elsevier B.V. All rights reserved.
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