A hybrid immune multiobjective optimization algorithm |
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Authors: | Jianyong Chen Qiuzhen LinZhen Ji |
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Institution: | Department of Computer Science and Technology, Shenzhen University, Shenzhen 518060, PR China |
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Abstract: | 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. |
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Keywords: | Multiple objective programming Artificial immune systems Clonal selection principle Hybrid mutation Artificial intelligence |
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