期刊
APPLIED SOFT COMPUTING
卷 13, 期 5, 页码 2577-2591出版社
ELSEVIER
DOI: 10.1016/j.asoc.2012.11.028
关键词
Hybrid algorithms; Particle Swarm Optimization; Genetic Algorithm; Single-objective problems; Multi-objective problems; Vehicle vibration model
In this paper, at first, a novel combination of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) is introduced. This hybrid algorithm uses the operators such as mutation, traditional or classical crossover, multiple-crossover, and PSO formula. The selection of these operators in each iteration for each particle or chromosome is based on a fuzzy probability. The performance of the proposed hybrid algorithm for solving both single and multi-objective optimization problems is challenged by using of some well-known benchmark problems. Obtained numerical results are compared with those of other optimization algorithms. At the end, the proposed multi-objective hybrid algorithm is used for the Pareto optimal design of a five-degree of freedom vehicle vibration model. The comparison of the obtained results with it in the literature demonstrates the superiority of this work. (C) 2012 Elsevier B. V. All rights reserved.
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