A novel combination of Particle Swarm Optimization and Genetic Algorithm for Pareto optimal design of a five-degree of freedom 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.

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► A novel combination of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) is introduced.
► 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.
► The proposed multi-objective hybrid algorithm is used to Pareto optimal design of a five-degree of freedom vehicle vibration model.