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.

Graphical abstractFigure optionsDownload full-size imageDownload as PowerPoint slideHighlights► 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.