Hybrid Particle Swarm Optimization Algorithm for Process Planning

Xu Zhang, Pan Guo, Hua Zhang and Jin Yao
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Xu Zhang: Business School, Sichuan University, Chengdu 610064, China
Pan Guo: Business School, Sichuan University, Chengdu 610064, China
Hua Zhang: School of Economics and Management, Zhaoqing University, Zhaoqing 526061, China
Jin Yao: School of Mechanical Engineering, Sichuan University, Chengdu 610064, China

Mathematics, 2020, vol. 8, issue 10, 1-22

Abstract: Process planning is a typical combinatorial optimization problem. When the scale of the problem increases, combinatorial explosion occurs, which makes it difficult for traditional precise algorithms to solve the problem. A hybrid particle swarm optimization (HPSO) algorithm is proposed in this paper to solve problems of process planning. A hierarchical coding method including operation layer, machine layer and logic layer is designed in this algorithm. Each layer of coding corresponds to the decision of a sub-problem of process planning. Several genetic operators of the genetic algorithm are designed to replace the update formula of particle position and velocity in the particle swarm optimization algorithm. The results of the benchmark example in case study show that the algorithm proposed in this paper has better performance.

Keywords: process planning; hybrid particle swarm optimization; hierarchical coding (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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