Flexible Job Shop Scheduling Problem Based on Hybrid Particle Swarm Algorithm
LIN Muqi
GAO Quanli
SHAO Lianhe
JIA Jianling
JIN Lei
LIU Jiachen
Abstract：In this paper,a new hybrid particle swarm algorithm is proposed to solve the problem that the traditional intelligent optimization algorithm has insufficient global search capability in the early stage of algorithm search and slow convergence speed in the later stage,which is easy to fall into local optimum.Firstly,the inertia weights of the particle swarm algorithm are dynamically adjusted using the cosine adaptive strategy,which effectively improves the search performance in the early and late iterations of the algorithm.Secondly,the explosion mechanism of the fireworks algorithm is introduced to perform explosion search at the historical optimal position of each particle to improve its local fine search capability.Finally,the health degree of each particle is detected,and the lazy particles below the health degree threshold are eliminated from the population by using the idea of"survival of the fit-test"of the wolf pack algorithm,and the same number of particles are randomly generated into the population by introducing the wolf pack update mechanism,which improves the population diversity and makes the algorithm have a strong pioneering ability.The proposed hybrid particle swarm algorithm is proved to be feasible,reasonable and efficient in solving the FJSP problem by conduct-ing simulation experiments on the FJSP arithmetic case and comparing with other algorithms.
Keywords：flexible job shop schedulingparticle swarm optimizationadaptive inertia weightfireworks algorithmwolf pack renewal mechanism
Publication Date：2025-12-20
Online Publishing Date：2026-03-09(First online date of this platform, not the publication date of the document)
Pages：7( 3435-3441 )