Open AccessAn Improved Particle Swarm Algorithm with the Elite Retain Strategy for Solving Flexible Jobshop Scheduling Problem
Author(s) -
Manman Wu,
Dongsheng Yang,
Tianyi Liu
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2173/1/012082
Subject(s) - particle swarm optimization , computer science , mathematical optimization , job shop scheduling , scheduling (production processes) , multi swarm optimization , convergence (economics) , imperialist competitive algorithm , algorithm , mathematics , economics , schedule , economic growth , operating system
As a combination optimization problem that an enterprise often encounters in the process of jobshop production management, the flexible job shop scheduling problem has become one of the hot issues that scholars at home and abroad have continuously studied and explored in recent years. With the development of computer technology and the rise of interdisciplinary, intelligent optimization algorithms have gradually become the primary method to solve such problems. Particle swarm algorithm is an evolutionary search calculation method that simulates birds gathering flight, constantly changing their position and speed during movement, and finally reaching the optimal state. However, the application of this algorithm in the field of solving jobshop scheduling is not particularly mature, and there are some shortcomings in its application. Aiming at the disadvantages of particle swarm algorithm that it is easy to fall into the optimal local solution and the slow convergence speed of the algorithm in the later stage, this paper proposes an elite retention strategy and integrates it into the PSO to enhance its later solution ability.