Solving the Problem of Multi-objective Flexible Job Shop Based on Hybrid Genetic Algorithm and Particle Swarm Optimization

DOI

10.2991/mmsa-18.2018.42How to use a DOI?

Keywords

flexible job shop scheduling problem; genetic algorithm; particle swarm optimization; hybrid algorithm

Abstract

A teaching-learning-based hybrid genetic-particle swarm optimization algorithm is proposed for multi-objective flexible job shop scheduling problem. It includes three modules: genetic algorithm (GA), bi-memory learning (BL) and particle swarm optimization (PSO). Firstly, in the BL module, a learning mechanism is introduced into GA to generate chromosomes which have a self-learning characteristic. During the process of evolution, the offspring in GA learn the characteristics of good chromosomes in the BL. Then, a discretization PSO algorithm which iterates the genetic population and particle population simultaneously is proposed. Finally, experiments are conducted to compare the rationality and validity of the proposed algorithm with others.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xiabao Huang
PY  - 2018/03
DA  - 2018/03
TI  - Solving the Problem of Multi-objective Flexible Job Shop Based on Hybrid Genetic Algorithm and Particle Swarm Optimization
BT  - Proceedings of the 2018 International Conference on Mathematics, Modelling, Simulation and Algorithms (MMSA 2018)
PB  - Atlantis Press
SP  - 190
EP  - 194
SN  - 1951-6851
UR  - https://doi.org/10.2991/mmsa-18.2018.42
DO  - 10.2991/mmsa-18.2018.42
ID  - Huang2018/03
ER  -