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

摘要

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.