A self-adaptive hybrid particle swarm optimization algorithm for flow shop scheduling problem

A hybrid self-adaptive algorithm is proposed to solve the flow shop scheduling problem with the objective of minimizing makespan, which combined the particle swarm optimization algorithm and genetic operators together. The particle similarity and particle energy are defined. The threshold of particle similarity dynamically changes with iterations and the particle energy depends on the swarm evolving degree and the particle's evolving speed. In order to improve the proposed algorithm performance further, a neighborhood based random greedy search strategy is introduced to overcome the shortcoming of evolving slowly in the later running phase. Finally, the proposed algorithm is tested on different scale benchmarks and compared with the recently proposed efficient algorithms. The result shows that the solution quality and the stability of the HPGA both precede the other two algorithms. It can be used to solve large scale flow shop scheduling problem.