A hybrid alternate two phases particle swarm optimization algorithm for flow shop scheduling problem

Abstract

A hybrid alternate two phases particle swarm optimization (PSO) algorithm called ATPPSO is proposed to solve the flow shop scheduling problem (FSSP) with the objective of minimizing makespan which combines the PSO with genetic operators and annealing strategy. In the ATPPSO algorithm, each particle contains two states, the attractive state and the repulsive state. In order to refrain from the shortcoming of premature convergence, a two point reversal crossover operator is defined and in the repulsive process each particle is repelled away from some inferior solution in the current tabu list to fly towards some promising areas which can introduce some new information to guide the swarm searching process. To preserve the swarm diversity, an annealing criterion is used to update the personal best of each particle. Moreover an easy understanding makespan computation method based on matrix is designed. Finally, the proposed algorithm is tested on different scale benchmarks and compared with the recently proposed efficient algorithms. The results show that both the solution quality and the convergence speed of the ATPPSO algorithm precede the other two recently proposed algorithms. It can be used to solve large scale flow shop scheduling problem effectively.