Genetic algorithms for flowshop scheduling problems
Computers and Industrial Engineering
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
A very fast Tabu search algorithm for the permutation flow shop problem with makespan criterion
Computers and Operations Research
An exact approach to early/tardy scheduling with release dates
Computers and Operations Research
Design and Analysis of Experiments
Design and Analysis of Experiments
An effective hybrid PSO-based algorithm for flow shop scheduling with limited buffers
Computers and Operations Research
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
IEEE Transactions on Evolutionary Computation
An Effective PSO-Based Memetic Algorithm for Flow Shop Scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Combining PSO and local search to solve scheduling problems
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Novel fish swarm heuristics for bound constrained global optimization problems
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part III
A new ant colony algorithm for makespan minimization in permutation flow shops
Computers and Industrial Engineering
An improved hybrid particle swarm optimization algorithm for fuzzy p-hub center problem
Computers and Industrial Engineering
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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.