Analysis of the publications on the applications of particle swarm optimisation
Journal of Artificial Evolution and Applications - Regular issue
Analysis of the publications on the applications of particle swarm optimisation
Journal of Artificial Evolution and Applications - Regular issue
Designing Neural Networks Using PSO-Based Memetic Algorithm
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Computers and Operations Research
Conservation of information in search: measuring the cost of success
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
A hybrid alternate two phases particle swarm optimization algorithm for flow shop scheduling problem
Computers and Industrial Engineering
Bacterial foraging with quorum sensing based optimization algorithm
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Adaptive particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Towards a memetic feature selection paradigm
IEEE Computational Intelligence Magazine
Evolutionary tristate PSO for strategic bidding of pumped-storage hydroelectric plant
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A particle swarm optimization based memetic algorithm for dynamic optimization problems
Natural Computing: an international journal
A rotary chaotic PSO algorithm for trustworthy scheduling of a grid workflow
Computers and Operations Research
A novel cyclic discrete optimization framework for particle swarm optimization
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Expert Systems with Applications: An International Journal
Mortal particles: particle swarm optimization with life span
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
A novel parallel hybrid intelligence optimization algorithm for a function approximation problem
Computers & Mathematics with Applications
A memetic particle swarm optimization algorithm for multimodal optimization problems
Information Sciences: an International Journal
A novel multi-objective particle swarm optimization algorithm for flow shop scheduling problems
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
Population-based dynamic scheduling optimisation for complex production process
International Journal of Computer Applications in Technology
Parallel cooperative micro-particle swarm optimization: A master-slave model
Applied Soft Computing
A region-based quantum evolutionary algorithm (RQEA) for global numerical optimization
Journal of Computational and Applied Mathematics
Advances in Engineering Software
Computers and Industrial Engineering
Journal of Parallel and Distributed Computing
A block-based evolutionary algorithm for flow-shop scheduling problem
Applied Soft Computing
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This paper proposes an effective particle swarm optimization (PSO)-based memetic algorithm (MA) for the permutation flow shop scheduling problem (PFSSP) with the objective to minimize the maximum completion time, which is a typical non-deterministic polynomial-time (NP) hard combinatorial optimization problem. In the proposed PSO-based MA (PSOMA), both PSO-based searching operators and some special local searching operators are designed to balance the exploration and exploitation abilities. In particular, the PSOMA applies the evolutionary searching mechanism of PSO, which is characterized by individual improvement, population cooperation, and competition to effectively perform exploration. On the other hand, the PSOMA utilizes several adaptive local searches to perform exploitation. First, to make PSO suitable for solving PFSSP, a ranked-order value rule based on random key representation is presented to convert the continuous position values of particles to job permutations. Second, to generate an initial swarm with certain quality and diversity, the famous Nawaz-Enscore-Ham (NEH) heuristic is incorporated into the initialization of population. Third, to balance the exploration and exploitation abilities, after the standard PSO-based searching operation, a new local search technique named NEH_1 insertion is probabilistically applied to some good particles selected by using a roulette wheel mechanism with a specified probability. Fourth, to enrich the searching behaviors and to avoid premature convergence, a simulated annealing (SA)-based local search with multiple different neighborhoods is designed and incorporated into the PSOMA. Meanwhile, an effective adaptive meta-Lamarckian learning strategy is employed to decide which neighborhood to be used in SA-based local search. Finally, to further enhance the exploitation ability, a pairwise-based local search is applied after the SA-based search. Simulation results based on benchmarks demonstrate the effectiveness of- - the PSOMA. Additionally, the effects of some parameters on optimization performances are also discussed