Hybrid fuzzy logic-based particle swarm optimization for flow shop scheduling problem

Ling, Sai Ho, Jiang, Frank, Nguyen, Hung T and Chan, Kit Yan 2011, Hybrid fuzzy logic-based particle swarm optimization for flow shop scheduling problem, International Journal of Computational Intelligence and Applications, vol. 10, no. 3, pp. 335-356, doi: 10.1142/S1469026811003136.

Title Hybrid fuzzy logic-based particle swarm optimization for flow shop scheduling problem
Author(s) Ling, Sai Ho
Jiang, FrankORCID iD for Jiang, Frank orcid.org/0000-0003-3088-8525
Nguyen, Hung T
Chan, Kit Yan
Journal name International Journal of Computational Intelligence and Applications
Volume number 10
Issue number 3
Start page 335
End page 356
Total pages 22
Publisher World Scientific Pub Co Pte Lt
Publication date 2011-09-01
ISSN 1469-0268
Summary This paper, proposes a hybrid fuzzy logic-based particle swarm optimization (PSO) with cross-mutated operation method for the minimization of makespan in permutation flow shop scheduling problem. This problem is a typical non-deterministic polynomial-time (NP) hard combinatorial optimization problem. In the proposed hybrid PSO, fuzzy inference system is applied to determine the inertia weight of PSO and the control parameter of the proposed cross-mutated operation by using human knowledge. By introducing the fuzzy system, the inertia weight becomes adaptive. The cross-mutated operation effectively forces the solution to escape the local optimum. To make PSO suitable for solving flow shop scheduling problem, a sequence-order system based on the roulette wheel mechanism is proposed to convert the continuous position values of particles to job permutations. Meanwhile, a new local search technique namely swap-based local search for scheduling problem is designed and incorporated into the hybrid PSO. Finally, a suite of flow shop benchmark functions are employed to evaluate the performance of the proposed PSO for flow shop scheduling problems. Experimental results show empirically that the proposed method outperforms the existing hybrid PSO methods significantly.
DOI 10.1142/S1469026811003136
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Field of Research 0801 Artificial Intelligence and Image Processing
0806 Information Systems
HERDC Research category C1.1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30138256

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