Adaptive discrete cat swarm optimisation algorithm for the flexible job shop problem
by Tian-hua Jiang; Chao Zhang
International Journal of Bio-Inspired Computation (IJBIC), Vol. 13, No. 3, 2019

Abstract: Flexible job shop scheduling problem (FJSP) is a discrete combinatorial optimisation problem. By considering its characteristics, a kind of adaptive discrete cat swarm optimisation algorithm (ADCSO) is proposed with three objectives to be optimised simultaneously, i.e., makespan, maximal machine workload and total workload. Firstly, a two-phase encoding mechanism is proposed to represent each individual, and a heuristic-based strategy is employed to generate the initial population. Secondly, the discrete seeking mode and tracing mode are developed to make the algorithm directly work in a discrete domain. Thirdly, four adaptive adjustment approaches with different decline curves are used to balance the abilities of global search and local search. Furthermore, a local search strategy is embedded into the algorithm to improve the searching capacity. Finally, extensive experimental data are obtained based on the benchmark instances. The comparison results show that ADCSO is effective for the problem under study.

Online publication date: Fri, 19-Apr-2019

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