Title: Adaptive discrete cat swarm optimisation algorithm for the flexible job shop problem

Authors: Tian-hua Jiang; Chao Zhang

Addresses: School of Transportation, Ludong University, Yantai, Shandong, China ' Department of Computer Science and Technology, Henan Institute of Technology, Xinxiang, Henan, China

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.

Keywords: flexible job shop scheduling; combinatorial optimisation; discrete cat swarm optimisation; adaptive adjustment strategy; local search.

DOI: 10.1504/IJBIC.2019.099186

International Journal of Bio-Inspired Computation, 2019 Vol.13 No.3, pp.199 - 208

Received: 04 Dec 2017
Accepted: 17 Nov 2018

Published online: 08 Apr 2019 *

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