期刊
MATHEMATICS
卷 7, 期 5, 页码 -出版社
MDPI
DOI: 10.3390/math7050384
关键词
whale optimization algorithm; flexible job shop scheduling problem; nonlinear convergence factor; adaptive weight; variable neighborhood search
类别
资金
- National Natural Science Foundation of China [11072192]
- Project of Shaanxi Province Soft Science Research Program [2018KRM090]
- Project of Xi'an Science and Technology Innovation Guidance Program [201805023YD1CG7(1)]
- Shandong Provincial Natural Science Foundation of China [ZR2016GP02]
In this paper, a novel improved whale optimization algorithm (IWOA), based on the integrated approach, is presented for solving the flexible job shop scheduling problem (FJSP) with the objective of minimizing makespan. First of all, to make the whale optimization algorithm (WOA) adaptive to the FJSP, the conversion method between the whale individual position vector and the scheduling solution is firstly proposed. Secondly, a resultful initialization scheme with certain quality is obtained using chaotic reverse learning (CRL) strategies. Thirdly, a nonlinear convergence factor (NFC) and an adaptive weight (AW) are introduced to balance the abilities of exploitation and exploration of the algorithm. Furthermore, a variable neighborhood search (VNS) operation is performed on the current optimal individual to enhance the accuracy and effectiveness of the local exploration. Experimental results on various benchmark instances show that the proposed IWOA can obtain competitive results compared to the existing algorithms in a short time.
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