A hybrid biogeography-based optimization algorithm for job shop scheduling problem |
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Affiliation: | 1. School of computer, Liaocheng University, Liaochheng, 252059, P. R.China;2. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore;3. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, P. R. China;4. Singapore Institute of Manufacturing Technology, Nanyang Drive 638075, Singapore |
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Abstract: | In this paper, a hybrid biogeography-based optimization (HBBO) algorithm has been proposed for the job-shop scheduling problem (JSP). Biogeography-based optimization (BBO) is a new bio-inpired computation method that is based on the science of biogeography. The BBO algorithm searches for the global optimum mainly through two main steps: migration and mutation. As JSP is one of the most difficult combinational optimization problems, the original BBO algorithm cannot handle it very well, especially for instances with larger size. The proposed HBBO algorithm combines the chaos theory and “searching around the optimum” strategy with the basic BBO, which makes it converge to global optimum solution faster and more stably. Series of comparative experiments with particle swarm optimization (PSO), basic BBO, the CPLEX and 14 other competitive algorithms are conducted, and the results show that our proposed HBBO algorithm outperforms the other state-of-the-art algorithms, such as genetic algorithm (GA), simulated annealing (SA), the PSO and the basic BBO. |
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Keywords: | Job-shop scheduling problem (JSP) Biogeography-based optimization (BBO) Chaos theory Hybrid biogeography-based optimization (HBBO) |
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