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A chemotaxis-enhanced bacterial foraging algorithm and its application in job shop scheduling problem

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In this article, a chemotaxis-enhanced bacterial foraging optimisation (CEBFO) is proposed to solve the job shop scheduling problem more effectively. The new approach, which is based on a new chemotaxis with the differential evolution (DE) operator added, aims at solving the tumble failure problem in the tumble step and accelerates the convergence speed of the original algorithm. The effectiveness of the new chemotaxis and the convergence are proved theoretically and tested in continuous problems. Furthermore, a local search operator was designed, which can improve the local search ability of novel algorithm greatly. Finally, the experiments were conducted on a set of 38 benchmark problems of job shop scheduling and the results demonstrated the outperformance of the proposed algorithm.
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Keywords: bacterial foraging algorithm; chemotaxis; difference mutation; hybrid optimisation; job shop scheduling

Document Type: Research Article

Affiliations: 1: School of Computer and Communication, Lanzhou University of Technology, Lanzhou, China 2: H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA 3: Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, Xi’an, China

Publication date: October 3, 2015

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