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

Abstract

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.

Keywords:

• bacterial foraging algorithm
• chemotaxis
• difference mutation
• hybrid optimisation
• job shop scheduling

Additional information

Funding

This work was financially supported by the National Natural Science Foundation of China [grant number 51365030]. It was also supported by scientific research funds from Gansu University, the General Program of the  Postdoctoral Science Foundation of China, Special Program of the Postdoctoral Science Foundation of China, the Science Foundation for Excellent Youth Scholars of Lanzhou University of Technology, Lanzhou Science Bureau project [grant numbers 1114ZTC139, 2012M521802, 2013T60889, 1014ZCX017 and 2013-4-64, respectively].