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

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.