An improved adaptive genetic algorithm for job-shop scheduling problem

Indexed by:会议论文

Date of Publication:2007-08-24

Included Journals:EI、CPCI-S、Scopus

Volume:4

Page Number:287-+

Abstract:An adaptive genetic algorithm with some improvement is proposed to solve the job-shop scheduling problem (JSSP) better The improved adaptive genetic algorithm (IAGA) obtained by applying the improved sigmoid function to adaptive genetic algorithm. And in IA GA for JSSP the fitness of algorithm is represented by completion time of jobs. Therefore, this algorithm making the crossover and mutation probability adjusted adaptively and nonlinearly with the completion time, can avoid such disadvantages as premature convergence, low convergence speed and low stability. Experimental results demonstrate that the proposed genetic algorithm does not get stuck at a local optimum easily, and it is fast in convergence, simple to be implemented Several examples testify the effectiveness of the proposed genetic algorithm for JSSP.

Date of Publication:2007-08-24