An Improved Clonal Selection Algorithm for Job Shop Scheduling

Authors:
Hong Lu;Jing Yang
Affiliations:
-;-
Venue:
IUCE '09 Proceedings of the 2009 International Symposium on Intelligent Ubiquitous Computing and Education
Year:
2009

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Abstract

The job shop scheduling problem (JSSP) is a notoriously difficult problem in combinatorial optimization. Extensive investigation has been devoted to developing efficient algorithms to find optimal or near-optimal solutions. This paper proposes an improved immune clonal selection algorithm, called improved clonal selection algorithm for the JSSP. The new algorithm has the advantage of preventing from prematurity and fast convergence speed. Numerous well-studied benchmark examples in job-shop scheduling problems were utilized to evaluate the proposed approach. The computational results show that the proposed algorithm could obtain the high-quality solutions within reasonable computing times, and the results indicate the effectiveness and flexibility of the immune memory clonal selection algorithm.