College of computer science, Soochow university, Suzhou, 215006, China

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Abstract—Genetic algorithm is a combinatorial optimization problem solving in the field of search algorithm, because of its versatility and robustness, it has been widely used in various fields of science. However, there are some defects in traditional genetic algorithm. for its shortcomings, this paper proposed an improved genetic algorithm for multi-objective Flexible JSP (job shop scheduling) problem. The algorithm construct the initial solution based on judging similarity strategy and immune mechanisms, proposed a self-adaptation cross and mutation operator, and using simulated annealing algorithm strategy combined with immune mechanisms in the selection operator, the experiment proof shows that, the improved genetic algorithm can improve the performance.

Index Terms—Similarity; adaptive cross-variation; immune mechanism; simulated annealing; multi-objective flexible job shop scheduling.

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Cite: Meng Lan, Ting-rong Xu, Ling Peng, "Solving Flexible Multi-objective JSP Problem Using A Improved Genetic Algorithm," Journal of Software vol. 5, no. 10, pp. 1107-1113, 2010.