Department of Electronics and Information Technology, Jiangmen Polytechnic, Jiangmen, China

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Abstract—In this paper, established a mathematical model for multi-objective flexible scheduling problems, combined Pareto non-dominated sorting method, put forward a hybrid genetic algorithm based on improved DNA computation. To ensure the diversity of the optimal solution sets, designed RNA quaternary encoder mode and genetic operator based on improved DNA computation, adopted sub-area crossover and dynamic mutation, imposed on manipulation of the molecular level. Through simulation, tested the performance of the designed algorithm, compared it with the standard genetic algorithm test results. Simulation results showed the proposed algorithm can provide an optimum searching, owned better seeking abilities; the obtained scheduling results were fairly reasonable. This algorithm can effectively solve the multiobjective flexible scheduling optimization problems.

Index Terms—DNA computation, RNA genetic operators, Genetic Algorithm, Pareto sorting, Multi-objective flexible scheduling.

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Cite: Nie Shuzhi, Zhong Yanhua, "Multi-objective Flexible Scheduling Optimization Scheme base on Improved DNA Genetic Algorithm," Journal of Computers vol. 7, no. 8, pp. 1982-1989, 2012.