Solving job shop scheduling problem using a hybrid parallel micro genetic algorithm
Applied Soft Computing
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In order to solve problem of the speed of convergence and early-maturing of the serial genetic algorithm, people begin to study the parallel genetic algorithm. However most researcher devoted to study the single way parallel genetic algorithm(PGA), while solving some combination optimizing problem, have made good result, but as to the more complicated combination optimizing problem, all have certain deficiencies. This paper propose a coarse grain size-master slaver model PGA(CGS-MSM PGA), which is make up of several coarse grain size PGA(CGS-PGA) that is combine of several master-slaver model parallel genetic algorithm(MSM-PGA), and every sub-algorithm is one Agent. This algorithm has improved the speed and precision of calculation greatly through the good communication coordination among many Agents, utilized the advantages of CGS-PGA and MSM-PGA, overcome the shortcoming of their two.