A Novel Parallel Hybrid Algorithms for Job Shop Problem

To make up for the deficiency of single algorithm for solving Job Shop problem and improve the quality of solutions, a novel parallel hybrid algorithm search method is proposed. Genetic Algorithm (GA) and Particle Swarm Algorithm (PSO) are both adopted to search in parallel way, and Migration Operator is adopted to achieve the intercommunication between them. Within limited time, several best solutions of typical benchmark problems such as FT10 , LA37 are found, and the average relative error percentage of the average value for ten times result is 2.54% and 0.16%, which are respectively smaller than ones by Parallel Genetic Algorithm (PGA)and Taboo Search Algorithm with Back Jump Tracking (TSAB). The proposed method has improved total search ability of hybrid algorithm, and the validity of the parallel hybrid search method is validated.