Application research of an annealing parallel genetic algorithm based on learning

Combining parallel genetic algorithm with simulated annealing algorithm,a new hybrid optimization strategy with simulated annealing and parallel genetic algorithm is proposed.The algorithm solves the problem of weak local searching ability of parallel genetic algorithm,SA is regarded as the mutation operator of GA population,then the local searching ability is improved.At the same time,the theories of machine_learning are applied to the hybrid algorithm.The average fitness of chromosomes is improved,the loss of the best solution is avoided and the speed of the evolution is increased,then the best solution can be obtained earlier.The results are compared through the optimization calculation of the new algorithm and the traditional genetic algorithm in solving classic problem of job-shop scheduling problem,and the simulation results show the effectiveness of the new algorithm.