Development of a parallel optimization method based on genetic simulated annealing algorithm

This paper presents a parallel genetic simulated annealing (PGSA) algorithm that has been developed and applied to optimize continuous problems. In PGSA, the entire population is divided into subpopulations, and in each subpopulation the algorithm uses the local search ability of simulated annealing after crossover and mutation. The best individuals of each subpopulation are migrated to neighboring ones after certain number of epochs. An implementation of the algorithm is discussed and the performance evaluation is made against a standard set of test functions. PGSA shows some remarkable improvement in comparison with the conventional simulated annealing, parallel genetic algorithm.