Parallel adaptive hybrid genetic optimization algorithm and its application

A pragmatic hybrid genetic algorithm named parallel adaptive genetic simulated annealing (PAGSA) is developed. The proposed hybrid approach combines the merits of genetic algorithm (GA) with simulated annealing (SA) to construct a more efficient genetic simulated annealing (GSA) algorithm for global search, while the iterative hill climbing (IHC) method is used as a local search technique to incorporate into GSA loop for speeding up the convergence of the algorithm. In addition, a self-adaptive hybrid mechanism is developed to maintain a tradeoff between the global and local optimizer searching then to efficiently locate quality solution to complicated optimization problem. The computational results and application have illustrated that the global searching ability and the convergence speed of this hybrid algorithm are significantly improved.