A hybrid strategy based on niche genetic algorithm and Tabu search and its convergence property

Genetic algorithm and Tabu search algorithm are powerful tools to solve complex large-scale optimization problems. To deal with the prematurity and low convergence speed problems when the genetic algorithm being used for global optimization, we introduce a hybrid optimization algorithm through comprehensive contrast and comparison between the above two algorithms. In our approach, we use Tabu search algorithm for local search in order to speed up convergence speed and get satisfied results, and we use Genetic algorithm for global search, and we import niche to control prematurity and to avoid the converging to a local optimum. The convergence analysis manifests that the proposed algorithm converges to the global optimal value with probability 1, and the excremental results show that both calculations speed and output are improved.