关键词: 遗传算法, 网格蚂蚁算法, 高维多峰函数优化

Abstract: Genetic algorithm is good at global search in function optimization，but it is slow in convergence speed in the late stage and weak in local search.Grid based ant algorithm is capable in local search and has high precision，but its global convergence is slow.Therefore this paper proposes Genetic and Grid Based Ant Colony Algorithm（GGACO） for continuous optimization.The algorithm combines genetic algorithm with grid based ant algorithm，which genetic algorithm is for global research and grid based ant algorithm is used for local research.After iterating several times the final results will be found.Simulation results show that when solving the complex global convergence functions，the algorithm is fast converged and has good performance in global convergence，especially in tackling high-dimensional and multi-peak function optimization problems.

Key words: genetic algorithm, grid based ant algorithm, high-dimensional and multi-peak function optimization