用于连续域优化的遗传网格蚂蚁融合算法

在进行函数优化时，遗传算法具有全局搜索能力强的特点，但其存在早熟收敛和后期收敛速度慢及局部搜索能力弱的问题。网格蚂蚁算法具有局部搜索能力强、优化精度高等特点，但其全局收敛速度较慢。因此提出了用于连续优化的遗传网格蚂蚁融合算法（Genetic and Grid Based Ant Colony Algorithm，GGACO）。该算法将遗传算法和网格蚂蚁算法相结合，用遗传算法进行全局搜索，用网格蚂蚁算法进行局部迭代寻优，经过若干次循环迭代产生最终结果。仿真实验结果表明，该算法在解决复杂函数优化时全局收敛性能好、速度快，尤其在解决高维多峰函数优化问题时效果更显著。

Genetic and grid based ant colony algorithm for solving continuous optimization problems

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.