关键词: 蚁群优化算法, 动态划分, 全局搜索, 随机搜索, 预处理

Abstract: Ant Colony Optimization for continuous domains（ACOR） is easy to fall into local optimum when dealing with high dimensional problems, and its convergence rate is slow. So ACO for continuous domains algorithm is put forward to solve these problems. In the proposed algorithm, the solution is divided into two parts, the optimal solution and the inferior solution, and the number of the optimal solution and the inferior solution is adjusted dynamically in the iterative process. For the optimal solution, the global search strategy is used to pre process, which can improve the convergence speed and convergence accuracy of the algorithm. For inferior solution, using a random search strategy for pretreatment, this can expand the search scope, and enhance search ability. The test results show that the DPHACO algorithm can effectively improve the convergence speed and the quality of the solution. Compared with continuous ant colony algorithm and other intelligent optimization algorithms, the proposed algorithm is more effective and better than the global search capability.

Key words: Ant Colony Optimization（ACO） algorithm, dynamic partition, global search, random search, pretreatment