关键词: 蚁群算法, 连续域, 多样性, 维变异, 全局寻优

Abstract: Concerning the disadvantages of ant colony optimization such as easily plunging into a local optimum and slow convergence speed in continuous optimization, a new Ant Colony Algorithm (ACO) with dimension mutation operator (DMCACO) was presented. In this algorithm, target individuals which led the ant colony to do global rapid search were determined by dynamic and stochastic extraction and the current optimal ant searches in small step nearly. The concept of dimension diversity was defined and the worst of diversity was mutated with introducing the dimension mutation operator: the positions of all ants in this dimension were distributed in the feasible range evenly. The simulation on typical test functions indicates that this algorithm has excellent global optimization and rapid convergence.

Key words: Ant Colony Algorithm (ACO), continuous domain, diversity, dimension mutation, global optimization