The ant colony optimization algorithm has been used for constrained optimization problems solving and applied to practical engineering domains due to its strong superiority. Considering its vulnerabilities to initial parameter and easiness to fall into local extremes, an improved particle swarm intelligence algorithm based on particle swarm grouping and information-sharing mechanisms reorganization was developed, which can effectively reduce the probability of falling into local minimums, thus a better approximation of the optimal solution was possible. 10-machine system and 26-machine system simulation were run to verify its effectiveness and feasibility, simulation results demonstrated that the improved method could converge to a better solution and the computation time was greatly reduced.%蚁群优化算法由于其具有较强的优越性,现已被用于约束优化问题的求解,并在相关的工程领域得到了实用.针对粒子群优化算法初始参数依赖性强和易陷入局部最优的问题,提出了对粒子群分组并重组信息共享机制的改进粒子群体智能算法.该算法有效地降低了陷入局部极小的概率,从而能够获取更佳的近似最优解.为验证算法的有效性和可行性,将改进粒子群优化算法用于10机系统和26机系统组合问题的仿真求解,结果表明该改进方法能收敛到更好的解,而且计算时间也大大减小.
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