改进多目标蚁群算法在电网规划中的应用..doc

第 33 卷 第 18 期 2009 年 10 月 文章编号：1000-3673（2009）18-0057-06 电网技术 Power System Technology 中图分类号：TM715 文献标志码：A Vol. 33 No. 18 Oct. 2009 学科代码：470·4051 改进多目标蚁群算法在电网规划中的应用 符杨 1，孟令合 2，胡荣 1，曹家麟 1 （1．上海电力学院 电力工程系，上海市 杨浦区 200090； 2．上海大学 机电工程与自动化学院，上海市 闸北区 200072） Application of Improved Multi-Objective Ant Colony Algorithm in Power Network Planning FU Yang1，MENG Ling-he2，HU Rong1，CAO Jia-lin1 （1．Department of Electrical Engineering，Shanghai University of Electric Power，Yangpu District，Shanghai 200090，China； 2．School of Mechatronics Engineering and Automation，Shanghai University，Zhabei District，Shanghai 200072，China） ABSTRACT: For the reason that both economy and reliability should be considered during power network planning, an improved multi-objective ant colony algorithm (IMACA) is proposed. In the proposed algorithm, the modified quick sort method is adopted to construct Pareto optimal solution set, thus the slow-chain is shortened and the time complexity of this algorithm is mitigated; the clustering algorithm is adopted to modify non-dominated solution, thus the obtained solution can possess good diversity and distributivity in whole Pareto solution space; the sociohormone is adopted to update variable parameter control, thus the global convergence is speeded up; the sociohormone volatilization coefficient is used to dynamic adaptive regulation mechanism, thus the global search ability of the proposed algorithm is improved. The calculation results of an 18-bus power network planning show that more Pareto optimal solutions can be obtained by the proposed algorithm than by basic multi-objective ant colony algorithm, and the Pareto frontier distribution is more uniform, meanwhile, the convergence and rapidity are improved. KEY WORDS: multi-objective ant colony algorithm ； clustering analysis；Pareto optimal；power network planning 摘要：针对电网规划需综合考虑经济性和可靠性的问题，提 出一种改进的多目标蚁群算法。该算法采用改进的快速排序 方法构造 Pareto 最优解集，缩短了“慢速链”，降低了算法 的时间复杂度；采用聚类算法裁剪非支配解，使所得解在整 个 Pareto 解空间具有良好的多样性和分布性；采用信