双局部粒子群算法解决环境经济调度问题  

Two local best based multi-objective particle swarm optimization algorithm to solve environmental/economic dispatch problem

作　　者：瞿博阳[1,2] 梁静[3] Ponnuthurai Nagaratnam Suganthan 

机构地区：[1]中原工学院电子信息学院,郑州450007 [2]南洋理工大学电气电子工程学院,新加坡639798 [3]郑州大学电气工程学院,郑州450001

出　　处：《计算机工程与应用》2014年第11期1-6,共6页Computer Engineering and Applications

基　　金：国家自然科学基金(No.60905039);中国博士后科学基金特别资助项目(No.2012T50639);教育部高等学校博士学科点专项科研基金(No.20114101110005);河南省科技攻关项目(No.132102210521)

摘　　要：提出一种基于双局部最优的多目标粒子群优化算法,与可行解为优的约束处理方法相结合,来求解决非线性带约束的多目标电力系统环境经济调度问题。该算法针对传统多目标粒子群算法多样性低的局限性,通过对搜索空间的分割归类来增加帕累托最优解的多样性;并采用一种新的双局部最优来引导粒子的搜索,从而增强了算法的全局搜索能力。算法加入了可行解为优的约束处理方法对IEEE30节点六发电机电力系统环境经济负荷分配模型分别在几个不同复杂性问题的情况进行仿真测试,并与文献中的其他算法进行了比较。结果表明,改进的算法能够在保持帕累托最优解多样性的同时具有良好的收敛性能,更有效地解决电力系统环境经济调度问题。A two local best based Multi-Objective Particle Swarm Optimization algorithm（2lb-MOPSO） is integrated with superiority of feasible solution constraint handling method in this paper to solve the nonlinear constrained multi-objective Environmental Economic Dispatch（EED） problem. One of the main drawbacks of classical multi-objective particle swarm optimization algorithm is low diversity. To overcome this disadvantage, the searching space is partitioned into fixed number of bins in the proposed algorithm. The algorithm uses two local best to lead the search particles which can increase the diversity of the population. The algorithm is combined with superiority of feasible solution constraint han-dling method and applied to the standard IEEE 30-bus six-generator test system. The performance is compared against several method obtained from the literature. The results show that the proposed algorithm is able to generate good performance in terms of both diversity and convergence in solving EED problems.

关 键 词：环境 经济调度 多目标优化 粒子群优化 约束处理方法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]