采用多目标蚁狮优化算法(MALO)的环境经济调度  

Environmental economic scheduling using multi-objective ant lion optimization algorithm(MALO)

作　　者：何旺 刘敏[1] HE Wang;LIU Min(College of Electrical Engineering,Guizhou University,Guiyang 550025,China)

机构地区：[1]贵州大学电气工程学院,贵阳550025

出　　处：《智能计算机与应用》2023年第6期117-121,共5页Intelligent Computer and Applications

摘　　要：本文针对传统优化算法求解电力系统环境经济调度(EED)容易陷入局部最优解的问题,采用了一种多目标进化算法——多目标蚁狮优化算法(MALO),通过在计及阀点效应和没有阀点效应的测试案例上实现。实验表明,MALO算法在可行域内更容易搜索出全局最优解。This paper adopts a new multi-objective evolutionary algorithm-Multi-objective ant lion optimization algorithm(MALO)to solve the problem that the environmental economic dispatch(EED)of the power system is easy to fall into the local optimal solution by the traditional optimization algorithm.We test MALO under the cases when the valve point effect is taken into account and when it is not.The experiments show that the MALO algorithm is easier to reach the global optimal solution in the feasible region.

关 键 词：环境经济调度 多目标优化算法 阀点效应 蚁狮算法 

分 类 号：TM73.4[电气工程—电力系统及自动化]