Optimal configuration of distributed generation based on improved cuckoo search algorithm

XIE Haibo; WEI Minglei; CHEN Zhuoyou; XIE Xuquan; LIN Xiang

doi:10.3969/j.issn.1672-3643.2016.05.002

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Optimal configuration of distributed generation based on improved cuckoo search algorithm

更新时间：2025-09-24

- Optimal configuration of distributed generation based on improved cuckoo search algorithm
- Ningxia Electric Power Issue 5, (2016)
- 作者机构：
- 作者简介：
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- DOI：10.3969/j.issn.1672-3643.2016.05.002
  CLC：
- Published Online：26 October 2020，
  
  Published：2016
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XIE Haibo, WEI Minglei, CHEN Zhuoyou, et al. Optimal configuration of distributed generation based on improved cuckoo search algorithm[J]. Ningxia electric power, 2016, (5).
DOI：

XIE Haibo, WEI Minglei, CHEN Zhuoyou, et al. Optimal configuration of distributed generation based on improved cuckoo search algorithm[J]. Ningxia electric power, 2016, (5). DOI： 10.3969/j.issn.1672-3643.2016.05.002.

摘要

针对布谷鸟搜索算法在处理多目标优化问题上的不足

基于混沌映射、非支配排序等多目标优化策略

提出一种改进的多目标布谷鸟搜索(improved muti-objective cuckoo search

IMOCS)算法

对分布式电源多目标优化配置问题进行求解。为增加初始种群的多样性

利用logistic混沌映射产生初始解;自适应改变搜索步长

以提高算法的寻优能力和收敛速度。仿真结果表明:IMOCS算法寻优能力强

收敛速度快

能够有效求解分布式电源的多目标优化配置问题。

Abstract

For lack of the basic cuckoo search algorithm in dealing with multi-objective optimizationproblems

based on the multi- objective optimized strategy such as the chaotic maping and nondominated sorting

puts forward an improved multi- objective cuckoo search (IMOCS) algorithm tosolve the multi-objective optimization and configuration problem of distributed generation. In order toincrease the diversity of initial population

using logistic chaotic maping generates the initial solution;self adaptability changes the search step to improve the searching ability and convergence speed of thealgorithm. By the simulation of IEEE33 node system verifies the validity and feasibility of the method.The result shows that the IMOCS algorithm has strong optimization capability and fast convergencespeed

can effectively solve the multi-objective optimization problem of distributed generation.

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