[1]闫李,李超,柴旭朝,等.基于多学习多目标鸽群优化的动态环境经济调度[J].郑州大学学报(工学版),2019,40(04):2.[doi:10.13705/j.issn.1671-6833.2019.04.023]
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基于多学习多目标鸽群优化的动态环境经济调度()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
40
期数:
2019年04期
页码:
2
栏目:
出版日期:
2019-07-10

文章信息/Info

Title:
Dynamic Economic Emission Dispatch Based On Multiple Learning Multi-objective Pigeon-inspired Optimization
作者:
闫李; 李超; 柴旭朝; 瞿博阳
中原工学院电子信息学院
Author(s):
Yan Li; Li Chao; Chai Xuchao; Qu Boyang
School of Electronic Information, Zhongyuan Institute of Technology
关键词:
环境经济调度; 多目标优化; 鸽群优化; 多学习; 小概率变异
Keywords:
Environmental economic dispatch; Multi-objective optimization; Pigeon flock optimization; learn more; small probability variation
DOI:
10.13705/j.issn.1671-6833.2019.04.023
文献标志码:
A
摘要:
针对电力系统动态环境经济调度（DEED）问题，本文提出了一种基于多学习策略的多目标鸽群优化（MLMPIO）算法。在多学习策略中，种群个体可以向外部存档集中的多个全局最优位置以及个体的历史最优位置进行学习，进而保持种群的多样性和全局搜索能力，避免陷入早熟收敛；引入了小概率变异扰动机制，来进一步增强种群的多样性和搜索能力；为提升算法的运行效率，采用容量自适应变化的外部存档集来存储当前Pareto最优解集。为验证所提算法的性能，将MLMPIO应用于10机组电力系统的DEED问题求解；仿真结果证明了MLMPIO算法解决此类问题的可行性和有效性。
Abstract:
For solving the dynamic economic emission dispatch problem (DEED), a multiple learning based multi-objective pigeon-inspired optimization (MLMPIO) algorithm is proposed in this paper. In the proposed multiple learning strategy, individuals of the population are allowed to learn from multiple global best positions of the external archive and from the personal historical best positions. This learning strategy enables the preservation of diversity and global search ability of the population to prevent premature convergence. Meanwhile, small probability mutation is introduced to MLMPIO to enhance the swarm diversity and search ability further. The external archive with adaptive changing capacity is used to store the current Pareto optimal solutions. To verify the performance of the proposed method, the DEED problem of the IEEE 10-generator power system has been solved. And the results demonstrate the feasibility and effectiveness of the proposed method

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更新日期/Last Update: 2019-07-28