稳健性(进化)
数学优化
差异进化
最大值和最小值
人口
经济调度
计算机科学
非线性系统
趋同(经济学)
电力系统
功率(物理)
数学
基因
物理
量子力学
数学分析
社会学
人口学
经济
化学
生物化学
经济增长
作者
Qinghua Liu,Guojiang Xiong,Xiaofan Fu,Ali Wagdy Mohamed,Jing Zhang,Mohammed Azmi Al-Betar,Hao Chen,Jun Chen,Sheng Xu
出处
期刊:Journal of Computational Design and Engineering
[Oxford University Press]
日期:2023-03-10
卷期号:10 (2): 615-631
被引量:2
摘要
Abstract Economic dispatch (ED) of thermal power units is significant for optimal generation operation efficiency of power systems. It is a typical nonconvex and nonlinear optimization problem with many local extrema when considering the valve-point effects, especially for large-scale systems. Considering that differential evolution (DE) is efficient in locating global optimal region, while gain-sharing knowledge-based algorithm (GSK) is effective in refining local solutions, this study presents a new hybrid method, namely GSK-DE, to integrate the advantages of both algorithms for solving large-scale ED problems. We design a dual-population evolution framework in which the population is randomly divided into two equal subpopulations in each iteration. One subpopulation performs GSK, while the other executes DE. Then, the updated individuals of these two subpopulations are combined to generate a new population. In such a manner, the exploration and the exploitation are harmonized well to improve the searching efficiency. The proposed GSK-DE is applied to six ED cases, including 15, 38, 40, 110, 120, and 330 units. Simulation results demonstrate that GSK-DE gives full play to the superiorities of GSK and DE effectively. It possesses a quicker global convergence rate to obtain higher quality dispatch schemes with greater robustness. Moreover, the effect of population size is also examined.
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