计算机科学
早熟收敛
趋同(经济学)
局部搜索(优化)
理论(学习稳定性)
算法
数学优化
群体行为
太阳辐照度
粒子群优化
数学
人工智能
气象学
物理
机器学习
经济
经济增长
作者
Sathishkumar Dr.,V. Nanthakumari
出处
期刊:International journal of innovative research in advanced engineering
日期:2023-07-31
卷期号:10 (07): 533-536
标识
DOI:10.26562/ijirae.2023.v1007.16
摘要
In this paper, the whale optimization algorithm (WOA), a potent swarm intelligence technique that has been widely applied in numerous sectors, including the parameter identification of solar cells and PV modules, is discussed in this work. A unique modified WOA (MWOA) that introduces both a mutation method based on Levy flight and a local search mechanism of pattern search to better balance the exploration and exploitation of WOA. On the one hand, Levy flight can force the algorithm to abandon the local optimum and avoid stagnation; as a result, it can stop the algorithm from losing diversity and improve the algorithm's ability to conduct global searches. However, pattern search, a direct search technique, has a high convergence rate and good stability, which can increase local search efficiency. Therefore, the ability of WOA to find the optimal solution can be significantly increased by combining these two techniques. Additionally, MWOA may be used to determine the unknown parameters of two different types of PV modules under various light irradiance and temperature circumstances, as well as to estimate parameters in the single diode model (SDM), double diode model (DDM), and PV modules.
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