Modified Whale Optimization Algorithm for Solar Cell and PV Module Parameter Identification

The whale optimization algorithm (WOA) is a powerful swarm intelligence method which has been widely used in various fields such as parameter identification of solar cells and PV modules. In order to better balance the exploration and exploitation of WOA, we propose a novel modified WOA (MWOA) in which both the mutation strategy based on Levy flight and a local search mechanism of pattern search are introduced. On the one hand, Levy flight can make the algorithm get rid of the local optimum and avoid stagnation; thus, it is able to prevent the algorithm from losing diversity and to increase the global search capability. On the other hand, pattern search, a direct search method, has not only high convergence rate but also good stability, which can boost the local optimization ability of the WOA. Therefore, the combination of these two mechanisms can greatly improve the capability of WOA to obtain the best solution. In addition, MWOA may be employed to estimate parameters in single diode model (SDM), double diode model (DDM), and PV modules and to identify unknown parameters of two different types of PV modules under diverse light irradiance and temperature conditions. The analytical results demonstrate the validity and the practicality of MWOA for estimating parameters of solar cells and PV modules.

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An application of the whale optimization algorithm with Levy flight strategy for clustering of medical datasets

An International Journal of Optimization and Control Theories & Applications (IJOCTA) ◽

10.11121/ijocta.01.2021.001091 ◽

2021 ◽

Vol 11 (2) ◽

pp. 216-226

Author(s):

Ayşe Nagehan Mat ◽

Onur İnan ◽

Murat Karakoyun

Keyword(s):

Optimization Algorithm ◽

Clustering Algorithm ◽

Heuristic Algorithms ◽

Clustering Algorithms ◽

Whale Optimization Algorithm ◽

Lévy Flight ◽

Hunting Behavior ◽

Levy Flight ◽

Whale Optimization ◽

Clustering Problems

Clustering, which is handled by many researchers, is separating data into clusters without supervision. In clustering, the data are grouped using similarities or differences between them. Many traditional and heuristic algorithms are used in clustering problems and new techniques continue to be developed today. In this study, a new and effective clustering algorithm was developed by using the Whale Optimization Algorithm (WOA) and Levy flight (LF) strategy that imitates the hunting behavior of whales. With the developed WOA-LF algorithm, clustering was performed using ten medical datasets taken from the UCI Machine Learning Repository database. The clustering performance of the WOA-LF was compared with the performance of k-means, k-medoids, fuzzy c-means and the original WOA clustering algorithms. Application results showed that WOA-LF has more successful clustering performance in general and can be used as an alternative algorithm in clustering problems.

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Lévy Flight Trajectory-Based Whale Optimization Algorithm for Global Optimization

IEEE Access ◽

10.1109/access.2017.2695498 ◽

2017 ◽

Vol 5 ◽

pp. 6168-6186 ◽

Cited By ~ 105

Author(s):

Ying Ling ◽

Yongquan Zhou ◽

Qifang Luo

Keyword(s):

Global Optimization ◽

Optimization Algorithm ◽

Whale Optimization Algorithm ◽

Lévy Flight ◽

Flight Trajectory ◽

Levy Flight ◽

Whale Optimization

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A Mixed-Strategy-Based Whale Optimization Algorithm for Parameter Identification of Hydraulic Turbine Governing Systems with a Delayed Water Hammer Effect

Energies ◽

10.3390/en11092367 ◽

2018 ◽

Vol 11 (9) ◽

pp. 2367 ◽

Cited By ~ 4

Author(s):

Tan Ding ◽

Li Chang ◽

Chaoshun Li ◽

Chen Feng ◽

Nan Zhang

Keyword(s):

Parameter Identification ◽

Optimization Algorithm ◽

Mixed Strategy ◽

Hydraulic Turbine ◽

Water Hammer ◽

Whale Optimization Algorithm ◽

Local Optimum ◽

Movement Strategies ◽

Mutation Strategy ◽

Whale Optimization

For solving the parameter optimization problem of a hydraulic turbine governing system (HTGS) with a delayed water hammer (DWH) effect, a Mixed-Strategy-based Whale Optimization Algorithm (MSWOA) is proposed in this paper, in which three improved strategies are designed and integrated to promote the optimization ability. Firstly, the movement strategies of WOA have been improved to balance the exploration and exploitation. In the improved movement strategies, a dynamic ratio based on improved JAYA algorithm is applied on the strategy of searching for prey and a chaotic dynamic weight is designed for improving the strategies of bubble-net attacking and encircling prey. Secondly, a guidance of the elite’s memory inspired by Particle swarm optimization (PSO) is proposed to lead the movement of the population to accelerate the convergence speed. Thirdly, the mutation strategy based on the sinusoidal chaotic map is employed to avoid prematurity and local optimum points. The proposed MSWOA are compared with six popular meta-heuristic optimization algorithms on 23 benchmark functions in numerical experiments and the results show that the MSWOA has achieved significantly better performance than others. Finally, the MSWOA is applied on parameter identification problem of HTGS with a DWH effect, and the comparative results confirm the effectiveness and identification accuracy of the proposed method.

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