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Horizontal and vertical crossover of Harris hawk optimizer with Nelder-Mead simplex for parameter estimation of photovoltaic models

Energy Conversion and Management 223, 2020, 113211

An improved Harris hawks optimization is proposed in this work to facilitate the simulation of an efficient photovoltaic system and extraction of unknown parameters, which combines horizontal and vertical crossover mechanism of the crisscross optimizer and Nelder-Mead simplex algorithm, named CCNMHHO. In CCNMHHO, the cores appeared in the crisscross optimizer are utilized to enrich the information exchange between the individuals and avoid the problem of dimensional stagnation of individuals all through the iterations. Hence, it enhances to change to improve the population quality and prevent the shortcoming of falling into a local optimum. In contrast, the Nelder-Mead simplex algorithm is employed in the proposed CCNMHHO methodology. Nelder-Mead simplex helps to improve individual searching capabilities in performing the local search phase and showing a faster convergence to optimal values. Compared to some algorithms that have a competitive performance in dealing with this type of problem, CCNMHHO has a faster convergence speed, and it shows high stability. In different environments, the experimental data obtained by this improved Harris hawks Optimization can reveal a high agreement with the measurement data. The experimental results show that the proposed method not only is very competitive in extracting the unknown parameters of different PV models compared to other state-of-the-art algorithms but also perform well in dealing with the complex outdoor environments such as different temperature and radiance. Therefore, we observed that the CCNMHHO could be considered as a reliable and efficient method in solving a class of cases for the assessment of unknown parameters of solar cells and photovoltaic models.

Online access to Elsevier full-text PDF

Analysis and Source Codes of the Original Harris Hawks Optimizer (HHO)

The population-based HHO was the most successful and popular optimization method currently. The HHO focuses on performance and provides a variety of search patterns based on random switching statements. It is a gradient-free optimization algorithm with several energetic and time-varying stages of exploration and exploitation tendencies. In spite of previous methods published in lower impact journals, the HHO was published in the Journal of Future Generation Computer Systems (FGCS) with an impact factor of 6.125 in 2019, and from the first day of publication, it has gotten growing consideration among researchers owing to its flexible structure, high performance, and first-rate results. The leading logic of the HHO technique is created according to some successful life patterns of Harris' hawks in nature called "surprise pounce". Due to the HHO technique's efficacy, there are many variants of HHO now in the best leading Elsevier and IEEE transaction journals.

Go to Webpage of Harris Hawks Optimization (HHO) for full info

Download MATLAB source codes of Harris Hawks Optimization (HHO)

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Download Python source codes of Harris Hawks Optimization (HHO)

Download LATEX source codes of Harris Hawks Optimization (HHO)

Download Visio source files of Harris Hawks Optimization (HHO)

The HHO algorithm is a high performance and easy to code, and straightforward to understand optimizer, while it has some time-varying components. The primary method was published in a top prestigious computer science journal. In 2020, it turned into the most used method for solving any problem. This method's source codes are widely available in almost all programming languages, and it has both a latex template and word office file for the pleasure of users. This method is backed up with a 24-h online service for reacting to users' questions on the code..

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  • What is Code Editor or programming language?
    This CCNMHHO code is written in MATLAB programming language and allows editing the code of algorithm in the app. Also, it's possible to get the codes of CCNMHHO in other available programming languages.
How to cite?

Liu, Yun, Guoshuang Chong, Ali Asghar Heidari, Huiling Chen, Guoxi Liang, Xiaojia Ye, Zhennao Cai, and Mingjing Wang. "Horizontal and vertical crossover of Harris hawk optimizer with Nelder-Mead simplex for parameter estimation of photovoltaic models." Energy Conversion and Management 223 (2020): 113211.

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