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Harris hawks optimization (HHO) is one of the leading optimization approaches due to its efficacy and multi-choice structure with time-varying components. The HHO has been applied in various areas due to its simplicity and outstanding performance. However, the original HHO can be improved and evolved in terms of convergence trends, and it is prone to local optimization under certain circumstances. Therefore, the performance and robustness of the algorithm need to be further improved. In our research, based on the core principle of evolutionary methods, we first developed an elite evolutionary strategy (EES) and then utilized it to advance HHO’s convergence speed and ability to jump out of the local optimum. We named such an enhanced hybrid algorithm EESHHO in this paper. To verify the effectiveness and robustness of the EESHHO, we tested it on 29 numerical optimization test functions, including 23 classic basic test functions and 6 composite test functions from the IEEE CEC2017 special session. Moreover, we apply the EESHHO on resource-constrained project scheduling and QoS-aware web service composition problems to further validate the effectiveness of EESHHO. The experimental results show that proposed EESHHO has faster convergence speed and better optimization performance by comparing it with other mainstream algorithms. The supplementary info and answers to possible queries will be publicly available at https://aliasgharheidari.com/publications/EESHHO.html. Also, the codes and info of HHO are available at: https://aliasgharheidari.com/HHO.html.
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.
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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..
How to cite?
Li, ChenYang, Jun Li, HuiLing Chen, and Ali Asghar Heidari. "Memetic Harris Hawks Optimization: Developments and Perspectives on Project Scheduling and QoS-aware Web Service Composition." Expert Systems with Applications (2020): 114529. https://doi.org/10.1016/j.eswa.2020.114529
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