Harris Hawks optimization with information exchange |
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| Institution: | 1. College of Mathematics and Statistics, Hunan Normal University, Changsha 410081, China;2. Key Laboratory of computing and Stochastic Mathematics (Ministry of Education), Hunan Normal University, Changsha 410081, China;3. School of Information Engineering, Baise University, Baise 533000, China;4. Department of Mathematics, The Ohio State University, Columbus 43210, OH, USA;5. Department of Pathology and Pathophysiology, Hunan Normal University School of Medicine, Changsha 410013, China;6. Department of Pathophysiology, Jishou University School of Medicine, Jishou 416000, China;1. Department of Agricultural Sciences, University of Naples – Federico II, Italy;2. Department of Civil Engineering, University of Calabria, Italy;3. Department of Engineering for Innovation, University of Salento, Italy;1. Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt;2. Department of Electrical Engineering, Faculty of Engineering, Fayoum University, Fayoum, Egypt;3. Torrens University Australia, Fortitude Valley, Brisbane QLD 4006, Australia;4. Yonsei Frontier Lab, Yonsei University, Seoul, South Korea;5. King Abdulaziz University, Jeddah, Saudi Arabia |
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| Abstract: | The basic Harris Hawks optimization algorithm cannot take full advantage of the information sharing capability of the Harris Hawks while cooperatively searching for prey, and it is difficult to balance the exploration and development capacities of this algorithm. These factors limit the Harris Hawks optimization algorithm, such as in terms of premature convergence and ease of falling into a local optimum. To this end, an improved Harris Hawks optimization algorithm based on information exchange is proposed to optimize the continuous function and its application to engineering problems. First, an individual Harris Hawk obtains information from the shared area of cooperative foraging and the location area of collaborators, thereby realizing information exchange and sharing. Second, a nonlinear escaping energy factor with chaos disturbance is designed to better balance the local searching and the global searching of the algorithm. Finally, a numerical experiment is conducted with four benchmark test functions and five CEC-2017 real-parameter numerical optimization problems as well as seven practical engineering problems. The results show that the proposed algorithm outperforms the basic Harris Hawks optimization algorithm and other intelligence optimization algorithms in terms of the convergence rate, solution accuracy, and robustness. |
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