![]() Soft Comput 1–17īoettcher S, Percus A (2000) Nature’s way of optimizing. Phys Rev A 38:364īalamurugan R, Ratheesh S, Venila YM (2021) Classification of heart disease using adaptive harris hawk optimization-based clustering algorithm and enhanced deep genetic algorithm. Phys Rev Lett 71:4083īak P, Tang C, Wiesenfeld K (1988) Self-organized criticality. Soft Comput 23:715–734īak P, Sneppen K (1993) Punctuated equilibrium and criticality in a simple model of evolution. Expert Syst Appl 168:114243Īrora S, Singh S (2019) Butterfly optimization algorithm: a novel approach for global optimization. Expert Syst Appl 90:484–500Īl-Betar MA, Awadallah MA, Heidari AA, Chen H, Al-Khraisat H, Li C (2021) Survival exploration strategies for Harris hawks optimizer. The results show that IHHO-EO has an excellent performance in terms of accuracy, reliability and statistical tests.Ībd Elaziz M, Oliva D, Xiong S (2017) An improved opposition-based sine cosine algorithm for global optimization. In addition, the proposed approach is applied to solving the pressure vessel design problem. The experimental results verify the effectiveness of the added strategies. IHHO-EO is compared with HHO, other newly proposed optimization algorithms and some improved variants of HHO. ![]() The proposed algorithm is applied to 23 classical benchmark test functions and 29 CEC2017 test functions. Finally, the exploitation ability is improved by performing EO operation which has strong local search ability. Thirdly, refracted opposition-based learning (ROBL) with a dynamic parameter is proposed and combined with HHO, which can improve the quality of solutions and convergence speed. Secondly, a nonlinear prey energy escaping factor is proposed to better balance the exploration and exploitation phases. Aiming at the defect of insufficient information utilization and excessive randomization in the exploration phase of the algorithm, the own historical optimal position of Harris hawks is introduced to better guide the individuals to search for better positions and improve the global search ability. In order to improve the performance of HHO, an improved HHO hybridized with extremal optimization (IHHO-EO) is proposed. However, HHO also has the shortcomings of low convergence accuracy and easy to fall into local optimum. It seeks the optimal solution by simulating the predation strategy of Harris hawks and many previous experiments show that HHO has a good effect on solving optimization problems. Harris Hawks optimizer (HHO) is a new swarm intelligence optimization algorithm proposed in recent years.
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