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• W4225527189 abstract "The Harris Hawks optimization (HHO) is a population-based metaheuristic algorithm; however, it has low diversity and premature convergence in certain problems. This paper proposes an adaptive relative reflection HHO (ARHHO), which increases the diversity of standard HHO, alleviates the problem of stagnation of local optimal solutions, and improves the search accuracy of the algorithm. The main features of the algorithm define nonlinear escape energy and adaptive weights and combine adaptive relative reflection with the HHO algorithm. Furthermore, we prove the computational complexity of the ARHHO algorithm. Finally, the performance of our algorithm is evaluated by comparison with other well-known metaheuristic algorithms on 23 benchmark problems. Experimental results show that our algorithms performs better than the compared algorithms on most of the benchmark functions." @default.
• W4225527189 created "2022-05-05" @default.
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• W4225527189 date "2022-04-02" @default.
• W4225527189 modified "2023-10-18" @default.
• W4225527189 title "Adaptive Relative Reflection Harris Hawks Optimization for Global Optimization" @default.
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• W4225527189 doi "https://doi.org/10.3390/math10071145" @default.
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