Journal
MATHEMATICS
Volume 10, Issue 7, Pages -Publisher
MDPI
DOI: 10.3390/math10071145
Keywords
Harris Hawks optimization; escape energy; adaptive relative reflection; computational complexity
Categories
Funding
- National Natural Science Foundation of China [61972063, 12175028]
- Fundamental Research Funds for the Central Universities of China [3132022260]
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This paper proposes an improved Harris Hawks optimization algorithm (ARHHO), which addresses the drawbacks of the standard HHO algorithm by increasing diversity and improving search accuracy.
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.
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