Adaptive Relative Reflection Harris Hawks Optimization for Global Optimization

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 st...

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Main Authors: Tingting Zou, Changyu Wang
Format: Article
Language:English
Published: MDPI AG 2022-04-01
Series:Mathematics
Subjects:
Online Access:https://www.mdpi.com/2227-7390/10/7/1145
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author Tingting Zou
Changyu Wang
author_facet Tingting Zou
Changyu Wang
author_sort Tingting Zou
collection DOAJ
description 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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spelling doaj.art-41c80576e90a4b248ae643a00c63b3cc2023-11-30T23:37:48ZengMDPI AGMathematics2227-73902022-04-01107114510.3390/math10071145Adaptive Relative Reflection Harris Hawks Optimization for Global OptimizationTingting Zou0Changyu Wang1Information Science and Technology College, Dalian Maritime University, Dalian 116026, ChinaInformation Science and Technology College, Dalian Maritime University, Dalian 116026, ChinaThe 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.https://www.mdpi.com/2227-7390/10/7/1145Harris Hawks optimizationescape energyadaptive relative reflectioncomputational complexity
spellingShingle Tingting Zou
Changyu Wang
Adaptive Relative Reflection Harris Hawks Optimization for Global Optimization
Mathematics
Harris Hawks optimization
escape energy
adaptive relative reflection
computational complexity
title Adaptive Relative Reflection Harris Hawks Optimization for Global Optimization
title_full Adaptive Relative Reflection Harris Hawks Optimization for Global Optimization
title_fullStr Adaptive Relative Reflection Harris Hawks Optimization for Global Optimization
title_full_unstemmed Adaptive Relative Reflection Harris Hawks Optimization for Global Optimization
title_short Adaptive Relative Reflection Harris Hawks Optimization for Global Optimization
title_sort adaptive relative reflection harris hawks optimization for global optimization
topic Harris Hawks optimization
escape energy
adaptive relative reflection
computational complexity
url https://www.mdpi.com/2227-7390/10/7/1145
work_keys_str_mv AT tingtingzou adaptiverelativereflectionharrishawksoptimizationforglobaloptimization
AT changyuwang adaptiverelativereflectionharrishawksoptimizationforglobaloptimization