Enhanced Harris hawks optimization with multi-strategy for global optimization tasks

Li, Chen-Yang; Li, Jun; Chen, Huiling; Jin, Ming; Ren, Hao

doi:10.1016/j.eswa.2021.115499

Cited by 50 publications

(28 citation statements)

References 124 publications

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“…These studies can be summarized, as shown in Table 7. [121] tried to speed the HHO convergence by introducing an enhanced the HHO version using two strategies: (1) enhancing the HHO exploration by using oppositebased learning and logarithmic spiral and (2) the Modify Rosenbrock Method (RM) to fuse HHO in order to improve convergence accuracy and enhance the HHO local search capability. The authors tested their algorithm, which is called (RLHHO), using 30 IEEE CEC 2014 and 23 traditional benchmark functions.…”

Section: Enhancement To Hhomentioning

confidence: 99%

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

et al. 2022

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The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search. In this review research, we focused on the applications and developments of the recent well-established robust optimizer Harris hawk optimizer (HHO) as one of the most popular swarm-based techniques of 2020. Moreover, several experiments were carried out to prove the powerfulness and effectivness of HHO compared with nine other state-of-art algorithms using Congress on Evolutionary Computation (CEC2005) and CEC2017. The literature review paper includes deep insight about possible future directions and possible ideas worth investigations regarding the new variants of the HHO algorithm and its widespread applications.

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Section: Enhancement To Hhomentioning

confidence: 99%

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

et al. 2022

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“…Very recent approaches were done by several researchers where they proposed many modification schemes to improve HHO. For instance, a multi-strategy approach was given by Li et al [ 39 ]. The main idea of their approach is to incorporate different enhancement strategies namely opposition-based learning, logarithmic spiral, and a modified Rosenbrock local search.…”

Section: Introductionmentioning

confidence: 99%

“…Motivated by HHO popularity, simplicity, and efficiency, this study aims to further improve HHO performances when dealing with large-scale problems that encounter a lot of local optima points. It should be noted that previously mentioned studies mainly focused on enhancing HHO exploration by incorporating chaotic re-initialization schemes [ 43 ], embedding opposition-based schemes [ 36 , 39 ], or using other search operations inside HHO [ 33 , 33 – 35 , 40 ]. Others suggested using an external local search algorithm with HHO to improve exploitation performance [ 39 , 42 ].…”

Section: Introductionmentioning

confidence: 99%

“…It should be noted that previously mentioned studies mainly focused on enhancing HHO exploration by incorporating chaotic re-initialization schemes [ 43 ], embedding opposition-based schemes [ 36 , 39 ], or using other search operations inside HHO [ 33 , 33 – 35 , 40 ]. Others suggested using an external local search algorithm with HHO to improve exploitation performance [ 39 , 42 ]. Despite that, there is still room for improvements by utilizing HHO population status at run-time search progress.…”

Section: Introductionmentioning

confidence: 99%

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Rules embedded harris hawks optimizer for large-scale optimization problems

Samma

Sama

2022

Neural Comput & Applic

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Harris Hawks Optimizer (HHO) is a recent optimizer that was successfully applied for various real-world problems. However, working under large-scale problems requires an efficient exploration/exploitation balancing scheme that helps HHO to escape from possible local optima stagnation. To achieve this objective and boost the search efficiency of HHO, this study develops embedded rules used to make adaptive switching between exploration/exploitation based on search performances. These embedded rules were formulated based on several parameters such as population status, success rate, and the number of consumed search iterations. To verify the effectiveness of these embedded rules in improving HHO performances, a total of six standard high-dimensional functions ranging from 1000-D to 10,000-D and CEC’2010 large-scale benchmark were employed in this study. In addition, the proposed Rules Embedded Harris Hawks Optimizer (REHHO) applied for one real-world high dimensional wavelength selection problem. Conducted experiments showed that these embedded rules significantly improve HHO in terms of accuracy and convergence curve. In particular, REHHO was able to achieve superior performances against HHO in all conducted benchmark problems. Besides that, results showed that faster convergence was obtained from the embedded rules. Furthermore, REHHO was able to outperform several recent and state-of-the-art optimization algorithms.

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“…Suresh et al[42] proposed a Chaotic Multi Verse Harris Hawks Optimization algorithm based Deep Kernel Machine Learning Classifier (CMVHHO-DKMLC) method for medical diagnostics, by which the feature selection (FS) is done for finding ideal feature subset of medical documents. Li et al[43] presented a called RLHHO, where two novel strategies are integrated into the original HHO to enhance exploration and exploitation capabilities. An exploration strategy based on logarithmic spiral and oppositionbased learning and a local search technique for Rosenbrock Method (RM) are proposed to improve the exploration ability and enhance algorithm's local search capability of HHO.…”

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confidence: 99%

Multiobjective Harris Hawks Optimization With Associative Learning and Chaotic Local Search for Feature Selection

Zhang

et al. 2022

IEEE Access

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In the classification problem, datasets often have a large number of features, but not all features are useful for classification. A lot of irrelevant features may even reduce the performance. Feature selection is to remove irrelevant features by minimizing the number of the feature subset and minimizing the classification error rate.So it can be regarded as a multi-objective optimization problem. Beacuse of its simple structure and easy implementation, Harris Hawks Optimization algorithm(HHO) is widely employed in optimization problems.In this paper, the multi-objective HHO is applied to address the feature selection problem. In order to improve the search ability of the algorithm, associative learning, grey wolf optimization and chaotic local search are introduced into it. An external repository is used to save non-dominant solution set. The results of feature selection on the sixteen University of CaliforniaIrvine (UCI) datasets show that the proposed method can effectively remove redundant features and improve the classification performance of the algorithm.INDEX TERMS multi-objective harris hawks optimization; feature selection; associative learning;chaotic local search

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Enhanced Harris hawks optimization with multi-strategy for global optimization tasks

Cited by 50 publications

References 124 publications

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

Rules embedded harris hawks optimizer for large-scale optimization problems

Multiobjective Harris Hawks Optimization With Associative Learning and Chaotic Local Search for Feature Selection

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