Modified Harris Hawks Optimization Algorithm for Global Optimization Problems

Zhang, Yang; Zhou, Xizhao; Shih, Po-Chou

doi:10.1007/s13369-020-04896-7

Cited by 47 publications

(28 citation statements)

References 51 publications

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“…Akdag et al [133] developed a modified HHO version by using seven different random distribution functions in order to show how stochastic search affects HHO performance. Moreover, Zhang et al [134] tried to balance the exploration and exploitation phases by focusing on prey escaping energy (E). They introduced six different strategies to update E.…”

Section: Modified 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: Modified Hhomentioning

confidence: 99%

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

et al. 2022

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“…whereas E 0 represents an arbitrary value in the range of (−1, 1); sin and cos represent sine and cosine functions, correspondingly [19]. The amount of iterations is represented by t while T is the maximal amount of iterations.…”

Section: Design Of Mhho Algorithmmentioning

confidence: 99%

Modified Harris Hawks Optimization Based Test Case Prioritization for Software Testing

Hamza¹,

Abdelmaboud²,

Marie-Sainte³

et al. 2022

Computers, Materials &Amp; Continua

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Generally, software testing is considered as a proficient technique to achieve improvement in quality and reliability of the software. But, the quality of test cases has a considerable influence on fault revealing capability of software testing activity. Test Case Prioritization (TCP) remains a challenging issue since prioritizing test cases is unsatisfactory in terms of Average Percentage of Faults Detected (APFD) and time spent upon execution results. TCP is mainly intended to design a collection of test cases that can accomplish early optimization using preferred characteristics. The studies conducted earlier focused on prioritizing the available test cases in accelerating fault detection rate during software testing. In this aspect, the current study designs a Modified Harris Hawks Optimization based TCP (MHHO-TCP) technique for software testing. The aim of the proposed MHHO-TCP technique is to maximize APFD and minimize the overall execution time. In addition, MHHO algorithm is designed to boost the exploration and exploitation abilities of conventional HHO algorithm. In order to validate the enhanced efficiency of MHHO-TCP technique, a wide range of simulations was conducted on different benchmark programs and the results were examined under several aspects. The experimental outcomes highlight the improved efficiency of MHHO-TCP technique over recent approaches under different measures.

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“…where the value of R is 95% of the length AB and =0. 125. Load ( Q f ) causes bending stress ( bf ) and shear stress ( f ).…”

Section: Stress Analysis Of Trbmentioning

confidence: 99%

Multi-strategy Gaussian Harris hawks optimization for fatigue life of tapered roller bearings

Abbasi

Firouzi

Şendur

et al. 2021

Engineering with Computers

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Bearing is one of the most fundamental components of rotary machinery, and its fatigue life is a crucial factor in designing. The design optimization of tapered roller bearing (TRB) is a complex design problem because various arrays of designing parameters and functional requirements should be fulfilled. Since there are many design variables and nonlinear constraints, presenting an optimal design of TRBs poses some challenges for metaheuristic algorithms. The Harris hawks optimization (HHO) algorithm is a robust nature-inspired method with unique exploitation and exploration phases due to its time-varying structure. However, this metaheuristic algorithm may still converge to local optima for more challenging problems such as the design of TRBs. Therefore, this study aims to improve the accuracy and efficiency of the shortcomings of this algorithm. The performance of the proposed algorithm is first evaluated for the TRB optimization problem. The TRB optimization design has nine design variables and 26 constraints because of geometrical dimensions and strength conditions. The productivity of the proposed method is compared with diverse metaheuristic algorithms in the literature. The results demonstrate the significant development of dynamic load capacity in comparison to the standard value. Furthermore, the enhanced version of the HHO algorithm presented in this study is benchmarked with various well-known engineering problems. For supplementary materials regarding algorithms in this research, readers can refer to https://aliasgharheidari.com .

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Modified Harris Hawks Optimization Algorithm for Global Optimization Problems

Cited by 47 publications

References 51 publications

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

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

Modified Harris Hawks Optimization Based Test Case Prioritization for Software Testing

Multi-strategy Gaussian Harris hawks optimization for fatigue life of tapered roller bearings

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