Harris hawks optimization: a comprehensive review of recent variants and applications

Alabool, Hamzeh; Alarabiat, Deemah; Abualigah, Laith; Heidari, Ali Asghar

doi:10.1007/s00521-021-05720-5

Cited by 132 publications

(69 citation statements)

References 23 publications

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“…The use of adaptive and time-varying escape energy parameters enables it to perform a smooth transition between exploration and exploitation, which can well address the difficulties of the search space, including local optimal solutions, multi-modality, and deceptive optima. Besides, this technique has been applied to tackle different real-world cases, such as manufacturing industry, environmental quality, image segmentation, power systems (Alabool et al 2021), etc. Furthermore, in order to make the global exploration capability of HHO significantly strengthened, the logistic chaos map sequence, DE/current-to-best/1 operator and Levy mutation operator have been embedded in the standard version of HHO.…”

Section: Introductionmentioning

confidence: 99%

A hybrid parallel Harris hawks optimization algorithm for reusable launch vehicle reentry trajectory optimization with no-fly zones

Dai

Liu

2021

Soft Comput

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Reentry trajectory optimization is a critical optimal control problem for reusable launch vehicle (RLV) with highly nonlinear dynamic characteristics and complex constraints. In this paper, a hybrid parallel harris hawks optimization (HPHHO) algorithm is proposed to address the problem.HPHHO aims to enhance the performance of existing harris hawks optimization (HHO) algorithm by three strategies including oppositional learning, smoothing technique and parallel optimization mechanism. At the beginning of each iteration, the opposite population is calculated from the current population by the oppositional learning strategy. Following that, the individuals in the two populations are arranged in ascending order on the basis of the fitness function values, and the top half of the resulting population is selected as the initial population. The selected initial population is divided into two equal subpopulations which are assigned to the differential evolution and the HHO algorithm, respectively. The both algorithms operate in parallel to search and update the solutions of each subpopulation simultaneously. Then the solutions are smoothed for each iteration by the smoothing technique to reduce fluctuations. As a result, the optimal solution obtained by the parallel optimization mechanism avoids falling into local optima. The performance of HPHHO is evaluated by 4 CEC 2005 benchmark functions and 3 constrained continuous optimal control problems, showing better efficiency and robustness in terms of performance metrics, convergence rate and stability. Finally, the simulation results show that the proposed algorithm is very effective, practical and feasible in solving the RLV reentry trajectory optimization problem. Keywords Hybrid parallel harris hawks optimization • Oppositional learning • Smoothing technique • Parallel optimization mechanism • Reentry trajectory optimization

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Section: Introductionmentioning

confidence: 99%

A hybrid parallel Harris hawks optimization algorithm for reusable launch vehicle reentry trajectory optimization with no-fly zones

Dai

Liu

2021

Soft Comput

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“…Another shortcoming is that HHO does not always maintain the proper balance between the cores, or it does not always find genuine moments, or the transition is not always smooth, particularly when dealing with very complex attribute spaces. Most of the HHO-based research works have been pointed to these two reasons to improve the convergence speed or local optima avoidance [58].…”

Section: Hard Besiege Progressive Rapid Divesmentioning

confidence: 99%

A Hybrid Approach for Solving Systems of Nonlinear Equations Using Harris Hawks Optimization and Newton’s Method

et al. 2021

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Systems of nonlinear equations are known as the basis for many models of engineering and data science, and their accurate solutions are very critical in achieving progress in these fields. However, solving a system with multiple nonlinear equations, usually, is not an easy task. Consequently, finding a robust and accurate solution can be a very challenging problem in complex systems. In this work, a novel hybrid method namely Newton-Harris hawks optimization (NHHO) for solving systems of nonlinear equations is proposed. The proposed NHHO combines Newton's method, with a second-order convergence where the correct digits roughly double in every step, and the Harris hawks optimization (HHO) to enhance the search mechanism, avoid local optima, improve convergence speed, and find more accurate solutions. We tested a group of six well-known benchmark systems of nonlinear equations to evaluate the efficiency of NHHO. Further, comparisons between NHHO and other optimization algorithms, including the original HHO algorithm, Particle Swarm Optimization (PSO), Ant Lion Optimizer (ALO), Butterfly Optimization Algorithm (BOA), and Equilibrium Optimization (EO) were performed. The norm of the equation system was calculated as a fitness function to measure the optimization algorithms' performance. A solution with less fitness value is considered a better solution. Furthermore, the experimental results confirmed the superiority of NHHO over the other optimization algorithms, in the comparisons, in different aspects, including best solution, average fitness value, and convergence speed. Accordingly, the proposed NHHO is powerful and more effective in all benchmark problems in solving systems of nonlinear equations compared to the other optimization algorithms. Finally, NHHO overcomes the limitations of Newton's method, including selecting the initial point and divergence problems.INDEX TERMS Hybridization; Harris hawks optimization; Newton's method; optimization; systems of nonlinear equations.

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“…HHO and its progressive variants also applied to parameters identification of photovoltaic cells [ 60 , 68 ], image segmentation [ 69 , 70 ], web service composition [ 71 ], diagnosing coronavirus disease [ 72 ], predicting di-2-ethylhexyl phthalate toxicity [ 65 ], parameter estimation of photovoltaic models [ 73 , 74 ], real-world engineering optimization problem [ 75 ], and feature selection [ 76 , 77 ]. For a review of recent works on HHO, please refer to work in [ 78 ].…”

Section: Introductionmentioning

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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Harris hawks optimization: a comprehensive review of recent variants and applications

Cited by 132 publications

References 23 publications

A hybrid parallel Harris hawks optimization algorithm for reusable launch vehicle reentry trajectory optimization with no-fly zones

A hybrid parallel Harris hawks optimization algorithm for reusable launch vehicle reentry trajectory optimization with no-fly zones

A Hybrid Approach for Solving Systems of Nonlinear Equations Using Harris Hawks Optimization and Newton’s Method

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

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