Genghis Khan shark optimizer: A novel nature-inspired algorithm for engineering optimization

Hu, Gang; Guo, Yuxuan; Wei, Guo; Abualigah, Laith

doi:10.1016/j.aei.2023.102210

Cited by 94 publications

(14 citation statements)

References 104 publications

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“…The question of how to simplify or build the mathematical model in a simple way so that it can be applied to the approximate merging of disk curves is a problem to be solved in the future. In addition, BEESO can be used to solve optimization problems in areas such as cryptosystem design [ 63 ], path planning, geometry optimization [ 40 , 64 ], engineering design [ 65 , 66 ], and feature selection [ 67 ].…”

Section: Discussionmentioning

confidence: 99%

Ameliorated Snake Optimizer-Based Approximate Merging of Disk Wang–Ball Curves

Lu,

Yang,

et al. 2024

Biomimetics

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A method for the approximate merging of disk Wang–Ball (DWB) curves based on the modified snake optimizer (BEESO) is proposed in this paper to address the problem of difficulties in the merging of DWB curves. By extending the approximate merging problem for traditional curves to disk curves and viewing it as an optimization problem, an approximate merging model is established to minimize the merging error through an error formulation. Considering the complexity of the model built, a BEESO with better convergence accuracy and convergence speed is introduced, which combines the snake optimizer (SO) and three strategies including bi-directional search, evolutionary population dynamics, and elite opposition-based learning. The merging results and merging errors of numerical examples demonstrate that BEESO is effective in solving approximate merging models, and it provides a new method for the compression and transfer of product shape data in Computer-Aided Geometric Design.

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Section: Discussionmentioning

confidence: 99%

Ameliorated Snake Optimizer-Based Approximate Merging of Disk Wang–Ball Curves

Lu,

Yang,

et al. 2024

Biomimetics

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“…The goal of optimization is to identify the best solution to a problem within the specified parameters [ 37 , 38 ]. There are two popular approaches to solving optimization problems: the metaheuristic approach and the mathematical approach.…”

Section: Methodsmentioning

confidence: 99%

Numerical investigation of the optimal porosity of titanium foam for dental implants

Farroukh,

Kaddah,

Wehbe

2024

Heliyon

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“…Experiments prove that the optimization performance of YDSW outperforms the other 12 algorithms on CEC2014, CE2017, and CEC2022. Genghis Khan shark optimizer (GKSE) 75 : The animal-based metaheuristic algorithm. The search for the optimal position is achieved by simulating the predation process of Genghis Khan sharks.…”

Section: Performance Testsmentioning

confidence: 99%

“…Genghis Khan shark optimizer (GKSE) 75 : The animal-based metaheuristic algorithm. The search for the optimal position is achieved by simulating the predation process of Genghis Khan sharks.…”

Section: Performance Testsmentioning

confidence: 99%

Symmetric projection optimizer: concise and efficient solving engineering problems using the fundamental wave of the Fourier series

Su,

Dong,

Liu

et al. 2024

Sci Rep

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The fitness function value is a kind of important information in the search process, which can be more targeted according to the guidance of the fitness function value. Most existing meta-heuristic algorithms only use the fitness function value as an indicator to compare the current variables as good or bad but do not use the fitness function value in the search process. To address this problem, the mathematical idea of the fitting is introduced into the meta-heuristic algorithm, and a symmetric projection optimizer (SPO) is proposed to solve numerical optimization and engineering problems more efficiently. The SPO algorithm mainly utilizes a new search mechanism, the symmetric projection search (SP) method. The SP method quickly completes the fitting of the projection plane, which is located through the symmetry of the two points and finds the minima in the projection plane according to the fitting result. Fitting by using the fitness function values allows the SP to find regions where extreme values may exist more quickly. Based on the SP method, exploration and exploitation strategies are constructed, respectively. The exploration strategy is used to find better regions, and the exploitation strategy is used to optimize the discovered regions continuously. The timing of the use of the two strategies is designed so that the SPO algorithm can converge faster while avoiding falling into local optima. The effectiveness of the SPO algorithm is extensively evaluated using seven test suites, including CEC2017, CEC2019, CEC2020, and CEC2022. It is also compared with two sets of 19 recent competitive algorithms. Statistical analyses are performed using five metrics such as the Wilcoxon test, the Friedman test, and variance. Finally, the practicality of the SPO algorithm is verified by four typical engineering problems and a real spacecraft trajectory optimization problem. The results show that the SPO algorithm can find superior results in 94.6% of the comparison tests and is a promising alternative for solving real-world problems.

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Genghis Khan shark optimizer: A novel nature-inspired algorithm for engineering optimization

Cited by 94 publications

References 104 publications

Ameliorated Snake Optimizer-Based Approximate Merging of Disk Wang–Ball Curves

Ameliorated Snake Optimizer-Based Approximate Merging of Disk Wang–Ball Curves

Numerical investigation of the optimal porosity of titanium foam for dental implants

Symmetric projection optimizer: concise and efficient solving engineering problems using the fundamental wave of the Fourier series

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