ESO: An enhanced snake optimizer for real-world engineering problems

Yao, Liguo; Yuan, Panliang; Tsai, Chieh-Yuan; Zhang, Taihua; Lu, Yao; Ding, Shilin

doi:10.1016/j.eswa.2023.120594

Cited by 35 publications

(19 citation statements)

References 84 publications

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“…In addition to the original versions of metaheuristic algorithms, many researchers have tried to improve the performance of existing algorithms by developing their improved versions, such as the Enhanced Snake Optimizer (ESO) [ 71 ], Improved Sparrow Search Algorithm (ISSA) [ 72 ], and multi-strategy-based Adaptive Sine–Cosine Algorithm (ASCA) [ 73 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Botox Optimization Algorithm: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems

Hubálovská,

Hubálovský,

Trojovský

2024

Biomimetics

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This paper introduces the Botox Optimization Algorithm (BOA), a novel metaheuristic inspired by the Botox operation mechanism. The algorithm is designed to address optimization problems, utilizing a human-based approach. Taking cues from Botox procedures, where defects are targeted and treated to enhance beauty, the BOA is formulated and mathematically modeled. Evaluation on the CEC 2017 test suite showcases the BOA’s ability to balance exploration and exploitation, delivering competitive solutions. Comparative analysis against twelve well-known metaheuristic algorithms demonstrates the BOA’s superior performance across various benchmark functions, with statistically significant advantages. Moreover, application to constrained optimization problems from the CEC 2011 test suite highlights the BOA’s effectiveness in real-world optimization tasks.

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Section: Literature Reviewmentioning

confidence: 99%

Botox Optimization Algorithm: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems

Hubálovská,

Hubálovský,

Trojovský

2024

Biomimetics

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“…Therefore, in this paper, the perturbation of the worst value is removed, and the perturbation of the global optimal solution is newly added to couple the current solution with the optimal solution, while retaining α × k × z i (t − 1) to keep its deflection unchanged and retain the advantages of the original formula. The improved formula is shown in Equation (9).…”

Section: Improved Rolling Dung Beetlesmentioning

confidence: 99%

“…Population-based intelligent optimization algorithms have the potential for global search capability, allowing them to efficiently find the optimal solution to optimization problems, and thus have become a hotspot for scholarly research today [6][7][8]. Li et al proposed an enhanced snake optimization algorithm (ESO) that has the capability of fast convergence when solving four real engineering optimization problems [9]. Teng and colleagues suggested an enhanced multi-strategy Sparrow Search Algorithm (ISSA) to address the Sparrow Search Algorithm's limitations, and its application in channel estimation frequency optimization significantly lowers the Bit Error Rate (BER), showcasing the algorithm's efficacy in engineering [10].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Dung Beetle Optimization Algorithm for Practical Engineering Optimization

Li,

Shi,

Zhao

et al. 2024

Mathematics

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An enhanced dung beetle optimization algorithm (EDBO) is proposed for nonlinear optimization problems with multiple constraints in manufacturing. Firstly, the dung beetle rolling phase is improved by removing the worst value interference and coupling the current solution with the optimal solution to each other, while retaining the advantages of the original formulation. Subsequently, to address the problem that the dung beetle dancing phase focuses only on the information of the current solution, which leads to the overly stochastic and inefficient exploration of the problem space, the globally optimal solution is introduced to steer the dung beetle, and a stochastic factor is added to the optimal solution. Finally, the dung beetle foraging phase introduces the Jacobi curve to further enhance the algorithm’s ability to jump out of the local optimum and avoid the phenomenon of premature convergence. The performance of EDBO in optimization is tested using the CEC2017 function set, and the significance of the algorithm is verified by the Wilcoxon rank-sum test and the Friedman test. The experimental results show that EDBO has strong optimization-seeking accuracy and optimization-seeking stability. By solving four engineering optimization problems of varying degrees, EDBO has proven to have good adaptability and robustness.

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“…In recent years, in pursuit of the effectiveness of heuristic algorithms, many improved algorithms have emerged, mainly consisting of strategy-based improvement and algorithm combinations. In recent years, our research team has been committed to obtaining better-performing heuristic algorithms through algorithmic combinations, such as the beetle antenna strategy based on grey wolf optimization [ 21 ], grey wolf optimization based on the Aquila exploration method (AGWO) [ 22 ], hybrid golden jackal optimization and the golden sine algorithm [ 23 ], enhanced snake optimization [ 24 ], etc.…”

Section: Introductionmentioning

confidence: 99%

Reptile Search Algorithm Considering Different Flight Heights to Solve Engineering Optimization Design Problems

Yao

Yuan

et al. 2023

Biomimetics

Self Cite

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The reptile search algorithm is an effective optimization method based on the natural laws of the biological world. By restoring and simulating the hunting process of reptiles, good optimization results can be achieved. However, due to the limitations of natural laws, it is easy to fall into local optima during the exploration phase. Inspired by the different search fields of biological organisms with varying flight heights, this paper proposes a reptile search algorithm considering different flight heights. In the exploration phase, introducing the different flight altitude abilities of two animals, the northern goshawk and the African vulture, enables reptiles to have better search horizons, improve their global search ability, and reduce the probability of falling into local optima during the exploration phase. A novel dynamic factor (DF) is proposed in the exploitation phase to improve the algorithm’s convergence speed and optimization accuracy. To verify the effectiveness of the proposed algorithm, the test results were compared with ten state-of-the-art (SOTA) algorithms on thirty-three famous test functions. The experimental results show that the proposed algorithm has good performance. In addition, the proposed algorithm and ten SOTA algorithms were applied to three micromachine practical engineering problems, and the experimental results show that the proposed algorithm has good problem-solving ability.

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ESO: An enhanced snake optimizer for real-world engineering problems

Cited by 35 publications

References 84 publications

Botox Optimization Algorithm: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems

Botox Optimization Algorithm: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems

Enhanced Dung Beetle Optimization Algorithm for Practical Engineering Optimization

Reptile Search Algorithm Considering Different Flight Heights to Solve Engineering Optimization Design Problems

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