Equalized Grey Wolf Optimizer with Refraction Opposite Learning

Sun, Lijun; Feng, Binbin; Chen, Tianfei; Zhao, Dongliang; Xin, Yan

doi:10.1155/2022/2721490

Cited by 6 publications

(6 citation statements)

References 61 publications

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“…This kind of opposition-based learning can be considered more advanced to avoid sub-optimality. Refraction learning is used in Whale Optimization Algorithm (WOA) and Equalized Grey Wolf Optimizer (EGWO) 24,56 . In both of the applications, it can be seen from the statistical results that the local optimality is avoided via RL method.…”

Section: Methodsmentioning

confidence: 99%

“…Physics-inspired algorithms imitate the physical laws that govern how individuals engage with each other and their search space. These laws include the laws of inertia, light refraction, gravitation and many others 24 . Few of the popular algorithms in this category are Gravitational Search Algorithm (GSA) 25 , Colliding Bodies Optimization (CBO) 26 and Henry Gas Solubility Optimization (HGSO) 27 .…”

Section: Related Workmentioning

confidence: 99%

“…Each of the numerous metaheuristic algorithms has its own drawbacks. The local and global search balance in Grey Wolf Optimizer (GWO) is weak 24 . Rodríguez et al investigated the possibility of altering the initial control parameters to improve the exploration process in GWO 32 .…”

Section: Related Workmentioning

confidence: 99%

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Hybrid artificial electric field employing cuckoo search algorithm with refraction learning for engineering optimization problems

Adegboye

Ülker

2023

Sci Rep

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Due to its low dependency on the control parameters and straightforward operations, the Artificial Electric Field Algorithm (AEFA) has drawn much interest; yet, it still has slow convergence and low solution precision. In this research, a hybrid Artificial Electric Field Employing Cuckoo Search Algorithm with Refraction Learning (AEFA-CSR) is suggested as a better version of the AEFA to address the aforementioned issues. The Cuckoo Search (CS) method is added to the algorithm to boost convergence and diversity which may improve global exploration. Refraction learning (RL) is utilized to enhance the lead agent which can help it to advance toward the global optimum and improve local exploitation potential with each iteration. Tests are run on 20 benchmark functions to gauge the proposed algorithm's efficiency. In order to compare it with the other well-studied metaheuristic algorithms, Wilcoxon rank-sum tests and Friedman tests with 5% significance level are used. In order to evaluate the algorithm’s efficiency and usability, some significant tests are carried out. As a result, the overall effectiveness of the algorithm with different dimensions and populations varied between 61.53 and 90.0% by overcoming all the compared algorithms. Regarding the promising results, a set of engineering problems are investigated for a further validation of our methodology. The results proved that AEFA-CSR is a solid optimizer with its satisfactory performance.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Hybrid artificial electric field employing cuckoo search algorithm with refraction learning for engineering optimization problems

Adegboye

Ülker

2023

Sci Rep

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“…The performance of algorithms in specific problems deteriorates with increasing dimensions. For this issue, Sun et al [20] proposed an equalized GWO with refraction reverse learning, which surmounted the low swarm population diversity of GWO in the final iteration. Nevertheless, it only accelerates convergence and considers that the direction of individual solutions is limited, which has the possibility of falling into local optimum in the early stages of iteration.…”

Section: Related Workmentioning

confidence: 99%

Improved Grey Wolf Optimization Algorithm Based on Hyperbolic Tangent Inertia Weight

Lin

2023

IEEE Access

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The Grey Wolf Optimization Algorithm (GWO) is an algorithm that replicates the leadership and foraging mechanisms of the natural grey wolf, which excels at solving problems in a variety of domains. However, the algorithm tends to converge on local optimal and has a slow convergence rate. This paper proposes an enhanced Grey Wolf optimization algorithm (HTGWO) based on hyperbolic tangent inertia weights to solve this problem. HTGWO employs inertia weight based on hyperbolic tangent functions to balance GWO's global and local search capabilities. HTGWO has a quicker convergence rate and more accurate solutions than GWO. Five classical test functions construct comparative experiments between HWGWO and five classical intelligent optimization algorithms. The comparison results indicate that HWGWO has superior convergence speed, solution precision, and stability compared to the other five classical intelligent optimization algorithms. Moreover, experimental evidence suggests that HWGWO is more effective at solving multi-modal than uni-modal functions.

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“…The position update of the current search wolf does not need to wait for the comparison between other search wolves and the three leading wolves; therefore, it can update the position in time and improve the speed of iterative convergence. Sun et al [20] proposed an equilibrium Gray Wolf Optimization algorithm with refracted reverse learning, which overcame the problem of the low population diversity of GWO groups in the later stage and reduced the possibility of falling into local extremes. Li et al [21] introduced a differential evolution algorithm and nonlinear convergence factor into the traditional GWO algorithm to solve the problem that the algorithm can easily fall into the local optimum.…”

Section: Introductionmentioning

confidence: 99%

Pressure Vessel Design Problem Using Improved Gray Wolf Optimizer Based on Cauchy Distribution

Li,

Sun

2023

Applied Sciences

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The Gray Wolf Optimizer (GWO) is an established algorithm for addressing complex optimization tasks. Despite its effectiveness, enhancing its precision and circumventing premature convergence is crucial to extending its scope of application. In this context, our study presents the Cauchy Gray Wolf Optimizer (CGWO), a modified version of GWO that leverages Cauchy distributions for key algorithmic improvements. The innovation of CGWO lies in several areas: First, it adopts a Cauchy distribution-based strategy for initializing the population, thereby broadening the global search potential. Second, the algorithm integrates a dynamic inertia weight mechanism, modulated non-linearly in accordance with the Cauchy distribution, to ensure a balanced trade-off between exploration and exploitation throughout the search process. Third, it introduces a Cauchy mutation concept, using inertia weight as a probability determinant, to preserve diversity and bolster the capability for escaping local optima during later search phases. Furthermore, a greedy strategy is employed to incrementally enhance solution accuracy. The performance of CGWO was rigorously evaluated using 23 benchmark functions, demonstrating significant improvements in convergence rate, solution precision, and robustness when contrasted with conventional algorithms. The deployment of CGWO in solving the engineering challenge of pressure vessel design illustrated its superiority over traditional methods, highlighting its potential for widespread adoption in practical engineering contexts.

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Equalized Grey Wolf Optimizer with Refraction Opposite Learning

Cited by 6 publications

References 61 publications

Hybrid artificial electric field employing cuckoo search algorithm with refraction learning for engineering optimization problems

Hybrid artificial electric field employing cuckoo search algorithm with refraction learning for engineering optimization problems

Improved Grey Wolf Optimization Algorithm Based on Hyperbolic Tangent Inertia Weight

Pressure Vessel Design Problem Using Improved Gray Wolf Optimizer Based on Cauchy Distribution

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