Group-based synchronous-asynchronous Grey Wolf Optimizer

Rodríguez, Alma; Camarena, Octavio; Cuevas, Erik; Aranguren, Itzel; Valdivia-G, Arturo; Morales-Castañeda, Bernardo; Zaldívar, Daniel; Pérez‐Cisneros, Marco

doi:10.1016/j.apm.2020.12.016

Cited by 19 publications

(7 citation statements)

References 22 publications

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“…A synchronous-asynchronous processing scheme was adapted for the GWO using a set of nonlinear functions and operations to increase diversity by Rodríguez et al [120]. In that sense, a better balance between exploration and exploitation was achieved.…”

Section: ) Structured Population Grey Wolf Optimizermentioning

confidence: 99%

“…This is because the complexity is increased and the search space will be huge. Therefore, to deal with a large-scale optimization problem, the structured GWO is proposed in many pieces of research [118], [119], [120]. To empower the research behaviour, the hybrid mechanism with local search has also revealed very successful outcomes such as Neighbour GWO [135], [75] and memetic GWO [163], [164].…”

Section: Critical Analysis Of Grey Wolf Optimizer Theorymentioning

confidence: 99%

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Recent Advances in Grey Wolf Optimizer, its Versions and Applications: Review

et al. 2024

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The Grey Wolf Optimizer (GWO) has emerged as one of the most captivating swarm intelligence methods, drawing inspiration from the hunting behavior of wolf packs. GWO's appeal lies in its remarkable characteristics: it is parameter-free, derivative-free, conceptually simple, user-friendly, adaptable, flexible, and robust. Its efficacy has been demonstrated across a wide range of optimization problems in diverse domains, including engineering, bioinformatics, biomedical, scheduling and planning, and business. Given the substantial growth and effectiveness of GWO, it is essential to conduct a recent review to provide updated insights. This review delves into the GWO-related research conducted between 2019 and 2022, encompassing over 200 research articles. It explores the growth of GWO in terms of publications, citations, and the domains that leverage its potential. The review thoroughly examines the latest versions of GWO, categorizing them based on their contributions. Additionally, it highlights the primary applications of GWO, with computer science and engineering emerging as the dominant research domains. A critical analysis of the accomplishments and limitations of GWO is presented, offering valuable insights. Finally, the review concludes with a brief summary and outlines potential future developments in GWO theory and applications. Researchers seeking to employ GWO as a problem-solving tool will find this comprehensive review immensely beneficial in advancing their research endeavors.

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Section: ) Structured Population Grey Wolf Optimizermentioning

confidence: 99%

Section: Critical Analysis Of Grey Wolf Optimizer Theorymentioning

confidence: 99%

Recent Advances in Grey Wolf Optimizer, its Versions and Applications: Review

et al. 2024

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“…In the GWO based on the incremental model (I-GWO), each wolf updates its position based on all the wolves selected before it [22]. In the GWO based on the Group-based Synchronous-Asynchronous, the method incorporates a synchronous-asynchronous processing scheme, a set of different nonlinear functions and an operation to increase diversity [23].…”

Section: ) Improve the Position-updated Strategymentioning

confidence: 99%

Chaos Gray Wolf global optimization algorithm based on Opposition- based Learning

Luo

Tan

Huang

et al. 2022

Preprint

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Gray wolf optimizer (GWO) is a new heuristic algorithm. It has few parameters and strong optimization ability and is used in many fields. However, when solving complex and multimodal functions, it is also easy to trap into the local optimum and premature convergence. In order to boost the performance of GWO, a tent chaotic map and opposition-based learning Grey Wolf Optimizer (CO-GWO) is proposed. Firstly, some better values of the population in the current generation are retained to avoid deterioration in the next generation. Secondly, tent chaotic map and opposition-based(OBL)are introduced to generate values that can traverse the whole feasible region as much as possible, which is conducive to jumping out of local optimization and accelerating convergence. Then, the coefficient \(\vec {a}\) is dynamically adjusted by the polynomial attenuation function of the 2-decay method. Finally, the proposed algorithm is tested on 23 benchmark functions. The results show that the proposed algorithm is superior to the conventional heuristic algorithms, GWO and its variants in search-efficiency, solution accuracy and convergence rate.

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“…Some scholars have proposed improvement strategies to solve the problem of premature convergence of the original algorithm. Alma Rodríguez proposed a Group-based Synchronous-Asynchronous Grey Wolf Optimizer that avoids convergence of local minima by combining a synchronous asynchronous processing scheme, a set of different nonlinear functions, and an operation that increases diversity [16] . Erik Cuevas proposed set of search patterns is integrated into a complete search strategy called secondorder algorithm (SOA) to obtain a global solution to complex optimization problems [17].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Stage Adaptive Sequential Parameter Exploration Hunger Games Search Algorithm for Solving Complex Optimization Problems

Zhan,

2023

IEEE Access

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Hunger Games Search (HGS) is a newly developed metaheuristic algorithm that models the hunger-driven activities and behaviors of animals. It incorporates the concept of hunger to devise an adaptive weight that mimics the impact of hunger on each search step. In this paper, a Multi-Stage Adaptive Sequential Parameter Exploration Hunger Games Search Algorithm (MASPE-HGSA) is proposed to alleviate the shortcomings of the original HGS in terms of insufficient optimization and convergence accuracy. In MASPE-HGSA, a Multi-Stage adaptive sequential parameter exploration is proposed to improve the search performance of the algorithm as well as to increase the search accuracy and global search capability, which can commendably achieve the balance of exploration and exploitation. The effectiveness of MASPE-HGSA is verified by comparing with original HGS algorithm and several classical algorithms using 23 benchmark functions, CEC2014 test set and three advanced algorithmic problems from classical engineering. Experimental results show that the performance of MASPE-HGSA is significantly better than other similar algorithms. The proposed algorithm can effectively search for high-quality solutions and prevent premature convergence, with better convergence robustness than the original HGS algorithm. In addition, an analysis of probability-based algorithm mechanisms approaching zero is presented in this paper from a theoretical perspective, while providing a reference and verification method for the design of algorithm mechanisms.

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Group-based synchronous-asynchronous Grey Wolf Optimizer

Cited by 19 publications

References 22 publications

Recent Advances in Grey Wolf Optimizer, its Versions and Applications: Review

Recent Advances in Grey Wolf Optimizer, its Versions and Applications: Review

Chaos Gray Wolf global optimization algorithm based on Opposition- based Learning

A Multi-Stage Adaptive Sequential Parameter Exploration Hunger Games Search Algorithm for Solving Complex Optimization Problems

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