An Enhanced GWO Algorithm with Improved Explorative Search Capability for Global Optimization and Data Clustering

Shial, Gyanaranjan; Sahoo, Sabita; Panigrahi, Sibarama

doi:10.1080/08839514.2023.2166232

Cited by 13 publications

(5 citation statements)

References 100 publications

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“…In 2023, Shial et al [10] proposed that the exponential decay equation helps in transforming the exploration to exploitation process from initial iterations to final iterations, with proportions of 70% and 30% respectively, enhancing the exploration capability significantly.…”

Section: Parameter Improvementmentioning

confidence: 99%

The Development and Application Research of Grey Wolf Optimization Algorithm

Chai,

Zhang,

Liu

2024

Frontiers in Artificial Intelligence and Applications

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The Grey Wolf Optimization (GWO) algorithm is an emerging swarm intelligence optimization technique known for its simplicity, minimal control parameters, fast convergence, and ease of implementation. This paper investigates the search mechanism and implementation process of the GWO algorithm, analyzing its shortcomings, including poor population diversity, slow convergence in later stages, and susceptibility to local optima. It provides an overview of improvement strategies for the Grey Wolf Optimization algorithm, encompassing enhancements to the population, parameters, search mechanism, and its integration with other optimization algorithms. Furthermore, the paper discusses the applications of the Grey Wolf Optimization algorithm in various domains.

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Section: Parameter Improvementmentioning

confidence: 99%

The Development and Application Research of Grey Wolf Optimization Algorithm

Chai,

Zhang,

Liu

2024

Frontiers in Artificial Intelligence and Applications

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“…Particularly in experimental research, they are utilized to observe and compare the performance of different algorithms. Among these, the Wilcoxon signed-rank test has gained favor due to its simplicity in computation and reliability in results [36,37]. To further assess INGO's performance, a Wilcoxon signed-rank test was conducted on the optimal results of the INGO and four other algorithms over 30 independent runs at a significance level of p = 5%, determining whether INGO significantly differed from other intelligent optimization algorithms.…”

Section: Algorithmmentioning

confidence: 99%

Tool Wear State Identification Based on SVM Optimized by the Improved Northern Goshawk Optimization

Wang,

Xiang,

Cheng

et al. 2023

Sensors

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Tool wear condition significantly influences equipment downtime and machining precision, necessitating the exploration of a more accurate tool wear state identification technique. In this paper, the wavelet packet thresholding denoising method is used to process the acquired multi-source signals and extract several signal features. The set of features most relevant to the tool wear state is screened out by the support vector machine recursive feature elimination (SVM-RFE). Utilizing these selected features, we propose a tool wear state identification model, which utilizes an improved northern goshawk optimization (INGO) algorithm to optimize the support vector machine (SVM), hereby referred to as INGO-SVM. The simulation tests reveal that INGO demonstrates superior convergence efficacy and stability. Furthermore, a milling wear experiment confirms that this approach outperforms five other methods in terms of recognition accuracy, achieving a remarkable accuracy rate of 97.9%.

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“…where n denotes the number of observations, K -the number of cluster centers, and i -the number of iterations to complete. Additionally, meta-heuristic-based partitional clustering algorithms handle such local convergence issues by using their stochastic operators, that ensures to adapt the global searches [7,13,48] for achieving near-optimal solutions. The traditional partitioning algorithms rely on some strict rules or mathematical logic for achieving optimal solutions, whereas meta-heuristic algorithms approach towards the optimal solutions by maintaining cooperation among population members with a few randomness [58].…”

Section: Partitional Clusteringmentioning

confidence: 99%

“…The recent literature reports that most of the meta-heuristic-based optimization algorithms are able to solve real-life optimization problems and those algorithms are such as: grey wolf optimization (GWO) [34], teaching learning-based optimization (TLBO) [44], bacteria foraging optimization (BFO) [17], the whale optimization algorithm (WOA) [33], the JAYA algorithm [43], ant colony optimization (ACO) [10], the sine cosine algorithm (SCA) [32], simulated annealing (SA) [29], the spotted hyena optimization algorithm (SHOA) [9], ant lion optimization (ALO) [31], particle swarm optimizations (PSO) [28], chemical reaction optimization (CRO) [30], differential evolution (DE) [50], etc. In recent times, most researchers have received immense interest in applying such nature-inspired algorithms on clustering problems [46][47][48][49]. Similarly, Jafer and Sivakumar [25] published a review article on a nature-inspired-based meta-heuristic optimization algorithm on ant-based clustering.…”

Section: Introductionmentioning

confidence: 99%

A Nature Inspired Hybrid Partitional Clustering Method Based on Grey Wolf Optimization and JAYA Algorithm

SHIAL,

Sahoo,

Panigrahi

2023

csci

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This paper presents a hybrid meta-heuristic algorithm using Grey Wolf optimization (GWO) and JAYA algorithm for data clustering. The idea is use exploitative capability of JAYA algorithm in the explorative phase of GWO to form compact clusters. Here, instead of using one best and one worst solution for generating offspring, three best wolfs and three worst omega wolfs of the population are used. So, the best wolfs and worst omega wolfs assist in moving the new solutions towards the best solutions and simultaneously helps in staying away from the worst solutions. This enhances the chances of reaching the near optimal solutions. The superiority of the proposed method is compared with five promising algorithms, namely GWO, Sine-Cosine Algorithm (SCA), Particle Swarm Optimization (PSO), JAYA and K-means algorithms. The result obtained from the Duncan’s multiple range test and Nemenyi hypothesis based statistical test confirms the superiority and robustness of our proposed method.

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An Enhanced GWO Algorithm with Improved Explorative Search Capability for Global Optimization and Data Clustering

Cited by 13 publications

References 100 publications

The Development and Application Research of Grey Wolf Optimization Algorithm

The Development and Application Research of Grey Wolf Optimization Algorithm

Tool Wear State Identification Based on SVM Optimized by the Improved Northern Goshawk Optimization

A Nature Inspired Hybrid Partitional Clustering Method Based on Grey Wolf Optimization and JAYA Algorithm

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