Data clustering using multivariant optimization algorithm

Zhang, Qin-Hu; Li, Baolei; Liu, Yajie; Gao, Lian; Liu, Lanjuan; Shi, Xinling

doi:10.1007/s13042-014-0294-5

Cited by 10 publications

(2 citation statements)

References 13 publications

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“…The first method utilizes the basic KH algorithm with all its operators, while the second method neglects the genetic operators in the basic KH algorithm. Zhang et al [36] introduced a new clustering method based on a proposed heuristic optimization algorithm called the multivariant optimization algorithm (MOA). This algorithm automatically identifies the optimal solution through a global and local alternating search conducted by a global exploration group and several local exploitation groups.…”

Section: Literature Reviewmentioning

confidence: 99%

Using Hybrid Mountain Gazelle Optimization and Particle Swarm Optimization Algorithms to Improve Clustering

Mosavi,

Fazeli,

Abbasi

et al. 2024

Preprint

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Clustering plays a crucial role in data mining and machine learning, with the primary objective being the identification of cohesive and distinct data groups, enabling the extraction of valuable information. However, clustering algorithms often encounter the challenge of getting trapped in local optima, hindering their ability to achieve optimal results. To address this issue, researchers have turned to using meta-heuristic algorithms. This article proposes an enhanced approach for clustering by combining the particle swarm optimization algorithm and the mountain gazelle algorithm. The utilization of this combined algorithm has shown superior performance compared to relying solely on the particle swarm algorithm. By using the strengths of both algorithms, our method overcomes the limitations posed by local optima, leading to more accurate and robust clustering results. The proposed algorithm utilizes the minimum fitness measure to locate the optimal centroid, which is determined based on three constraints: intra-cluster distance, inter-cluster distance, and cluster density. The data is then clustered using the optimal centroid corresponding to the minimal value of the fitness. The performance of our proposed approach has been evaluated on real-world datasets such as Iris, Wine, and Vowel. Our method and PSO and MGO algorithms have been compared on these datasets. The results of the experiments indicate that our proposed method outperforms the PSO and MGO algorithms in terms of clustering quality and convergence speed.

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Section: Literature Reviewmentioning

confidence: 99%

Using Hybrid Mountain Gazelle Optimization and Particle Swarm Optimization Algorithms to Improve Clustering

Mosavi,

Fazeli,

Abbasi

et al. 2024

Preprint

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show abstract

“…As proven by literatures [8][9][10], this algorithm has good asymptotic property, convergence, and reachability. Later, some scholars applied it in dynamic route optimization [11], data clustering [12], three-dimensional container loading problem [13] and other practical applications and achieved good results.…”

Section: Introductionmentioning

confidence: 99%

Application of Multivariant Optimization Algorithm in Multiobjective Optimization

Tian,

Shi,

et al. 2023

Preprint

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The multiobjective optimization problem is a key research direction in the field of optimization. Based on the multivariant optimization algorithm (MOA), this paper puts forward a multiobjective multivariant optimization algorithm (MMOA). A good point set is adopted for population initialization and to ensure the homogeneity of the initial solution set and avoid prematurity; global atoms are acquired by non-dominated sorting and an external archiving strategy; a nonlinear search radius is designed to balance global and local searches and enhance the convergence precision of the algorithm. Levy Flight and Sine chaotic mapping are introduced to redesign the update formula for local atoms, increase the convergence rate of the algorithm, and boost the optimization efficiency. Comparative experiments are conducted between MMOA and NSGA-II, MOEA/D, NSWOA, MOGWO, NSMFO on multiobjective test functions and an engineering example. The simulation results show that MMOA has superior convergence and diversity to the other five algorithms.

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Hybrid Harris Hawks Optimization with Differential Evolution for Data Clustering

Abualigah

Elaziz

Shehab

et al. 2021

Metaheuristics in Machine Learning: Theory and Applications

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Data clustering using multivariant optimization algorithm

Cited by 10 publications

References 13 publications

Using Hybrid Mountain Gazelle Optimization and Particle Swarm Optimization Algorithms to Improve Clustering

Using Hybrid Mountain Gazelle Optimization and Particle Swarm Optimization Algorithms to Improve Clustering

Application of Multivariant Optimization Algorithm in Multiobjective Optimization

Hybrid Harris Hawks Optimization with Differential Evolution for Data Clustering

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