Automatic clustering using a local search-based human mental search algorithm for image segmentation

Mousavirad, Seyed Jalaleddin; Ebrahimpour-Komleh, Hossein; Schaefer, Gerald

doi:10.1016/j.asoc.2020.106604

Cited by 21 publications

(3 citation statements)

References 42 publications

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“…Verma et al [41] proposed hybrid FCM and particle swarm optimization (PSO) algorithms (Hybrid FCM-PSO), while the global optimization property of PSO is used to search for cluster centers. In [42], an Automatic Clustering Local Search HMS (ACLSHMS) algorithm was proposed for image segmentation, incorporating a local search operator in the algorithm aimed at optimizing the cluster configuration of the clusters. In addition, given the effectiveness of unsupervised learning for medical image diagnosis, Mittal et al [43] proposed a novel k-means-based improved gravitational search algorithm clustering (KIGSA-C) method for diagnosing medical images of coronavirus .…”

Section: Literature Reviewmentioning

confidence: 99%

Gradient-based elephant herding optimization for cluster analysis

Duan

Liu

et al. 2022

Appl Intell

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Clustering analysis is essential for obtaining valuable information from a predetermined dataset. However, traditional clustering methods suffer from falling into local optima and an overdependence on the quality of the initial solution. Given these defects, a novel clustering method called gradient-based elephant herding optimization for cluster analysis (GBEHO) is proposed. A well-defined set of heuristics is introduced to select the initial centroids instead of selecting random initial points. Specifically, the elephant optimization algorithm (EHO) is combined with the gradient-based algorithm GBO for assigning initial cluster centers across the search space. Second, to overcome the imbalance between the original EHO exploration and exploitation, the initialized population is improved by introducing Gaussian chaos mapping. In addition, two operators, i.e., random wandering and variation operators, are set to adjust the location update strategy of the agents. Nine datasets from synthetic and real-world datasets are adopted to evaluate the effectiveness of the proposed algorithm and the other metaheuristic algorithms. The results show that the proposed algorithm ranks first among the 10 algorithms. It is also extensively compared with state-of-the-art techniques, and four evaluation criteria of accuracy rate, specificity, detection rate, and F-measure are used. The obtained results clearly indicate the excellent performance of GBEHO, while the stability is also more prominent.

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Section: Literature Reviewmentioning

confidence: 99%

Gradient-based elephant herding optimization for cluster analysis

Duan

Liu

et al. 2022

Appl Intell

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“…The last column of each row is a randomly generated number

{rand}_{kk} ((), italickk = 1, 2, \dots, K_{\max})

in the range of (0, 1), which is used to decide which cluster is selected. The widely used mask or activation thresholds are adopted here (Mousavirad et al, 2020; Nguyen & Kuo, 2019). If

{rand}_{kk} \geq 0.5

, this cluster is active and selected; if

{rand}_{kk} < 0.5

, this cluster is inactivated and not selected.…”

Section: Archive Maintenance Strategymentioning

confidence: 99%

Evolutionary state‐based novel multi‐objective periodic bacterial foraging optimization algorithm for data clustering

Chang

Tang²,

Niu

2021

Expert Systems

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Clustering divides objects into groups based on similarity. However, traditional clustering approaches are plagued by their difficulty in dealing with data with complex structure and high dimensionality, as well as their inability in solving multi-objective data clustering problems. To address these issues, an evolutionary state-based novel multi-objective periodic bacterial foraging optimization algorithm (ES-NMPBFO) is proposed in this article. The algorithm is designed to alleviate the high-computing complexity of the standard bacterial foraging optimization (BFO) algorithm by introducing periodic BFO. Moreover, two learning strategies, global best individual (gbest) and personal historical best individual (pbest), are used in the chemotaxis operation to enhance the convergence speed and guide the bacteria to the optimum position. Two elimination-dispersal operations are also proposed to prevent falling into local optima and improve the diversity of solutions. The proposed algorithm is compared with five other algorithms on six validity indexes in two data clustering cases comprising nine general benchmark datasets and four credit risk assessment datasets.The experimental results suggest that the proposed algorithm significantly outperforms the competing approaches. To further examine the effectiveness of the proposed strategies, two variants of ES-NMPBFO were designed, and all three forms of ES-NMPBFO were tested. The experimental results show that all of the proposed strategies are conducive to the improvement of solution quality, diversity and convergence.

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“…Here, mental search, a main part of the algorithm, employs Levy flight to explore the vicinity of candidate solutions. HMS has been shown to solve effectively a wide range of optimisation problems, including unimodal, multi-modal, high-dimensional, rotated, shifted, and complex functions [10], as well as various machine vision applications including multilevel thresholding [11], [12], colour quantisation [13], [14], image segmentation [15], and image clustering [16].…”

Section: Introductionmentioning

confidence: 99%

MCS-HMS: A Multi-Cluster Selection Strategy for the Human Mental Search Algorithm

Bojnordi¹,

Mousavirad²,

Schaefer³

et al. 2021

Preprint

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Population-based metaheuristic algorithms have received significant attention in global optimisation. Human Mental Search (HMS) is a relatively recent population-based metaheuristic that has been shown to work well in comparison to other algorithms. However, HMS is time-consuming and suffers from relatively poor exploration. Having clustered the candidate solutions, HMS selects a winner cluster with the best mean objective function. This is not necessarily the best criterion to choose the winner group and limits the exploration ability of the algorithm.In this paper, we propose an improvement to the HMS algorithm in which the best bids from multiple clusters are used to benefit from enhanced exploration. We also use a onestep k-means algorithm in the clustering phase to improve the speed of the algorithm. Our experimental results show that MCS-HMS outperforms HMS as well as other population-based metaheuristic algorithms.

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Automatic clustering using a local search-based human mental search algorithm for image segmentation

Cited by 21 publications

References 42 publications

Gradient-based elephant herding optimization for cluster analysis

Gradient-based elephant herding optimization for cluster analysis

Evolutionary state‐based novel multi‐objective periodic bacterial foraging optimization algorithm for data clustering

MCS-HMS: A Multi-Cluster Selection Strategy for the Human Mental Search Algorithm

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