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

Chang, Guo‐Qing; Tang, Tianhao; Niu, Ben

doi:10.1111/exsy.12812

Cited by 9 publications

(7 citation statements)

References 79 publications

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“…Through this analysis here, we are re-evaluating by comparison with the said algorithms and the additional datasets, which usually give poor results, as was reported in [30]. We also compare CM-BBPSO results with the existing cluster-based MOO technique HT-MOC [20], MOC techniques MOFC-TMS [25], VAMOSA [31], GenClustMOO [32], MOAC [26], ES-NMPBFO [27] and BBPSO clustering technique CBPSO [24] based on commonly reported datasets and evaluation metrics.…”

Section: Methodsmentioning

confidence: 98%

“…The algorithm is not sensitive to initialization, and the particle update is based on the magnetic resultant force. Guo et al [27] proposed the evolutionary state-based novel multiobjective periodic bacterial foraging optimization algorithm (ES-NMPBFO). This is a novel multiobjective periodic bacterial foraging optimization (BFO) algorithm for data clustering, incorporating PSO mechanisms into the chemotaxis operation.…”

Section: Multiobjective Clusteringmentioning

confidence: 99%

“…However, HT-MOC, MOFC-TMS, GenClustMOO and ES-NMPBFO outperformed CM-BBPSO in the available common datasets. [20] 0.961 0.951 MOFC-TMS [25] 0.653 0.97 0.836 0.501 0.696 GenClustMOO [32] 0.647 0.969 0.823 0.503 0.698 VAMOSA [31] 0.593 0.816 0.682 0.415 0.516 MOAC [26] 0.401 0.218 0.893 ES-NMPBFO [27] 0.448 0.478 0.886 0.834 CBPSO [24] 0.398 0.961 0.908 0.509 0.711…”

Section: Moo Comparisonsmentioning

confidence: 99%

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An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering †

Lai

Sato

2021

Algorithms

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Previously, cluster-based multi or many objective function techniques were proposed to reduce the Pareto set. Recently, researchers proposed such techniques to find better solutions in the objective space to solve engineering problems. In this work, we applied a cluster-based approach for solution selection in a multiobjective evolutionary algorithm based on decomposition with bare bones particle swarm optimization for data clustering and investigated its clustering performance. In our previous work, we found that MOEA/D with BBPSO performed the best on 10 datasets. Here, we extend this work applying a cluster-based approach tested on 13 UCI datasets. We compared with six multiobjective evolutionary clustering algorithms from the existing literature and ten from our previous work. The proposed technique was found to perform well on datasets highly overlapping clusters, such as CMC and Sonar. So far, we found only one work that used cluster-based MOEA for clustering data, the hierarchical topology multiobjective clustering algorithm. All other cluster-based MOEA found were used to solve other problems that are not data clustering problems. By clustering Pareto solutions and evaluating new candidates against the found cluster representatives, local search is introduced in the solution selection process within the objective space, which can be effective on datasets with highly overlapping clusters. This is an added layer of search control in the objective space. The results are found to be promising, prompting different areas of future research which are discussed, including the study of its effects with an increasing number of clusters as well as with other objective functions.

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Section: Methodsmentioning

confidence: 98%

Section: Multiobjective Clusteringmentioning

confidence: 99%

Section: Moo Comparisonsmentioning

confidence: 99%

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An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering †

Lai

Sato

2021

Algorithms

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“…The original BFOA is theorized as swarming, chemotaxis, elimination-dispersal, and reproduction. Generally, the swarming process has adverse impact on the BFOA accuracy [18].…”

Section: Hbfoa Based Parameter Optimizationmentioning

confidence: 99%

Hybrid Bacterial Foraging Optimization with Sparse Autoencoder for Energy Systems

L¹,

Vedaiyan²,

Xavier³

et al. 2023

Computer Systems Science and Engineering

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The Internet of Things (IoT) technologies has gained significant interest in the design of smart grids (SGs). The increasing amount of distributed generations, maturity of existing grid infrastructures, and demand network transformation have received maximum attention. An essential energy storing model mostly the electrical energy stored methods are developing as the diagnoses for its procedure was becoming further compelling. The dynamic electrical energy stored model using Electric Vehicles (EVs) is comparatively standard because of its excellent electrical property and flexibility however the chance of damage to its battery was there in event of overcharging or deep discharging and its mass penetration deeply influences the grids. This paper offers a new Hybridization of Bacterial foraging optimization with Sparse Autoencoder (HBFOA-SAE) model for IoT Enabled energy systems. The proposed HBFOA-SAE model majorly intends to effectually estimate the state of charge (SOC) values in the IoT based energy system. To accomplish this, the SAE technique was executed to proper determination of the SOC values in the energy systems. Next, for improving the performance of the SOC estimation process, the HBFOA is employed. In addition, the HBFOA technique is derived by the integration of the hill climbing (HC) concepts with the BFOA to improve the overall efficiency. For ensuring better outcomes for the HBFOA-SAE model, a comprehensive set of simulations were performed and the outcomes are inspected under several aspects. The experimental results reported the supremacy of the HBFOA-SAE model over the recent state of art approaches.

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“…SI algorithms have been used in real-world applications to solve complex problems [18]. A wide variety of the optimization algorithms have been proposed to solve the parameter optimization problem, such as particle swarm optimization (PSO) [19], genetic algorithm (GA) [20], differential evolution (DE) algorithm [21], bacterial foraging optimization (BFO) algorithm [22], and ant colony algorithm (ACO) [23]. The specific objective of this study was to use a new method for the PCP.…”

Section: Introductionmentioning

confidence: 99%

Pipeline Corrosion Prediction Using the Grey Model and Artificial Bee Colony Algorithm

Cui

Liu

et al. 2022

Axioms

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Pipeline corrosion prediction (PCP) is an important technology for pipeline maintenance and management. How to accurately predict pipeline corrosion is a challenging task. To address the drawback of the poor prediction accuracy of the grey model (GM(1,1)), this paper proposes a method named ETGM(1,1)-RABC. The proposed method consists of two parts. First, the exponentially transformed grey model (ETGM(1,1)) is an improvement of the GM(1,1), in which exponential transformation (ET) is used to preprocess the raw data. Next, dynamic coefficients, instead of background fixed coefficients, are optimized by the reformative artificial bee colony (RABC) algorithm, which is a variation of the artificial bee colony (ABC) algorithm. Experiments are performed on actual pipe corrosion data, and four different methods are included in the comparative study, including GM(1,1), ETGM(1,1), and three ETGM(1,1)-ABC variants. The results show that the proposed method proves to be superior for the PCP in terms of Taylor diagram and absolute error.

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

Cited by 9 publications

References 79 publications

An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering †

An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering †

Hybrid Bacterial Foraging Optimization with Sparse Autoencoder for Energy Systems

Pipeline Corrosion Prediction Using the Grey Model and Artificial Bee Colony Algorithm

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