Ant-based and swarm-based clustering

Handl, Julia; Meyer, Bernd

doi:10.1007/s11721-007-0008-7

Cited by 139 publications

(51 citation statements)

References 78 publications

(87 reference statements)

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“…According to Handl & Meyer (2007), algorithms can be categorized into two groups: the first group of algorithms is inspired by nature and animals' behavior such as ants' methods in building colonies and where researchers followed the same strategy; the other group of algorithms uses techniques such as clustering-methods and data mining.…”

Section: Related Workmentioning

confidence: 99%

Polar Particle Swarm Algorithm for Solving Cloud Data Migration Optimization Problem

Al-Sayyed¹,

Fakhouri²,

Rodan³

et al. 2017

MAS

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Particle Swarm Optimization (PSO) has proved to be a common meta-heuristic algorithm for determining the minimum value among a set of values but it is known to suffer from the local minima problem. In this paper, we propose a novel optimization algorithm called POLARPSO that enhances the behavior of PSO and avoids the local minima problem by using a polar function to search for more points in the search space. The algorithm has been tested on 23 well-known benchmark factions and the results are verified by comparing them with state of the art algorithms: Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA), Multi-Verse Optimizer (MVO) as well as PSO. The paper also considers a solution to the cloud data migration problem where data migrates from highly loaded nodes to less loaded nodes in a process aims at achieving a kind of load balancing. The results prove that the proposed algorithm is applicable to solve this challenging problem in cloud environment and is able to find the best node to migrate to quickly and effectively. Our empirical results show that the proposed algorithm has enhanced the PSO behavior in reaching the best solution and outperformed the other algorithms over the tested benchmarked functions.

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Section: Related Workmentioning

confidence: 99%

Polar Particle Swarm Algorithm for Solving Cloud Data Migration Optimization Problem

Al-Sayyed¹,

Fakhouri²,

Rodan³

et al. 2017

MAS

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“…A number of modifications have been introduced to the basic LF algorithm to improve clustering quality and convergent speed. A latest review of ant-based clustering can be referred to [29].…”

Section: The Basic Ant-based Clustering Algorithmmentioning

confidence: 99%

Performance Assessment for a Fleet of Machines Using a Combined Method of Ant-Based Clustering and CMAC

Zhang

Cao

Lee

2013

Advances in Mechanical Engineering

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This paper proposes a combined method of ant-based clustering and cerebellar model articulation controller for performance assessment for a fleet of machines. A novel ant-based clustering algorithm with kernel method is used to cluster machines in a fleet. The algorithm has two features. First, a projection based on kernel principal component analysis replaces random projection to improve the efficiency. Second, the clustering is performed on the feature space after kernel mapping to improve the clustering accuracy. The algorithm can cluster machines in a self-organizing way to achieve the horizontal assessment. The vertical assessment for the single machine based on CMAC is presented. Then, how to combine the vertical and horizontal assessment results is discussed. The outlier mining method to detect abnormal machines based on the clustering results is also proposed. Cluster-based global outlying factor is suggested to measure the outlying degree of abnormal machine. Finally, the case study on axial fans shows that the combined method can give a more comprehensive assessment for fans' performance monitoring.

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“…While pseudo code and a detailed explanation of this basic algorithm can be found in [11], only the significant changes and modifications will be outlined in this thesis. The first major difference is in terms of the properties used to define the characteristics of the data that the ants used for sorting.…”

Section: Lumer and Faieta's Sorting Extensionmentioning

confidence: 99%

Pseudo-hierarchical ant-based clustering using a heterogeneous agent hierarchy and automatic boundary formation

Brown

Huber

2010

Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation

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Ant-based and swarm-based clustering

Cited by 139 publications

References 78 publications

Polar Particle Swarm Algorithm for Solving Cloud Data Migration Optimization Problem

Polar Particle Swarm Algorithm for Solving Cloud Data Migration Optimization Problem

Performance Assessment for a Fleet of Machines Using a Combined Method of Ant-Based Clustering and CMAC

Pseudo-hierarchical ant-based clustering using a heterogeneous agent hierarchy and automatic boundary formation

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