Extended PSO Algorithm for Improvement Problems K-Means Clustering Algorithm

Lashkari, Maryam; Rostami, Amin; Branch, Ferdows

doi:10.5121/ijmit.2014.6302

Cited by 11 publications

(6 citation statements)

References 11 publications

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“…PSO has found widespread application in two main component methodologies: one in artificial life and another one based to bird flocking, fishes schooling, and swarm theory. As mentioned in [23], the advantages of using PSO in task scheduling are as the following: a PSO algorithm can maintain useful information about characteristics of the environment; PSO as characterized by its fast convergence behavior, has an in-built ability to adjust to a dynamic environment; PSO is effective for locating and tracking optima in both static and dynamic environments. The particle swarm optimizer has been found to be fast in solving nonlinear, non-differentiable, multimodal problems [24].…”

Section: Particle Swarm Optimization (Pso) Versus Genetic Algorimentioning

confidence: 99%

“…PSO have no overlapping and mutation calculation [12]. The disadvantages of PSO algorithms are cited in [23]as the following: (1)The method suffers from the partial optimism, which causes the less exact at the regulation of its speed and the direction. (2)The method cannot work out the problems of scattering and optimization.…”

Section: Particle Swarm Optimization (Pso) Versus Genetic Algorimentioning

confidence: 99%

“…GAs and PSO can both be applied in pattern discovery, signal processing, neural networks, cloud computing, manufacturing, power Electronics to control power System such as scheduling power flow, providing voltage support, limiting short-circuit, etc [27,28]. The task scheduling aim [23] is to assign incoming tasks to the available resources. According to the scheduling strategies used, the task scheduling algorithms can significantly affect the efficiency of the whole system.…”

Section: Comparison Of Genetic Algorithms and Particle Swarm Optimmentioning

confidence: 99%

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Analysis of Particle Swarm Optimization and Genetic Algorithm based on Task Scheduling in Cloud Computing Environment

Nzanywayingoma¹,

Yang²

2017

ijacsa

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Abstract-Since the beginning of cloud computing technology, task scheduling problem has never been an easy work. Because of its NP-complete problem nature, a large number of task scheduling techniques have been suggested by different researchers to solve this complicated optimization problem. It is found worth to employ heuristics methods to get optimal or to arrive at near-optimal solutions. In this work, a combination of two heuristics algorithms was proposed: particle swarm optimization (PSO) and genetic algorithm (GA). Firstly, we list pros and cons of each algorithm and express its best interest to maximize the resource utilization. Secondly, we conduct a performance comparison approach based on two most critical objective functions of task scheduling problems which are execution time and computation cost of tasks in cloud computing. Thirdly, we compare our results with other existing heuristics algorithms from the literatures. The experimental results was examined with benchmark functions and results showed that the particle swarm optimization (PSO) performs better than genetic algorithm (GA) but they both present a similarity because of their population based search methods. The results also showed that the proposed hybrid models outperform the standard PSO and reduces dramatically the execution time and lower the processing cost on the computing resources.

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Section: Particle Swarm Optimization (Pso) Versus Genetic Algorimentioning

confidence: 99%

Section: Particle Swarm Optimization (Pso) Versus Genetic Algorimentioning

confidence: 99%

Section: Comparison Of Genetic Algorithms and Particle Swarm Optimmentioning

confidence: 99%

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Analysis of Particle Swarm Optimization and Genetic Algorithm based on Task Scheduling in Cloud Computing Environment

Nzanywayingoma¹,

Yang²

2017

ijacsa

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“…Take as the best position of a particle i and its neighbors' best position. Then, the velocity and position of every particle is updated using the two Equations below [39]:…”

Section: Particle Swarm Optimization Algorithm Overviewmentioning

confidence: 99%

Effective Task Scheduling and Dynamic Resource Optimization based on Heuristic Algorithms in Cloud Computing Environment

2017

KSII TIIS

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Cloud computing system consists of distributed resources in a dynamic and decentralized environment. Therefore, using cloud computing resources efficiently and getting the maximum profits are still challenging problems to the cloud service providers and cloud service users. It is important to provide the efficient scheduling. To schedule cloud resources, numerous heuristic algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Cuckoo Search (CS) algorithms have been adopted. The paper proposes a Modified Particle Swarm Optimization (MPSO) algorithm to solve the above mentioned issues. We first formulate an optimization problem and propose a Modified PSO optimization technique. The performance of MPSO was evaluated against PSO, and GA. Our experimental results show that the proposed MPSO minimizes the task execution time, and maximizes the resource utilization rate.

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“…When PSO applies to data clustering many issues are arise such as convergence to local optima and slow rate of convergence [29]. We need to improve the parameters of PSO such as inertia factor, velocity clamping, and acceleration constant.…”

Section: B Variants Of Psomentioning

confidence: 99%

Study and analysis of particle swarm optimization for improving partition clustering

Patel

Dabhi

Prajapati

2015

2015 International Conference on Advances in Computer Engineering and Applications

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Clustering is a widely used technique for finding the similar hidden patterns from a dataset. Many techniques are available for data clustering such as partition clustering, hierarchical clustering, density based clustering, and grid based clustering. This paper discusses various clustering techniques along with their benefits, drawbacks, characteristics, and applications. The paper also discusses various validity measures, which are useful in evaluating cluster quality. The paper discusses issues involved in Particle Swarm Optimization (PSO) and compares various variants of PSO that address the discussed issues. PSO can be applied to partition based clustering for improving performance and quality of resulting clusters. In that connection, the paper discusses about how PSO is useful to solve issues present in partition clustering. Moreover, the paper presents a survey of partition clustering using PSO. This paper would become useful to beginners and researchers in advancing the field of applying data clustering using PSO.

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Extended PSO Algorithm for Improvement Problems K-Means Clustering Algorithm

Abstract: The clustering is a without monitoring process and one of the most common data mining techniques.

Cited by 11 publications

References 11 publications

Analysis of Particle Swarm Optimization and Genetic Algorithm based on Task Scheduling in Cloud Computing Environment

Analysis of Particle Swarm Optimization and Genetic Algorithm based on Task Scheduling in Cloud Computing Environment

Effective Task Scheduling and Dynamic Resource Optimization based on Heuristic Algorithms in Cloud Computing Environment

Study and analysis of particle swarm optimization for improving partition clustering

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