Comparison of Cat Swarm Optimization with particle swarm optimization for IIR system identification

So, J.; Jenkins, W.K.

doi:10.1109/acssc.2013.6810419

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…The simulation performance is carried out using LabVIEW in windows based computer. The simulation parameters for LEACH-C have been referred from [18] and PSO from [21] and for CSO [30] as listed in Table.2. In our proposed work, the BS station is positioned at the centre for the initial test run.…”

Section: Resultsmentioning

confidence: 99%

Cso Based Energy Efficient Cluster Protocol for Wireless Sensor Networks

Chandirasekaran¹,

Jayabarathi²

2018

IJCT

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In wireless sensor networks (WSNs) energy saving has become one of the prime optimization problem and clustering technique has been considered as most efficient techniques to achieve the same. The selection of cluster heads (CHs) plays a vital role in hierarchical based WSNs as it consume more energy owing to its additional duty of receiving, aggregating the data from the cluster member nodes and transmitting the same to the base station (BS). Improper selection of CHs causes impact on network life time. In order to have an energy efficient network a suitable optimization algorithm is to be adopted to select the CHs. We propose a cluster protocol based on Cat Swarm Optimization (CSO) algorithm to reduce the energy consumption during cluster setup phase and transmission phase. The CSO cluster protocol is developed by considering intra-cluster distance of nodes to CH and residual energy of cluster member nodes. The algorithm is tested extensively on various scenarios of WSNs, varying number of sensor nodes and the CHs. The energy efficient scheme of proposed CSO performance is compared with other well-known protocols such as Low Energy Adaptive Clustering Hierarchy -Centralized (LEACH-C) and Particle Swarm Optimization (PSO) based protocol to prove the superiority of it.

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Section: Resultsmentioning

confidence: 99%

Cso Based Energy Efficient Cluster Protocol for Wireless Sensor Networks

Chandirasekaran¹,

Jayabarathi²

2018

IJCT

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“…Within this branch, the main techniques are Particle Swarm Optimization (PSO) designed and presented by Eberhart et al [7, 9] in 1995; Ant Colony Optimization (ACO), which is a family of algorithms derived from Dorigo's 1991 work based on the social behavior of ants [15, 16]; Migrating Birds Optimization (MBO) [17] algorithm based on the alignment of migratory birds during flight; Artificial Fish Swarm Algorithm (AFSA) [18], based on the behavior of fish to find food by themselves or by following other fish; and the discrete Cat Swarm optimization (CSO) Technique presented in 2007 by Chu and Tsai [9], which is based on the behavior of cats. Interestingly, the CSO cat corresponds to a particle in PSO, with a small difference in its algorithms [19, 20]. CSO and PSO were originally developed for continuous value spaces, but there are a number of optimization problems where the values are discrete [21].…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Solving the Manufacturing Cell Design Problem through Binary Cat Swarm Optimization with Dynamic Mixture Ratios

Soto

Crawford

Toledo

et al. 2019

Computational Intelligence and Neuroscience

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In this research, we present a Binary Cat Swarm Optimization for solving the Manufacturing Cell Design Problem (MCDP). This problem divides an industrial production plant into a certain number of cells. Each cell contains machines with similar types of processes or part families. The goal is to identify a cell organization in such a way that the transportation of the different parts between cells is minimized. The organization of these cells is performed through Cat Swarm Optimization, which is a recent swarm metaheuristic technique based on the behavior of cats. In that technique, cats have two modes of behavior: seeking mode and tracing mode, selected from a mixture ratio. For experimental purposes, a version of the Autonomous Search algorithm was developed with dynamic mixture ratios. The experimental results for both normal Binary Cat Swarm Optimization (BCSO) and Autonomous Search BCSO reach all global optimums, both for a set of 90 instances with known optima, and for a set of 35 new instances with 13 known optima.

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“…The behavior of a cat in searching for its prey is modeled to solve the optimization problem by employing a new set of learning mechanisms. Interestingly, the CSO cat corresponds to a particle in PSO, and little bit difference is in its algorithm [22]. Cat activities are divided into two phases or modes, that is, the seeking phase and the tracing phase.…”

Section: Cso Based Flann Filtermentioning

confidence: 99%

Cat Swarm Optimization Based Functional Link Artificial Neural Network Filter for Gaussian Noise Removal from Computed Tomography Images

Kumar

Mishra

Sahu

2016

Applied Computational Intelligence and Soft Computing

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Gaussian noise is one of the dominant noises, which degrades the quality of acquired Computed Tomography (CT) image data. It creates difficulties in pathological identification or diagnosis of any disease. Gaussian noise elimination is desirable to improve the clarity of a CT image for clinical, diagnostic, and postprocessing applications. This paper proposes an evolutionary nonlinear adaptive filter approach, using Cat Swarm Functional Link Artificial Neural Network (CS-FLANN) to remove the unwanted noise. The structure of the proposed filter is based on the Functional Link Artificial Neural Network (FLANN) and the Cat Swarm Optimization (CSO) is utilized for the selection of optimum weight of the neural network filter. The applied filter has been compared with the existing linear filters, like the mean filter and the adaptive Wiener filter. The performance indices, such as peak signal to noise ratio (PSNR), have been computed for the quantitative analysis of the proposed filter. The experimental evaluation established the superiority of the proposed filtering technique over existing methods.

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Comparison of Cat Swarm Optimization with particle swarm optimization for IIR system identification

Cited by 9 publications

References 9 publications

Cso Based Energy Efficient Cluster Protocol for Wireless Sensor Networks

Cso Based Energy Efficient Cluster Protocol for Wireless Sensor Networks

Solving the Manufacturing Cell Design Problem through Binary Cat Swarm Optimization with Dynamic Mixture Ratios

Cat Swarm Optimization Based Functional Link Artificial Neural Network Filter for Gaussian Noise Removal from Computed Tomography Images

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