Direct and Inverse Modeling of Plants Using Cat Swarm Optimization

Panda, Ganapati; Pradhan, Pyari Mohan; Majhi, Babita

doi:10.1007/978-3-642-17390-5_20

Cited by 10 publications

(8 citation statements)

References 20 publications

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“…Panda et al used CSO algorithm for IIR system identification [67]. e authors also applied CSO algorithm as an optimization mechanism to do direct and inverse modeling of linear and nonlinear plants [68]. Al-Asadi combined CSO Algorithm with SVM for electrocardiograms signal classification [38].…”

Section: Signalmentioning

confidence: 99%

Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

Ahmed

Rashid

Saeed

2020

Computational Intelligence and Neuroscience

101

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This paper presents an in-depth survey and performance evaluation of cat swarm optimization (CSO) algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems, and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely, dragonfly algorithm (DA), butterfly optimization algorithm (BOA), and fitness dependent optimizer (FDO). These algorithms are then ranked according to Friedman test, and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.

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Section: Signalmentioning

confidence: 99%

Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

Ahmed

Rashid

Saeed

2020

Computational Intelligence and Neuroscience

101

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“…Panda, G., P.M. Pradhan, and B. Majhi used the CSO algorithm for IIR system identification [68]. The authors also applied the CSO algorithm as an optimization mechanism to do direct and inverse modeling of linear and nonlinear plants [69]. Abed, M.A., and H.A.A.…”

Section: 3mentioning

confidence: 99%

Cat Swarm Optimization Algorithm - A Survey and Performance Evaluation

Ahmed¹,

Rashid²,

Saeed³

2020

Preprint

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This paper presents an in-depth survey and performance evaluation of the Cat Swarm Optimization (CSO) Algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely Dragonfly algorithm (DA), Butterfly optimization algorithm (BOA) and Fitness Dependent Optimizer (FDO). These algorithms are then ranked according to Friedman test and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.

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“…It was also extended to solve multi-objective problems in 2012 by Pradhan and Panda (2012). These improvements were applied to solve some difficult application problems, such as IIR system identification by Panda et al (2011b), optimizing least-significant-bit Wang et al(2012), optimal placement of multiple UPFC's in voltage stability enhancement under contingency by Kumar and Kalavathi (2014), direct and inverse modeling of plants by Panda et al (2011a), single bitmap block truncation coding of color images by Cui et al (2013), linear antenna array synthesis by Pappula and Ghosh (2014), improved metaheuristic techniques for simultaneous capacitor and DG allocation in radial distribution networks by Kawtar et al (2015).…”

Section: Cat Swarm Optimization Algorithmmentioning

confidence: 99%

Cat swarm optimization for solving the open shop scheduling problem

2018

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This paper aims to prove the efficiency of an adapted computationally intelligence-based behavior of cats called the cat swarm optimization algorithm, that solves the open shop scheduling problem, classified as NP-hard which its importance appears in several industrial and manufacturing applications. The cat swarm optimization algorithm was applied to solve some benchmark instances from the literature. The computational results, and the comparison of the relative percentage deviation of the proposed metaheuristic with other's existing in the literature, show that the cat swarm optimization algorithm yields good results in reasonable execution time.

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Direct and Inverse Modeling of Plants Using Cat Swarm Optimization

Cited by 10 publications

References 20 publications

Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

Cat Swarm Optimization Algorithm - A Survey and Performance Evaluation

Cat swarm optimization for solving the open shop scheduling problem

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