Improved cat swarm optimization algorithm for solving global optimization problems and its application to clustering

Kumar, Yugal; Singh, Pradeep Kumar

doi:10.1007/s10489-017-1096-8

Cited by 59 publications

(31 citation statements)

References 41 publications

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“…Singh made two modifications to the improved CSO algorithm and called it ICSO [21]. ey first improved the tracing mode by modifying the velocity and updating position equations.…”

Section: Improved Cat Swarm Optimization (Icso) Kumar Andmentioning

confidence: 99%

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Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

Ahmed

Rashid

Saeed

2020

Computational Intelligence and Neuroscience

100

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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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“…Singh made two modifications to the improved CSO algorithm and called it ICSO [21]. ey first improved the tracing mode by modifying the velocity and updating position equations.…”

Section: Improved Cat Swarm Optimization (Icso) Kumar Andmentioning

confidence: 99%

“…Secondly, they changed the value of the new position so that SRD value and current position have no correlations [29]. It is worth mentioning that there are four versions of CSO referenced in [17,20,21,29], all having the same name (ICSO). However, their structures are different.…”

Section: Improved Cat Swarm Optimization (Icso) Lin Et Almentioning

confidence: 99%

Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

Ahmed

Rashid

Saeed

2020

Computational Intelligence and Neuroscience

100

View full text Add to dashboard Cite

show abstract

“…[32]. It is worth mentioning that there are four versions of CSO referenced in [19,23,24,32], all having the same name (ICSO). However, their structures are different.…”

Section: Modified Cat Swarm Optimization (Mcso): Lin Et Al Combined mentioning

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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“…Some of are Simulated Annealing (SA) algorithm [2], Genetic Algorithm (GA) [3], Particle Swarm Optimization (PSO) Y. Kumar et al [4], Ant Colony Optimization (ACO) [5], Harmony Search (HS) [6], Artificial Bee Colony (ABC) [7,33], Firefly Algorithm (FA) [8], League Championship Algorithm (LCA) [9], Water Cycle Algorithm (WCA) [10], CSS [11,12], MCSS [13,14,15], TLBO [16, 17, ], CSO [47,48] and Mine Blast Algorithm (MBA) [18].…”

Section: Introductionmentioning

confidence: 99%

A New Variant of Teaching Learning Based Optimization Algorithm for Global Optimization Problems

Kumar

Dahiya²,

Malik³

et al. 2019

IJCAI

Self Cite

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This paper presents a new variant of teaching learning based optimization (TLBO) algorithm for solving global optimization problems. The performance of the TLBO algorithm depends on coordination of teacher phase and learner phase. It is noticed that sometimes performance of TLBO algorithm is affected due to lack of diversity in teacher and learner phases. In this work, a new variant of TLBO algorithm is proposed based on genetic crossover and mutation strategies. These strategies are inculcated in TLBO algorithm for improving its search mechanism and convergence rate. Genetic mutation strategy is applied in teacher phase of TLBO algorithm for improving the mean knowledge of leaners. While, Crossover strategy is applied in learner phase of TLBO algorithm to find the good learner. The effectiveness of the proposed algorithm is tested on several bench mark test functions of CEC'14. From simulation results, it is stated that the proposed algorithm provides more optimized results in comparison to same class of algorithms.Povzetek: V prispevku je opisan nov globalni optimizacijski algoritem na osnovi učenja optimizacije (TLBO).

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Improved cat swarm optimization algorithm for solving global optimization problems and its application to clustering

Cited by 59 publications

References 41 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

A New Variant of Teaching Learning Based Optimization Algorithm for Global Optimization Problems

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