Chaotic Kbest gravitational search algorithm (CKGSA)

Mittal, Himanshu; Pal, Raju; Kulhari, Ankur; Saraswat, Mukesh

doi:10.1109/ic3.2016.7880252

Cited by 55 publications

(14 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…CKGSA, proposed by Mittal et al . [39], is a variant of GSA [36] which defines a chaotic trade‐off between exploration and exploitation. It performs mathematical computation according to the Newton's law of gravity and motion.…”

Section: Chaotic Kbest Gsamentioning

confidence: 99%

“…The GSA uses a linear kbest function to determine the number of objects exerting force over an object while the CKGSA redefines the kbest function as a chaotic, which introduces non‐linearity, ergodicity, and divergent in the system [40]. It has been experimentally validated that the chaotic behaviour of CKGSA advantages in escaping local optima, better precision, and faster convergence [39].…”

Section: Chaotic Kbest Gsamentioning

confidence: 99%

“…To alleviate the same, Mittal et al . [39] proposed an efficient variant of GSA, namely chaotic kbest GSA (CKGSA), which shows prominent results on various benchmark functions. Therefore, this paper uses CKGSA to obtain the optimal set of embedding factors.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimised robust watermarking technique using CKGSA in DCT‐SVD domain

2020

Self Cite

View full text Add to dashboard Cite

Section: Chaotic Kbest Gsamentioning

confidence: 99%

Section: Chaotic Kbest Gsamentioning

confidence: 99%

See 1 more Smart Citation

Optimised robust watermarking technique using CKGSA in DCT‐SVD domain

2020

Self Cite

View full text Add to dashboard Cite

“…However, single solution-based algorithm suffers with premature convergence due to the lack of information sharing. Furthermore, the success of a meta-heuristic algorithm majorly depends on the way in which exploration and exploitation is performed [27,28]. Exploration controls the diversification of the search agents, whereas the convergence of the individuals is controlled by the exploitation.…”

Section: Introductionmentioning

confidence: 99%

A new recommendation system using map-reduce-based tournament empowered Whale optimization algorithm

Tripathi

Mittal

Saxena

et al. 2020

Complex Intell. Syst.

Self Cite

View full text Add to dashboard Cite

In the era of Web 2.0, the data are growing immensely and is assisting E-commerce websites for better decision-making. Collaborative filtering, one of the prominent recommendation approaches, performs recommendation by finding similarity. However, this approach fails in managing large-scale datasets. To mitigate the same, an efficient map-reduce-based clustering recommendation system is presented. The proposed method uses a novel variant of the whale optimization algorithm, tournament selection empowered whale optimization algorithm, to attain the optimal clusters. The clustering efficiency of the proposed method is measured on four large-scale datasets in terms of F-measure and computation time. The experimental results are compared with state-of-the-art map-reduce-based clustering methods, namely map-reduce-based K-means, map-reduce-based bat algorithm, map-reduce-based Kmeans particle swarm optimization, map-reduce-based artificial bee colony, and map-reduce-based whale optimization algorithm. Furthermore, the proposed method is tested as a recommendation system on the publicly available movie-lens dataset. The performance validation is measured in terms of mean absolute error, precision and recall, over a different number of clusters. The experimental results assert that the proposed method is a permissive approach for the recommendation over large-scale datasets.

show abstract

“…In [22], a new attractive-repulsive gravitational search algorithm is proposed, in which the uniform circular motion and centripetal force are introduced. A chaotic optimization mechanism making Kbest, one of the parameters in the algorithm, decrease chaotically is applied in [23]. And a fuzzy logic controller is utilized in [24] to control the changes of some parameters and improve the convergence rate of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Modified Gravitational Search Algorithm for Function Optimization

Zhu

Wang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Gravitational search algorithm (GSA) is a population-based heuristic algorithm, which is inspired by Newton's laws of gravity and motion. Although GSA provides a good performance in solving optimization problems, it has a disadvantage of premature convergence. In this paper, the concept of repulsive force is introduced and the definition of exponential Kbest is used in a new version of GSA, which is called repulsive GSA with exponential Kbest (EKRGSA). In this algorithm, heavy particles repulse or attract all particles according to distance, and all particles search the solution space under the combined action of repulsive force and gravitational force. In this way, the exploration ability of the algorithm is improved and a proper balance between exploration and exploitation is established. Moreover, the exponential Kbest significantly decreases the computational time. The proposed algorithm is tested on a set of benchmark functions and compared with other algorithms. The experimental results confirm the high efficiency of EKRGSA.

show abstract

Chaotic Kbest gravitational search algorithm (CKGSA)

Cited by 55 publications

References 12 publications

Optimised robust watermarking technique using CKGSA in DCT‐SVD domain

Optimised robust watermarking technique using CKGSA in DCT‐SVD domain

A new recommendation system using map-reduce-based tournament empowered Whale optimization algorithm

A Modified Gravitational Search Algorithm for Function Optimization

Contact Info

Product

Resources

About