Decision function estimation using intelligent gravitational search algorithm

Askari, Hossein; Zahiri, Seyed Hamid

doi:10.1007/s13042-011-0052-x

Cited by 27 publications

(18 citation statements)

References 13 publications

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“…These properties are advantageous compared to the state-of-the-art because of the improved search process offered by our new algorithm. Therefore our approach, which is different to that proposed by Askari and Zahiri (2011), exhibits an additional improvement with respect to GSAs (Precup et al, 2011b). This paper offers twofold new contributions.…”

Section: Introductionmentioning

confidence: 86%

Adaptive Gravitational Search Algorithm for PI-fuzzy Controller Tuning

David

Precup

Petriu

et al. 2012

Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics

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This paper proposes an adaptive Gravitational Search Algorithm (aGSA) focused on tuning of Takagi-Sugeno PI-fuzzy controllers (T-S PI-FCs). The algorithm adapts two depreciation laws of the gravitational constant to the iteration index, a parameter in the weighted sum of all forces exerted from the other agents to the iteration index, and the reset at each stage of agents' worst fitnesses and positions to their best values. Two fuzzy logic blocks carry out the adaptation of the ratios of exploration runs and explanation runs using the ratio between the minimum and maximum Popov sums as an input variable. A tuning method for T-S PI-FCs dedicated to a class of nonlinear servo systems with an integral component and is offered, and T-S PI-FCs with reduced process gain sensitivity are tuned. A case study and digital simulation results illustrate the optimal tuning of a T-S PI-FC for the position control of a laboratory servo system.

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Section: Introductionmentioning

confidence: 86%

Adaptive Gravitational Search Algorithm for PI-fuzzy Controller Tuning

David

Precup

Petriu

et al. 2012

Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics

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“…swarm size and gravitational coefficient. The intelligent GSA is used to find the optimum set of hyper planes from the feature space to distinguish regions so that unknown data set can be classified [6].…”

Section: Gsa In Classificationmentioning

confidence: 99%

Gravitational Search Algorithms in Data Mining: A Survey

Patil¹,

Ratnoo²

2017

International Journal of Advanced Research in Computer and Comm

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A Gravitational Search Algorithm is a meta-heuristic search algorithm inspired by Newton"s laws of motion and gravitation force. Gravitational search based algorithms have attracted the attention of data mining community due to their ability to effectively search large and complex search spaces as is the case with the domain of data mining and knowledge discovery. Gravitational search algorithms have been applied to the data mining tasks like classification, clustering objects and mining association rules etc. This paper presents a survey on the applications of Gravitational Search based algorithms in data mining and indicates the scope of further research on using GSAs in the field of data mining.

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“…Such datasets are Iris, wine, glass & cancer and performance of classification methods evaluate with misclassified instance, accuracy, rank parameters etc. Table 3 [66,85,86,87] provides the comparisons of GSA & its variants (that are proposed for classification problems) with other techniques using above discussed common datasets with two parameters: accuracy & rank as well as some other methods also include such as ID3, J48, SVM, IBK etc. (9) 52 (11) 53 (12) 55 (13) 55 (13) 56 (14) VII.…”

Section: Gsa In Classificationmentioning

confidence: 99%

A Review on Gravitational Search Algorithm and its Applications to Data Clustering & Classification

Kumar¹,

Sahoo²

2014

IJISA

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Abstract− Natural phenomenon's and swarms behavior are the warm area of research among the researchers. A large number of algorithms have been developed on the account of natural phenomenon's and swarms behavior. These algorithms have been implemented on the various computational problems for the sake of solutions and provided significant results than conventional methods but there is no such algorithm which can be applied for all of the computational problems. In 2009, a new algorithm was developed on the behalf of theory of gravity and was named gravitational search algorithm (GSA) for continuous optimization problems. In short span of time, GSA algorithm gain popularity among researchers and has been applied to large number of problems such as clustering, classification, parameter identification etc. This paper presents the compendious survey on the GSA algorithm and its applications as well as enlightens the applicability of GSA in data clustering & classification.

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Decision function estimation using intelligent gravitational search algorithm

Cited by 27 publications

References 13 publications

Adaptive Gravitational Search Algorithm for PI-fuzzy Controller Tuning

Adaptive Gravitational Search Algorithm for PI-fuzzy Controller Tuning

Gravitational Search Algorithms in Data Mining: A Survey

A Review on Gravitational Search Algorithm and its Applications to Data Clustering & Classification

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