Disruption: A new operator in gravitational search algorithm

Sarafrazi, Soroor; Nezamabadi‐pour, Hossein; Saryazdi, Saeı̈d

doi:10.1016/j.scient.2011.04.003

Cited by 160 publications

(92 citation statements)

References 22 publications

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“…26 The performance of the proposed approach (BH-GSA), is compared with some well-known optimization algorithms such as Particle Swarm Optimization, PSO, 3 Real Genetic Algorithm, RGA, 40 Gravitational Search Algorithm, GSA, 26 and Disruption Gravitational Search Algorithm, DGSA. 28 The details of comparative algorithms are as follow:…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, new operators should be appended to GSA in order to increase its flexibility for solving more complex problems. More details regarding the GSA stagnation have been reported by Sarafrazi et al 28 . In this order, Sarafrazi et al 28 , proposed an operator called "disruption", originating from astrophysics, to improve the ability of GSA in exploration.…”

Section: Introductionmentioning

confidence: 82%

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Black Hole: A New Operator for Gravitational Search Algorithm

Doraghinejad¹,

Nezamabadi‐pour²

2014

IJCIS

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Inspiring by nature have motivated many researchers in many fields of sciences and engineering. The Gravitational search algorithm (GSA) is a recent created metaheuristic algorithm by using law of gravity and mass interactions. In this paper, a new operator inspired by some of the characteristics of the black hole as an astronomy phenomenon for GSA is presented. When a star is converted to a black hole under situations, it has the extremely strong gravity that prevents anything to escape from, and the objects that are closed to the black hole, experience very strong force called tidal force which it causes to collapse them to the black hole. We propose a new operator using these features and hybridize it with GSA (BH-GSA) in order to prevent facing the premature convergence and to improve the abilities of GSA in exploration and exploitation. The proposed algorithm is applied to two sets of standard benchmark functions. The first set includes 23 standard benchmark functions and in this set the performance of the proposed algorithm is compared with the standard GSA, the disruption GSA, the particle swarm optimization (PSO), and the real genetic algorithm (GA). The second set contains the CEC 2005 benchmark functions. In this set, we compare the BH-GSA with some well-known metaheuristic algorithms. The obtained results and comparing with the competing algorithms prove that the BH-GSA has merit in the field of continuous space optimization.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 82%

Black Hole: A New Operator for Gravitational Search Algorithm

Doraghinejad¹,

Nezamabadi‐pour²

2014

IJCIS

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“…Sarafrazi et al [31] introduced a novel operator called "Disruption," originating from astrophysics, into GSA and obtained the improved GSA in nonlinear functions by comparison with PSO and GA was a valid method.…”

Section: The Prior Workmentioning

confidence: 99%

Two Kinds of Classifications Based on Improved Gravitational Search Algorithm and Particle Swarm Optimization Algorithm

Xia-xia

Bai

2017

Advances in Mathematical Physics

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Gravitational Search Algorithm (GSA) is a widely used metaheuristic algorithm. Although fewer parameters in GSA were adjusted, GSA has a slow convergence rate. In this paper, we change the constant acceleration coefficients to be the exponential function on the basis of combination of GSA and PSO (PSO-GSA) and propose an improved PSO-GSA algorithm (written as I-PSO-GSA) for solving two kinds of classifications: surface water quality and the moving direction of robots. I-PSO-GSA is employed to optimize weights and biases of backpropagation (BP) neural network. The experimental results show that, being compared with combination of PSO and GSA (PSO-GSA), single PSO, and single GSA for optimizing the parameters of BP neural network, I-PSO-GSA outperforms PSO-GSA, PSO, and GSA and has better classification accuracy for these two actual problems.

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“…Researchers have applied several modifications in order to cope with these issues. Operators like Disruption operator [12] and black hole operator [13] have been introduced in GSA to achieve a better balance of exploration and exploitation to avoid premature convergence. Yu Zhang et al (2012) have proposed immunity based gravitational search algorithm [14].…”

Section: ) Computing Accelerationmentioning

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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Disruption: A new operator in gravitational search algorithm

Cited by 160 publications

References 22 publications

Black Hole: A New Operator for Gravitational Search Algorithm

Black Hole: A New Operator for Gravitational Search Algorithm

Two Kinds of Classifications Based on Improved Gravitational Search Algorithm and Particle Swarm Optimization Algorithm

Gravitational Search Algorithms in Data Mining: A Survey

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