Convergence analysis and performance of an improved gravitational search algorithm

Jiang, Shanhe; Wang, Yan; Ji, Zhicheng

doi:10.1016/j.asoc.2014.07.016

Cited by 65 publications

(33 citation statements)

References 42 publications

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“…NP denotes the size of the population. The velocity of each particle is initialized to zeros and the update relies on the gravitational forces exerted by its neighbours following the law of gravity [17]. According to the law of gravity, the gravitational force between two particles is directly proportional to their masses and inversely proportional to their distance.…”

Section: Basic Gsamentioning

confidence: 99%

“…Owing to its simple concept and superior performance, GSA has attracted much attention from researchers in different application areas [17][18][19]. Various experimental results have demonstrated the high computational efficiency and the competitive convergence performance over many other NAs [17,[20][21]. Thanks to these advantages, GSA has attracted increasing interest in the field of engineering optimization, such as parameter 2 identification [22], data clustering [23], image classification [24], and thresholding [25].…”

Section: Introductionmentioning

confidence: 99%

“…Gravitational Search Algorithm (GSA) is a recently proposed NA inspired by the law of Newton's gravity and mass interactions [17]. Owing to its simple concept and superior performance, GSA has attracted much attention from researchers in different application areas [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hyperspectral band selection using crossover‐based gravitational search algorithm

Zhang

Liu

et al. 2019

IET Image Processing

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Band selection is an important data dimensionality reduction tool in hyperspectral images (HSIs). To identify the most informative subset band from the hundreds of highly corrected bands in HSIs, a novel hyperspectral band selection method using a crossover based gravitational search algorithm (CGSA) is presented in this paper. In this method, the discriminative capability of each band subset is evaluated by a combined optimization criterion, which is constructed based on the overall classification accuracy and the size of the band subset. As the evolution of the criterion, the subset is updated using the V-shaped transfer function based CGSA. Ultimately, the band subset with the best fitness value is selected. Experiments on two public hyperspectral datasets, i.e. the Indian Pines dataset and the Pavia University dataset, have been conducted to test the performance of the proposed method. Comparing experimental results against the basic GSA and the PSOGSA (hybrid PSO and GSA) revealed that all of the three GSA variants can considerably reduce the band dimensionality of HSIs without damaging their classification accuracy. Moreover, the CGSA shows superiority on both the effectiveness and efficiency compared to the other two GSA variants.

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Section: Basic Gsamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hyperspectral band selection using crossover‐based gravitational search algorithm

Zhang

Liu

et al. 2019

IET Image Processing

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“…Hence, for enhancing particle memory ability and improve its search accuracy, Gu et al [30] uses the idea of local optimum solution and global optimum solution from PSO and proposed modified GSA. Jiang et al [31] proposed an improved GSA, in which the chaos operator and memory strategy are applied.…”

Section: Introductionmentioning

confidence: 99%

Real Coded Genetic Algorithm Operators Embedded in Gravitational Search Algorithm for Continuous Optimization

Singh¹,

Deep²

2015

IJISA

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Abstract-The objective of this paper is to propose three modified versions of the Gravitational Search Algorithm for continuous optimization problems. Although the Gravitational Search Algorithm is a recently introduced promising memory-less heuristic but its performance is not so satisfactory in multimodal problems particularly during the later iterations. With a view to improve the exploration and exploitation capabilities of GSA, it is hybridized with well-known real coded genetic algorithm operators. The first version is the hybridization of GSA with Laplace Crossover which was initially designed for real coded genetic algorithms. The second version is the hybridization of GSA with Power Mutation which also was initially designed for real coded genetic algorithms. The third version hybridizes the GSA with both the Laplace Crossover and the Power mutation. The performance of the original GSA and the three proposed variants is investigated over a set of 23 benchmark problems considered in the original paper of GSA. Next, all the four variants are implemented on 30 rotated and shifted benchmark problems of CEC 2014. The extensive numerical, graphical and statistical analysis of the results show that the third version incorporating the Laplace Crossover and Power mutation is a definite improvement over the other variants.

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Robust Control of Agroindustrial Drying Process of Grains Based on Sliding Modes and Gravitational Search Algorithm

Oliveira

Boaventura‐Cunha

Oliveira

2016

Lecture Notes in Electrical Engineering

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Convergence analysis and performance of an improved gravitational search algorithm

Cited by 65 publications

References 42 publications

Hyperspectral band selection using crossover‐based gravitational search algorithm

Hyperspectral band selection using crossover‐based gravitational search algorithm

Real Coded Genetic Algorithm Operators Embedded in Gravitational Search Algorithm for Continuous Optimization

Robust Control of Agroindustrial Drying Process of Grains Based on Sliding Modes and Gravitational Search Algorithm

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