A Modified Gravitational Search Algorithm for Function Optimization

He, Shoushuai; Zhu, Lei; Wang, Lei; Lu, Yu; Yao, Changhua

doi:10.1109/access.2018.2889854

Cited by 12 publications

(10 citation statements)

References 27 publications

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“…The first term in (22) is the field produced by the solenoids of the length (2l+s) and radius "rc1" and the second term is the field produced by the solenoid of length "s" and radius "rc2".…”

Section: Pseudo Code Of Igsamentioning

confidence: 99%

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An improved gravitational search algorithm for solving an electromagnetic design problem

Khan

Ling

2020

J Comput Electron

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Gravitational Search Algorithm (GSA) is a novel optimization technique that depends upon the law of motion and law of gravity of masses interaction between the agents. GSA has shown outstanding performance but has a drawback of having a slow exploitation process due to the fitness function influence on masses of the agents. As a result, after each iteration, the masses are getting heavier and restricts the movement. For that reason, masses cancel out the gravitational forces on each other and restricting them from quickly exploiting the optimum. To overcome this limitation, an improved GSA (IGSA) with a modified exploitation strategy is proposed in this paper. The primary aim of modification is to enhance the proficiency of the algorithm in terms of faster convergence and avoid premature convergence. An electromagnetic optimization problem is used to validate the performance of the presented method. Simulation results confirmed that the proposed method has outstanding results in solving the Loney's Solenoid design problem and solution stability is much better as compared to the Standard gravitational search algorithm and various other state-of-the-art techniques previously applied for solving this problem.

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Section: Pseudo Code Of Igsamentioning

confidence: 99%

“…The amalgamation of all these factors that are: the relation between masses and the objective function, dependency on the distance, and combine the behavior of all the agents' gravitational force make the GSA algorithm unique. [22]. This algorithm works on the following mechanism.…”

Section: Introductionmentioning

confidence: 99%

An improved gravitational search algorithm for solving an electromagnetic design problem

Khan

Ling

2020

J Comput Electron

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“…To further validate the performance of the proposed algorithm, the three latest GSA variants, i.e., NFGSA [21], PSGSA [21] and EKRGSA [20], are selected for comparison.…”

Section: Comparison With the Latest Gsa Variantsmentioning

confidence: 99%

“…S. He et al proposed EKRGSA and modified the function of Kbest, which decreases exponentially with the number of iterations from N to 1 in Eq. (2) [20]:…”

Section: Introductionmentioning

confidence: 99%

An Improved Parameter Control Based on a Fuzzy System for Gravitational Search Algorithm

Yu¹,

Yu²,

Li³

et al. 2020

IJCIS

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Recently, a kind of heuristic optimization algorithm named gravitational search algorithm (GSA) has been rapidly developed. In GSA, there are two main parameters that control the search process, namely, the number of applied agents (Kbest) and the gravity constant (G). To balance exploration and exploitation, a fuzzy system containing twelve fuzzy rules is proposed to intelligently control the parameter setting of the GSA. The proposed method can enhance the convergence ability and yield better optimization results. The performance of fuzzy GSA (FGSA) is examined by fifteen benchmark functions. Extensive experimental results are tested and compared with those of the original GSA, CGSA, CLPSO, NFGSA, PSGSA and EKRGSA.

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“…In [27], chaotic operators were utilized to enhance the performance of GSA. Besides, Kepler operator [28], escaping operator [29] and repulsive operator [30] were introduced to optimize particles' positions. These operators effectively improve particles' movements.…”

Section: Introductionmentioning

confidence: 99%

A Gravitational Search Algorithm With Chaotic Neural Oscillators

Wang

Gao

et al. 2020

IEEE Access

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Gravitational search algorithm (GSA) inspired from physics emulates gravitational forces to guide particles' search. It has been successfully applied to diverse optimization problems. However, its search performance is limited by its inherent mechanism where gravitational constant plays an important role in gravitational forces among particles. To improve it, this paper uses chaotic neural oscillators to adjust its gravitational constant, named GSA-CNO. Chaotic neural oscillators can generate various chaotic states according to their parameter settings. Thus, we select four kinds of chaotic neural oscillators to form distinctive chaotic characteristics. Experimental results show that chaotic neural oscillators effectively tune the gravitational constant such that GSA-CNO has good performance and stability against four GSA variants on functions. Three real-world optimization problems demonstrate the promising practicality of GSA-CNO.

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A Modified Gravitational Search Algorithm for Function Optimization

Cited by 12 publications

References 27 publications

An improved gravitational search algorithm for solving an electromagnetic design problem

An improved gravitational search algorithm for solving an electromagnetic design problem

An Improved Parameter Control Based on a Fuzzy System for Gravitational Search Algorithm

A Gravitational Search Algorithm With Chaotic Neural Oscillators

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