Gravitational search algorithm with both attractive and repulsive forces

Zandevakili, Hamed; Rashedi, Esmat; Mahani, Ali

doi:10.1007/s00500-017-2785-2

Cited by 27 publications

(26 citation statements)

References 49 publications

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“…Satisfactory solution and reasonable computational costs are two major criteria for heuristic algorithms to solve practical problems. In this section, in order to investigate the performance of EKRGSA, it is compared with the original algorithm and another modified algorithm: Attractive-Repulsive Gravitational Search Algorithm (AR-GSA), in which the uniform circular motion in physics is introduced, and particles exert centripetal force on each other based on the hypothetical absorption radius to improve the exploration ability [22].…”

Section: Resultsmentioning

confidence: 99%

“…In [21], a novel gravitational search algorithm with negative mass is proposed, which simulates the phenomenon of antigravity and modifies the definition of mass. 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].…”

Section: Introductionmentioning

confidence: 99%

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A Modified Gravitational Search Algorithm for Function Optimization

Zhu

Wang

et al. 2019

IEEE Access

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Modified Gravitational Search Algorithm for Function Optimization

Zhu

Wang

et al. 2019

IEEE Access

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“…Molecular interactions, a ubiquitous physical phenomenon, have inspired many researchers to apply the concepts in different applications, such as medical treatment, materials, sensors and so on. Hamed et al [19] proposed the gravitational search algorithm (GSA) by using centrifugal force, which was called as the repulsion. A distance threshold value was used, beyond which the repulsion worked according to the gravity formula.…”

Section: B Some Repulsive-attractive Algorithmsmentioning

confidence: 99%

“…In the limited work, we are aware that the repulsion is generally used to increase the population diversity while the attraction is used to increase the population convergence. Studies have different focuses, such as the diversity among population [18] and the diversity and convergence equilibrium among the particle swarms [19].…”

Section: Introductionmentioning

confidence: 99%

A Molecular Interactions-Based Social Learning Particle Swarm Optimization Algorithm

et al. 2020

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Social learning particle swarm optimization (SL-PSO) allows individuals to learn from others to improve the scalability with easy parameter settings. However, it still suffers from the poor convergence for those multi-modal problems due to the loss of swarm diversity. To improve both the diversity and the convergence, this paper proposes a novel algorithm to apply the mechanism of molecular interactions to SL-PSO, in which the molecular attraction aims to improve the convergence, and the molecular repulsion intends to enhance the diversity. In the experiments, we compare our algorithm with the SL-PSO algorithm and other representative PSO and evolutionary algorithms on 49 benchmark functions. The results show the performance of the proposed algorithm is better than that of the SL-PSO algorithm and other representative PSO and evolutionary algorithms on average. This work builds the solid foundation for the integration of the molecular interaction mechanism with PSO and other optimization algorithms.

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“…For different optimization problems, GSA variants have continuous, discrete and mixed types. Continuous type with real-valued variables can resolve real problems [19], [45], dynamic constrained problem [46], multi-modal and multi-objective problems [24], [26]. Discrete type with discrete values can address binary problem [47], graph planarization problem [48] and knapsack problem [49].…”

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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Gravitational search algorithm with both attractive and repulsive forces

Cited by 27 publications

References 49 publications

A Modified Gravitational Search Algorithm for Function Optimization

A Modified Gravitational Search Algorithm for Function Optimization

A Molecular Interactions-Based Social Learning Particle Swarm Optimization Algorithm

A Gravitational Search Algorithm With Chaotic Neural Oscillators

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