GSA: A Gravitational Search Algorithm

Rashedi, Esmat; Nezamabadi‐pour, Hossein; Saryazdi, Saeı̈d

doi:10.1016/j.ins.2009.03.004

Cited by 6,064 publications

(3,137 citation statements)

References 34 publications

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“…The foraging behavior of ants as in ant colony optimization (ACO) [13], [19], [20], the choreography of bird flocks as in the partial swarm optimization (PSO) [21], the intelligent behavior of honey bee swarms as in an artificial bee colony (ABC) [22], [23] are examples that inspired from animal collective life. In addition, the gravitational search algorithm (GSA) [15], [24] is based on the mass interactions and the law of gravity, Krill herd algorithm (KHA) [25] is inspired from herding behavior of krill individuals, and Intelligent water drops (IWD) [26] are simulated natural rivers and how they find almost optimal paths to their destination. Onthe rest of paper the first-fourth algorithms as most popular will be explained and reviewed their application in GrSC.…”

Section: Swarm Based Algorithmsmentioning

confidence: 99%

“…One of the novel meta-heuristic stochastic optimization algorithm inspired by the law of gravity and mass interactions is Gravitational Search Algorithm [15]. GSA, the individuals, called agents, is the collection of masses which interact with each other based on the Newtonian gravity and the laws of motion.…”

Section: Gravitational Search Algorithmmentioning

confidence: 99%

“…By changing the velocities over time, the agents are likely to move toward the global optima. Results of experiments undertaken previously show the high performance and the global search ability of GSA in solving various nonlinear functions comparing PSO [15], [55]. The superior solution and convergence rate cause GSA have been interested by researchers to be supply chain problems.…”

Section: Gravitational Search Algorithmmentioning

confidence: 99%

“…These techniques can help researchers to overcome the complexity issues in GrSC [14] and have gained popularity in the optimization of grass problems because usually they use a collection of agents (like ants or honey bees) and perform a parallel search with multiple starting points in solution space. It is noticeable that meta-heuristic algorithm may solve some problems better and some problems worse than other methods [15], [16] so that researchers should select proper algorithms regarding to the problem characteristics, available time to implementation of the model, computational requirement, and required solution quality.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Review of Nature-Based Algorithms Applications in Green Supply Chain Problems

Sadrnia¹,

Soltani²,

Zulkifli³

et al. 2014

IJET

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Abstract-In the recent years, there is a significant attention among researchers and practitioners to environmental issues and green supply chain (GrSC) because of legislations and profit oriented motivations.Because of considering environmental issues GrSC besides the economic variables, the models are very difficult to find optimal. Hence, complicated green problems call for development of modern optimization methods and algorithms to solve optimization models by efficient techniques. In recent decades, meta-heuristic algorithms have been developed to overcome the problem that most of them are inspired from nature. Some of the algorithms have been inspired from natural generation, some of them inspired from swarm behavior, and others simulate natural processes. In this research we summarize the recent advance evolutionary optimization algorithms and swarm intelligence algorithms which are applied to GrSC and green logistics. Literature reviewed in this paper shows the current state of the art and discusses the potential future research trends.Index Terms-Evolutionary algorithms, green supply chain, meta-heuristic, swarm intelligence algorithms.

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Section: Swarm Based Algorithmsmentioning

confidence: 99%

Section: Gravitational Search Algorithmmentioning

confidence: 99%

Section: Gravitational Search Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Review of Nature-Based Algorithms Applications in Green Supply Chain Problems

Sadrnia¹,

Soltani²,

Zulkifli³

et al. 2014

IJET

View full text Add to dashboard Cite

show abstract

“…Gravitational search algorithm (GSA) [19] is a novel metaheuristic algorithm, which has been confirmed to be able to achieve higher performance in solving various non-linear problems compared with some well-known swarm algorithms [20]. In this paper, we use graph coloring theory to formulate the spectrum assignment problem, and we also design a new version of GSA (IGSA) to maximize the spectrum utilization.…”

mentioning

confidence: 99%

Spectrum Allocation Based on an Improved Gravitational Search Algorithm

et al. 2018

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In cognitive radio networks (CRNs), improving system utility and ensuring system fairness are two important issues. In this paper, we propose a spectrum allocation model to construct CRNs based on graph coloring theory, which contains three classes of matrices: available matrix, utility matrix, and interference matrix. Based on the model, we formulate a system objective function by jointly considering two features: system utility and system fairness. Based on the proposed model and the objective problem, we develop an improved gravitational search algorithm (IGSA) from two aspects: first, we introduce the pattern search algorithm (PSA) to improve the global optimization ability of the original gravitational search algorithm (GSA); second, we design the Chebyshev chaotic sequences to enhance the convergence speed and precision of the algorithm. Simulation results demonstrate that the proposed algorithm achieves better performance than traditional methods in spectrum allocation.

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Swarm‐Based Optimization Algorithms

Parsopoulos

de²

2017

Wiley Encyclopedia of Electrical and Electronics Engineering

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Swarm‐based optimization algorithms have prevailed in the field of metaheuristics for the past decades. With their application field spanning from combinatorial problems to continuous and mixed integer problems, swarm‐based algorithms are currently part of the state‐of‐the‐art in search and optimization. The main search mechanisms of swarm‐based algorithms are based on models of swarms found in the natural world. These models include swarms ranging from the macroscopic level (living organisms) to the microscopic level (elementary particles). It is surprising that similar dynamics and motion patterns are discovered in such diverse systems. These properties are responsible for the collective intelligent behavior exhibited by swarms, even when their constituent entities lack intelligent behavior properties. The present article aims at discussing the main properties that govern the swarming behavior and reviewing some of the most popular swarm‐based optimization algorithms.

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GSA: A Gravitational Search Algorithm

Cited by 6,064 publications

References 34 publications

A Review of Nature-Based Algorithms Applications in Green Supply Chain Problems

A Review of Nature-Based Algorithms Applications in Green Supply Chain Problems

Spectrum Allocation Based on an Improved Gravitational Search Algorithm

Swarm‐Based Optimization Algorithms

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