Grey wolf gravitational search algorithm

Joshi, Susheel Kumar; Bansal, Jagdish Chand

doi:10.1109/iwci.2016.7860371

Cited by 3 publications

(2 citation statements)

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“…According to the velocity update equation of GSA (equation ( 9)), acceleration plays a crucial role in balancing the exploration and exploitation. It is clear from equation (7) that the acceleration is a function of gravitational constant G(t), masses M i (t) and distances R ij . It is directly proportional to gravitational constant G(t).…”

Section: Fitness Varying Gravitational Constant In Gsamentioning

confidence: 99%

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Fitness varying gravitational constant in GSA

2018

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Gravitational Search Algorithm (GSA) is a recent metaheuristic algorithm inspired by Newton's law of gravity and law of motion. In this search process, position change is based on the calculation of step size which depends upon a constant namely, Gravitational Constant (G). G is an exponentially decreasing function throughout the search process. Further, inspite of having different masses, the value of G remains same for each agent, which may cause inappropriate step size of agents for the next move, and thus leads the swarm towards stagnation or sometimes skipping the true optima.To overcome stagnation, we first propose a gravitational constant having different scaling characteristics for different phase of the search process. Secondly, a dynamic behavior is introduced in this proposed gravitational constant which varies according to the fitness of the agents. Due to this behavior, the gravitational constant will be different for every agent based on its fitness and thus will help in controlling the acceleration and step sizes of the agents which further improve exploration and exploitation of the solution search space.The proposed strategy is tested over 23 well-known classical benchmark functions and 11 shifted and biased benchmark functions. Various statistical analyses and a comparative study with original GSA, Chaos-based GSA (CGSA), Bio-geography Based Optimization (BBO) and DBBO has been carried out.

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Section: Fitness Varying Gravitational Constant In Gsamentioning

confidence: 99%

“…Chen et al [3] introduced a hybrid GSA, in which a multi-type local improvement scheme is used as a local search operator. To improve the exploitation ability of GSA, Susheel et al [7] introduced the encircle behavior of grey wolf in GSA.…”

Section: Introductionmentioning

confidence: 99%

Fitness varying gravitational constant in GSA

2018

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A novel neighborhood archives embedded gravitational constant in GSA

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A Hybrid Technique for Server Consolidation in Cloud Computing Environment

Vijaya

Srinivasan

2020

Cybernetics and Information Technologies

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The goal of data centers in the cloud computing environment is to provision the workloads and the computing resources as demanded by the users without the intervention of the providers. To achieve this, virtualization based server consolidation acts as a vital part in virtual machine placement process. Consolidating the Virtual Machines (VMs) on the Physical Machines (PMs) cuts down the unused physical servers, decreasing the energy consumption, while keeping the constraints for CPU and memory utilization. This technique also reduces the resource wastage and optimizes the available resources efficiently. Ant Colony Optimization (ACO) that is a well-known multi objective heuristic algorithm and Grey Wolf Algorithm (GWO) has been used to consolidate the servers used in the virtual machine placement problem. The proposed Fuzzy HAGA algorithm outperforms the other algorithms MMAS, ACS, FFD and Fuzzy ACS compared against it as the number of processors and memory utilization are lesser than these algorithms.

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Grey wolf gravitational search algorithm

Cited by 3 publications

References 11 publications

Fitness varying gravitational constant in GSA

Fitness varying gravitational constant in GSA

A novel neighborhood archives embedded gravitational constant in GSA

A Hybrid Technique for Server Consolidation in Cloud Computing Environment

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