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ABSTRACT-This work recommends a new meta-heuristic based on optimization algorithm inspired by nature(NIA) practiced by grey wolves(Canis lupus) called Grey Wolf Optimizer (GWO).These algorithms have been gaining much popularity in recent years due to the fact that many real-world optimization problems have become increasingly large, complex and dynamic. GWO algorithm mainly shows the hunting and searching techniques that are in practice among grey wolves in nature. Alpha, beta, delta, and omega, four types of grey wolves are used to replicate the leadership attribute among grey wolves. Over and above the three main steps of poaching, finding the prey, trapping it, and attacking are implemented. The experimental studies conducted over it for past few times have proven that the GWO algorithm is best among all NIA algorithms and is applicable to problems involving unknown search spaceKeywords-Cloud Computing, Grey Wolf Optimizer (GWO), Nature Inspired Algorithm (NIA), Alpha Beta wolves, leadership, poaching,metahuristicINTRODUCTIONEnergy efficient Cloud resources allocation consists in identifying and assigning resources to each incoming user request in such a way, that the user requirements are met, that the least possible number of resources is used and that data centre energy efficiency is optimized. The main focus is the selection of algorithms for energy efficient resource allocation in Cloud data centers. Nature Influenced Algorithm (NIA) has been gaining much popularity. In recent years due to the fact that many real-world optimization problems have become increasingly large, complex and dynamic and it consists of algorithms that imitate the way how nature performs .The goal is to propose, develop and evaluate optimization algorithms of resource allocation for IaaS architectures that are widely used to manage clouds. Grey Wolf Optimization algorithm (GWOA) is basically a swarm-intelligence based method that mimics the leadership hierarchy and hunting behavior of grey wolves in nature . Grew wolves are considered to be apex predators; it means that in the food chain they are at the top. The mathematical model for hunting behavior of grey wolves comprises of: Tracking, chasing and approaching the prey; Pursuing, encircling and harassing the prey until it stops moving; Attacking the prey (3). SOME DEFINITIONSA. Cloud ComputingCloud computing is a method for delivering information technology (IT) services in which resources are retrieved from the Internet through web-based tools and applications, as opposed to a direct connection to a server.B. Nature Inspired AlgorithmNature Inspired Algorithm (NIA) is a category of algorithms that imitate the way how nature performs. These algorithms have solution to many large, complex, dynamic real-world optimization problem.C. Grey Wolf AlgorithmThe GWO algorithm mimics the leadership hierarchy and hunting mechanism of gray wolves in nature proposed by Mirjalili et al. in 2014.Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented to perform optimization.D. MetahuristicIn computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacityE. OptimisationIn mathematics, computer science and operations research, mathematical optimization or mathematical programming, alternatively spelled optimisation, is the selection of a best element (with regard to some criterion) from some set of available alternatives.PRACTICAL APPLICATION(EXISTING)The grey wolf algorithm(GWO) has been successfully implemented in various fields. The GWO algorithm cover a vast field of practical application.It includes being applied in train operations to minimise cost due to accidental damage by finding optimal path and conservation of energy in wind power farms by optimising an algorithm that decides in which way the running of wind mills give the most optimal result. Furthermore based on experimental calculation it has even been proved that out of all nature inspired algorithm the GWO is most efficient. It has even been used in places where the search space is unknown. All these point have been published by we’ll known scientists and the same has been illustrated below1 Majid Siavash, Christoph Pfeifer, Abolfazl Rahiminejad and Behrooz Vahidi(2015) proposed to solve the problem of optimal power flow which is a non linear optimization task. The problem is a non linear task with lots of constraints. It mentions that several numerical methods are sufficient for finding local maxima but aren’t that reliable in finding solution for global maxima. They recommended that for such increasingly dynamic search space problems with undefined parameters Evolutionary algorithms needed to be used. Such algorithms include genetic algorithm (GA), particle swarm optimization (PSO), tabu search (TS), Grey wolf algorithm (GWO).Some of the most well-known targets of OPF problem include total cost minimization, loss reduction, voltage profile improvement, reducing the environmental pollution, and reducing the load shedding.environmental pollution, and reducing the load shedding.They conclude by saying that the aim of optimal power flow is met with minimization of generation cost. They say that the problem statement was met by using grey wolf algorithm.2 Kemal Keskin and Abdurrahman Karamancioglu(2017) proposed optimization in train operation by minimizing its traction energy subject to various constraints which are carried out using nature-inspired evolutionary algorithms. Due to nonlinear optimization formulation of the problem, nature-inspired evolutionary search algorithms were employed in this study all having different accuracy and convergence characteristics. Total energy consumption for an optimal strategy throughout successive stations is calculated by the following equation:E(total)= E(ma) + E(cr);On these equations various nature inspired optimization techniques were implemented. The algorithms were in very close proximity to the real time solution and the minimized cost to a great extent.

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3 Md. Julfikar Islam, Md. Siddiqur Rahman Tanveer and M. A. H. Akhand(2016) This paper basically compares all the NIA and based on experimental calculation came up with a result that grey wolf algorithm is the most efficient among all. The tests are performed on function optimization where several factors are taken into consideration while coming up with a particular result. The result is computed based on values calculated as fitness factor which are calculated on 22 functions. The table below shows the same.

Based on the experimental calculation it was proved that GWO provided optimal solution for 17 out of 22 experiments.The benchmark values for 17 of 22 function optimisation functions were achieved.

4 This paper deals with Challenging problems of unknown search spaces. GWO is proven to be the best algorithm because of its simplicity, flexibility, derivation free mechanism and local optima avoidance.

It shows its simplicity as it is based on animal’s behavior and allows scientists to stimulate different concepts. As they assume problems too be in black box which means they consider only the input and output which make the algorithm flexible. It is derivation free as it starts from Random solution and there is no need to calculate the derivative of search spaces to find the optimum value. The ability to avoid local optima can be seen as they avoid stagnation in local solution and search the entire search space.

GWO is chosen to be best as it gives the correct output for the unknown search spaces.

PROPOSED SYSTEM

Based on the conclusion derived from all the above paper surveys we get a notion that with this dynamical changing search space and with lots of constraints the Nature Inspired Algorithm (NIA) have been gaining huge popularity because of the optimization technique. The best among these is grey wolf algorithm which uses just two parameters to fond the state of its prey even in unknown search space problems. In today’s

World where energy is of great concern and where almost all development on hardware side is made, going forward with software changes to make the program more optimal seems to be the most vial solution to the existing problem. In the proposed system we would concentrate on reducing the time

taken to search elements by localizing the global prey and then hunting it down by encircling it with reducing state space.

We propose to divide the entire work into three major steps.

First module containing the grey wolf algorithm setup, second being implementing the same with large datasets. The final module will be setup of cloud and sending the local dataset into cloud server. The result expected is reduction in buffer time, reduction in conservation of resources.With this algorithm usage we even hope to achieve the problem of dealing with unknown search space problems.

In the proposed system we hope to implement the well known grey wolf algorithm in the field of web search engine and have a well optimised solution with least data consumption and reduced buffer time.The algorithm used comprise of two constants A,C. The other parameters are calculated on random functions and by reducing the search space from 2 to 0 thereby reducing the search space exponentially and the random function generation ensures that entire search space is searched even those whondont lie within the search space.

The formulae used for grey wolf algorithm are:

D-> = |C->*X(t)-> – X->(t)|

X(t+1)=X->(t) -A*D

t indicates current iteration

A->,C-> represent coefficient vectors,

X->indicates position of grey wolf

A,C the constant are calculated as follows

A->=2a->*r1-> -a->

C->=2r2->

a-> is decreased from 2 to 0 over course of iterations and r1,r2 are random vector in 0,1

The random vector allow the wolf to update its position inside search space around the prey in any random location by the equations

X(t+1)=(X1+X2+X3)/3

The search agent updates its position according to the position of alphabet, dealta.

CONCLUSION

Based on the proposed model and systemic implementation of those modules we propose that the current problem of unknown space solution will be solved.The successful implementation of grey wolf algorithm in various field helps us understand that it can be successfully implemented in the filed of web search engine also. The result thus obtained will be the most optimised one and will be obtained in least amount of time, resources and thus supporting our energy conservation theroy.