Abstract
In daily life there exist many problems whose objective are to either maximize or minimize some value with following some constraints (like load balancing in cloud with aim to maximizing QoS, Travelling salesman problem with aim to minimize total length of trip). These types of problems are optimization problems. Out of these problems there exist many problems which comes under NP-Hard category. To get nearby optimal solution of these problems in polynomial time the metaheuristics approaches are used. Metaheuristics are nature inspired algorithms which provides optimal solution by utilizing combination of exploration and exploitation. This paper provides a survey of Metaheuristic approaches (consisting of need, applications, characteristics, general classification and fourteen approaches under it). Compared all approaches corresponding to key parameters, mechanism. On the basis of literature survey and comparison, cuckoo search has been considered better due to global search via levy flight and generality (because of single parameter setting in cuckoo search). Implemented Randomized algorithm, Genetic Algorithm and Cuckoo Search to solve Load Balancing problem in Cloud Computing with aim to minimize makespan time and proved through results that cuckoo search is better. These experimental results were obtained using CloudSim 3.0.3 toolkit by extending few base classes.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Boussaid, I., Lepagnot, J., Patrick S.: A survey on optimization metaheuristics. In: Web of Science, Elsevier Information Science, vol. 237(5), pp. 82–117 (2013)
Glover, F.: Future paths for integer programming and links to artificial intelligence. Comput. Op. Res. 13(5), 533–549 (1986)
Holland, J.H.: Adaption in Natural and Artificial Systems. The University of Michigan Press, Ann Harbor, MI (1975)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
Dorigo, M., Maniezzo, V., Colorni, A.: Ant system- optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B 26(1), 29–41 (1996)
Moscato, P., Norman, M.G.: A memetic approach for the traveling salesman problem implementation of a computational ecology for combinatorial optimization on message-passing systems. In: International Conference on Parallel Computing and Transputer Application, pp. 86–177 (1992)
Kennedy, J., Eberhart R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)
Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. In: IEEE Control Systems Magazine, pp. 52–67 (2006)
Eusuff, M., Lansey, K., Pasha, F.: Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng. Optim. 38(2), 129–154 (2006)
Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. 8, 687–697 (2007)
Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization- artificial bee colony (ABC) algorithm. J. Glob. Optim. 39, 459–471 (2007)
Yang, X.S., He, X.: Firefly algorithm- recent advances and applications. Int. J. Swarm Intell. 1(1), 36–50 (2013)
Yang, X.S.: Firefly algorithm, stochastic test functions and design optimization. Int. J. Bio-Inspir. Comput. 2(2), 78–84 (2010)
Simon, D.: Biogeography-based optimization. IEEE Trans. Evol. Comput. 12(6), 702–713 (2008)
Yang, X.S., Deb, S.: Cuckoo search via Lévy flights. In: IEEE Conference Publication World Congress on Nature & Biologically Inspired Computing (NaBIC), pp. 210–214 (2009)
Yang, X.S., Deb, S.: Engineering optimisation by cuckoo search. Int. J. Math. Modell. Numer. Optim. 1(4), 330–343 (2010)
Yang, X.S., Deb, S.: Cuckoo search- recent advances and applications. Neural Comput. Appl. 24(1), 169–174 (2014)
Yang, X.S.: Bat algorithm- literature review and applications. Int. J. Bio-Inspir. Comput. 5(3), 141–149 (2013)
Yang, X.S., Karamanoglu M.: Multi-objective flower algorithm for optimization. In: International Conference on Computational Science, Elsevier Science, pp. 861–868 (2013)
Yang, X.S.: Flower pollination algorithm for global optimization, unconventional computation and natural computation. Lect. Notes Comput. Sci. 44(5), 240–249 (2012)
Yang, X.S., Deb, S., Fong, S., Xingshi, H., Zhao, Y.: From swarm intelligence to metaheuristics- nature-inspired optimization algorithms. IEEE Comput. Soc. 49(9), 52–59 (2016)
Wang, F., Yang, X.S., Yang, S.M.: Markov model and convergence analysis based on cuckoo search algorithm. Comput. Eng. 38(11), 180–185 (2012)
Gandomi, A.H., Yang, X.S., Alavi, A.H.: Cuckoo search algorithm- a metaheuristic approach to solve structural optimization problems. Eng. Comput. 29(1), 17–35 (2013)
Gandomi, A.H., Yang, X.S., Talatahari, S., Deb, S.: Coupled eagle strategy and differential evolution for unconstrained and constrained global optimization. Comput. Math Appl. 63(1), 191–200 (2012)
Srivastava, P.R., Chis, M., Deb, S., Yang, X.S.: An efficient optimization algorithm for structural software testing. Int. J. Artif. Intell. 9(12), 68–77 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Garg, D., Kumar, P. (2019). A Survey on Metaheuristic Approaches and Its Evaluation for Load Balancing in Cloud Computing. In: Luhach, A., Singh, D., Hsiung, PA., Hawari, K., Lingras, P., Singh, P. (eds) Advanced Informatics for Computing Research. ICAICR 2018. Communications in Computer and Information Science, vol 955. Springer, Singapore. https://doi.org/10.1007/978-981-13-3140-4_53
Download citation
DOI: https://doi.org/10.1007/978-981-13-3140-4_53
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-3139-8
Online ISBN: 978-981-13-3140-4
eBook Packages: Computer ScienceComputer Science (R0)