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International Journal of Mathematical, Engineering and Management Sciences

ISSN: 2455-7749


A Hybrid Artificial Grasshopper Optimization (HAGOA) Meta-Heuristic Approach: A Hybrid Optimizer For Discover the Global Optimum in Given Search Space

A Hybrid Artificial Grasshopper Optimization (HAGOA) Meta-Heuristic Approach: A Hybrid Optimizer For Discover the Global Optimum in Given Search Space

Brahm Prakash Dahiya
Department of Computer Science and Engineering, I. K. G. Punjab Technical University, Punjab, India.

Shaveta Rani
Department of Computer Science and Engineering, Giani Zail Singh Campus College of Engineering and Technology, Punjab, India.

Paramjeet Singh
Department of Computer Science and Engineering, Giani Zail Singh Campus College of Engineering and Technology, Punjab, India.

DOI https://dx.doi.org/10.33889/IJMEMS.2019.4.2-039

Received on October 17, 2018
  ;
Accepted on December 11, 2018

Abstract

Meta-heuristic algorithms are used to get optimal solutions in different engineering branches. Here four types of meta-heuristics algorithms are used such as evolutionary algorithms, swarm-based algorithms, physics based algorithms and human based algorithms respectively. Swarm based meta-heuristic algorithms are given more effective result in optimization problem issues and these are generated global optimal solution. Existing swarm intelligence techniques are suffered with poor exploitation and exploration in given search space. Therefore, in this paper Hybrid Artificial Grasshopper Optimization (HAGOA) meta-heuristic algorithm is proposed to improve the exploitation and exploration in given search space. HAGOA is inherited Salp swarm behaviors. HAGOA performs balancing in exploitation and exploration search space. It is capable to make chain system between exploitation and exploration phases. The efficiency of HAGOA meta-heuristic algorithm will analyze using 19 benchmarks functions from F1 to F19. In this paper, HAGOA algorithm is performed efficiency analyze test with Artificial Grasshopper optimization (AGOA), Hybrid Artificial Bee Colony with Salp (HABCS), Modified Artificial Bee Colony (MABC), and Modify Particle Swarm Optimization (MPSO) swarm based meta-heuristic algorithms using uni-modal and multi-modal functions in MATLAB. Comparison results are shown that HAGOA meta-heuristic algorithm is performed better efficiency than other swarm intelligence algorithms on the basics of high exploitation, high exploration, and high convergence rate. It also performed perfect balancing between exploitation and exploration in given search space.

Keywords- Swarm intelligence (SI), Hybrid artificial grasshopper optimization (HAGOA), Modified artificial bee colony (MABC), Modify particle swarm optimization (MPSO).

Citation

Dahiya, B. P., Rani, S., & Singh, P. (2019). A Hybrid Artificial Grasshopper Optimization (HAGOA) Meta-Heuristic Approach: A Hybrid Optimizer For Discover the Global Optimum in Given Search Space. International Journal of Mathematical, Engineering and Management Sciences, 4(2), 471-488. https://dx.doi.org/10.33889/IJMEMS.2019.4.2-039.

Conflict of Interest

The authors confirm that this article contents have no conflict of interest.

Acknowledgements

The authors acknowledge I. K. Gujral Punjab Technical University, Kapurthala, India for providing research facilities.

References

Abro, A. G., & Mohamad-Saleh, J. (2012, November). Enhanced global-best artificial bee colony optimization algorithm. In Computer Modeling and Simulation (EMS), 2012 Sixth UKSim/AMSS European Symposium on (pp. 95-100). IEEE.

Adams, R. (2013). Social behavior and communication in elephants- it's true! elephants don't forget! Available at: http://www.wildlifepictures-online.com/ elephant-communication.html.

Alatas, B. (2011). ACROA: Artificial chemical reaction optimization algorithm for global optimization. Expert Systems with Applications, 38(10), 13170-13180.

Archie, E. A., & Chiyo, P. I. (2012). Elephant behavior and conservation: social relationships, the effects of poaching, and genetic tools for management. Molecular Ecology, 21(3), 765-778.

Archie, E. A., Moss, C. J., & Alberts, S. C. (2006). The ties that bind: genetic relatedness predicts the fission and fusion of social groups in wild African elephants. Proceedings of the Royal Society of London B: Biological Sciences, 273(1586), 513-522.

Ari, A., Yenke, B. O. , Labraoui, N., Damakoa, I., & Gueroui, A. (2016). A power efficient cluster based routing algorithm for wireless sensor networks: Honey bees swarm intelligence based approach. Elsevier, Journal of Network and Computer Applications, 69, 77-97.

Aswani, R., Ghrera, S. P., & Chandra, S. (2016). A novel approach to outlier detection using modified grey wolf optimization and k-nearest neighbors algorithm. Indian Journal of Science and Technology, 9(44), 1-8.

Back, T. (1996). Evolutionary algorithms in theory and practice, evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press.

Blum, C., & Li, X. (2008). Swarm intelligence in optimization. In Swarm Intelligence: Introduction and Applications (pp. 43-85). Springer International Publishing.

Bose, G. K., & Pain P. (2018). Metaheuristic approach of multi-objective optimization during EDM process. International Journal of Mathematical, Engineering and Management Sciences. 3(3), 301-314.

Bramer, M., Ellis, R., & Petridis, M. (2010). Group counseling optimization: a novel approach. In Research and Development in Intelligent Systems (pp. 195-208). Springer International Publishing.

Cerny, V. (1985). Thermo dynamical approach to the traveling salesman problem: an efficient simulation algorithm. Journal of Optimization Theory and Applications, 45(1), 41-51.

Dai, C., Zhu, Y., & Chen, W. (2007). Seeker optimization algorithm. In Computational Intelligence and Security (pp. 167-276). Springer International Publishing.

Davies, N. B., & Krebs, J. (1993). An introduction to behavioral ecology. Third Edition. Blackwell Publishing, Oxford, UK.

Du, H., Wu, X., & Zhuang, J. (2006). Small-world optimization algorithm for function optimization. In International Conference on Natural Computation ICNC 2006. ‘Advances in Natural Computation’. Springer, 264-273.

Eita, M. A., & Fahmy, M. M. (2014). Group counseling optimization. Applied Soft Computing, 22, 585-604.

Erol, O. K., & Eksin, I., (2006). A new optimization method: big bang-big crunch, Advances in Engineering Software, 37(2), 106-111.

Fogel, L. J., Owens A. J., & Walsh, M. J. (1966). Artificial intelligence through simulated evolution. Oxford, England: John Wiley & Sons.

Formato, R. A. (2007). Central force optimization: A new meta-heuristic with applications in applied electromagnetics, Progress in Electromagnetics Research, 77, 425–491.

Gandomi, A. H. (2014). Interior search algorithm (ISA): a novel approach for global optimization, ISA Transaction, 53(4), 1168-1183.

Gao, W. F., Liu, S. Y., & Huang, L. L. (2013). A novel artificial bee colony algorithm based on modified search equation and orthogonal learning. IEEE Transactions on Cybernetics, 43(3), 1011-1024.

Gao, W., & Liu, S. (2011). Improved artificial bee colony algorithm for global optimization. Information Processing Letters, 111(17), 871-882.

Gao, W., Liu, S., & Huang, L. (2012). A global best artificial bee colony algorithm for global optimization. Journal of Computational and Applied Mathematics, 236(11), 2741-2753.

Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: harmony search. Simulation, 76(2), 60-68.

Ghorbani, N., & Babaei, E. (2014). Exchange market algorithm, Applied Soft Computing, 19, 177–187.

Gogna, A., & Tayal, A. (2013). Metaheuristics: review and application. Journal of Experimental & Theoretical Artificial Intelligence, 25(4), 503-526.

Hatamlou, A. (2013). Black hole: a new heuristic optimization approach for data clustering. Information Sciences, 222, 175-184.

He, G., & Huang, N. J. (2012). A modified particle swarm optimization algorithm with applications. Applied Mathematics and Computation, 219(3), 1053-1060.

Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459-471.

Kashan, A. H. (2011). An efficient algorithm for constrained global optimization and application to mechanical engineering design: League championship algorithm (LCA). Computer-Aided Design, 43(12), 1769-1792.

Kaveh, A. (2014). Colliding bodies optimization. In Advances in Meta-Heuristic Algorithms for Optimal Design of Structures (pp. 195-232). Springer International Publishing.

Kaveh, A., & Farhoudi, N. (2013). A new optimization method: dolphin echolocation. Advances in Engineering Software, 59, 53-70.

Kaveh, A., & Khayatazad, M. (2012). A new meta-heuristic method: ray optimization. Computers and Structures, 112-113, 283-294.

Kaveh, A., & Khayatazad, M. (2013). Ray optimization for size and shape optimization of truss structures. Computers & Structures, 117, 82-94.

Kaveh, A., & Mahdavi, V. R. (2014). Colliding bodies optimization: a novel meta-heuristic method. Computers & Structures, 139, 18-27.

Kaveh, A., & Nasrollahi, A., (2014). A new hybrid meta-heuristic for structural design: ranked particles optimization. Structural Engineering and Mechanics, 52(2), 405-426.

Kaveh, A., & Talatahari, S (2010). A novel heuristic optimization method: charged system search. Acta Mechanica, 213(3-4), 267–289.

Kaveh, A., Bakhshpoori, T., & Afshari, E. (2014). An efficient hybrid particle swarm and swallow swarm optimization algorithm. Computers & Structures, 143, 40-59.

Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680.

Kumar, V., Chhabra, J., & Kumar, D. (2015). A hybrid approach for data clustering using expectation-maximization and parameter adaptive harmony search algorithm. In: Proceedings of International Conference on Future Computational Technologies, pp. 61-67.

Kumar, V., Chhabra, J., & Kumar, D. (2017). Grey wolf algorithm-based clustering technique. Journal of Intelligent Systems, 26(1), 153-168.

Liang, Y., & Yu, H. (2005). PSO-based energy efficient gathering in sensor networks. In International Conference on Mobile Ad-hoc and Sensor Networks (pp. 362-369). Springer, Berlin, Heidelberg.

Mann, P. S., & Singh, S. (2017). Improved artificial bee colony metaheuristic for energy-efficient clustering in wireless sensor networks. Artificial Intelligence Review, 1-26, DOI: 10.1007/s10462-017-9564-4

Mirjalili, S. (2015). The ant lion optimizer. Advances in Engineering Software, 83, 80-98.

Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51-67.

Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163-191.

Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46-61.

Moghaddam, F. F., Moghaddam, R. F., & Cheriet, M. (2012). Curved space optimization: a random search based on general relativity theory. arxiv preprint arXiv:1208.2214.

Moosavian, N., & Roodsari, B. K. (2014a). Soccer league competition algorithm, a new method for solving systems of nonlinear equations. International Journal of Intelligence Science, 4(1), 7-16.

Moosavian, N., & Roodsari, B. K. (2014b). Soccer league competition algorithm: a novel meta-heuristic algorithm for optimal design of water distribution networks. Swarm and Evolutionary Computation, 17, 14-24.

Neri, F., Iacca, G., & Mininno, E. (2013). Compact optimization. In Handbook of Optimization (pp. 337-364). Springer, Berlin, Heidelberg.

Pramy, F. A. (2018). An approach for solving fuzzy multi-Objective linear fractional programming problems. International Journal of Mathematical, Engineering and Management Sciences, 3(3), 280–293.

Ramezani, F., & Lotfi, S. (2013). Social-based algorithm (SBA). Applied Soft Computing, 13(5), 2837-2856.

Rashedi, E., Nezamabadi, H., & Saryazdi, S. (2009). GSA: a gravitational search algorithm. Information Science, 179(13), 2232-2248.

Rechenberg, I. (1973). Evolution strategy: optimization of technical systems by means of biological evolution. Fromman-Holzboog, Stuttgart, 104, 15-16.

Sadollah, A., Bahreininejad, A., Eskandar. H., & Hamdi. M., (2013). Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems, Applied Soft Computing, 13(5), 2592-2612.

Saremi, S., Mirjalili, S., & Lewis, A. (2017). Grasshopper optimization algorithm: theory and application. Advances in Engineering Software, 105, 30-47.

Simon, D. (2008). Biogeography-based optimization. IEEE Transactions on Evolutionary Computation, 12(6), 702-713.

Storn, R., & Price, K. (1997). Differential evolution a simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization, 11(4), 341-359.

Tan, Y., & Zhu, Y. (2010, June). Fireworks algorithm for optimization. In International Conference in Swarm Intelligence (pp. 355-364). Springer, Berlin, Heidelberg.

Wang, J., Ren, X. L., Shen, Y. L., & Liu, S. Y. (2010, January). A remote wireless sensor networks for water quality monitoring. In Innovative Computing & Communication, 2010 International Conference on and Information Technology & Ocean Engineering, 2010 Asia-Pacific Conference on (CICC-ITOE) (pp. 7-12). IEEE

Webster, B., & Bernhard, P, (2003). A local search optimization algorithm based on natural principles of gravitation”, In Proceedings of the International Conference 2003, on Information and Knowledge Engineering (IKE’03), 255-261.

Wilson, E. O. (2000). Sociobiology: The New Synthesis. 25th Anniversary Editions. The Belknap Press of Harvard University Press Cambridge, Massachusetts and London, England.

Yang, X.-S. (2010). Firefly algorithm, In Engineering Optimization (pp. 221-230). Wiley Online Publishing.

Yang, X. S., & Deb, S. (2009). Cuckoo search via Levy flights. In: Nature & biologically inspired computing (NaBIC), World Congress, 210-214.

Yang, X.-S. (April-2010). A new meta-heuristic bat-inspired algorithm, in: nature inspired cooperative strategies for optimization (NISCO 2010), Studies in Computational Intelligence, Springer Berlin, 284, 65-74.

Yao, X., Liu, Y., & Lin, G. (1999). Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation, 3(2), 82-102.