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African Buffalo Optimization for Global Optimization


Affiliations
1 Department of Mathematical Sciences, Anchor University, Lagos, Nigeria
2 Universiti Malaysia Pahang, Kuantan 26300, Malaysia
 

In this study we apply the African buffalo optimization (ABO) to solve benchmark global optimization problems. Such problems which are artificial representation of different search landscapes ranging from unimodal to multimodal, separable to non-separable, constrained to unconstrained search landscapes have become a veritable instrument to test the search capacities of optimization algorithms. After a number of experimental procedures involving 28 benchmark problems, results from ABO prove to be rather competitive leading to the conclusion that it is a worthy addition to the body of swarm intelligence techniques.

Keywords

African Buffalo Optimization, Global Optimization, Search Landscapes, Swarm Intelligence Techniques.
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  • Pricopie, A. and Costache, A., In The 1940 Vrancea Earthquake. Issues, Insights and Lessons Learnt, Springer, New York, 2016, pp. 363–375.
  • Kennedy, J. In Encyclopedia of Machine Learning, Springer, New York, 2011, pp. 760–766.
  • Karaboga, D., Artificial bee colony algorithm. Scholarpedia, 2010, 5, 6915.
  • Yang, X.-S., Firefly algorithm, stochastic test functions and design optimisation. Int. J. Bio-Inspired Comput., 2010, 2, 78–84.
  • Yang, X.-S., In Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), Springer, New York, 2010, pp. 65–74.
  • Gandomi, A. H., Yang, X.-S. and Alavi, A. H., Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng. Comput., 2013, 29, 17–35.
  • Rao, R. V., Savsani, V. J. and Vakharia, D., Teaching–learningbased optimization: a novel method for constrained mechanical design optimization problems. Comput-Aided Des., 2011, 43, 303–315.
  • Rao, R., Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int. J. Ind. Eng. Comput., 2016, 7, 19–34.
  • Odili, J. B., Kahar, M. N. M. and Anwar, S., African buffalo optimization: a swarm-intelligence technique. Proc. Comput. Sci., 2015, 76, 443–448.
  • Odili, J. B. and Mohmad Kahar, M. N., Solving the traveling salesman’s problem using the African buffalo optimization. Comput. Intell. Neurosci., 2015, 501, 1–12.
  • Yeomans, J. S. and Yang, X.-S., Municipal waste management optimisation using a firefly algorithm–driven simulationoptimisation approach. Int. J. Process Manage. Benchmark., 2014, 4, 363–375.
  • Matsushita, H., In IEEE Congress on Evolutionary Computation, Sendai, Japan, 2015, pp. 2672–2677.
  • Fister, I., Yang, X.-S. and Brest, J., A comprehensive review of firefly algorithms. Swarm Evol. Comput., 2013, 13, 34–46.
  • Yang, X.-S. Nature-inspired metaheuristic algorithms: success and new challenges. arXiv preprint arXiv:1211.6658, 2012.
  • Kavousi-Fard, A., Samet, H. and Marzbani, F., A new hybrid modified firefly algorithm and support vector regression model for accurate short term load forecasting. Expert Syst. Appl., 2014, 41, 6047–6056.
  • Yang, X.-S. and Deb, S., In IEEE World Congress on Nature and Biologically Inspired Computing (NaBIC 2009), Coimbatore, India, 2009, pp. 210–214.
  • Kamat, S. and Karegowda, A., A brief survey on cuckoo search applications. Int. J. Innov. Res. Comput. Commun. Eng., 2014, 2, 7–14.
  • Ouaarab, A., Ahiod, B. and Yang, X.-S., Discrete cuckoo search algorithm for the travelling salesman problem. Neural Comput. Appl., 2014, 24, 1659–1669.
  • Marichelvam, M., Prabaharan, T. and Yang, X.-S., Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan. Appl. Soft Comput., 2014, 19, 93–101.
  • Fister, I., Rauter, S., Yang, X.-S. and Ljubič, K., Planning the sports training sessions with the bat algorithm. Neurocomputing, 2015, 149, 993–1002.
  • Yang, X.-S. and He, X., Bat algorithm: literature review and applications. Int. J. Bio-Inspired Comput., 2013, 5, 141–149.
  • Warid, W., Hizam, H., Mariun, N. and Abdul-Wahab, N. I., Optimal power flow using the Jaya algorithm. Energies, 2016, 9, 678.
  • Pandey, H. M., Cloud System and Big Data Engineering (Confluence), In 6th IEEE International Conference, Noida, India, 2016, pp. 728–730.
  • Rao, R. V., Savsani, V. J. and Vakharia, D., Teaching–learningbased optimization: an optimization method for continuous nonlinear large scale problems. Inf. Sci., 2012, 183, 1–15.
  • Baghlani, A. and Makiabadi, M., Teaching–learning-based optimization algorithm for shape and size optimization of truss structures with dynamic frequency constraints. Iran. J. Sci. Technol. Trans. Civil Eng., 2013, 37, 409.
  • Rao, R., Review of applications of TLBO algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems. Decis. Sci. Lett., 2016, 5, 1–30.
  • Ge, F., Hong, L. and Shi, L., An autonomous teaching–learning based optimization algorithm for single objective global optimization. Int. J. Comput. Intel. Syst., 2016, 9, 506–524.
  • Ali, M. M., Khompatraporn, C. and Zabinsky, Z. B., A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems. J. Global Optim., 2005, 31, 635–672.
  • Hedar, A.-R. and Fukushima, M., Tabu search directed by direct search methods for nonlinear global optimization. Eur. J. Oper. Res., 2006, 170, 329–349.
  • Bingham, D., Virtual library of simulation experiments: test functions and databases, 2015; https://www.sfu.ca/~ssurjano.
  • Mishra, S. K., Some new test functions for global optimization and performance of repulsive particle swarm method, 2006; SSRN 926132.
  • Fateen, S.-E. K. and Bonilla-Petriciolet, A., In Cuckoo Search and Firefly Algorithm, Springer, 2014, pp. 315–330.
  • Odili, J. B., Kahar, M. N. M. and Noraziah, A., African buffalo optimization and the randomized insertion algorithm for the asymmetric travelling Salesman’s problems. J. Theoret. Appl. Infor. Technol., 2016, 87(3) 356–364.
  • Odili J. B., Kahar, M. N. M. and Noraziah, A., Convergence analysis of the African buffalo optimization algorithm. Int. J. Simul. Sci. Technol, United Kingdom Simulation Society, 2016, 17(33), 44.1–44.7.
  • Odili, J. B., Kahar, M. N. M. and Noraziah, A., African buffalo optimization strategy for tuning parameters of a PID controller in automatic voltage regulators. Int. J. Simul., Sci. Technol., 2016, 17(33), 45.1–45.6.
  • de Oliveira, J. V., Semantic constraints for membership function optimization. IEEE Trans. Syst. Man, Cybern. – Part A, 1999, 29, 128–138.

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  • African Buffalo Optimization for Global Optimization

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Authors

Julius Beneoluchi Odili
Department of Mathematical Sciences, Anchor University, Lagos, Nigeria
A. Noraziah
Universiti Malaysia Pahang, Kuantan 26300, Malaysia

Abstract


In this study we apply the African buffalo optimization (ABO) to solve benchmark global optimization problems. Such problems which are artificial representation of different search landscapes ranging from unimodal to multimodal, separable to non-separable, constrained to unconstrained search landscapes have become a veritable instrument to test the search capacities of optimization algorithms. After a number of experimental procedures involving 28 benchmark problems, results from ABO prove to be rather competitive leading to the conclusion that it is a worthy addition to the body of swarm intelligence techniques.

Keywords


African Buffalo Optimization, Global Optimization, Search Landscapes, Swarm Intelligence Techniques.

References





DOI: https://doi.org/10.18520/cs%2Fv114%2Fi03%2F627-636