This paper introduces hybridized monarch butterfly optimization algorithm for solving global optimization problems. Despite of the fact that the monarch butterfly optimization algorithm is relatively new approach, it has already showed great potential when tackling NP-hard optimization tasks. However, by analyzing original monarch butterfly algorithm, we noticed some deficiencies in the butterfly adjusting operator that in early iterations exceedingly directs the search process towards the current best solution. To overcome this deficiency, we incorporated firefly’s algorithm search mechanism into the original monarch optimization approach. We tested our algorithm on six standard global optimization benchamarks, and performed comparative analysis with original monarch butterfly optimization, as well as with other five state-of-the-art metaheuristics. Experimental results are promising.
monarch butterfly optimization, algorithms, global optimization, swarm intelligence, metaheuristics
 E. Bonabeau, M. Dorigo, and G. Theraulaz, Swarm intelligence: from natural to artificial systems. Oxford University Press, 1999.
 D. Karaboga, “An idea based on honey bee swarm for numerical optimization,” Technical Report - TR06, pp. 1–10, 2005.
 M. Tuba and N. Bacanin, “Hybridized bat algorithm for multi-objective radio frequency identification (RFID) network planning,” in Proceedings of the 2015 IEEE Congress on Evolutionary Computation (CEC 2015), May 2015.
 N. Bacanin and M. Tuba, “Multiobjective RFID network planning by artificial bee colony algorithm with genetic operators,” Advances in Swarm and Computational Intelligence, ICSI 2015, Lecture Notes in Computer Science, vol. 9140, pp. 247–254, 2015.
 I. Brajevic, M. Tuba, and M. Subotic, “Improved artificial bee colony algorithm for constrained problems,” in Proceedings of the 11th WSEAS International Conference on Evolutionary Computing, pp. 185–190, 2010.
 X.-S. Yang, “Firefly algorithms for multimodal optimization,” Stochastic Algorithms: Foundations and Applications, LNCS, vol. 5792, pp. 169–178, 2009.
 I. Strumberger, N. Bacanin, and M. Tuba, “Enhanced firefly algorithm for constrained numerical optimization, ieee congress on evolutionary computation,” in Proceedings of the IEEE International Congress on Evolutionary Computation (CEC 2017), pp. 2120–2127, June 2017.
 E. Tuba, M. Tuba, and M. Beko, “Mobile wireless sensor networks coverage maximization by firefly algorithm,” in 27th International Conference Radioelektronika, pp. 1–5, IEEE, 2017.
 M. Tuba and N. Bacanin, “JPEG quantization tables selection by the firefly algorithm,” in International Conference on Multimedia Computing and Systems (ICMCS), pp. 153–158, IEEE, 2014.
 E. Tuba, M. Tuba, and M. Beko, “Two stage wireless sensor node localization using firefly algorithm,” in Smart Trends in Systems, Security and Sustainability, LNNS, vol. 18, pp. 113–120, Springer, 2018.
 X.-S. Yang and S. Deb, “Cuckoo search via levy flights,” in Proceedings of World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), pp. 210–214, 2009.
 I. Brajevic and M. Tuba, “Cuckoo search and firefly algorithm applied to multilevel image thresholding,” in Cuckoo Search and Firefly Algorithm: Theory and Applications (X.-S. Yang, ed.), vol. 516 of Studies in Computational Intelligence, pp. 115–139, Springer International Publishing, 2014.
 N. Bacanin, “Implementation and performance of an object-oriented software system for cuckoo search algorithm,” International Journal of Mathematics and Computers in Simulation, vol. 6, pp. 185–193, December 2010.
 G.-G.Wang, S. Deb, and L. dos S. Coelho, “Elephant herding optimization,” in Proceedings of the 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI), pp. 1–5, December 2015.
 E. Tuba and Z. Stanimirovic, “Elephant herding optimization algorithm for support vector machine parameters tuning,” in Proceedings of the 2017 International Conference on Electronics, Computers and Artificial Intelligence (ECAI), pp. 1–5, June 2017.
 E. Tuba, I. Ribic, R. Capor-Hrosik, and M. Tuba, “Support vector machine optimized by elephant herding algorithm for erythemato-squamous diseases detection,” Procedia Computer Science, vol. 122, pp. 916–923, 2017.
 E. Tuba, A. Alihodzic, and M. Tuba, “Multilevel image thresholding using elephant herding optimization algorithm,” in Proceedings of 14th International Conference on the Engineering of Modern Electric Systems (EMES), pp. 240–243, June 2017.
 A. Alihodzic, E. Tuba, R. Capor-Hrosik, E. Dolicanin, and M. Tuba, “Unmanned aerial vehicle path planning problem by adjusted elephant herding optimization,” in 25th Telecommunication Forum (TELFOR), pp. 1–4, IEEE, 2017.
 I. Strumberger, N. Bacanin, M. Beko, S. Tomic, and M. Tuba, “Static drone placement by elephant herding optimization algorithm,” in Proceedings of the 24th Telecommunications Forum (TELFOR), November 2017.
 N. Bacanin and M. Tuba, “Fireworks algorithm applied to constrained portfolio optimization problem,” in Proceedings of the 2015 IEEE Congress on Evolutionary Computation (CEC 2015), May 2015.
 I. Strumberger, N. Bacanin, and M. Tuba, “Constrained portfolio optimization by hybridized bat algorithm,” in 2016 7th International Conference on Intelligent Systems, Modelling and Simulation (ISMS), pp. 83–88, Jan 2016.
 E. Tuba, M. Tuba, and M. Beko, “Node localization in ad hoc wireless sensor networks using fireworks algorithm,” in Proceedings of the 5th International Conference on Multimedia Computing and Systems (ICMCS), pp. 223–229, September 2016.
 E. Tuba, M. Tuba, and E. Dolicanin, “Adjusted fireworks algorithm applied to retinal image registration,” Studies in Informatics and Control, vol. 26, pp. 33–42, March 2017.
 E. Dolicanin, I. Fetahovic, E. Tuba, R. Capor- Hrosik, and M. Tuba, “Unmanned combat aerial vehicle path planning by brain storm optimization algorithm,” Studies in Informatics and Control, vol. 27, no. 1, pp. 15–24, 2018.
 E. Tuba, M. Tuba, and D. Simian, “Adjusted bat algorithm for tuning of support vector machine parameters,” in IEEE Congress on Evolutionary Computation (CEC), pp. 2225–2232, IEEE, 2016.
 G.-G. Wang, S. Deb, and Z. Cui, “Monarch butterfly optimization,” Neural Computing and Applications, pp. 1–20, May 2015.
 G. A. Breed, P. M. Severns, and A. M. Edwards, “Apparent power-law distributions in animal movements can arise from intraspecific interactions,” Journal of the The Royal Society Interface, vol. 12, February 2015.
 X.-S. Yang, “Firefly algorithm, stochastic test functions and design optimisation,” International Journal of Bio-Inspired Computation, vol. 2, no. 2, pp. 78–84, 2010.
Cite this paper
Ivana Strumberger, Marko Sarac, Dusan Markovic, Nebojsa Bacanin. (2018) Hybridized Monarch Butterfly Algorithm for Global Optimization Problems. International Journal of Computers, 3, 63-68
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