Performance Analysis of Earthworm Optimization Algorithm for Bound Constrained Optimization Problems

AUTHOR(S):

Eva Tuba, Romana Capor-Hrosik, Viktor Tuba

TITLE

Performance Analysis of Earthworm Optimization Algorithm for Bound Constrained Optimization Problems

ABSTRACT

Numerous real life problems represents hard optimization problems that cannot be solved by deterministic algorithm. In the past decades various different methods were proposed for these kind of problems and one of the methods are nature inspired algorithms especially swarm intelligence algorithms. Earthworm optimization algorithm (EWA) is one of the recent swarm intelligence algorithm that has not been thoroughly researched. In this paper we tested EWA algorithm on 28 standard benchmark functions and compared results with particle swarm optimization algorithm. Comparison show that EWA has good characteristics and it outperformed other approach from literature.

KEYWORDS

hard optimization problems, optimization algorithms, swarm intelligence, earthworm optimization, EWA

REFERENCES

[1] A. Alihodzic and M. Tuba, “Bat algorithm (BA) for image thresholding,” Recent Researches in Telecommunications, Informatics, Electronics and Signal Processing, pp. 17–19, 2013.

[2] N. Bacanin and M. Tuba, “Firefly algorithm for cardinality constrained mean-variance portfolio optimization problem with entropy diversity constraint,” The Scientific World Journal, special issue Computational Intelligence and Metaheuristic Algorithms with Applications,, vol. 2014, no. Article ID 721521, p. 16, 2014.

[3] ——, “Firefly algorithm for cardinality constrained mean-variance portfolio optimization problem with entropy diversity constraint,” The Scientific World Journal, vol. 2014, 2014.

[4] I. Brajevic and M. Tuba, “An upgraded artifi- cial bee colony algorithm (ABC) for constrained optimization problems,” Journal of Intelligent Manufacturing, vol. 24, no. 4, pp. 729–740, August 2013.

[5] ——, Cuckoo Search and Firefly Algorithm: Theory and Applications. Springer International Publishing, 2014, ch. Cuckoo Search and Firefly Algorithm Applied to Multilevel Image Thresholding, pp. 115–139.

[6] 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. World Scientific and Engineering Academy and Society (WSEAS), 2010, pp. 185–190.

[7] C. Dai, W. Chen, Y. Song, and Y. Zhu, “Seeker optimization algorithm: a novel stochastic search algorithm for global numerical optimization,” Journal of Systems Engineering and Electronics, vol. 21, no. 2, pp. 300–311, 2010.

[8] R. Jovanovic and M. Tuba, “An ant colony optimization algorithm with improved pheromone correction strategy for the minimum weight vertex cover problem,” Applied Soft Computing, vol. 11, no. 8, pp. 5360–5366, December 2011.

[9] ——, “Ant colony optimization algorithm with pheromone correction strategy for the minimum connected dominating set problem,” Computer Science and Information Systems (ComSIS), vol. 10, no. 1, pp. 133–149, January 2013.

[10] D. Karaboga and B. Akay, “A modified artificial bee colony (ABC) algorithm for constrained optimization problems,” Applied Soft Computing, vol. 11, no. 3, pp. 3021–3031, 2011.

[11] J. Kennedy and R. Eberhart, “Particle swarm optimization,” Proceedings of the IEEE International Conference on Neural Networks (ICNN ’95), vol. 4, pp. 1942–1948, 1995.

[12] J. Liang, B. Qu, P. Suganthan, and A. G. Hernandez-D ´ ´ıaz, “Problem definitions and evaluation criteria for the cec 2013 special session on real-parameter optimization,” Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore, Technical Report, vol. 201212, 2013.

[13] M. Subotic and M. Tuba, “Parallelized multiple swarm artificial bee colony algorithm (MSABC) for global optimization,” Studies in Informatics and Control, vol. 23, no. 1, pp. 117–126, 2014.

[14] Y. Tan and Y. Zhu, “Fireworks algorithm for optimization,” Advances in Swarm Intelligence, LNCS, vol. 6145, pp. 355–364, June 2010.

[15] I. Tsoulos and A. Stavrakoudisb, “Enhancing pso methods for global optimization,” Applied Mathematics and Computation, vol. 216, no. 10, pp. 2988–3001, July 2010.

[16] E. Tuba, M. Tuba, and M. Beko, Support Vector Machine Parameters Optimization by Enhanced Fireworks Algorithm. Springer International Publishing, 2016, vol. 9712, pp. 526–534.

[17] E. Tuba, M. Tuba, and E. Dolicanin, “Adjusted fireworks algorithm applied to retinal image registration,” Studies in Informatics and Control, vol. 26, no. 1, pp. 33–42, 2017.

[18] E. Tuba, M. Tuba, and D. Simian, “Adjusted bat algorithm for tuning of support vector machine parameters,” in Congress on Evolutionary Computation (CEC),. IEEE, 2016, pp. 2225–2232.

[19] ——, “Handwritten digit recognition by support vector machine optimized by bat algorithm,” in 24th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision,(WSCG 2016), 2016, pp. 369– 376.

[20] M. Tuba, “Asymptotic behavior of the maximum entropy routing in computer networks,” Entropy, vol. 15, no. 1, pp. 361–371, January 2013.

[21] ——, “Multilevel image thresholding by natureinspired algorithms-a short review.” The Computer Science Journal of Moldova, vol. 22, no. 3, pp. 318–338, 2014.

[22] M. Tuba and N. Bacanin, “Artificial bee colony algorithm hybridized with firefly metaheuristic for cardinality constrained mean-variance portfolio problem,” Applied Mathematics & Information Sciences, vol. 8, no. 6, pp. 2831–2844, November 2014.

[23] ——, “Improved seeker optimization algorithm hybridized with firefly algorithm for constrained optimization problems,” Neurocomputing, vol. 143, pp. 197–207, 2014.

[24] ——, “Improved seeker optimization algorithm hybridized with firefly algorithm for constrained optimization problems,” Neurocomputing, vol. 143, pp. 197–207, 2014.

[25] M. Tuba, N. Bacanin, and N. Stanarevic, “Adjusted artificial bee colony (ABC) algorithm for engineering problems,” WSEAS Transactions on Computers, vol. 11, no. 4, pp. 111–120, 2012.

[26] M. Tuba, R. Jovanovic, and S. SERBIA, “An analysis of different variations of ant colony optimization to the minimum weight vertex cover problem,” WSEAS Transactions on Information Science and Applications, vol. 6, no. 6, pp. 936– 945, 2009.

[27] X.-S. Yang, “Firefly algorithms for multimodal optimization,” Stochastic Algorithms: Foundations and Applications, LNCS, vol. 5792, pp. 169–178, 2009.

[28] ——, “A new metaheurisitic bat-inspired algorithm,” Studies in Computational Intelligence, vol. 284, pp. 65–74, 2010.

[29] ——, “Efficiency analysis of swarm intelligence and randomization techniques,” Journal of Computational and Theoretical Nanoscience, vol. 9, no. 2, pp. 189–198, 2012.

[30] M. Zambrano-Bigiarini, M. Clerc, and R. Rojas, “Standard particle swarm optimisation 2011 at CEC-2013: A baseline for future PSO improvements,” in IEEE Congress on Evolutionary Computation (CEC 2013). IEEE, 2013, pp. 2337–2344.

Cite this paper

Eva Tuba, Romana Capor-Hrosik, Viktor Tuba. (2017) Performance Analysis of Earthworm Optimization Algorithm for Bound Constrained Optimization Problems. International Journal of Computers, 2, 94-98