Performance of Elephant Herding Optimization Algorithm on CEC 2013 Real Parameter Single Objective Optimization

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. Elephant herding optimization algorithm (EHO) is one of the recent swarm intelligence algorithm that has not been thoroughly researched. In this paper we tested EHO algorithm on 28 standard benchmark functions and compared results with particle swarm optimization algorithm. Comparison show that EHO has good characteristics and it outperformed other approach from literature.

[1]  Milan Tuba,et al.  Handwritten digit recognition by support vector machine optimized by Bat algorithm , 2016 .

[2]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.

[3]  Yonghua Song,et al.  Seeker optimization algorithm: A novel stochastic search algorithm for global numerical optimization , 2010 .

[4]  Milan Tuba,et al.  Ant colony optimization algorithm with pheromone correction strategy for the minimum connected dominating set problem , 2013, Comput. Sci. Inf. Syst..

[5]  Milan Tuba,et al.  An ant colony optimization algorithm with improved pheromone correction strategy for the minimum weight vertex cover problem , 2011, Appl. Soft Comput..

[6]  Adis ALIHODZIC,et al.  Bat Algorithm ( BA ) for Image Thresholding , 2013 .

[7]  Milan Tuba,et al.  Parallelized Multiple Swarm Artificial Bee Colony Algorithm (MS-ABC) for Global Optimization , 2014 .

[8]  Milan Tuba,et al.  Improved seeker optimization algorithm hybridized with firefly algorithm for constrained optimization problems , 2014, Neurocomputing.

[9]  Mauricio Zambrano-Bigiarini,et al.  Standard Particle Swarm Optimisation 2011 at CEC-2013: A baseline for future PSO improvements , 2013, 2013 IEEE Congress on Evolutionary Computation.

[10]  Milan Tuba,et al.  Adjusted bat algorithm for tuning of support vector machine parameters , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[11]  Milan Tuba,et al.  Firefly Algorithm for Cardinality Constrained Mean-Variance Portfolio Optimization Problem with Entropy Diversity Constraint , 2014, TheScientificWorldJournal.

[12]  Milan Tuba,et al.  Adjusted Fireworks Algorithm Applied to Retinal Image Registration , 2017 .

[13]  Milan Tuba,et al.  Multilevel image thresholding by nature-inspired algorithms - A short review , 2014, Comput. Sci. J. Moldova.

[14]  Ying Tan,et al.  Fireworks Algorithm for Optimization , 2010, ICSI.

[15]  Leandro dos Santos Coelho,et al.  A new metaheuristic optimisation algorithm motivated by elephant herding behaviour , 2017 .

[16]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[17]  Milan Tuba,et al.  Adjusted artificial bee colony (ABC) algorithm for engineering problems , 2012 .

[18]  Milan Tuba Asymptotic Behavior of the Maximum Entropy Routing in Computer Networks , 2013, Entropy.

[19]  Xin-She Yang,et al.  Efficiency Analysis of Swarm Intelligence and Randomization Techniques , 2012, 1303.6342.

[20]  Ivona Brajevic,et al.  An upgraded artificial bee colony (ABC) algorithm for constrained optimization problems , 2012, Journal of Intelligent Manufacturing.

[21]  Marko Beko,et al.  Support Vector Machine Parameters Optimization by Enhanced Fireworks Algorithm , 2016, ICSI.

[22]  Xin-She Yang,et al.  Firefly Algorithms for Multimodal Optimization , 2009, SAGA.

[23]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[24]  Dervis Karaboga,et al.  A modified Artificial Bee Colony (ABC) algorithm for constrained optimization problems , 2011, Appl. Soft Comput..

[25]  Ivona Brajevic,et al.  Improved artificial bee colony algorithm for constrained problems , 2010 .

[26]  M. Tuba,et al.  An analysis of different variations of ant colony optimization to the minimum weight vertex cover problem , 2009 .

[27]  Ioannis G. Tsoulos,et al.  Enhancing PSO methods for global optimization , 2010, Appl. Math. Comput..