Coyote Optimization Algorithm: A New Metaheuristic for Global Optimization Problems

The behavior of natural phenomena has become one of the most popular sources for researchers to design optimization algorithms for scientific, computing and engineering fields. As a result, a lot of nature-inspired algorithms have been proposed in the last decades. Due to the numerous issues of the global optimization process, new algorithms are always welcome in this research field. This paper introduces the Coyote Optimization Algorithm (COA), which is a population based metaheuristic for optimization inspired on the canis latrans species. It contributes with a new algorithmic structure and mechanisms for balancing exploration and exploitation. A set of boundary constrained real parameter optimization benchmarks is tested and a comparative study with other nature-inspired metaheuristics is provided to investigate the performance of the COA. Numerical results and non-parametric statistical significance tests indicate that the COA is capable of locating promising solutions and it outperforms other metaheuristics on most tested functions.

[1]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[2]  Ben Niu,et al.  Bacterial colony foraging optimization , 2014, Neurocomputing.

[3]  Xin-She Yang,et al.  Nature-Inspired Optimization Algorithms: Challenges and Open Problems , 2020, J. Comput. Sci..

[4]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[5]  Xin-She Yang,et al.  Flower Pollination Algorithm for Global Optimization , 2012, UCNC.

[6]  Paul W. Box,et al.  An individual-based model of canid populations: modelling territoriality and social structure , 2003 .

[7]  Andrew Lewis,et al.  The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..

[8]  S. Salcedo-Sanz.  Modern meta-heuristics based on nonlinear physics processes: A review of models and design procedures , 2016 .

[9]  Hui Zhao,et al.  A novel nature-inspired algorithm for optimization: Virus colony search , 2016, Adv. Eng. Softw..

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

[11]  Jing Hu,et al.  A Diversity-Guided Particle Swarm Optimizer for Dynamic Environments , 2007, LSMS.

[12]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[13]  Eric M. Gese,et al.  Foraging ecology of coyotes (Canis latrans): the influence of extrinsic factors and a dominance hierarchy , 1996 .

[14]  Shahryar Rahnamayan,et al.  Metaheuristics in large-scale global continues optimization: A survey , 2015, Inf. Sci..

[15]  Patrick Siarry,et al.  A survey on optimization metaheuristics , 2013, Inf. Sci..

[16]  Marco Dorigo,et al.  Ant colony optimization for continuous domains , 2008, Eur. J. Oper. Res..

[17]  Francisco Herrera,et al.  Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power , 2010, Inf. Sci..

[18]  Julie K. Young,et al.  Influence of habitat structure and food on patch choice of captive coyotes , 2014 .

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

[20]  Ponnuthurai N. Suganthan,et al.  Real-parameter evolutionary multimodal optimization - A survey of the state-of-the-art , 2011, Swarm Evol. Comput..

[21]  Frederick F. Knowlton,et al.  Evaluating coyote management strategies using a spatially explicit, individual-based, socially structured population model , 2008 .

[22]  Min-Yuan Cheng,et al.  Symbiotic Organisms Search: A new metaheuristic optimization algorithm , 2014 .

[23]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[24]  A. Kaveh,et al.  A new optimization method: Dolphin echolocation , 2013, Adv. Eng. Softw..

[25]  Xin-She Yang,et al.  New directional bat algorithm for continuous optimization problems , 2017, Expert Syst. Appl..

[26]  Antonio LaTorre,et al.  A comprehensive comparison of large scale global optimizers , 2015, Inf. Sci..

[27]  Ponnuthurai Nagaratnam Suganthan,et al.  Problem Definitions and Evaluation Criteria for CEC 2015 Special Session on Bound Constrained Single-Objective Computationally Expensive Numerical Optimization , 2015 .

[28]  Yu Liu,et al.  A novel bat algorithm with habitat selection and Doppler effect in echoes for optimization , 2015, Expert Syst. Appl..

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

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

[31]  Trung Nguyen-Thoi,et al.  An improved constrained differential evolution using discrete variables (D-ICDE) for layout optimization of truss structures , 2015, Expert Syst. Appl..