Artificial gorilla troops optimizer: A new nature‐inspired metaheuristic algorithm for global optimization problems

Metaheuristics play a critical role in solving optimization problems, and most of them have been inspired by the collective intelligence of natural organisms in nature. This paper proposes a new metaheuristic algorithm inspired by gorilla troops' social intelligence in nature, called Artificial Gorilla Troops Optimizer (GTO). In this algorithm, gorillas' collective life is mathematically formulated, and new mechanisms are designed to perform exploration and exploitation. To evaluate the GTO, we apply it to 52 standard benchmark functions and seven engineering problems. Friedman's test and Wilcoxon rank‐sum statistical tests statistically compared the proposed method with several existing metaheuristics. The results demonstrate that the GTO performs better than comparative algorithms on most benchmark functions, particularly on high‐dimensional problems. The results demonstrate that the GTO can provide superior results compared with other metaheuristics.

[1]  Farhad Soleimanian Gharehchopogh,et al.  A multi-objective optimization algorithm for feature selection problems , 2021, Engineering with Computers.

[2]  Farhad Soleimanian Gharehchopogh,et al.  Discrete farmland fertility optimization algorithm with metropolis acceptance criterion for traveling salesman problems , 2020, Int. J. Intell. Syst..

[3]  A. L. Sangal,et al.  Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization , 2020, Eng. Appl. Artif. Intell..

[4]  Mohammad Heidarinejad,et al.  Equilibrium optimizer: A novel optimization algorithm , 2020, Knowl. Based Syst..

[5]  Farhad Soleimanian Gharehchopogh,et al.  A comprehensive survey: Whale Optimization Algorithm and its applications , 2019, Swarm Evol. Comput..

[6]  James McDermott,et al.  When and why metaheuristics researchers can ignore “No Free Lunch” theorems , 2019, ArXiv.

[7]  Syed Fawad Hussain,et al.  CCGA: Co-similarity based Co-clustering using genetic algorithm , 2018, Appl. Soft Comput..

[8]  Farhad Soleimanian Gharehchopogh,et al.  Farmland fertility: A new metaheuristic algorithm for solving continuous optimization problems , 2018, Appl. Soft Comput..

[9]  Yunlong Zhu,et al.  A new meta-heuristic butterfly-inspired algorithm , 2017, J. Comput. Sci..

[10]  Chee Peng Lim,et al.  An artificial bee colony algorithm with a modified choice function for the Traveling Salesman Problem , 2017, 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[11]  V. Mukherjee,et al.  A novel chaos-integrated symbiotic organisms search algorithm for global optimization , 2017, Soft Computing.

[12]  Quan-Ke Pan,et al.  Self-adaptive fruit fly optimizer for global optimization , 2017, Natural Computing.

[13]  Hui Li,et al.  A modified ABC algorithm based on improved-global-best-guided approach and adaptive-limit strategy for global optimization , 2016, Appl. Soft Comput..

[14]  Min-Yuan Cheng,et al.  A Hybrid Harmony Search algorithm for discrete sizing optimization of truss structure , 2016 .

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

[16]  Ali Kaveh,et al.  Water Evaporation Optimization , 2016 .

[17]  Seyedali Mirjalili,et al.  SCA: A Sine Cosine Algorithm for solving optimization problems , 2016, Knowl. Based Syst..

[18]  Seyed Mohammad Mirjalili,et al.  Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm , 2015, Knowl. Based Syst..

[19]  Seyed Mohammad Mirjalili,et al.  Multi-Verse Optimizer: a nature-inspired algorithm for global optimization , 2015, Neural Computing and Applications.

[20]  Morteza Dadash Naslian,et al.  A new stochastic search algorithm bundled honeybee mating for solving optimization problems , 2014, Neural Computing and Applications.

[21]  Ali Kaveh,et al.  Colliding bodies optimization: A novel meta-heuristic method , 2014 .

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

[23]  Alireza Askarzadeh,et al.  Bird mating optimizer: An optimization algorithm inspired by bird mating strategies , 2014, Commun. Nonlinear Sci. Numer. Simul..

[24]  Ali Husseinzadeh Kashan,et al.  League Championship Algorithm (LCA): An algorithm for global optimization inspired by sport championships , 2014, Appl. Soft Comput..

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

[26]  Harish Sharma,et al.  Spider Monkey Optimization algorithm for numerical optimization , 2014, Memetic Computing.

[27]  Erik Valdemar Cuevas Jiménez,et al.  A swarm optimization algorithm inspired in the behavior of the social-spider , 2013, Expert Syst. Appl..

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

[29]  A. Kaveh,et al.  A new meta-heuristic method: Ray Optimization , 2012 .

[30]  Ardeshir Bahreininejad,et al.  Water cycle algorithm - A novel metaheuristic optimization method for solving constrained engineering optimization problems , 2012 .

[31]  Sriram Devanathan,et al.  Optimizing properties of nanoclay–nitrile rubber (NBR) composites using Face Centred Central Composite Design , 2012 .

[32]  Amir Hossein Gandomi,et al.  Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems , 2011, Engineering with Computers.

[33]  Hamed Shah-Hosseini,et al.  Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation , 2011, Int. J. Comput. Sci. Eng..

[34]  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..

[35]  Xin-She Yang.  17. Firefly Algorithm , 2010 .

[36]  Marco Sciandrone,et al.  Machine learning for global optimization , 2010, Computational Optimization and Applications.

[37]  P. Sicotte.  Effect of Male Competition on Male‐Female Relationships in Bi‐male Groups of Mountain Gorillas , 2010 .

[38]  D. Watts.  Infanticide in Mountain Gorillas: New Cases and a Reconsideration of the Evidence , 2010 .

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

[40]  A. Kaveh,et al.  A novel heuristic optimization method: charged system search , 2010 .

[41]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[42]  Debasish Ghose,et al.  Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions , 2009, Swarm Intelligence.

[43]  J. Lockard,et al.  Competition Coalitions and Conflict Interventions among Captive Female Gorillas , 2007, International Journal of Primatology.

[44]  M. Hoare.  Structure and Dynamics of Simple Microclusters , 2007 .

[45]  Andries Petrus Engelbrecht,et al.  A study of particle swarm optimization particle trajectories , 2006, Inf. Sci..

[46]  Moawad I. Dessouky,et al.  A Novel Tapered Beamforming Window for Uniform Concentric Circular Arrays , 2006 .

[47]  M. M. Ali,et al.  An Iterative Global Optimization Algorithm for Potential Energy Minimization , 2005, Comput. Optim. Appl..

[48]  M. Robbins,et al.  Social structure and life‐history patterns in western gorillas (Gorilla gorilla gorilla) , 2004, American journal of primatology.

[49]  J. Yamagiwa,et al.  Intra-specific variation in social organization of gorillas: implications for their social evolution , 2003, Primates.

[50]  Richard J Parnell,et al.  Female dispersal and reproductive success in wild western lowland gorillas (Gorilla gorilla gorilla) , 2003, Behavioral Ecology and Sociobiology.

[51]  Maliha S. Nash,et al.  Handbook of Parametric and Nonparametric Statistical Procedures , 2001, Technometrics.

[52]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

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

[54]  Dan Boneh,et al.  On genetic algorithms , 1995, COLT '95.

[55]  Miroslav L. Dukic,et al.  A Method of a Spread-Spectrum Radar Polyphase Code Design , 1990, IEEE J. Sel. Areas Commun..

[56]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[57]  A. Harcourt,et al.  Male emigration and female transfer in wild mountain gorilla , 1976, Nature.

[58]  Tantikorn Pichpibul,et al.  An Improved Golden Ball Algorithm for the Capacitated Vehicle Routing Problem , 2014, BIC-TA.

[59]  M. M. Fahmy,et al.  Group Counseling Optimization: A Novel Approach , 2009, SGAI Conf..

[60]  D. Watts.  Mountain gorilla life histories, reproductive competition, and sociosexual behavior and some implications for captive husbandry , 1990 .