Over the last decade, several metaheuristic algorithms have emerged to solve numerical function optimization problems. Since the performance of these algorithms presents a suboptimal behavior, a large number of studies have been carried out to find new and better algorithms. Therefore, this paper proposes a new metaheuristic algorithm, namely the car tracking optimization algorithm; it is inspired by observing the programming methods of other metaheuristic algorithms. And the proposed algorithm has been tested over 55 benchmark functions, and the results have been compared with firefly algorithm (FA), cuckoo searching algorithm (CS), and vortex search algorithm (VS). The results indicate that the performance of the proposed algorithm surpasses FA, CS, and VS algorithm.
This is a preview of subscription content, log in to check access.
Compliance with ethical standards
Conflict of interest
The authors declared that they have no conflicts of interest to this work.
This article does not contain any studies with human participants performed by any of the authors.
Askarzadeh A, Rezazadeh A (2011) A grouping-based global harmony search algorithm for modeling of proton exchange membrane fuel cell. Int J Hydrogen Energy 36:5047–5053CrossRefGoogle Scholar
Choi S, Yeung D (2006) Learning-based SMT processor resource distribution via hill-climbing. ACM SIGARCH Comput Archit News 34:239–251CrossRefGoogle Scholar
Civicioglu P (2013) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219:8121–8144MathSciNetzbMATHGoogle Scholar
Colorni A, Dorigo M, Maniezzo V et al (1991) Distributed optimization by ant colonies. In: Proceedings of the first European conference on artificial life, pp 134–142Google Scholar
Dai C, Chen W, Ran L et al (2011) Human group optimizer with local search. In: International conference in swarm intelligence, pp 310–320Google Scholar
Doğugan B, Ölmez T (2015) A new metaheuristic for numerical function optimization: vortex search algorithm. Inf Sci (Ny) 293:125–145CrossRefGoogle Scholar
Dorigo M, Stützle T (1999) The ant colony optimization metaheuristic. In: New ideas in optimization, pp 11–32Google Scholar
Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B Cybern A Publ IEEE Syst Man Cybern Soc 26:29–41CrossRefGoogle Scholar
Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: International symposium on micro machine and human science, pp 39–43Google Scholar
Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39:459–471MathSciNetCrossRefzbMATHGoogle Scholar
Li X, Shao Z, Qian J (2002) An optimizing method based on autonomous animats: fish-swarm algorithm. Syst Eng Theory Pract 22:32–38Google Scholar
Li H-Z, Guo S, Li C-J, Sun J-Q (2013) A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm. Knowl-Based Syst 37:378–387CrossRefGoogle Scholar
Osuna-Enciso V, Cuevas E, Oliva D et al (2016) A bio-inspired evolutionary algorithm: allostatic optimisation. Int J Bio-Inspired Comput 8:154–169CrossRefGoogle Scholar
Pan W-T (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl-Based Syst 26:69–74CrossRefGoogle Scholar
Rechenberg I (1965) Cybernetic solution path of an experimental problemGoogle Scholar
Shi Y, Eberhart R (1998) Modified particle swarm optimizer. In: IEEE international conference on evolutionary computation proceedings, 1998. IEEE world congress on computational intelligence, pp 69–73Google Scholar
Wang C-R, Zhou C-L, Ma J-W (2005) An improved artificial fish-swarm algorithm and its application in feed-forward neural networks. In: 2005 International conference on machine learning and cybernetics, pp 2890–2894Google Scholar
Yang X-S (2010a) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, pp 65–74Google Scholar
Yang X-S (2010b) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio-Inspired Comput 2:78–84CrossRefGoogle Scholar
Yang X-S (2010c) Nature-inspired metaheuristic algorithms. Luniver Press, FromeGoogle Scholar
Yang XS, Deb S (2009) Cuckoo Search via Lévy flights. In: World congress on nature and biologically inspired computing, 2009. NaBIC 2009, pp 210–214Google Scholar
Yang XS, Deb S (2010) Engineering optimisation by cuckoo search. Int J Math Model Numer Optim 1:330–343zbMATHGoogle Scholar
Yang X-S, Hossein Gandomi A (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29:464–483CrossRefGoogle Scholar
Yang XS, Hosseini SSS, Gandomi AH (2012) Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect. Appl Soft Comput 12:1180–1186CrossRefGoogle Scholar