Optimization using lion algorithm: a biological inspiration from lion’s social behavior
- 91 Downloads
Nature-inspired optimization algorithms, especially evolutionary computation-based and swarm intelligence-based algorithms are being used to solve a variety of optimization problems. Motivated by the obligation of having optimization algorithms, a novel optimization algorithm based on a lion’s unique social behavior had been presented in our previous work. Territorial defense and territorial takeover were the two most popular lion’s social behaviors. This paper takes the algorithm forward on rigorous and diverse performance tests to demonstrate the versatility of the algorithm. Four different test suites are presented in this paper. The first two test suites are benchmark optimization problems. The first suite had comparison with published results of evolutionary and few renowned optimization algorithms, while the second suite leads to a comparative study with state-of-the-art optimization algorithms. The test suite 3 takes the large-scale optimization problems, whereas test suite 4 considers benchmark engineering problems. The performance statistics demonstrate that the lion algorithm is equivalent to certain optimization algorithms, while outperforming majority of the optimization algorithms. The results also demonstrate the trade-off maintainability of the lion algorithm over the traditional algorithms.
KeywordsLion algorithm Optimization Bio-inspired Large-scale Crossover Mutation
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- 1.Rozenberg G, Bck T, Kok JN (2011) Handbook of natural computing, 1st edn. Springer Publishing Company, New YorkGoogle Scholar
- 5.Corne D, Deb K, Knowles J, Yao X (2010) Selected applications of natural computing. In: Rozenberg G, Back T, Kok JN (eds) Handbook of natural computing. Springer, BerlinGoogle Scholar
- 6.Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann ArborGoogle Scholar
- 11.Rajakumar BR (2014) Lion algorithm for standard and large scale bilinear system identification: a global optimization based on lion’s social behavior. In: 2014 IEEE congress on evolutionary computation (CEC), pp 2116–2123Google Scholar
- 13.Babers R, Hassanien AE, Ghali NI (2015) A nature-inspired metaheuristic lion optimization algorithm for community detection. In: 2015 11th IEEE international computer engineering conference (ICENCO)Google Scholar
- 19.Jong De KA (1975) An analysis of the behavior of a class of genetic adaptive systems. Doctoral thesis, Dept. Computer and Communication Sciences, University of Michigan, Ann ArborGoogle Scholar
- 23.Packer C, Pusey AE (1982) Cooperation and competition within coalition of male lions: Kin selection or game theory. Macmillan J 296(5859):740–742Google Scholar
- 29.Fogel LJ, Owens AJ, Walsh MJ (1965) Artificial intelligence through a simulation of evolution. In: Proc. 2nd cybern. sci. symp. biophysics cybern. syst., Washington: Spartan Books, pp 131–155Google Scholar
- 32.Hansen N, Ostermeier A (1996) Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation. In: IEEE int. conf. evolution. comput. (ICEC) proc, pp 312–317Google Scholar
- 33.Hansen N (2006) The CMA evolution strategy: a comparing review. In: Lozano JA, Larraaga P, Inza I, Bengoetxea E (eds) Towards a new evolutionary computation. Springer, BerlinGoogle Scholar
- 34.Hedar A, Fukushima M (2006) Evolution strategies learned with automatic termination criteria. In: Proceedings of SCIS-ISIS 2006, Tokyo, JapanGoogle Scholar
- 35.Goldberg DE (1989) Genetic algorithms in search optimization and machine learning. Addison Wesley, Reading, p 41Google Scholar
- 36.Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, IV, pp 1942–1948. https://doi.org/10.1109/ICNN.1995.488968
- 37.Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering DepartmentGoogle Scholar
- 40.Yang X-S, Deb S (2009) Cuckoo search via Lévy flights. In: World congress on nature and biologically inspired computing (NaBIC 2009), IEEE Publications, pp 210–214Google Scholar
- 41.Yang X-S (2010) Nature inspired metaheuristic algorithms, 2nd edn. Luniver Press, LondonGoogle Scholar