Modified bat algorithm based on covariance adaptive evolution for global optimization problems
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Bat algorithm is a newly proposed swarm intelligence algorithm inspired by the echolocation behavior of bats, which has been successfully used in many optimization problems. However, due to its poor exploration ability, it still suffers from problems such as premature convergence and local optimum. In order to enhance the search ability of the algorithm, we propose an improved bat algorithm, which is based on the covariance adaptive evolution process. The information included in the covariance adaptive evolution diversifies the search directions and sampling distributions of the population, which is of great benefit to the search process. The proposed approaches have been tested on a set of benchmark functions. Experimental results indicate that the proposed algorithm obtains superior performance over the majority of the test problems.
KeywordsBat algorithm Swarm intelligence Covariance adaptive evolution
This work was supported in part by the National Nature Science Foundation of China under Grants 61402534, by the Shandong Provincial Natural Science Foundation, China under grant ZR2014FQ002, and by the Fundamental Research Funds for the Central Universities under grants 16CX02010A.
Compliance with ethical standards
Conflict of interest
The authors declare that there is no conflict of interests regarding the publication of this paper.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Biswal S, Barisal AK, Behera A, Prakash T (2013) Optimal power dispatch using BAT algorithm. In: Proceedings of the 2013 international conference on energy efficient technologies for sustainability, pp 1018–1023Google Scholar
- Chen YT, Shieh CS, Horng MF, Liao BY, Pan JS, Tsai MT (2014) A guidable bat algorithm based on doppler effect to improve solving efficiency for optimization problems. In: Proceedings of the 2014 ICCCI technologies and applications, pp 373–383Google Scholar
- Fister IJ, Yang XS, Fong S, Zhuang Y (2014) Bat algorithm: recent advances. In: Proceedings of the 2014 IEEE 15th international symposium on computational intelligence and informatics, pp 163–167Google Scholar
- Jewajinda Y, Pathom N (2016) Covariance matrix compact differential evolution for embedded intelligence. In: Proceedings of the 2016 IEEE region 10 symposium, pp 349–354Google Scholar
- Kennedy J, Eberhart R (2002) Particle swarm optimization. In: Proceedings of the 2002 IEEE international conference on neural networks, pp 1942–1948Google Scholar
- Khan K, Sahai A (2012) A comparison of BA, GA, PSO, BP and LM for training feed forward neural networks in e-learning context. Int J Intell Syst Appl 4(7):23–29Google Scholar
- Lemma TA (2011) Use of fuzzy systems and bat algorithm for energy modeling in a gas turbine generator. In: Proceedings of the 2011 IEEE colloquium on humanities, science and engineering, pp 305–310Google Scholar
- Li X, Zhang J, Yin M (2013) Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Comput Appl 24(7–8):1867–1877Google Scholar
- Li ZY, Ma L, Zhang HZ (2014) Genetic mutation bat algorithm for 0–1 knapsack problem. Comput Eng Appl 35:1–10 (in Chinese)Google Scholar
- Mirjalili S, Mirjalili SM, Lewis A (2014a) Grey wolf optimizer. Adv Eng Softw 69(3):46–61Google Scholar
- Mirjalili S, Mirjalili SM, Yang XS (2014b) Binary bat algorithm. Neural Comput Appl 25(3–4):663–681Google Scholar
- Pan TS, Dao TK, Nguyen TT, Chu SC (2015) Hybrid particle swarm optimization with bat algorithm. Genet Evolut Comput 329:37–47Google Scholar
- Wang GG, Gandomi AH, Zhao X, Chu HCE (2016a) Hybridizing harmony search algorithm with cuckoo search for global numerical optimization. Soft Comput 20(1):273–85Google Scholar
- Wang Y, Liu ZZ, Li JB (2016b) Utilizing cumulative population distribution information in differential evolution. Appl Soft Comput 48:329–346Google Scholar
- Wang X, Wang W, Wang Y (2013) An adaptive bat algorithm. In: Proceedings of the 2013 ICIC on intelligent computing theories and technology, pp 216–223Google Scholar
- Xie J, Zhou Y, Chen H (2013) A novel bat algorithm based on differential operator and Levy flights trajectory. Comput Intell Neurosci 2013(2013):13–13Google Scholar
- Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: Proceedings of world congress on nature and biologically inspired computing, pp 210–214Google Scholar
- Yang XS (2010a) A new metaheuristic bat-inspired algorithm. In: Proceedings of the 2010 NICSO computational intelligence, pp 65–74Google Scholar
- Yang XS (2010b) Nature-inspired metaheuristic algorithms, 2nd edn. Luniver Press, Beckington, pp 97–103Google Scholar