Swarm bat algorithm with improved search (SBAIS)
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Bat algorithm (BA) is a powerful nature-inspired swarm algorithm which finds applicability to a diverse range of problem domains. Though it is efficient, it suffers from two handicaps: possibility of being trapped in local optima and lost convergence speed as the algorithm progresses. This paper proposes swarm bat algorithm with improved search (SBAIS). SBAIS gains superior exploration capabilities by employing swarming characteristics inspired by shuffled complex evolution (SCE) algorithm. Best bats of the population are kept in a super-swarm, while all other bats are partitioned according to SCE. The super-swarm uses the search mechanism of bat algorithm with improved search to perform refined search around the best solution, which makes sure that the convergence speed of the algorithm is not lost. Every other swarm gets one solution from the super-swarm before starting their evolution process. These swarms evolve using standard bat algorithm, helping the algorithm to escape any possible local optima. SBAIS further keeps a check on the overall diversity of the population. If the diversity drops below a given threshold value, new random solutions are added to the population. Performance of SBAIS is validated by comparing it to BA and fourteen recent variants of bat algorithm over 30 standard benchmark optimization functions, CEC’05 and CEC’14 function sets. Results established the superiority of SBAIS over the compared algorithms.
KeywordsBat algorithm Bat algorithm with improved search Shuffled complex evolution algorithm Numerical optimization
Compliance with ethical standards
Conflict of interest
All 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.
- Akhtar S, Ahmad AR, Abdel-Rahman EM (2012) A metaheuristic bat-inspired algorithm for full body human pose estimation. In: 2012 9th conference on computer and robot vision (CRV), pp 369–375Google Scholar
- Banati H, Chaudhary R (2016) Enhanced shuffled bat algorithm (EShBAT). In: 2016 international conference on advances in computing, communications and informatics (ICACCI), Jaipur, pp 731–738Google Scholar
- Biswal S, Barisal AK, Behera A, Prakash T (2013) Optimal power dispatch using BAT algorithm. In: 2013 international conference on energy efficient technologies for sustainability (ICEETS), pp 1018–1023Google Scholar
- Chaudhary R, Banati H (2017) Shuffled multi-population bat algorithm (SMPBat). In: 2017 international conference on advances in computing, communications and informatics (ICACCI), Udupi, pp 541–547Google Scholar
- Crepinsek M, Liu S-H, Mernik M (2013) Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput Surv 45(3): Article 35 (June 2013), 33 pagesGoogle Scholar
- Dorigo M, Caro GD (1999) The ant colony optimization meta-heuristic. In: Corne D, Dorigo M, Glover F (eds) New ideas in optimization. McGraw-Hill, LondonGoogle Scholar
- Gupta N, Sharma K (2015) Optimizing intermediate COCOMO model using BAT algorithm. In: 2nd international conference on computing for sustainable global development. IEEE, pp 1649–1653Google Scholar
- Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: IEEE international conference neural networks, Australia, pp 1942–1948Google Scholar
- Ramawan MK, Othman Z, Sulaiman SI, Musirin I, Othman N (2014) A hybrid bat algorithm artificial neural network for grid-connected photovoltaic system output prediction. In: 2014 IEEE 8th international power engineering and optimization conference (PEOCO2014), Langkawi, pp 619–623Google Scholar
- Wang GG, Chang B, Zhang Z (2015) A multi-swarm bat algorithm for global optimisation. In: 2015 IEEE congress on evolutionary computation (CEC), pp 480–485Google Scholar
- Yang X-S (2009) Firefly algorithms for multimodal optimization. In: Watanabe O, Zeugmann T (eds) Stochastic algorithms: foundations and applications, SAGA 2009, Lecture notes in computer science, vol 5792. Springer, Berlin, pp 169–178Google Scholar
- Yang X-S (2010a) A new metaheuristic bat-inspired algorithm. In: Gonzalez JR et al (eds) Nature inspired cooperative strategies for optimization (NISCO 2010), studies in computational intelligence, vol 284, pp 65–74Google Scholar
- Yang X-S (2010b) Nature-inspired metaheuristic algorithms, 2nd edn. Luniver Press, LondonGoogle Scholar