A Novel Adaptive Bat Algorithm to Control Explorations and Exploitations for Continuous Optimization Problems

Kabir, Md. Wasi Ul; Sakib, Nazmus; Chowdhury, Syed Mustafizur Rahman; Alam, Mohammad Shafiul

doi:10.5120/16402-6079

Cited by 16 publications

(5 citation statements)

References 10 publications

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“…TheoriginalBatalgorithm,sinceitsinceptionintheyear2010 (Yang,2010),hasbeenimproved in a number of ways, e.g., Kabir, Sakib, Chowdhury, & Alam (2014), Pérez, Valdez, & Castillo (2015), Zhao&Li(2016),Yilmaz&Kucuksille(2013), Mirjalili,Mirjalili&Yang(2014).However, mostoftheseimprovementsarenotrelatedtofuzzylogic(exceptPérez,Valdez,&Castillo(2015). However,theauthorsin (Pérez,Valdez,&Castillo,2015)donotpresentanysignificantdescription oftheirfuzzysystem,noteventheinputandoutputvariables.Thisiswhywehaveignoredtheir study,whichmakesourworktheonlyfuzzyimprovementoftheoriginalBATalgorithmthatdeals withthemultidimensionalcontinuousfunctionoptimizationproblem.…”

Section: Existing Algorithmsmentioning

confidence: 99%

“…AnotherexistingBatalgorithmvariantthatiscloselyrelatedtoourworkistheNovelAdaptive Bat Algorithm (NABA) (Kabir, Sakib, Chowdhury & Alam, 2014). In each generation, NABA computesthesuccessrateofitsmutationoperationandusesthisinformationtobringabouteither moreexplorationormoreexploitationbyalteringthepulseemissionratesofthesimulatedbats.…”

Section: Existing Algorithmsmentioning

confidence: 99%

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A Novel Fuzzy Inspired Bat Algorithm for Multidimensional Function Optimization Problem

Hasan

Rahman

Jahan

et al. 2019

International Journal of Fuzzy System Applications

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This article introduces Fuzzy Inspired Bat Algorithm (FIBA), which is an improved variant of the original Bat algorithm. The novelty of FIBA lies in the integration of a fuzzy controller with the basic Bat algorithm that tries to bring balance between the degree of explorations and exploitations during the mutation operation. Another novelty of FIBA is the introduction of a step size parameter, maintained separately for every candidate solution, to customize and control the mix of explorative and exploitative operations around each candidate solution. FIBA is tested on a standard benchmark set that includes 10 complex, scalable, high dimensional functions. The results on benchmark functions reveal that FIBA can perform sufficiently well, and often better than the original Bat algorithm and another recently proposed improved Bat variant. Such improvements on the experimental results imply that the fuzzy technique adopted by FIBA might be effective on other existing problems as well, and hence demand further research and investigation.

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Section: Existing Algorithmsmentioning

confidence: 99%

Section: Existing Algorithmsmentioning

confidence: 99%

A Novel Fuzzy Inspired Bat Algorithm for Multidimensional Function Optimization Problem

Hasan

Rahman

Jahan

et al. 2019

International Journal of Fuzzy System Applications

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“…It is clear that, the possibility of flying near to the best position will increase rapidly for each bat after some iterations [14]. In another word, Bat Algorithm loses its exploration capability rapidly and increases its exploitation capability at the following iterations.…”

Section: Micro Bat Algorithmmentioning

confidence: 99%

Micro Bat Algorithm for High Dimensional Optimization Problems

Topal¹,

Altun²,

Yildiz³

2015

IJCA

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Very recently bat inspired algorithms have gained increasing attention as a powerful technique for solving optimization problems. Bat algorithm (BA) is the first one in this group. It is based on the echolocation behavior of bats. BA is very good at exploitation however it is generally poor at exploration. Dynamic Virtual Bats Algorithm (DVBA) is another bat inspired algorithm, which is proposed lately. Although the algorithm is fundamentally inspired from BA, it is conceptually very different. DVBA employs just two bats and uses role based search mechanism. It is very efficient in exploration but relatively poor in exploitation, when it comes to high dimensional problems. In this paper, a novel micro-bat algorithm (µBA) is proposed which possess the advantages of both algorithms. µBA employs a very small population compared to its classical version. It combines the swarming technique of bats in Bat Algorithm with the role based search in Dynamic Virtual Bats Algorithm. Our empirical results demonstrate that the proposed µBA achieves a good balance between exploration and exploitation. And it exhibits a better overall performance than the standard BA with larger and smaller populations on high dimensional problems.

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“…Once the model is developed, mathematical techniques can be applied to determine the inputs to the process that will satisfy a certain given criterion. Combustion engines [21][22][23][24], twostage combustor burning ethylene (doped with ammonia) in air [25], catalytic distillation [26] and desulphurization of hot metal and steel [27] those are the industrial process where the modelling and optimization research have been conducted.…”

Section: Introductionmentioning

confidence: 99%

Application of FPA and ANOVA in the optimization of liquid flow control process

Dutta¹,

Mandal²,

Kumar³

2019

RCES

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In process industry liquid flowrate is one of the important variable which need to be controlled in a process to obtain the better quality and reduce the cost of production. As the liquid Flow rate in a process industry depends upon a number of parameter so the process will give the unexpected output as it is caused by the improper setting of parameters. The improper parameter settings could threaten the processes. In this paper, we utilize the Flower Pollination Algorithm (FPA) methods and ANOVA to obtain the optimum conditions of a flowrate in a process industry and to gain the percentage of contributions of each parameter by. A verification test was carried out to inspect the optimum output among the ANOVA & FPA. For generating the objective function 120 sets of data is used in ANOVA while 18 sets of data are used for the verification purpose.

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A Novel Adaptive Bat Algorithm to Control Explorations and Exploitations for Continuous Optimization Problems

Cited by 16 publications

References 10 publications

A Novel Fuzzy Inspired Bat Algorithm for Multidimensional Function Optimization Problem

A Novel Fuzzy Inspired Bat Algorithm for Multidimensional Function Optimization Problem

Micro Bat Algorithm for High Dimensional Optimization Problems

Application of FPA and ANOVA in the optimization of liquid flow control process

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