Optimizing Support Vector Machine Parameters Using Bat Optimization Algorithm

  • Alaa TharwatEmail author
  • Aboul Ella Hassanien
Part of the Studies in Computational Intelligence book series (SCI, volume 801)


Support Vector Machine (SVM) parameters such as penalty parameter and kernel parameters have a great influence on the complexity and accuracy of predicting model. In this research, Bat algorithm (BA) has been employed to optimize the parameters of SVM, so that the classification performance can be increased. To evaluate the proposed model (BA-SVM), the experiment adopted different standard classification datasets which are obtained from the UCI machine learning data repository. The results of the BA-SVM algorithm are compared with grid search, which is a classical method of searching parameter values, and two other optimization algorithms, namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The experimental results showed that the proposed model achieved competitive results and it can find the optimal values of SVM parameters.


Optimization algorithms Support vector machine (SVM) Classification Parameter optimization Swarm intelligent Bat algorithm (BA) 


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Authors and Affiliations

  1. 1.Faculty of EngineeringSuez Canal UniversityIsmailiaEgypt
  2. 2.Faculty of Computer Science and EngineeringFrankfurt University of Applied SciencesFrankfurtGermany
  3. 3.Faculty of Computers and InformationCairo UniversityGizaEgypt
  4. 4.Scientific Research Group in Egypt (SRGE)GizaEgypt

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