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2017 | OriginalPaper | Buchkapitel

Hybrid Global Crossover Bees Algorithm for Solving Boolean Function Classification Task

verfasst von : Habib Shah, Nasser Tairan, Wali Khan Mashwani, Abdulrahman Ahmad Al-Sewari, Muhammad Asif Jan, Gran Badshah

Erschienen in: Intelligent Computing Methodologies

Verlag: Springer International Publishing

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Abstract

Using typical algorithms for training multilayer perceptron (MLP) creates some difficulties like slow convergence speed and local minima trapping in the solution space. Bio-inspired learning algorithms are famous for solving linear and nonlinear combinatorial problems. Artificial Bee Colony (ABC) algorithm is one among the famous bio-inspired algorithms. However, due to slow exploration process, it has been focused by researchers for further enhancement in optimization area. Therefore, this paper proposed a new hybrid swarm based learning algorithm called Global Crossover Artificial Bee Colony (GCABC) algorithm for training MLP for solving boolean classification problems. The simulation results of proposed GCABC algorithm compared with standard bio-inspired algorithms such as ABC, and Global Artificial Bee Colony (GABC) show that the proposed algorithm is achievable and efficient results in benchmark boolean function classification, with fast convergence speed.

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Titel: Hybrid Global Crossover Bees Algorithm for Solving Boolean Function Classification Task
verfasst von: Habib Shah
Nasser Tairan
Wali Khan Mashwani
Abdulrahman Ahmad Al-Sewari
Muhammad Asif Jan
Gran Badshah
Verlag: Springer International Publishing
Buch: Intelligent Computing Methodologies
Print ISBN: 978-3-319-63314-5

Electronic ISBN: 978-3-319-63315-2

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-63315-2_41

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