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A Novel Honey-Bees Mating Optimization Approach with Higher order Neural Network for Classification

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  • Janmenjoy Nayak

    (Sri Sivani College of Engineering)

  • Bighnaraj Naik

    (Veer Surendra Sai University of Technology)

Abstract

In the recent past, several biological and natural phenomena have extensively attracted researchers towards the rapid development of science and engineering. Basically solving the optimization problems in various Engineering discipline is a popular topic among the other problem solving strategies. Most of the biological processes include the swarm intelligence research areas where the activity and the behavior of real insects have been studied. One of the recently developed Swarm algorithms is the Honey Bee Mating Optimization (HBMO) algorithm which is based on the mating behavior of bees. In this work, a hybrid metaheuristic honey bee mating based Pi-Sigma Neural Network (PSNN) have been proposed to successfully solve the classification problem of data mining. The proposed approach combines HBMO with the PSNN and is compared with other techniques like GA (Genetic Algorithm), DE (Differential Evolution), and PSO (Particle Swarm Optimization). Experimental results reveal that the proposed approach is steady as well as reliable and provides better classification accuracy than others.

Suggested Citation

  • Janmenjoy Nayak & Bighnaraj Naik, 2018. "A Novel Honey-Bees Mating Optimization Approach with Higher order Neural Network for Classification," Journal of Classification, Springer;The Classification Society, vol. 35(3), pages 511-548, October.
    • Handle: RePEc:spr:jclass:v:35:y:2018:i:3:d:10.1007_s00357-018-9270-1
    DOI: 10.1007/s00357-018-9270-1
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    References listed on IDEAS

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    1. Sabar, Nasser R. & Ayob, Masri & Kendall, Graham & Qu, Rong, 2012. "A honey-bee mating optimization algorithm for educational timetabling problems," European Journal of Operational Research, Elsevier, vol. 216(3), pages 533-543.
    2. Omid Haddad & Abbas Afshar & Miguel Mariño, 2006. "Honey-Bees Mating Optimization (HBMO) Algorithm: A New Heuristic Approach for Water Resources Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(5), pages 661-680, October.
    3. Vera, David & Carabias, Julio & Jurado, Francisco & Ruiz-Reyes, Nicolás, 2010. "A Honey Bee Foraging approach for optimal location of a biomass power plant," Applied Energy, Elsevier, vol. 87(7), pages 2119-2127, July.
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    5. Szeto, W.Y. & Wu, Yongzhong & Ho, Sin C., 2011. "An artificial bee colony algorithm for the capacitated vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 215(1), pages 126-135, November.
    6. Guerra, Fábio A. & Coelho, Leandro dos S., 2008. "Multi-step ahead nonlinear identification of Lorenz’s chaotic system using radial basis neural network with learning by clustering and particle swarm optimization," Chaos, Solitons & Fractals, Elsevier, vol. 35(5), pages 967-979.
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