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A New Parallel Cuckoo Flower Search Algorithm for Training Multi-Layer Perceptron

Author

Listed:
  • Rohit Salgotra

    (Faculty of Physics and Applied Computer Science, AGH University of Science & Technology, 30-059 Krakow, Poland
    MEU Research Unit, Middle East University, Amman 11813, Jordan)

  • Nitin Mittal

    (University Centre for Research and Development, Chandigarh University, Mohali 140413, India)

  • Vikas Mittal

    (University Centre for Research and Development, Chandigarh University, Mohali 140413, India)

Abstract

This paper introduces a parallel meta-heuristic algorithm called Cuckoo Flower Search (CFS). This algorithm combines the Flower Pollination Algorithm (FPA) and Cuckoo Search (CS) to train Multi-Layer Perceptron (MLP) models. The algorithm is evaluated on standard benchmark problems and its competitiveness is demonstrated against other state-of-the-art algorithms. Multiple datasets are utilized to assess the performance of CFS for MLP training. The experimental results are compared with various algorithms such as Genetic Algorithm (GA), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Evolutionary Search (ES), Ant Colony Optimization (ACO), and Population-based Incremental Learning (PBIL). Statistical tests are conducted to validate the superiority of the CFS algorithm in finding global optimum solutions. The results indicate that CFS achieves significantly better outcomes with a higher convergence rate when compared to the other algorithms tested. This highlights the effectiveness of CFS in solving MLP optimization problems and its potential as a competitive algorithm in the field.

Suggested Citation

  • Rohit Salgotra & Nitin Mittal & Vikas Mittal, 2023. "A New Parallel Cuckoo Flower Search Algorithm for Training Multi-Layer Perceptron," Mathematics, MDPI, vol. 11(14), pages 1-25, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:14:p:3080-:d:1192599
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    References listed on IDEAS

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    1. Melanie Mitchell & John H. Holland, 1993. "When Will a Genetic Algorithm Outperform Hill-Climbing?," Working Papers 93-06-037, Santa Fe Institute.
    2. Uzlu, Ergun & Kankal, Murat & Akpınar, Adem & Dede, Tayfun, 2014. "Estimates of energy consumption in Turkey using neural networks with the teaching–learning-based optimization algorithm," Energy, Elsevier, vol. 75(C), pages 295-303.
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