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Research and Study of the Hybrid Algorithms Based on the Collective Behavior of Fish Schools and Classical Optimization Methods

Institute of Integrated Safety and Special Instrumentation, Federal State Budget Educational Institution of Higher Education, MIREA—Russian Technological University, 78, Vernadskogo avenye, 119454 Moscow, Russia
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This paper is the extended version of our paper published in the proceedings of the 8-th International Workshop on Mathematical Models and their Applications (IWMMA 2019) (Krasnoyarsk, Russia, 18–21 November 2019).
Algorithms 2020, 13(4), 85; https://doi.org/10.3390/a13040085
Received: 14 March 2020 / Revised: 31 March 2020 / Accepted: 2 April 2020 / Published: 3 April 2020
(This article belongs to the Special Issue Mathematical Models and Their Applications)
Inspired by biological systems, swarm intelligence algorithms are widely used to solve multimodal optimization problems. In this study, we consider the hybridization problem of an algorithm based on the collective behavior of fish schools. The algorithm is computationally inexpensive compared to other population-based algorithms. Accuracy of fish school search increases with the increase of predefined iteration count, but this also affects computation time required to find a suboptimal solution. We propose two hybrid approaches, intending to improve the evolutionary-inspired algorithm accuracy by using classical optimization methods, such as gradient descent and Newton’s optimization method. The study shows the effectiveness of the proposed hybrid algorithms, and the strong advantage of the hybrid algorithm based on fish school search and gradient descent. We provide a solution for the linearly inseparable exclusive disjunction problem using the developed algorithm and a perceptron with one hidden layer. To demonstrate the effectiveness of the algorithms, we visualize high dimensional loss surfaces near global extreme points. In addition, we apply the distributed version of the most effective hybrid algorithm to the hyperparameter optimization problem of a neural network. View Full-Text
Keywords: evolutionary optimization; swarm intelligence; fish school search; gradient descent; hybrid algorithm; Newton’s algorithm; neural network training; hyper parameter optimization; distributed computations evolutionary optimization; swarm intelligence; fish school search; gradient descent; hybrid algorithm; Newton’s algorithm; neural network training; hyper parameter optimization; distributed computations
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    Doi: 10.5281/zenodo.3710828
    Link: https://zenodo.org/record/3710828
    Description: Supplementary materials for the article «Research and Study of the Hybrid Algorithms Based on the Collective Behavior of Fish Schools and Classical Optimization Methods» for the MDPI Algorithms Journal
MDPI and ACS Style

Demidova, L.A.; Gorchakov, A.V. Research and Study of the Hybrid Algorithms Based on the Collective Behavior of Fish Schools and Classical Optimization Methods. Algorithms 2020, 13, 85.

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