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Improved Kangaroo Mob Optimization and Logistic Regression for Smart Grid Stability Classification

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Artificial Intelligence in Intelligent Systems (CSOC 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 229))

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Abstract

The paper presents an improved version of the Kangaroo Mob Optimization (KMO) which is further called Improved Kangaroo Mob Optimization (IKMO). IKMO modifies the standard equations for the updating of the positions of the kangaroos to avoid the premature convergence and to explore the search space more. IKMO is validated using 9 objective functions and is compared to other bio-inspired algorithms such as Horse Optimization Algorithm (HOA), Chicken Swarm Optimization (CSO), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Cat Swarm Optimization Algorithm (CSOA). Finally, the article applies IKMO in the tuning of the hyperparameters of a Logistic Regression (LR) model for the classification of the stability of a smart grid using the Electrical Grid Stability Simulated Dataset from the UCI Machine Learning Repository as experimental support.

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Authors and Affiliations

  1. Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

    Dorin Moldovan

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  1. Dorin Moldovan
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Correspondence to Dorin Moldovan .

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  1. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Radek Silhavy

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Moldovan, D. (2021). Improved Kangaroo Mob Optimization and Logistic Regression for Smart Grid Stability Classification. In: Silhavy, R. (eds) Artificial Intelligence in Intelligent Systems. CSOC 2021. Lecture Notes in Networks and Systems, vol 229. Springer, Cham. https://doi.org/10.1007/978-3-030-77445-5_44

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