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A Classification Model Based on Improved Self-Adaptive Fireworks Algorithm

A Classification Model Based on Improved Self-Adaptive Fireworks Algorithm
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Author(s): Yu Xue (Nanjing University of Information Science and Technology, China)
Copyright: 2020
Pages: 28
Source title: Handbook of Research on Fireworks Algorithms and Swarm Intelligence
Source Author(s)/Editor(s): Ying Tan (Peking University, China)
DOI: 10.4018/978-1-7998-1659-1.ch007

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Abstract

As a recently developed swarm intelligence algorithm, fireworks algorithm (FWA) is an optimization algorithm with good convergence and extensible properties. Moreover, it is usually able to find the global solutions. The advantages of FWA are both optimization accuracy and convergence speed which endue the FWA with a promising prospect of application and extension. This chapter mainly focuses on the application of FWA in classification problems and the improvement of FWA. Many prior studies around FWA have been produced. The author here probes improvement of FWA and its application in classification. The chapter studies FWA around: (1) Application of FWA in classification problems; (2) Improvement of FWA's candidate solution generation strategy (CSGS), including the employment of self-adaptive mechanisms; (3) Improved SaFWA and classification model. For each part, the author conducts research through theory, experimentation, and results analysis.

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