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A Deep Insight into Advanced Optimization

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Towards Augmented Slime Mould Kernel Extreme Learning Models for Bankruptcy Prediction: Algorithmic Behavior and Comprehensive Analysis

Neurocomputing, Available online 22 October 2020,

Bankruptcy prediction is a crucial application in financial fields to aid in accurate decision making for business enterprises. Many models may stagnate to low-accuracy results due to the uninformed choice of parameters. This paper presents a forward-thinking bankruptcy prediction model based on kernel extreme learning machine (KELM), which proposes a new efficient version of a fruit fly optimization (FOA) algorithm called LSEOFOA, to evolve and harmonize the penalty and the kernel parameter in KELM. The upgraded version of FOA is conceptualized based on three reorganizations. The first attempt is to include Levy's flight for improving exploration inclinations, and the second is slime mould-based process for avoiding premature convergence and enhancing the stability of the exploration and exploitation patterns. As the last modification, we utilized the elite opposition-based learning for accelerating the convergence. The algorithmic trends of this optimizer are verified, and then, it is verified on a bankruptcy prediction module. Therefore, to further demonstrate the superiority of the LSEOFOA method, comparison studies are performed using the conventional FOA and other variants of FOA and a set of advanced algorithms including EBOwithCMAR. Experimental results for every optimization task demonstrate that LSEOFOA can provide a high-performance and self-assured tradeoff between exploration and exploitation. Also, the developed KELM classifier is utilized for bankruptcy prediction, and its optimal parameters set are revealed by the proposed FOA. The effectiveness of the LSEOFOA-KELM model is rigorously evaluated using a financial dataset and comparison with KELM-based models with other competitive optimizers such as LSHADE-RSP. Overall research findings show that the proposed model is superior in terms of classification accuracy, Matthews correlation coefficient, sensitivity, and specificity. Towards more evolutionary and efficient prediction models, the proposed LSEOFOA-KELM prediction model can be regarded as a promising warning tool for financial decision making, with successful performance in bankruptcy prediction.

Online access to Elsevier full-text PDF

Analysis and Source Codes of the Original Slime Mould Algorithm (SMA)

Slime mould algorithm (SMA) is a powerful population-based optimizer based on the oscillation mode of slime mould in nature. In April 2020, the paper of SMA published in the prestigious journal of Future Generation Computer Systems (FGCS).

Go to Webpage of Slime Mould Algorithm (SMA) for full info

Download MATLAB source codes of Slime Mould Algorithm (SMA)

Download JAVA source codes of Slime Mould Algorithm (SMA)

Download Python source codes of Slime Mould Algorithm (SMA)

Download LATEX source codes of Slime Mould Algorithm (SMA)

Download Visio source files of Slime Mould Algorithm (SMA)

This method's source codes are widely available in almost all programming languages, and it has both a latex template and word office file for the pleasure of users. This method is backed up with a 24-h online service for reacting to users' questions on the code.

Frequently asked questions

  • How to download codes of SMAKELM or LSEOFOA or KELM Method?
    Press Contact me to copy my email address. Feel free to email me for any question or help I can do.
  • Can I download source codes of the basic SMA optimization algorithm or Slime mould algorithm for academic projects?
    Yes, source codes of basic optimization algorithms is available for both non-profit and academic uses.
  • How to download PDF source this paper?
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  • Can you help us to continue this research on LSEOFOA part, optimization or SMAKELM, or Slime mould algorithm for optimizing KELM?
    Yes, we can do great research together and I have all required material and team for collaboration and innovations. Press Contact me and it starts.
  • What is Code Editor or programming language?
    This SMAKELM code is written in MATLAB programming language and allows editing the code of algorithm in the app. Also, it's possible to get the codes of SMAKELM in other available programming languages.
How to cite?

Zhang, Y., Liu, R., Heidari, A. A., Wang, X., Chen, Y., Wang, M., & Chen, H. (2020). Towards Augmented Kernel Extreme Learning Models for Bankruptcy Prediction: Algorithmic Behavior and Comprehensive Analysis. Neurocomputing.

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