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Improved Binary Grasshopper Optimization Algorithm for Feature Selection Problem.

Gui-Ling Wang1, Shu-Chuan Chu1,2, Ai-Qing Tian1

  • 1College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China.

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Summary
This summary is machine-generated.

This study enhances the binary grasshopper optimization algorithm (BGOA) by modifying its step size and introducing new transfer functions. The improved BGOA demonstrates superior performance in optimization tasks and feature selection compared to existing methods.

Keywords:
binary versionfeature selectiongrasshopper optimizationtransfer function

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Area of Science:

  • Computational Intelligence
  • Metaheuristic Optimization
  • Swarm Intelligence

Background:

  • The grasshopper optimization algorithm (GOA) is a nature-inspired metaheuristic algorithm simulating grasshopper behavior.
  • The binary grasshopper optimization algorithm (BGOA) adapts GOA for binary optimization problems.
  • Existing BGOA methods may have limitations in exploration capability and solution quality.

Purpose of the Study:

  • To enhance the exploration capability and solution quality of the binary grasshopper optimization algorithm (BGOA).
  • To introduce novel modifications to the BGOA's step size and propose new transfer functions.
  • To validate the effectiveness of the improved BGOA through comparative experiments and real-world applications.

Main Methods:

  • Modification of the step size parameter within the BGOA framework.
  • Development and integration of three new transfer functions to improve solution space exploration.
  • Comparative analysis against BGOA, particle swarm optimization (PSO), and binary gray wolf optimizer (BGWO) on 23 benchmark functions.
  • Application of the improved algorithm for feature selection on 23 UCI datasets.
  • Statistical validation using Wilcoxon rank-sum and Friedman tests.

Main Results:

  • The improved BGOA demonstrated significantly superior performance across most benchmark test functions compared to BGOA, PSO, and BGWO.
  • The optimized algorithm achieved higher accuracy and selected fewer features in the feature selection task on UCI datasets.
  • The proposed modifications effectively enhanced the algorithm's exploration capability and the quality of the obtained solutions.

Conclusions:

  • The enhanced BGOA with modified step size and new transfer functions represents a significant improvement over existing binary optimization algorithms.
  • The improved algorithm is highly effective for both general optimization problems and practical applications like feature selection.
  • The study validates the proposed enhancements through rigorous testing and statistical analysis, confirming its superior performance and efficiency.