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Human-Inspired Optimization Algorithms: Theoretical Foundations, Algorithms, Open-Research Issues and Application for

Rebika Rai1, Arunita Das2, Swarnajit Ray3

  • 1Department of Computer Applications, Sikkim University, Gangtok, Sikkim India.

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Summary
This summary is machine-generated.

This study introduces Human-Inspired Optimization Algorithms (HIOAs), leveraging human intelligence for complex problem-solving. It surveys HIOAs, their applications in image segmentation, and future research directions.

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Area of Science:

  • Artificial Intelligence
  • Computational Intelligence
  • Optimization Techniques

Background:

  • Human intelligence, encompassing understanding, reasoning, and problem-solving, offers unique potential for advanced computational methods.
  • Nature-Inspired Optimization Algorithms (NIOAs) are expanding, with Human-Inspired Optimization Algorithms (HIOAs) emerging as a distinct category.
  • The rapid growth of HIOAs necessitates a structured understanding of their theoretical foundations, governing principles, and common structures.

Purpose of the Study:

  • To provide a comprehensive survey and analysis of Human-Inspired Optimization Algorithms (HIOAs).
  • To distinguish HIOAs based on various criteria and discuss their fundamental building blocks.
  • To explore the implications of HIOAs on color satellite image segmentation for Multi-Level Thresholding (MLT) models.

Main Methods:

  • Categorization and analysis of existing Human-Inspired Optimization Algorithms (HIOAs).
  • Examination of the theoretical underpinnings and common structures of HIOAs.
  • Application of HIOAs to Multi-Level Thresholding (MLT) models using Tsallis and t-entropy objective functions for color satellite image segmentation.

Main Results:

  • A structured overview distinguishing HIOAs based on key criteria.
  • Identification of common challenges and open research issues within the HIOA domain.
  • Evaluation of HIOA efficacy in developing advanced Multi-Level Thresholding (MLT) models for image segmentation.

Conclusions:

  • Human-Inspired Optimization Algorithms (HIOAs) represent a promising frontier in optimization, drawing from human cognitive abilities.
  • Further research into HIOAs is crucial for selecting appropriate algorithms and addressing complex real-world problems.
  • HIOAs demonstrate potential in enhancing image segmentation techniques, particularly for Multi-Level Thresholding (MLT) applications.