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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.
Archives of Computational Methods in Engineering : State of the Art Reviews
|June 13, 2022
Summary
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.
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.
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