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Assessing Spatial Learning and Memory in Small Squamate Reptiles

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MLBRSA: Multi-Learning-Based Reptile Search Algorithm for Global Optimization and Software Requirement Prioritization

Jeyaganesh Kumar Kailasam¹, Rajkumar Nalliah², Saravanakumar Nallagoundanpalayam Muthusamy³

¹Department of Artificial Intelligence and Data Science, M. Kumarasamy College of Engineering, Karur 639113, Tamilnadu, India.

Biomimetics (Basel, Switzerland)

|December 22, 2023

Summary

This summary is machine-generated.

This study introduces the Multi-Learning-Based Reptile Search Algorithm (MLBRSA), combining Q-learning, competitive, and adaptive learning. MLBRSA efficiently solves complex engineering problems and prioritizes software requirements.

Keywords:

Q-learning adaptive learning competitive learning multi-learning-based reptile search algorithm (MLBRSA)optimization software requirement prioritization

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Area of Science:

Computational intelligence
Optimization algorithms
Machine learning

Background:

Efficient algorithms are crucial for complex engineering and software requirement prioritization.
Existing methods may lack adaptability or robustness in diverse problem spaces.
The Reptile Search Algorithm (RSA) provides a foundation for optimization.

Purpose of the Study:

To introduce the Multi-Learning-Based Reptile Search Algorithm (MLBRSA).
To enhance problem-solving capabilities by integrating multiple learning strategies.
To demonstrate MLBRSA's effectiveness in engineering and software requirement prioritization.

Main Methods:

Synergistic integration of Q-learning, competitive learning, and adaptive learning.
Development of a multi-learning framework based on the Reptile Search Algorithm (RSA).
Application and evaluation on numerical benchmarks and real-world engineering problems.

Main Results:

MLBRSA successfully identified optimal solutions in complex numerical and engineering problem spaces.
The algorithm effectively prioritized software requirements, ensuring focus on critical functionalities.
Demonstrated superior performance compared to traditional approaches in tested scenarios.

Conclusions:

MLBRSA offers a robust and versatile solution for computational problem-solving.
The multi-learning approach enhances adaptability and competitiveness in optimization.
MLBRSA presents a valuable tool for researchers and practitioners in engineering and software development.