Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Nature-Inspired Computing

Background:

• Teaching-Learning-Based Optimization (TLBO) is a popular nature-inspired heuristic algorithm.
• Standard TLBO can suffer from slow convergence and premature convergence to local optima.

Purpose of the Study:

• To develop a novel metaheuristic algorithm to improve TLBO's performance.
• To enhance the convergence rate and global search capability of TLBO.
• To address the limitations of TLBO in handling complex optimization tasks.

Main Methods:

• Integration of chaos mechanism characteristics into the TLBO framework.
• Incorporation of Lévy flight properties to enhance exploration.
• Testing the proposed algorithm on diverse large-scale nonlinear benchmark functions.

Main Results:

• The proposed algorithm demonstrates superior performance compared to existing methods.
• Significant improvements in convergence rate and avoidance of local optima were observed.
• The enhanced TLBO algorithm achieved satisfactory results on benchmark test functions.

Conclusions:

• The novel metaheuristic effectively improves upon the basic TLBO algorithm.
• The integration of chaos and Lévy flight offers a promising approach for enhancing optimization algorithms.
• The proposed method shows potential for practical applications in complex optimization problems.