Modified teaching learning based optimization with hummingbird flight strategy to solve the single and multi-area...

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Engineering Applications

Background:

• Existing optimization algorithms face challenges in efficiency and solution quality for complex problems.
• The Teaching-Learning-Based Optimization (TLBO) algorithm is a population-based metaheuristic.
• The Hummingbird Flight (HBF) strategy offers unique exploration and exploitation mechanisms.

Purpose of the Study:

• To introduce an enhanced TLBO algorithm integrated with the HBF strategy (TLBO-HBF).
• To improve solution quality and computational efficiency for diverse optimization challenges.
• To validate the efficacy of TLBO-HBF against existing state-of-the-art algorithms and real-world problems.

Main Methods:

• Integration of TLBO with HBF operations, incorporating refined step sizes and adjustment coefficients.
• Extensive comparative analysis using IEEE CEC-2014 and CEC-2013 benchmark functions across 30 and 1000 dimensions.
• Application to the reserve-constrained dynamic economic dispatch (DED) problem in various power system configurations.

Main Results:

• TLBO-HBF demonstrated superior performance and solution quality compared to 8 TLBO variants and 10 other optimizers.
• The algorithm achieved a rank of 1 in all economic dispatch problem test cases.
• A maximum cost advantage of $27,064 was observed in economic dispatch applications.

Conclusions:

• The proposed TLBO-HBF algorithm significantly enhances optimization capabilities.
• TLBO-HBF proves effective and practical for complex real-world optimization problems like economic dispatch.
• The research highlights the potential of TLBO-HBF for high-quality optimization applications.