An improved African vultures optimization algorithm based on tent chaotic mapping and time-varying mechanism.

Area of Science:

• Engineering Optimization
• Computational Intelligence
• Metaheuristic Algorithms

Background:

• Metaheuristic algorithms are crucial for complex engineering problems.
• Algorithm performance hinges on exploration and exploitation balance.
• Existing African vultures optimization algorithm (AVOA) can be improved.

Purpose of the Study:

• To propose an improved African vultures optimization algorithm (AVOA) named TAVOA.
• To enhance the exploration and exploitation capabilities of the AVOA.
• To validate the effectiveness and efficiency of TAVOA on benchmark and real-world problems.

Main Methods:

• Introduced tent chaotic mapping for population initialization.
• Incorporated individual historical optimal positions for location updating.
• Developed a time-varying mechanism to balance exploration and exploitation.

Main Results:

• TAVOA significantly outperformed AVOA on 13 of 23 basic benchmark functions.
• TAVOA showed significant or similar performance on 26 of 28 CEC 2013 benchmark functions compared to AVOA.
• TAVOA demonstrated competitive performance on real-world engineering design problems against six other algorithms.

Conclusions:

• TAVOA effectively improves upon the AVOA by balancing exploration and exploitation.
• The proposed enhancements lead to superior or comparable performance across various optimization tasks.
• TAVOA presents a promising alternative for complex engineering optimization challenges.