Blackcap Optimization Algorithm (BCOA): A Novel Metaheuristic Algorithm for Global and Engineering Optimization...

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Nature-Inspired Computing

Background:

• Metaheuristic algorithms face challenges in balancing exploration/exploitation and avoiding local optima.
• Existing methods often rely on complex distance calculations, increasing computational cost.

Purpose of the Study:

• Introduce the Blackcap Optimization Algorithm (BCOA), a novel metaheuristic inspired by Blackcap bird navigation.
• Improve the balance between exploration and exploitation while reducing computational complexity.

Main Methods:

• BCOA utilizes angle-based movement vectors, avoiding complex distance computations.
• Employs a mathematical model integrating global best angle, neighboring angle, and adaptive disturbance.
• Incorporates a quasi-genetic mechanism for path transition and a territorial competition stage.

Main Results:

• BCOA demonstrated a strong ability to escape local optima across 32 benchmark functions.
• The algorithm achieved competitive convergence speed and cost reduction on engineering and network problems.
• Simulation results indicate superior performance compared to several existing optimization methods.

Conclusions:

• The Blackcap Optimization Algorithm (BCOA) offers an effective approach to complex optimization challenges.
• BCOA's unique mechanisms enhance exploration-exploitation balance and computational efficiency.
• This novel algorithm shows significant potential for various optimization applications.