A Hybrid Black-Winged Kite Algorithm with PSO and Differential Mutation for Superior Global Optimization and...

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Heuristic Search

Background:

• The Black-Winged Kite Algorithm (BKA) faces premature convergence in high-dimensional optimization.
• Existing algorithms often struggle with balancing exploration and exploitation.
• Parameter sensitivity and insufficient local search limit BKA's effectiveness.

Purpose of the Study:

• To propose an enhanced hybrid algorithm (BKAPI) to address BKA's limitations.
• To improve convergence speed and computational accuracy in high-dimensional optimization.
• To achieve a robust balance between exploration and exploitation.

Main Methods:

• Hybridization of Black-Winged Kite Algorithm (BKA) with Particle Swarm Optimization (PSO) and Differential Evolution (DE).
• Integration of PSO for local exploitation and DE for population diversity maintenance.
• Dynamic global exploration via BKA's strategies and local refinement via PSO's velocity-based search.

Main Results:

• BKAPI demonstrates a significant improvement in convergence speed and computational accuracy.
• The enhanced algorithm effectively overcomes premature convergence and parameter sensitivity.
• Experimental validation using CEC 2017 and CEC 2022 benchmark functions shows superiority over seven other algorithms.

Conclusions:

• The proposed BKAPI offers a robust solution for high-dimensional optimization problems.
• BKAPI exhibits strong performance and broad applicability in engineering design.
• The integrated strategy proves effective and versatile for complex optimization tasks.