A black-winged kite optimization algorithm enhanced by osprey optimization and vertical and horizontal crossover...

Area of Science:

• Computational Intelligence
• Swarm Intelligence
• Optimization Algorithms

Background:

• The black-winged kite algorithm (BKA) faces challenges with accuracy and consistency between global search and local development.
• Practical problem-solving requires robust optimization techniques that balance exploration and exploitation.

Purpose of the Study:

• To propose an enhanced black-winged kite optimization algorithm (DKCBKA) that addresses the limitations of the standard BKA.
• To improve convergence rate, prevent local optima, and enhance solution accuracy for practical optimization tasks.

Main Methods:

• Integration of the Osprey optimization algorithm and Crossbar enhancement into the BKA framework.
• Incorporation of an adaptive index factor and probability distribution factor updates.
• Implementation of a stochastic difference variant method and longitudinal/transversal crossover techniques.

Main Results:

• DKCBKA demonstrated superior performance over standard and improved swarm intelligence algorithms on CEC2017 and CEC2019 test sets.
• The algorithm achieved higher solution accuracy and faster convergence speeds compared to existing methods.
• DKCBKA showed significant improvements in optimizing real engineering problems, surpassing the original BKA.

Conclusions:

• The proposed DKCBKA algorithm effectively enhances accuracy and consistency in optimization tasks.
• DKCBKA offers a superior alternative for complex problem-solving, outperforming conventional and advanced optimization techniques.
• The integrated approach provides a more robust and efficient optimization tool for scientific and engineering applications.