A Lévy flight based chaotic black winged kite algorithm for solving optimization problems.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristic Computing

Background:

• The Black-Winged Kite Algorithm (BKA) is a bio-inspired metaheuristic for optimization.
• Existing BKA variants struggle with complex optimization scenarios requiring a balance between exploration and exploitation.

Purpose of the Study:

• To introduce improved variants of the Black-Winged Kite Algorithm (BKA) for enhanced complex optimization.
• To evaluate the performance of these novel algorithms against standard benchmarks and real-world engineering problems.

Main Methods:

• Developed three modified BKA variants: CBKA (chaos mapping), LBKA (Lévy flight), and CLBKA (combined mechanisms).
• Assessed algorithms on 23 standard benchmark functions (unimodal, multimodal, fixed-dimension).
• Validated performance on six engineering design problems and used Friedman and Wilcoxon signed-rank tests for statistical analysis.

Main Results:

• CLBKA achieved the global optimum on 20 out of 23 benchmark functions.
• CLBKA ranked first in the Friedman test (average rank 2.9348) and outperformed baseline BKA and other metaheuristics.
• CLBKA demonstrated superior solution accuracy, convergence speed, and robustness on engineering design problems.

Conclusions:

• The proposed hybrid strategies significantly enhance BKA's search efficiency.
• CLBKA is a reliable and versatile optimizer for complex, constrained optimization tasks.
• The enhanced BKA variants show strong potential for solving challenging optimization problems in science and engineering.