“Optimizing the Optimization”: A Hybrid Evolutionary-Based AI Scheme for Optimal Performance

Optimization algorithms for solving technological and scientific problems often face long convergence times and high computational costs due to numerous input/output parameters and complex calculations. This study focuses on proposing a method for minimizing response times for such algorithms across various scientific fields, including the design and manufacturing of high-performance, high-quality components. It introduces an innovative mixed-scheme optimization algorithm aimed at effective optimization with minimal objective function evaluations. Indicative key optimization algorithms—namely, the Genetic Algorithm, Firefly Algorithm, Harmony Search Algorithm, and Black Hole Algorithm—were analyzed as paradigms to standardize parameters for integration into the mixed scheme. The proposed scheme designates one algorithm as a “leader” to initiate optimization, guiding others in iterative evaluations and enforcing intermediate solution exchanges. This collaborative process seeks to achieve optimal solutions at reduced convergence costs. This mixed scheme was tested on challenging benchmark functions, demonstrating convergence speeds that were at least three times faster than the best-performing standalone algorithms while maintaining solution quality. These results highlight its potential as an efficient optimization approach for computationally intensive problems, regardless of the included algorithms and their standalone performance.