Optimization of Engineering Processes Using Hybrid Evolutionary Algorithms

Abstract

Optimization of engineering processes is essential for improving efficiency, reducing operational costs, and enhancing system performance in complex industrial applications. Traditional optimization techniques often struggle to handle nonlinear, multi-objective, and high-dimensional problems commonly encountered in engineering systems. This study presents a hybrid optimization approach using evolutionary algorithms to enhance the efficiency and accuracy of engineering process optimization. The methodology integrates multiple evolutionary techniques such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE) to form a hybrid framework capable of balancing exploration and exploitation in the search space. Engineering process parameters are formulated as objective functions with constraints representing real-world operational limitations. The hybrid algorithm is applied to optimize performance metrics such as energy consumption, production time, material usage, and system efficiency. Simulation studies are conducted on benchmark engineering optimization problems to evaluate the effectiveness of the proposed approach. The results indicate that the hybrid evolutionary algorithm outperforms individual optimization techniques in terms of convergence speed, solution accuracy, and robustness. It is also observed that the combination of GA’s global search capability, PSO’s fast convergence, and DE’s strong mutation strategy significantly improves optimization performance. The proposed method effectively avoids local optima and provides near-global optimal solutions for complex engineering problems. The study concludes that hybrid evolutionary algorithms offer a powerful and flexible tool for solving multi-objective engineering optimization problems.