Development of metaheuristic optimization algorithms and AI tools for advanced design/control of industrial components

We are living at the dawn of a new industrial era, one where intelligence, matter, and energy intertwine. Industry 4.0 is no longer a distant vision but a living ecosystem, where machines think, systems evolve, and data becomes the language of creation. Factories are transforming into sentient environments, capable of sensing, learning, and adapting in real time. This revolution, however, brings with it extraordinary complexity. Engineering design and control now unfold within vast spaces of uncertainty, shaped by nonlinear behaviours, interdependent dynamics, and ever-changing constraints. Traditional optimization methods, bound by rigid assumptions, often falter in such terrain. Here, metaheuristic algorithms emerge as explorers of the unknown, creative, adaptive, and free from the confines of problem-specific logic. From Genetic Algorithms to Particle Swarm Optimization and the Grey Wolf Optimizer, they mimic the elegance of natural intelligence to discover balance in chaos, efficiency in diversity, and order in complexity. In parallel, Artificial Intelligence and machine learning are redefining the concept of knowledge itself. When united with metaheuristics, they give rise to intelligent optimization, a form of computational intuition capable of learning, adapting, and self-improving. Hybrid Metaheuristics, blending the strengths of different paradigms, extend this vision even further, enabling systems that evolve as living organisms do. Together, these technologies shape the soul of Industry 4.0, an ecosystem of self-optimizing, resilient, and health-aware systems that reflect both human creativity and machine precision. This thesis stands at the intersection of Mechatronics, Artificial Intelligence Tools, and Computational Optimization, and seeks to contribute to this ongoing evolution: the pursuit of engineering systems that are not only efficient, but truly intelligent.