Evolutionary Algorithms for Complex Optimization Problems in Engineering

Abstract

Engineering problems often present complex, nonlinear, and multi-dimensional optimization challenges that traditional methods struggle to address effectively. Evolutionary algorithms (EAs), inspired by natural selection and biological evolution, have emerged as robust and adaptable solutions for these problems. This paper explores the application of various EAs, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Evolution Strategies (ES), and Genetic Programming (GP), in diverse engineering domains such as structural design, energy systems, robotics, and supply chain management. The study emphasizes their effectiveness in solving nonconvex, multi-objective, and constrained problems. Recent advancements, including hybrid approaches that integrate EAs with machine learning techniques and metaheuristics, are also examined. Benchmarking results on standard test problems and real-world engineering scenarios demonstrate the superior performance of EAs compared to conventional optimization techniques. The paper concludes by outlining future research directions, including enhancing computational efficiency, integrating domain-specific knowledge, and leveraging parallel computing for large-scale problem-solving.