A Review of Multi-Objective Optimization: Methods and Algorithms in Mechanical Engineering Problems

Abstract

Modern mechanical engineering problems often involve multiple conflicting objectives such as minimizing cost while maximizing performance, strength, and efficiency. Traditional optimization approaches, which focus on a single objective, are no longer sufficient to address such complex design challenges. Multi-objective optimization (MOO) provides a systematic framework to handle trade-offs among competing objectives and identify optimal design solutions. This review paper presents a comprehensive overview of various multi-objective optimization methods and algorithms widely used in mechanical engineering applications. Classical techniques, evolutionary algorithms, swarm intelligence approaches, and hybrid optimization strategies are discussed in detail. The paper also highlights practical applications in design optimization, thermal systems, manufacturing, and structural engineering. Finally, current challenges and future research directions in the field are outlined.