Many-Objective Real-World Engineering Problems: A Comparative Study of State-of-the-Art Algorithms

Abstract

Many-objective optimization has recently gained popularity as it poses significant challenges for the existing algorithms. Therefore, numerous optimization algorithms have been developed to handle many-objective optimization in the literature. In addition, several studies have conducted experimental comparisons to assess the performance of optimization algorithms. Nevertheless, existing empirical studies have analyzed the performance of optimization algorithms on well-defined test problems, but it remains unclear whether the results translate to real-world scenarios. Furthermore, empirical studies on validating the performance of algorithms on real-world many-objective problems are intriguing but not yet fully explored. Therefore, in this article, we present a comprehensive comparative study evaluating the performance of 15 state-of-the-art algorithms on ten real-world many-objective applications with four to ten objectives from various domains. Further, these ten applications exhibit various mathematically challenging properties, including stochastic objectives, complex Pareto frontiers, and strong nonlinearity. In addition, four performance metrics are employed to visualize the performance of MOEAs in experimental settings. Based on comparative results, the performance of state-of-the-art algorithms with respect to different problems is evaluated herein.

Keywords

• Convergence
• diversity
• many-objective optimization
• multi-objective optimization
• real-world application