Multi-Objective Optimizations and Their Applications

Dear Colleagues,

Multi-objective optimization addresses the fundamental challenge of simultaneously optimizing multiple, often conflicting objectives in engineering design, scientific research, and industrial applications. Unlike single-objective problems, it seeks Pareto-optimal solutions representing optimal trade-offs between competing goals, providing decision-makers with alternatives rather than a single solution. This approach is essential in scenarios where objectives like minimizing cost while maximizing performance cannot be combined without losing critical trade-off information.

Evolutionary multi-objective optimization (EMO) algorithms have emerged as powerful tools due to their population-based nature, naturally aligning with the simultaneous discovery of multiple Pareto-optimal solutions. However, real-world applications present significant computational challenges when objective function evaluations require expensive simulations, physical experiments, or complex models—such as computational fluid dynamics for aerodynamic optimization or finite element analysis for structural design.

Surrogate-assisted and data-driven multi-objective optimization approaches bridge the gap between theoretical capabilities and practical constraints by integrating machine learning models as surrogates for expensive functions and leveraging available historical data, experimental measurements, and computational results. These data-driven methods enable effective design space exploration with dramatically reduced costs while incorporating prior knowledge and uncertainty quantification. Advanced infill strategies, model management, and data fusion techniques ensure judicious surrogate use while maintaining solution quality through the intelligent exploitation of all available information sources. These innovations have transformed multi-objective optimization into a practical tool for complex applications across the aerospace, automotive, drug discovery, and environmental engineering industries.

Dr. Xilu Wang
Dr. Jie Tian
Dr. Hangyu Zhu
Guest Editors

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• multi-objective optimization
• surrogate-assisted
• multi-objective
• data-driven optimization