A5101443526- Advanced Multi-Objective Optimization Algorithms
- Metaheuristic Optimization Algorithms Research
- Evolutionary Algorithms and Applications
- Scheduling and Timetabling Solutions
- Advanced Numerical Analysis Techniques
- Manufacturing Process and Optimization
East China Normal University
2023-2024
Due to computationally and/or financially costly evaluation, tackling expensive multi-objective optimization problems is quite challenging for evolutionary algorithms. One popular approach these building cheap surrogate models replace the real function evaluations. To this end, various kinds of surrogate-assisted algorithms (SAEAs) have been proposed, which predict fitness values, classifications, or relation candidate solutions. However, off-spring generation, despite its important role in...
Optimizing multiple conflicting black-box objectives simultaneously is a prevalent occurrence in many real-world applications, such as neural architecture search, and machine learning. These problems are known expensive multi-objective optimization (EMOPs) when the function evaluations computationally or financially costly. Multi-objective Bayesian (MOBO) offers an efficient approach to discovering set of Pareto optimal solutions. However, data deficiency issue caused by limited has posed...
Multi-objective combinatorial optimization (MOCO) problems are prevalent in various real-world applications. Most existing neural methods for MOCO rely solely on decomposition and utilize precise hypervolume to enhance diversity. However, these often approximate only limited regions of the Pareto front spend excessive time diversity enhancement because ambiguous time-consuming calculation. To address limitations, we design a Geometry-Aware set Learning algorithm named GAPL, which provides...
Multi-objective Bayesian optimization (MOBO) has shown promising performance on various expensive multi-objective problems (EMOPs). However, effectively modeling complex distributions of the Pareto optimal solutions is difficult with limited function evaluations. Existing set learning algorithms may exhibit considerable instability in such scenarios, leading to significant deviations between obtained solution and (PS). In this paper, we propose a novel Composite Diffusion Model based Set...