In the past few decades, many multiobjective evolutionary optimization algorithms (MOEAs) have been proposed to find a finite set of approximate Pareto solutions for a given problem in a single run. However, in many real-world applications, it could be desirable to have structure constraints on the entire optimal solution set, which define the patterns shared among all solutions. The current population-based MOEAs cannot properly handle such requirements. In this work, we make a first attempt to incorporate the structure constraints into the whole solution set. Specifically, we propose to model such a multiobjective optimization problem as a set optimization problem with structure constraints. The structure constraints define some patterns that all the solutions are required to share. Such patterns can be fixed components shared by all solutions, specific relations among decision variables, and the required shape of the Pareto set. In addition, we develop a simple yet efficient evolutionary stochastic optimization method to learn the set model, which only requires a low computational budget similar to classic MOEAs. With our proposed method, the decision-makers can easily tradeoff the Pareto optimality with preferred structures, which is not supported by other MOEAs. A set of experiments on benchmark test suites and real-world application problems demonstrates that our proposed method is effective.

Multi-objective optimization problems can be found in many real-world applications, where the objectives often conflict each other and cannot be optimized by a single solution. In the past few decades, numerous methods have been proposed to find Pareto solutions that represent optimal trade-offs among the objectives for a given problem. However, these existing methods could have high computational complexity or may not have good theoretical properties for solving a general differentiable multi-objective optimization problem. In this work, by leveraging the smooth optimization technique, we propose a lightweight and efficient smooth Tchebycheff scalarization approach for gradient-based multi-objective optimization. It has good theoretical properties for finding all Pareto solutions with valid trade-off preferences, while enjoying significantly lower computational complexity compared to other methods. Experimental results on various real-world application problems fully demonstrate the effectiveness of our proposed method.

We present Neural Radiance Fields (NeRF) with Template, dubbed Template-NeRF, for modeling 3D appearance and geometry and generating dense shape correspondence simultaneously among objects of the same category from only multi-view posed images. No 3D supervision or ground-truth correspondence knowledge is required. The learned dense correspondence can be directly used for various image-based tasks such as keypoint detection, part segmentation, and texture transfer that previously required specific model designs. Our method can also accommodate annotation transfer in a one or few-shot manner. Given only one or a few annotated instances of the category, our model can transfer to many others. We introduce deep implicit templates on 3D data into the 3D-aware image synthesis pipeline NeRF using periodic activation and feature-wise linear modulation (FiLM) conditioning. By representing object instances within the same category as shape and appearance variation of a shared NeRF template, our proposed method can achieve dense shape correspondence reasoning on images for a wide range of object classes.

Homotopy optimization is a traditional method to deal with a complicated optimization problem by solving a sequence of easy-to-hard surrogate subproblems. However, this method can be very sensitive to the continuation schedule design and might lead to a suboptimal solution to the original problem. In addition, the intermediate solutions, often ignored by classic homotopy optimization, could be useful for many real-world applications. In this work, we propose a novel model-based approach to learn the whole continuation path for homotopy optimization, which contains infinite intermediate solutions for any surrogate subproblems. Rather than the classic unidirectional easy-to-hard optimization, our method can simultaneously optimize the original problem and all surrogate subproblems in a collaborative manner. The proposed model also supports the real-time generation of any intermediate solution, which could be desirable for many applications. Experimental studies on different problems show that our proposed method can significantly improve the performance of homotopy optimization and provide extra helpful information to support better decision-making.

Expensive multi-objective optimization problems can be found in many real-world applications, where their objective function evaluations involve expensive computations or physical experiments. It is desirable to obtain an approximate Pareto front with a limited evaluation budget. Multi-objective Bayesian optimization (MOBO) has been widely used for finding a finite set of Pareto optimal solutions. However, it is well-known that the whole Pareto set is on a continuous manifold and can contain infinite solutions. The structural properties of the Pareto set are not well exploited in existing MOBO methods, and the finite-set approximation may not contain the most preferred solution(s) for decision-makers. This paper develops a novel learning-based method to approximate the whole Pareto set for MOBO, which generalizes the decomposition-based multi-objective optimization algorithm (MOEA/D) from finite populations to models. We design a simple and powerful acquisition search method based on the learned Pareto set, which naturally supports batch evaluation. In addition, with our proposed model, decision-makers can readily explore any trade-off area in the approximate Pareto set for flexible decision-making. This work represents the first attempt to model the Pareto set for expensive multi-objective optimization. Experimental results on different synthetic and real-world problems demonstrate the effectiveness of our proposed method.