Dezhen Yang

Multi-objective optimization problems (MOPs) are ubiquitous in scientific research and engineering applications, where multiple conflicting objectives must be optimized simultaneously. Unlike traditional multi-objective evolutionary algorithms, which aim to approximate the entire Pareto front, preference-based multi-objective evolutionary algorithms (PBMOEAs) incorporate decision maker preferences to guide the search toward a region of interest (ROI). This paper presents a focused survey of PBMOEAs using a three-dimensional analytical framework consisting of preference articulation mode, preference modeling approach, and preference incorporation mechanism. Under this framework, the survey reviews major preference modeling approaches and preference incorporation mechanisms, and further discusses three practical issues critical to PBMOEA design: the effectiveness of preference incorporation, the practical limits of interactive methods, and the role of visualization in ROI-oriented decision support. Findings indicate that while PBMOEAs enhance decision-making efficiency, their performance hinges on preference quality, problem characteristics, cognitive load, and method suitability. Future directions include unified modeling, comprehensive evaluation, human-centered design, and integrating surrogate models, explainable learning, and LLM-assisted interfaces.

Effective boundary scenario generation methods are critical for dynamic target tracking when evaluating the performance of unmanned aerial vehicles. However, traditional methods primarily rely on exhaustive testing or random sampling and often assume that different contributing factors are independent. This results in the ineffective generation of boundary scenarios and a lack of realism. In this study, a novel high-dimensional dynamic characteristics-based boundary test scenario generation method is proposed using reinforcement learning (RL). Boundary test scenarios can be explored more purposive, by designing an appropriate reward function. First, an innovative scenario modeling method is developed to model the influence of environment, occlusion, and other interference. A joint distribution model of the key correlation factors, such as light intensity and clouds, is also established. This ensures scenario authenticity and test accuracy. Subsequently, a high-dimensional dynamic spatial Markov decision process (HDDS-MDP) model is constructed to facilitate the generation of boundary scenarios based on the scenario modeling. Ultimately, RL is employed to solve the HDDS-MDP model and generate boundary test scenario, thereby substantially improves the effectiveness of boundary test scenario generation. The simulation results indicate that the boundary test scenario generation effectiveness is improved by 91% over random sampling methods.