  When addressing the challenge of complex multi-objective optimization
problems, particularly those with non-convex and non-uniform Pareto fronts,
Decomposition-based Multi-Objective Evolutionary Algorithms (MOEADs) often
converge to local optima, thereby limiting solution diversity. Despite its
significance, this issue has received limited theoretical exploration. Through
a comprehensive geometric analysis, we identify that the traditional method of
Reference Point (RP) selection fundamentally contributes to this challenge. In
response, we introduce an innovative RP selection strategy, the Weight
Vector-Guided and Gaussian-Hybrid method, designed to overcome the local optima
issue. This approach employs a novel RP type that aligns with weight vector
directions and integrates a Gaussian distribution to combine three distinct RP
categories. Our research comprises two main experimental components: an
ablation study involving 14 algorithms within the MOEADs framework, spanning
from 2014 to 2022, to validate our theoretical framework, and a series of
empirical tests to evaluate the effectiveness of our proposed method against
both traditional and cutting-edge alternatives. Results demonstrate that our
method achieves remarkable improvements in both population diversity and
convergence.
