Smooth Tchebycheff Scalarization for Multi-Objective Optimization
CoRR(2024)
摘要
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 different 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 novel and lightweight 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.
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