Balancing objective and switch cost for robust optimization over time

Abstract

Dynamic optimization problems (DOPs) commonly arise in real-world scenarios where objective functions and constraints change over time. While existing methods primarily focus on tracking optimal solutions across different environments, the cost of switching solutions cannot be overlooked in practical applications. Robust optimization over time (ROOT) addresses this challenge by simultaneously maximizing objective values and minimizing solution switch costs. This paper proposes a novel ROOT method, introducing a dynamic balancing mechanism that adjusts the search direction according to the correlation between objective value and switch cost. Additionally, a robust solution selection strategy is developed, utilizing switch cost as a constraint to pre-screen solutions before selecting the optimal robust solution. Comprehensive experiments on ROOT test problems of varying dimensions and switch cost weights validate the effectiveness of the proposed approach. Comparisons with ROOT algorithms, dynamic evolutionary algorithms, multi-objective evolutionary algorithms and single-objective evolutionary algorithms demonstrate that the proposed method achieves superior overall performance. Furthermore, ablation studies confirm the effectiveness of the balancing mechanism and the robust solution selection strategy in enhancing optimization quality.