ADNSGA-II: A Trend-Aware Evolutionary Algorithm for Dynamic Multi-Objective Optimization

A fundamental challenge in dynamic multi-objective optimization lies in simultaneously ensuring rapid adaptation to environmental changes and maintaining the convergence quality of the evolving solution set. Most existing dynamic multi-objective evolutionary algorithms rely on a stage-wise modeling paradigm, where dynamic problems are decomposed into a sequence of static subproblems solved independently. This structural assumption often leads to delayed adaptation and search drift when the objective functions evolve continuously. To overcome these limitations, we reformulate the static population evolution in the existing solutions as a trend-aware adaptive process and propose a novel trend-aware multi-objective optimization algorithm capable of dynamically adapting environment changes. The algorithm conducts trend-guided search based on the centroid movement of Pareto non-dominated front to ensure that the search process for each objective is consistent with the evolving direction of the optimization landscape. It estimates a global trend vector T_g based on the centroid shift of the non-dominated front across generations, and integrates a synergy factor to evaluate the directional consistency of each solution. This factor dynamically adjusts selection priorities, enabling the algorithm to adapt more effectively to continuous environmental changes. We conducted extensive experiments for both benchmark problems and a real-world application of microservice scheduling. The experimental results demonstrate the performance improvements of our proposed method over the popular baseline algorithms, and its cosistent achievement of globally or locally optimal values in key indicators for solving both benchmark and real-world application problems.