关键词: 动态鲁棒优化, 粒子群, 特征变化, 预测误差, 引导个体

Abstract: Robust optimization is a new method for solving dynamic optimization problems with the goal of finding solutions that are still acceptable for a long time. Most of the researchers in this field try to find new robust solutions based on their future predicted fitness values. However, the error of predicting the future fitness value is often too large, which makes it difficult to find a better robust solution. To solve this problem, an algorithm framework based on problem feature change guidance (ROOT-PFCG) is proposed for dynamic robust optimization. The change of problem characteristics mainly refers to the objective function value of the solution in the current environment and the floating value of the objective function in the corresponding adjacent environment, and three important indexes are proposed. In the case of prediction and non-prediction, three different fitness decision rules are proposed to select the solution based on the index to ensure that the selected solution is less or not affected by the prediction error, so as to find a better robust solution. On this basis, a new performance evaluation index is proposed. The experimental results on benchmark problems show that the proposed algorithm can better improve the performance of robust solutions, and further analyze the impact of indicators on performance in different situations. On this basis, a better indicator combination method is analyzed.

Key words: dynamic robust optimization, particle swarm, feature change, prediction error, guide-individual