Evolutionary algorithms face significant challenges when dealing with dynamic multi-objective optimization because Pareto optimal solutions and/or Pareto optimal fronts change. This paper proposes a unified paradigm, which combines the kernelized autoncoding evolutionary search and the centriod-based prediction (denoted by KAEP), for solving dynamic multi-objective optimization problems (DMOPs). Specifically, whenever a change is detected, KAEP reacts effectively to it by generating two subpopulations. The first subpoulation is generated by a simple centriod-based prediction strategy. For the second initial subpopulation, the kernel autoencoder is derived to predict the moving of the Pareto-optimal solutions based on the historical elite solutions. In this way, an initial population is predicted by the proposed combination strategies with good convergence and diversity, which can be effective for solving DMOPs. The performance of our proposed method is compared with five state-of-the-art algorithms on a number of complex benchmark problems. Empirical results fully demonstrate the superiority of our proposed method on most test instances.

翻译：进化算法在处理动态多目标优化问题时面临重大挑战，因为帕累托最优解集和/或帕累托最优前沿会随时间变化。本文提出了一种统一范式——融合核自编码进化搜索与基于质心的预测方法（简称KAEP），用于求解动态多目标优化问题。具体而言，每当检测到环境变化时，KAEP通过生成两个子种群来有效响应变化：第一个子种群采用简单的质心预测策略生成；第二个初始子种群则基于历史精英解集，利用核自编码器预测帕累托最优解的移动轨迹。通过这种组合策略预测生成的初始种群兼具良好的收敛性与多样性，可有效求解动态多目标优化问题。将所提方法与五种前沿算法在多个复杂基准问题上进行对比，实验结果充分验证了本方法在大多数测试实例中的优越性。