Multi-solution Robust Optimization over Time with Adaptive Population Control

Robust Optimization Over Time (ROOT) is used to solve dynamic optimization problems with the aim of finding solutions that can be accepted over a long time. Most of the researches in this field try to seek new robust solutions by predicting the future fitness values of candidate solutions. However, predicting future fitness value is error prone. Therefore, this paper propose a multi-solution robust optimization over time with adaptive population control (MROOT-AC). Firstly, a adaptive population control mechanism was proposed to optimize the population in the unexplored promising region according to the convergence state of the population, so as to improve the diversity of the population. Secondly, in order to prevent the inefficiency and resource waste caused by frequent changes of deployment scheme, a multi-solution archive management mechanism has been proposed. Compared with the existing ROOT algorithms, the experimental results on the Generalized Moving Peak Benchmark (GMPB) show that the proposed algorithm can significantly improve the performance of the robust solution.