A novel algorithm for optimizing the Pareto set in dynamic problem spaces
Ima Okon Essiet, Yanxia Sun, Zenghui Wang · 2018
This paper presents an algorithm based on dynamic multiobjective optimization (DMO) which employs a single randomly mutating time-variant archive to balance convergence and diversity in order to efficiently select the final, non-dominated Pareto set. The algorithm is tested on selected dynamic optimization benchmark functions, and the improvement in the performance of the single archive approach is validated by the improved performance metrics and overall computational time. Overall, the proposed single-archive algorithm (called DOAEA) generated better metrics and faster computational time for the Gee-Tan-Abbas (GTA) test suite for average MIGD and average MHV compared to previously proposed two-archive algorithm, DTAEA.