Abstract
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 improvement in the
performance of the single archive approach is demonstrated by
improved performance metrics and overall computational time.