Dynamic multi-objective optimization problems are very common in many real-world applications. Such problems are often characterized by time varying objectives, constraints or parameters. Consideration of dynamics is typically limited to a single dynamic time scale; a restriction on the realistic description of real-world scenarios. In this paper, we investigate the effects of asynchrony on algorithm performance for two and three objective benchmark optimization problems with two independent time variables. The independent update of these time variables is parameterized on a logarithmic scale between slow-relative change, synchronous change and fast-relative changes. To evaluate the effect of the asynchronous modes, six established multi-objective optimization algorithms, tailored specifically for dynamic problems, were used to solve the problems. The hybrid-based methods achieve significantly better hypervolume and generational distance measurements when compared to random re-initialization, diversity focused and population prediction methods. Interestingly, for selected values of the change-frequency parameter, the best operating ranges of the algorithms differ. The benefits of mutation over replacement in diversity schemes are observed. Future application to power, economic and chemical scenarios are proposed. © 2019 IEEE.
|Title of host publication
|2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - Jun 2019
- Evolutionary Algorithms