Enhancing Dynamic Multi-objective Optimization Using Opposition-based Learning and Simulated Annealing

There are many dynamic real-life optimization problems in which objectives increase or decrease over time, which usually leads to variations in the dimensions of a Pareto front. Dynamic multi-objective optimization (DyMO) approaches aim to keep track of the updated Pareto front to tackle the changes which are caused by the dynamic environment. However, the current DyMO approaches do not handle dynamic environments effectively. In this study, a new hybrid dynamic two-archive evolutionary algorithm with a newly added simulated annealing and opposition-based learning strategy is proposed. The proposed method helps to preserve solutions with reasonable diversity and improve convergence by searching for promising solutions within acceptable computational time and effort. To evaluate the efficacy of the suggested method, comprehensive experiments using different multi-objective quality measures such as generational distance, and inverted generational distance have been performed on several benchmark problems with varying numbers of objectives over time. The results of the experiments show that the suggested method outperforms the strategies already in use.