An Adaptive Diversity Introduction Method for Dynamic Evolutionary Multiobjective Optimization

This paper investigates how to use diversity introduction methods to enhance the dynamic evolutionary multiobjective optimization algorithms in dealing with dynamic multiobjective optimization problems (DMOPs). Although diversity introduction method is easy used to response to the dynamic change, current diversity introduction methods still have a difficulty in identifying the correct proportion of diversity introduction. To overcome this difficulty, this paper proposes an adaptive diversity introduction (ADI) method. Specifically, the proportion of diversity introduction can be dynamically adjusted rather than being hand designed and fixed in advance. In addition, an adaptive relocation operator is designed to adapt the evolving individuals to the new environmental condition. The effectiveness of the ADI method is validated against various diversity introduction methods upon five DMOPs test problems. The simulation results show that the proposed ADI has better robustness and total performance than other diversity introduction methods.