An integrated differential evolution of multi-population based on contribution degree

The differential evolution algorithm based on multi-population mainly improves its performance through mutation strategy and grouping mechanism. However, each sub-population plays a different role in different periods of iterative evolution. If each sub-population is assigned the same computing resources, it will waste a lot of computing resources. In order to rationally distribute computational resources, an integrated differential evolution of multi-population based on contribution degree (MDE-ctd) is put forth in this work. In MDE-ctd, the whole population is divided into three sub-populations according to different update strategies: archival, exploratory, and integrated sub-populations. MDE-ctd dynamically adjusts computing resources according to the contribution degree of each sub-population. It can effectively use computing resources and speed up convergence. In the updating process of integrated sub-populations, a mutation strategy pool and two-parameter value pools are used to maintain population diversity. The experimental results of CEC2005 and CEC2014 benchmark functions show that MDE-ctd outperforms other state-of-art differential evolution algorithms based on multi-population, especially when it deals with highly complex optimization problems.

Automated summary

The multi-population differential evolution algorithm improves its performance through mutation strategy and grouping mechanism. Each sub-population plays a different role in different stages of iterative evolution. An integrated differential evolution algorithm based on contribution degree (MDE-ctd) is proposed to rationally distribute computational resources. MDE-ctd dynamically adjusts computing resources according to the contribution degree of each sub-population and outperforms other state-of-art differential evolution algorithms in dealing with highly complex optimization problems.