Dynamic allocation of opposition-based learning in differential evolution for multi-role individuals

Opposition-based learning (OBL) is an optimization method widely applied to algorithms. Through analysis, it has been found that different variants of OBL demonstrate varying performance in solving different problems, which makes it crucial for multiple OBL strategies to co-optimize. Therefore, this study proposed a dynamic allocation of OBL in differential evolution for multi-role individuals. Before the population update in DAODE, individuals in the population played multiple roles and were stored in corresponding archives. Subsequently, different roles received respective rewards through a comprehensive ranking mechanism based on OBL, which assigned an OBL strategy to maintain a balance between exploration and exploitation within the population. In addition, a mutation strategy based on multi-role archives was proposed. Individuals for mutation operations were selected from the archives, thereby influencing the population to evolve toward more promising regions. Experimental results were compared between DAODE and state of the art algorithms on the benchmark suite presented at the 2017 IEEE conference on evolutionary computation (CEC2017). Furthermore, statistical tests were conducted to examine the significance differences between DAODE and the state of the art algorithms. The experimental results indicated that the overall performance of DAODE surpasses all state of the art algorithms on more than half of the test functions. Additionally, the results of statistical tests also demonstrated that DAODE consistently ranked first in comprehensive ranking.