Enhancing Differential Evolution on Continuous Optimization Problems by Detecting Promising Leaders

Due to that the performance of differential evolution (DE) significantly depends on offspring generation strategies, various DE variants have been reported with the improved mutation operators. However, on the one hand, the mutation operators in most DE variants are guided by the elites in terms of the fitness value, without considering their distribution information in the fitness landscape. It may lead to the population be evolving towards the unpromising areas more frequently if these elites are clustered in a locally optimal region. On the other hand, in most DE variants, the evolutionary information of the potential trial vectors is not fully utilized to guide the search, which will hamper the local exploitation in the promising regions that they are located in. To overcome these weaknesses, this article proposes an enhanced DE framework (DELDG) with a leaders-detection-and-guidance mechanism that consists of an adaptive leaders detection (ALD) and a neighborhood-based tournament selection (NTS). With these two novel operators, DELDG can not only guide the mutation process of each individual with multiple promising leaders detected by ALD, but also accelerate the convergence speed with the competition among the potential trial vectors by NTS. Therefore, DELDG is characterized by the explicit detection of the promising leaders according to their fitness values and distribution information and the effective use of the potential trial vectors in the neighborhood of each leader. Compared with 36 excellent DE variants and evolutionary algorithms (EAs), the experimental results on 28 IEEE CEC2013 real-parameter functions and 17 IEEE CEC2011 real-world problems have demonstrated the competitive performance of DELDG.