Elite-Transition-Potential Model-Based Adaptive Multi-Population Multi-Mutation Differential Evolution Algorithm

Differential Evolution (DE) often faces a critical challenge in striking an effective balance exploration and exploitation when tackling complex optimization problems. To address this issue, this paper proposes a novel Elite-Transition-Potential (ETP) model based adaptive multi-population multi-mutation DE algorithm, named ETPDE. This algorithm employs the ETP model to dynamically partition each generation's population into three complementary subpopulations, and tailors differentiated mutation strategies to them to further reinforce their respective roles, thereby enabling the algorithm to dynamically adapt to the search requirements of different evolutionary stages. Furthermore, ETPDE incorporates a population size reduction method, along with an adaptive size control strategy for these three subpopulations, dynamically adjusting their proportional distribution within the entire population during evolution. Experiments are conducted on the CEC 2017 benchmark suite and the Lennard-Jones potential problem, and the results indicate that ETPDE exhibits competitive performance.