A Novel Multi-population Optimization Algorithm: Adaptive Differential Evolution Integration Algorithm with Dynamic Tracking Strategy

Abstract In response to the limitations observed in existing single intelligent optimization algorithms, particularly their shortcomings in global search capability and population diversity, we propose the Adaptive Differential Evolution Integral (ADEI) algorithm. Drawing inspiration from collective behaviors observed in social organisms, this algorithm introduces four roles: "leaders," "followers," "contemplators," and "rationalists," employing a dynamic following strategy to effectively integrate these diverse particles and populations. Specifically, individuals in the Differential Evolution algorithm serve as the leader population, with tailored trial vector generation strategies implemented to enhance global search capability. Concurrently, improvements are made to the Particle Swarm Optimization algorithm to facilitate its role as the evolution strategy for other populations. By adopting this approach, the algorithm's population diversity is enhanced, striking a balance between global and local search performance, thereby augmenting search efficiency and convergence accuracy. To evaluate its performance, extensive testing was conducted using the CEC2013 test function suite. The results indicate that the proposed algorithm achieved superior performance in over half of the 28 tested problems, demonstrating robust convergence accuracy and speed across unimodal, multimodal, and composite problem domains.