Solving numerical and engineering optimization problems using a dynamic dual-population differential evolution algorithm

Differential evolution (DE) is a cutting-edge meta-heuristic algorithm known for its simplicity and low computational overhead. But the traditional DE cannot effectively balance between exploration and exploitation. To solve this problem, in this paper, a dynamic dual-population DE variant (ADPDE) is proposed. Firstly, the dynamic population division mechanism based on individual potential value is presented to divide the population into two subgroups, effectively improving the population diversity. Secondly, a nonlinear reduction mechanism is designed to dynamically adjust the size of potential subgroup to allocate computing resources reasonably. Thirdly, two unique mutation strategies are adopted for two subgroups respectively to better utilise the effective information of potential individuals and ensure fast convergence speed. Finally, adaptive parameter setting methods of two subgroups further achieve the balance between exploration and exploitation. The effectiveness of improved strategies is verified on 21 classical benchmark functions. Then, to verify the overall performance of ADPDE, it is compared with three standard DE algorithms, eight excellent DE variants and seven advanced evolutionary algorithms on CEC2013, CEC2017 and CEC2020 test suites, respectively, and the results show that ADPDE has higher accuracy and faster convergence speed. Furthermore, ADPDE is compared with eight well-known optimizers and CEC2020 winner algorithms on nine real-world engineering optimization problems, and the results indicate ADPDE has the development potential for constrained optimization problems as well.