Application of an Improved Differential Evolution Algorithm in Practical Engineering

ABSTRACT

The differential evolution algorithm, as a simple yet effective random search algorithm, often faces challenges in terms of rapid convergence and a sharp decline in population diversity during the evolutionary process. To address this issue, an improved differential evolution algorithm, namely the multi‐population collaboration improved differential evolution (MPC‐DE) algorithm, is introduced in this article. The algorithm proposes a multi‐population collaboration mechanism and a two‐stage mutation operator. Through the multi‐population collaboration mechanism, the diversity of individuals involved in mutation is effectively controlled, enhancing the algorithm's global search capability. The two‐stage mutation operator efficiently balances the requirements of the exploration and exploitation stages. Additionally, a perturbation operator is introduced to enhance the algorithm's ability to escape local optima and improve stability. By conducting comprehensive comparisons with 15 well‐known optimization algorithms on CEC2005 and CEC2017 test functions, MPC‐DE is thoroughly evaluated in terms of solution accuracy, convergence, stability, and scalability. Furthermore, validation on 57 real‐world engineering optimization problems in CEC2020 demonstrates the robustness of the MPC‐DE. Experimental results reveal that, compared to other algorithms, MPC‐DE exhibits superior convergence accuracy and robustness in both constrained and unconstrained optimization problems. These research findings provide strong support for the widespread applicability of multi‐population collaboration in differential evolution algorithms for addressing practical engineering problems.