Optimization of hydraulic electric energy-feeding suspension based on improved cloud particle swarm optimization-cuckoo search algorithm

To address the problems of slow steady-state response, poor optimization accuracy, and easy falling into local optima in the parameter optimization of hydraulic electric energy-feeding suspension systems, an improved cloud particle swarm optimization-cuckoo search (CPSO-CS) algorithm was proposed. The algorithm innovatively integrated cloud theory and Logistic chaotic initialization into the traditional particle swarm optimization (PSO) and, combined with the levy flight local search mechanism of cuckoo search (CS), achieved a balance between global exploration and local development. Under Class-B, Class-C, and convex road excitations, the dynamic characteristics of passive control, sliding mode control (SMC), PSO control, and CPSO-CS control strategy applied to the hydraulic electric energy-feeding suspension were analyzed. Vertical body acceleration, suspension dynamic deflection, and tire dynamic load were selected as the evaluation indices. The results indicate that the proposed improved algorithm enhances both the optimization accuracy and convergence speed of the suspension system’s dynamic performance indices, thereby significantly improving the ride comfort of the hydraulic electric energy-feeding suspension system.