## 1 Introduction
The safety factors of the tower are improved and the total construction cost can be reduced by about 20% after optimization. However, the optimization result is easily stuck at locally optimal values and the material utilization ratio of the optimization result is comparatively low. Different from the direct investment or construction cost for a wind turbine system investigated in the above studies, it is more important to minimize its LCOE in a life-cycle perspective, which is treated as the objective function for geometry optimization for PCSH wind turbine towers in this study. Particle swarm optimization (PSO) has proven to be a powerful method for optimization problems [17]. Adopting PSO, [PERSON] et al. [18] investigated the optimum floor configuration by minimizing the total mass or cost while satisfying all design criteria. [PERSON] et al. [19] conducted a comprehensive investigation on cold-formed steel beam designs using PSO techniques. [PERSON] et al. [20] proposed a computational approach based on PSO to obtain the lower bound of the buckling load of shell structures with geometric imperfections. Based on a PSO algorithm, [PERSON] et al. [21] optimized the active control strategy for machinery-equipment-induced structural vibrations. [PERSON] et al. [22] carried out structural optimization employing isogeometric tools in PSO for a two-dimensional truss or a frame tower. [PERSON] et al. [23] introduced a new hybrid algorithm based on PSO and GA to find optimal fiber orientation of stiffened laminated composite panels to reach their maximum buckling load. [PERSON] and [PERSON] optimized a series of usual-size skeletal structures by transplanting a harmony search-based mechanism to particle swarm optimization with an aging leader and challengers (HALC-PSO) and multistage particle swarm optimization (MSPSO) and obtained satisfactory results [24]. In this paper, in order to enhance the computation efficiency for the geometry optimization of the PCSH wind turbine towers, a parallel updated PSO (PUPSO) method is proposed to optimally design a PCSH wind turbine tower subjected to both wind and seismic excitations, considering the constraints of load-carrying capacity, fatigue, stability, natural frequency, and maximum top displacement. It employs the LCOE as the objective function. The proposed approach is used to optimally design a 2 MW PCSH wind turbine tower with a design height of 77.5 m as an alternative to the traditional steel tubular tower. The optimal result is evaluated by utilization ratio of the tower. Results show that the PUPSO algorithm efficiently optimizes the PCSH wind turbine towers when compared with the traditional particle swarm optimization (PSO) algorithm and the LCOE of the optimized PCSH wind turbine significantly decreases when compared with the benchmark wind turbine tower. The height of the steel segment of the optimized PUPSO tower is recommended to be 30% of total height of the PCSH wind turbine tower. Compared with the original PCSH tower, the increased utilization rates of both PC and steel segments illustrate the effectiveness of the PUPSO algorithm. Moreover, the fundamental natural frequency of the optimized PCSH wind turbine tower increases significantly when compared with that of the original wind turbine tower.