基于犹豫模糊语言术语集和粒子群优化算法的产品造型设计感性评价方法

产品造型感性评价反映了用户的意象感知，具有模糊性与不确定性，用户常难以准确描述其感 性偏好而表现出犹豫。针对该问题，引入犹豫模糊语言术语集(HFLTSs)描述用户感性评价，基于其数学算子构 建犹豫模糊语言共识模型以测度用户认知一致性程度，借助粒子群优化算法(PSO)实现非共识条件下用户评价 矩阵的优化与共识达成，通过逼近理想解排序法对产品造型方案进行优劣排序，提出了基于 HFLTSs 和 PSO 的 产品造型设计感性评价流程。以汽车充电桩造型方案感性评价为例，验证了 HFLTSs 有助于解决用户感性认知 的不确定问题，结合 PSO 能够提高犹豫模糊语言评价的一致性，从而提升产品造型感性评价质量。

Kansei evaluation method of product form design based on hesitant fuzzylinguistic term sets and particle swarm optimization

The Kansei evaluation of product form design reflects users’ perceptual cognition, of which the characteristics of fuzziness and uncertainty render users hesitant and unable to precisely express their Kansei preference. To address this problem, the hesitant fuzzy linguistic term sets (HFLTSs) were introduced to describe users’ Kansei evaluation. With the mathematical operators of HFLTSs, a consensus model was constructed to measure the consistency degree of users’ perceptual cognition about product form design schemes. The particle swarm optimization technique (PSO) was utilized to optimize users’ Kansei evaluation matrices for consensus reaching. With optimized Kansei evaluation matrices, the technique for order preference by similarity to an ideal solution (TOPSIS) was applied to rank product form design schemes. A Kansei evaluation process of product form design based on HFLTSs and PSO was proposed. The Kansei evaluation of car charging pile design schemes as an example verified that HFLTSs can be conducive to solving the uncertainty and hesitancy problems of users’ perceptual cognition, and that by integrating PSO for consensus reaching, the consistency of Kansei evaluation with HFLTSs can be improved, and finally the quality of Kansei evaluation of product forms can be enhanced.