Product Development and Evolution Innovation Redesign Method Based on Particle Swarm Optimization

In today's fiercely competitive global market, user demand has become an important input factor for companies and manufacturers to develop new products. In order to solve the problem of very little real user information in traditional perceptual design, and the large-scale KE needs to manually set up the questionnaire survey and the data is small and time-consuming. Therefore, This paper proposes a new integrated approach to product evaluation and innovative development method combining natural language and PSO. Firstly, based on the web crawler to obtain the user's evaluation data on the internet to construct the user's real perceptual semantic space; Secondly, use FA and PA to define the dimensions of the user's perceptual requirements initially. Meanwhile, the machine learning is effectively used to quantify the non-linear relationship between product form and intention semantics, and the multi-dimensional predictive model of the perceptual image value of the product is established and verified. Finally, the particle swarm optimization is used to iteratively generate product optimal selection design schemes based on multiple emotional responses. An illustrative study is demonstrated to justify the validity of the proposed framework.