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Computational Intelligence and Neuroscience
Volume 2017 (2017), Article ID 9740278, 8 pages
https://doi.org/10.1155/2017/9740278
Research Article

A Method for Consensus Reaching in Product Kansei Evaluation Using Advanced Particle Swarm Optimization

School of Construction Machinery, Chang’an University, Xi’an 710064, China

Correspondence should be addressed to Yan-pu Yang; nc.ude.dhc@upnaygnay

Received 17 November 2016; Accepted 16 January 2017; Published 12 February 2017

Academic Editor: Elio Masciari

Copyright © 2017 Yan-pu Yang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Consumers’ opinions toward product design alternatives are often subjective and perceptual, which reflect their perception about a product and can be described using Kansei adjectives. Therefore, Kansei evaluation is often employed to determine consumers’ preference. However, how to identify and improve the reliability of consumers’ Kansei evaluation opinions toward design alternatives has an important role in adding additional insurance and reducing uncertainty to successful product design. To solve this problem, this study employs a consensus model to measure consistence among consumers’ opinions, and an advanced particle swarm optimization (PSO) algorithm combined with Linearly Decreasing Inertia Weight (LDW) method is proposed for consensus reaching by minimizing adjustment of consumers’ opinions. Furthermore, the process of the proposed method is presented and the details are illustrated using an example of electronic scooter design evaluation. The case study reveals that the proposed method is promising for reaching a consensus through searching optimal solutions by PSO and improving the reliability of consumers’ evaluation opinions toward design alternatives according to Kansei indexes.