Koffka Khan; Ashok Sahai

Swarm-Optimization-Based Affective Product Design Illustrated by a Mobile Phone Case-Study

Full Text (PDF, 496KB), PP.23-29

Views: 0 Downloads: 0

Author(s)

1. Department of Computing and Information Technology, University of the West Indies, St. Augustine, Trinidad And Tobago (W.I.)

2. Department of Mathematics and Statistics, University of the West Indies, St. Augustine, Trinidad And Tobago (W.I.)

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2012.05.04

Received: 25 Jun. 2011 / Revised: 7 Oct. 2011 / Accepted: 6 Dec. 2011 / Published: 8 May 2012

Index Terms

Product design, particle swarm optimization, kansei engineering

Abstract

This paper presents a new approach of user-oriented design for transforming users’ perception into product elements design. An experimental study on mobile phones is conducted to examine how product form and product design parameters affect consumer’s perception of a product. The concept of Kansei Engineering is used to extract the experimental samples as a data base for neural networks (NNs) with particle swarm optimization (PSO) analysis. The result of numerical analysis suggests that mobile phone makers need to focus on particular design parameters to attract specific target user groups, in addition to product forms. This paper demonstrates the advantage of using KE-PSO for determining the optimal combination of product design parameters. Based on the analysis, we can use KE-PSO to suggest customers’ preferences for mobile phone design attributes that would be considered optimal by various user groups of all surveyed. They can be used for improvement and development of new future products.

Cite This Paper

Koffka Khan, Ashok Sahai, "Swarm-Optimization-Based Affective Product Design Illustrated by a Mobile Phone Case-Study", International Journal of Intelligent Systems and Applications(IJISA), vol.4, no.5, pp.23-29, 2012. DOI:10.5815/ijisa.2012.05.04

Reference

[1]Birge, B., PSOt - A Particle Swarm Optimization Toolbox for Use With Matlab. IEEE 2003 Swarm Intelligence Symposium, 2003, pp. 182-186.

[2]Clerc M. and Kennedy J., The Particle Swarm - Explosion, Stability, and Convergence in a Multidimensional Complex Space. IEEE Transactions on Evolutionary Computation, 2002, 6(1), 58-73.

[3]Ge Y. and Rubo Z., An Emotional Particle Swarm Optimization Algorithm. Springer Berlin / Heidelberg, 2005, pp. 553-561.

[4]Jiao J., Zhang Y., and Helander M., A Kansei mining system for affective design, Expert Systems with Applications. Expert Systems with Applications Vol. 30, No. 4, 2006, pp. 658-673.

[5]Kennedy J. and Eberhart R., Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks, IEEE Press, 1995, pp.1942-1948.

[6]Kennedy J., and Eberhart R., An Improved Particle Swarm Optimization for Evolving Feedforward Artificial Neural Networks. Kluwer Academic Publishers, 1995, pp.217-231.

[7]Lai H., Lin Y., and Yeh C., Form design of product image using grey relational analysis and neural network models. Computers & Operations Research, Vol. 32, No. 10, 2005, pp. 2689-2711.

[8]Lee S.H., Harada A. and P. J. Stappers P.J., Pleasure with Products: Design based on Kansei. Taylor and Francis, 2000, pp. 219-230.

[9]Lin Y., Lai H. and Yeh C., Consumer-oriented product form design based on fuzzy logic: A case study of mobile phones. Int. J. of Industrial Ergonomics, Vol. 37, No. 6, 2007, pp. 531-543.

[10]Nagamachi M., Kansei engineering as a powerful consumer-oriented technology for product development. Applied Ergonomics, Vol. 33, No. 3, 2002, pp. 289-294.

[11]Su Rijian, Kong Li, Song Shengli et al. A new ridgelet neural network training algorithm based on improved particle swarm optimization. Proceedings - Third International Conference on Natural Computation, ICNC 2007, 2007, v3, pp.411-415.

International Journal of Intelligent Systems and Applications (IJISA)