[3M1-CC] Synergy among Machine Learning, Computational Optimization, and Human Awareness / General Session – B

[3M1-CC-03] A Proposal of Interactive Tabu Search for Creating Beverage by Blending Source Juices

Makoto Fukumoto¹, Gan Haoran¹, Yoshiko Hanada² (1. Fukuoka Institute of Technology, 2. Kansai University)

Obtaining media content suited to user’s feelings is one of the essential targets of engineering. However, it is still difficult because feelings are different between users and are hard to be shown as a certain equation. As a method of a beverage, this study proposed Interactive Tabu Search (ITS) that blends source juices for creating new beverages suited to each user’s feelings. Tabu Search is one of stochastic local searches, and its properties are a continuous neighborhood search and a tabu list prohibiting cycling. A target of optimization was the ratio of the source juices. A concrete system based on the proposed ITS was constructed with the computer, Arduino, and peristaltic pumps. A tasting experiment composed of two steps was conducted. The target was delicious blended beverage. As a result, continuous increases in the fitness values related to deliciousness were observed, and a significant increase was observed in the maximum fitness. In the progress of the ratios, both different and common trends between the subjects were observed.