The Study of MapReduce based Genetic Algorithm on Tour Planning

• Main Authors: Hao Yang, 楊浩
• Other Authors: 翁頌舜
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/3fsjg5

碩士 === 國立臺北科技大學 === 資訊管理研究所 === 102 === In recent years, mobile networks and mobile devices are rapidly developed and popularized. Information is in circulation rapidly. In tourist industry, the type of independent travel has occurred more than eighty percent. Independent travelers must handle their own trips from planning to implementation. How to plan the most time-saving transportation during the travel period is the most critical concern, which often determines the success or failure of the trip. Most traveling services focus on the attractions recommendation, lack of research regarding travel planning. This study proposes a system that users can plan their own trips. This study also tries to improve the planning algorithm so that in such a structure to meet the needs of users in shorter time. In this study, a mobile device is used as the primary communication interface. It provides the user for searching information and planning the trip in any environment. Travel planning is helped based on Genetic Algorithm with MapReduce mechanism, the master-slave architecture on a Hadoop cloud platform. This study also proposes an enhanced Genetic Algorithm. It combines the Nearest Neighbor method and uses the unusual crossover approach to improve the performance and results. As the result shows, the system proposed in this study satisfies users’ needs. As the result shows, the proposed genetic algorithm for solving travel planning enhances the quality of the planning results of 44.89%. The algorithms based on MapReduce method also improves the efficiency of 27.45%. From the results of this study, it shows that the proposed framework has a good effect.