Contents I. Introduction
Phuket is a city in the southern part of Thailand and widely recognised as a world tourist destination. However, there are many tourism types classified into several categories. These complicated relationships can be troublesome for tourists who prefer to visit different tourist places that mostly suit their needs, but may be required to plan their trips. In this case, a path searching model for helping tourists obtain an appropriate path to different tourist attractions under limited time constraints is required. In Jarupunphol et al. [1], Phuket Tourism Planning Model (PTPM) was introduced as path finding model to achieve an organised schedule of tourist attractions under time and distance constraints. Initially, PTPM was based on two well-respected theories, including genetic algorithm (GA) and fuzzy sets, designed to address problems associated with travelling salesman algorithm. The authors [1], nevertheless, summarised that experimenting on Particle Swarm Optimization (PSO) should be considered as a future work due to some similarities between PSO and GA. For example, GA and PSO are determined by information shared among population members. Besides, these algorithms employ a combination of rules for determination and probability to optimise their searching processes [2]. A comparison of PSO and GA performance in this context can be useful due to their similarities in addressing complex issues. Thus, a combination of PSO and Fuzzy Set was experimented on PTPM and compared with that of GA and Fuzzy Set in this article. To ensure the experimental validity, the experiments were based on the same data and approaches conducted in the previous experiments on GA.
