Itinerary planning system using genetic algorithm

Tan, Wei Han (2023) Itinerary planning system using genetic algorithm. Project Report. Melaka, Malaysia, Universiti Teknikal Malaysia Melaka. (Submitted)

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Abstract

Travelling for pleasure, recreation, or business is referred to as tourism. It entails travelling to and staying in locations that are different from one's typical environment. During their trips, tourists frequently partake in several activities, including sightseeing, cultural encounters, outdoor leisure, and shopping. The tourism sector includes a wide range of companies, including tour operators, lodging and transportation providers, restaurants and food services, hotels and lodging, and other attractions and services. Generally, the arranging of tourism routes can be described as a travelling salesman problem (TSP). TSP is a well-known problem in computer science and optimization that involves finding the shortest possible route that visits a set of cities and returns to the starting city. The problem is NP-hard, which means that it is computationally expensive to solve for large numbers of cities. The TSP is often described as follows: Given a list of cities and the distances between each pair of cities, the goal is to find the shortest possible route that visits each city exactly once and returns to the starting city. Hence, the choice of route is a main concern for the itinerary planning process. This project is designed mainly to propose a system that provides itinerary planning features. It is also aimed to evaluate the path planned on its performance. This project is expected to plan the best route for the users, reducing the time taken for the itinerary. The optimization algorithms involved in this project to be compared are Genetic Algorithm, Firefly Algorithm, and Ant Colony Optimization. The result of the comparison of optimization algorithms states that the average fitness value is 55.1920 for Genetic Algorithm, 50.7961 for Firefly Algorithm, and 34.4488 for Ant Colony Optimization. The result shows that Genetic Algorithm is better than the other algorithms in terms of quality of solutions.

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