NSGA-III Performance in Multi-objective Tour Guide Assignment Problem

Abstract

Optimization of a multi-objective tour guide assignment problem considering total guiding time, total assignment cost, and service quality is conducted. Several multi-objective evolutionary algorithms (MOEAs), such as the Non-dominated Sorting Genetic Algorithm III (NSGA-III), ε -NSGA-II, the epsilon MOEA (ε -MOEA), NSGA-II, the Pareto archived evolution strategy (PAES), and the Pareto Envelope-based Selection Algorithm II (PESA-II), have been used to solve and evaluate the problem in the MOEA framework. Based on the results, we found that NSGA-III gives better performance than the other algorithms in terms of solution quality and running time.