Integrating reinforcement learning and metaheuristics for safe and sustainable health tourist trip design problem

Abstract

This study aims to address the critical challenge of enhancing safety within the domain of health and wellness tourism through the framework of sustainable travel design. Given the rising demand for secure and sustainable tourism experiences, this investigation explores the integration of safety considerations into the strategic planning and execution of tourist journeys. The central focus of this research revolves around the development and application of the Modified Reinforcement Learning-Artificial Multiple Intelligence System (MRL-AMIS). Operating within a multi-objective framework, the objective of this study is to optimize essential parameters encompassing environmental sustainability, economic viability, tourist preferences, and societal acceptability, with a specific emphasis on safety. The efficacy of the proposed methodology is rigorously evaluated across various tourist group scenarios, providing a comprehensive assessment of its applicability. Our computational analyses unequivocally demonstrate the superiority of the proposed approach in enhancing safety, surpassing existing methods such as genetic algorithms, differential evolution algorithms, particle swarm optimization, etc., by 15%-25%. These findings underscore the effectiveness of the proposed method in seamlessly integrating safety considerations into the realm of sustainable tourism planning. This study highlights the potential of the proposed method as an advanced tool in the strategic planning of sustainable tourism, particularly within the context of health and wellness tourism. It constitutes a significant contribution to the field of sustainable tourism by offering an innovative approach that prioritizes safety while upholding the principles of environmental stewardship, economic viability, and societal sustainability.