Towards equitable and immersive outdoor orienteering: An artificial intelligence-driven multi-objective route...

Area of Science:

• Recreational Geography
• Computational Optimization
• Sports Science

Background:

• Outdoor orienteering is a growing global sport demanding complex route design.
• Balancing competitive fairness and participant experience is a key challenge for planners.
• Existing methods often fall short in optimizing orienteering routes effectively.

Purpose of the Study:

• To establish fundamental design principles for equitable and engaging orienteering routes.
• To develop a novel computational framework for optimizing orienteering route design.
• To enhance the user experience in outdoor recreational activities.

Main Methods:

• Formulated route design as a constrained multi-objective optimization problem.
• Developed an enhanced sand cat swarm optimization (SCSO) algorithm for solution generation.
• Validated the framework through simulations on 50 diverse terrain profiles.

Main Results:

• The SCSO algorithm demonstrated efficient solution generation for route optimization.
• Consistent performance improvements in route optimality metrics were observed.
• The novel framework significantly outperformed conventional route design methods.

Conclusions:

• The proposed design principles and computational framework effectively balance competitive equity and user experience.
• This research offers theoretical insights and practical tools for recreational route planning.
• The enhanced SCSO algorithm provides an efficient method for complex optimization problems in outdoor sports.