Towards equitable and immersive outdoor orienteering: An artificial intelligence-driven multi-objective route planning framework with augmented sand cat swarm optimization - Qingzhu Lun; Boya Li; Yuehui Zhou (PLoS ONE, 2026)
Outdoor orienteering has emerged as a globally popular recreational activity and competitive sport, combining navigational challenges with physical endurance across diverse natural terrains. Despite its growing popularity, the design of optimal orienteering routes presents significant challenges for recreation planners, requiring careful consideration of both competitive fairness and participant engagement. To address these challenges, this study establishes five fundamental design principles that systematically balance competitive equity with user experience enhancement. Building upon these principles, we develop a novel computational framework that integrates mathematical modeling techniques with intelligent optimization algorithms. Specifically, our methodology reformulates the route design challenge as a constrained multi-objective optimization problem and introduces an enhanced sand cat swarm optimization (SCSO) algorithm for efficient solution generation. Through comprehensive simulations across 50 distinct terrain profiles representing varying levels of complexity, we demonstrate the efficacy of our approach. Quantitative results show consistent performance improvements in route optimality metrics compared to conventional methods, which contribute to both the theoretical understanding of recreational route optimization and practical applications in outdoor activity planning.