Dragon Boat Optimization: A Meta-Heuristic for Intelligent Systems

Abstract

Dragon boat racing, a popular aquatic folklore team sport, is traditionally held during the Dragon Boat Festival. Inspired by this event, we propose a novel human-based meta-heuristic algorithm called dragon boat optimization (DBO) in this paper. It models the unique behaviours of each crew member on the dragon boat during the race by introducing social psychology mechanisms (social loafing, social incentive). Throughout this process, the focus is on the interaction and collaboration among the crew members, as well as their decision-making in various situations. During each iteration, DBO implements different state updating strategies. By accurately modelling the crew's behaviour and employing adaptive state update strategies, DBO consistently achieves high optimization performance, as validated by comprehensive testing on 29 benchmark functions and 2 structural design problems. Experimental results indicate that DBO outperforms 7 and 16 state-of-the-art meta-heuristic algorithms across these test functions and problems, respectively.