An Improved Snake Optimization Algorithm with Opposition-Based Population Initialization

Snake Optimization (SO) is a newly developed swarm-intelligence-based optimization algorithm inspired by the behavior of snakes seeking better food sources, which potentially applies to multi-vehicle task assignments and trajectory optimization. The paper comes up with an improved SO that integrated with Opposition-based Learning (OBL) which helps to filter the snake population, reserving only the individuals closer to food. In contrast, the original SO with randomly generated population performs extra iteration steps for unfit snakes to survive, and spends more computational time. Simulations are conducted to illustrate a comparison between the proposed algorithm and the conventional one, where the former exhibits faster convergence to the optimal solutions and guarantees higher accuracy without population expansion, and these features gives rise to its applications in real-time embedded computing systems for automated vehicles.