@article{Lazarowska_2023, author = {Lazarowska, Agnieszka}, title = {A Nature Inspired Collision Avoidance Algorithm for Ships}, journal = {TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation}, volume = {17}, number = {2}, pages = {341-346}, year = {2023}, url = {./Article_A_Nature_Inspired_Collision_Avoidance_Lazarowska,66,1305.html}, abstract = {Nature inspired algorithms are regarded as a powerful tool for solving real life problems. They do not guarantee to find the globally optimal solution, but can find a suboptimal, robust solution with an acceptable computational cost. The paper introduces an approach to the development of collision avoidance algorithms for ships based on the firefly algorithm, classified to the swarm intelligence methods. Such algorithms are inspired by the swarming behaviour of animals, such as e.g. birds, fish, ants, bees, fireflies. The description of the developed algorithm is followed by the presentation of simulation results, which show, that it might be regarded as an efficient method of solving the collision avoidance problem. Such algorithm is intended for use in the Decision Support System or in the Collision Avoidance Module of the Autonomous Navigation System for Maritime Autonomous Surface Ships.}, doi = {10.12716/1001.17.02.10}, issn = {2083-6473}, publisher = {Gdynia Maritime University, Faculty of Navigation}, keywords = {Safe Own Ship's Trajectory, Path Planning, Safe Navigation, Ant Colony Optimization (ACO), Collision Avoidance Algorithm, Firefly Algorithm (FA), Swarm intelligence, Nature Inspired Computing} }