On Nature-Inspired Dynamic Route Planning: Hammerhead Shark Optimization Algorithm

Abstract

Dynamic route planning is a classical problem with interesting applications in optimizing car navigation, fleet management, urban evacuation planning, unmanned ground and airborne vehicle movement, and maritime route planning. While numerous traditional and heuristic-based algorithms have been proposed to address deficiencies in dynamic route planning, ongoing efforts are increasingly focusing on deriving scalable solutions from nature motivated schemes. The present work inspired by the behavior and prey hunting skills of sea predator, the hammerhead shark, seeks to devise a new dynamic route optimization algorithm. The proposed hammerhead shark optimization algorithm (HOA) finds destination in an unknown solution space by applying the natural route optimization behavior inherent in the hammerhead shark. Once the destination location is identified, HOA attempts to determine the path starting from source employing targeted movement towards the destination. The proposed algorithm can deal with static as well as dynamic environments. During validation, the proposed algorithm is performance tested against two state of the art algorithms A* and ant colony optimization (ACO). Attributes including the iteration count, path length (cells) and time complexity are compared. The results show that proposed algorithm outperforms A* in terms of latency, and ACO in terms of path optimality. The highly satisfactory performance of HOA leads us to recommend it as a suitable candidate for further adoption in optimal, scalable and dynamic path calculation concerns.