CSO: A Novel Crow Swarm Optimization Algorithm for Collaborative Path Planning with Collision Avoidance

Abstract

For autonomous systems operating in complex environments, it is a critical challenge to develop effective MAS path planning control mechanism that enables agents to collaborate and avoid collisions with each other. In response to this crucial need, this paper introduces a novel hybrid algorithm for MAS path planning called Crow Swarm Optimization (CSO). This solution leverages the strengths of two distinct metaheuristic approaches, the Crow Search algorithm (CSA) and the Particle Swarm Optimization (PSO). Although CSA is effective during search space exploration, it has slower convergence and weaknesses in finding global optima due to random location updates. To enhance the local search capability of the CSA, this research leverages the strength of PSO in exploitation. To accomplish this, the primary objective of this paper is to leverage the functionally segregated mechanism to overcome the inherent weaknesses of single-approach algorithms, thereby achieving optimal MAS path planning. Beyond path optimization, the presented work addresses collision prevention employing a velocity adjustment technique. This approach minimizes the agent’s velocity to prevent collisions with other agents. To validate the findings, the study conducted a comparative analysis against various versions of both CSA and PSO. The results clearly demonstrate the efficacy of the proposed approach in solving the path-planning problem with collision avoidance.