Competition and cooperation in evading threat meta-heuristic algorithm

This work introduces an innovative heuristic algorithm named "Competition and Collaboration in Evading Threat (CCET)". Inspired by the escape behavior of animals such as deer, buffalo, sheep, etc., from predators like lions, leopards, tigers, etc., and also drawing parallels with soldiers evading attacks in war zones involving missiles, cannons, tanks, enemy gunfire, etc., the algorithm has been devised. In this approach, it is assumed that soldiers in war zones or domesticated animals are fleeing from threats and, despite competing in their escape, they collaborate with each other to ensure their survival. Unlike existing heuristic algorithms that rely on convergence, this proposed algorithm focuses on a novel approach based on the concept of divergence. The optimal response is determined based on the divergence of prey from the threat of the predator. The algorithm undergoes testing on 23 well-known benchmark functions, including unimodal, multimodal, and fixed-dimensional functions. The performance of the proposed algorithm is validated against recognized heuristic algorithms. Comparative results indicate that the proposed algorithm significantly demonstrates the capability to compete with well-known and powerful algorithms.