Bounty Hunter Optimizer

Bounty Hunter Optimizer: A Novel Metaheuristic with an Application to Multi-UAV Mobile Edge Computing and Path Planning

Yu M, Yang H, Zhang J, Ouyang K, Fu S, Tan P, Jiang F, Xu J. Bounty Hunter Optimizer: A Novel Metaheuristic with an Application to Multi-UAV Mobile Edge Computing and Path Planning. Knowledge-Based Systems.

In this study, we proposed a novel metaheuristic called Bounty Hunter Optimizer (BHO), inspired by the search behavior of bounty hunters. Compared with traditional optimization methods that rely on mean aggregation, BHO adopts a decentralized position-update strategy based on local differences and random disturbances, which helps avoid center bias and population collapse. The algorithm further introduces the Explorpolis rule, a quantum probability rotation selection mechanism, and an evolutionary framework integrating “thorough investigation,” “rough search,” and “hunter reassignment,” together with a self-feedback adjustment mechanism to dynamically balance exploration and exploitation. This work was eventually published in Knowledge-Based Systems (KBS).