Dholes Hunting—A Multi-Local Search Algorithm Using Gradient Approximation and Its Application for Blockchain Consensus Problem

This study introduces a brand-new swarm-inspired algorithm dubbed dholes hunting-based optimization (DhoH) based on an animal hunting strategy to solve global optimization problems. The technique is a brilliant idea for simultaneously finding many local minima. The dhole’s hunting strategy is coordinated by members of a swarm, clustering and chasing prey. A clustering approach and finding an optimal global algorithm describe primarily based on gradient approximation. We use four benchmark function datasets to evaluate the DhoH’s performance. We compare the achieved results with several previous research from various well-known algorithms. The comparisons demonstrate that DhoH is better than other meta-heuristic algorithms in most cases and determines high-quality solutions with fewer control parameters. Besides, we also explore the application of DhoH in optimizing the decentralized level of Meta-heuristic Proof of Criteria consensus protocol (MPoC) in Blockchain Network to further demonstrate its potential in multi-dimensional problems. The results show the superior effectiveness of DhoH in terms of computational burden and solution precision compared with the existing optimization techniques in the literature.