Hunger games search: a comprehensive review of recent variants and applications

Hunger Games Search (HGS) is a novel metaheuristic algorithm favoured for its exploration ability and flexibility. Following the PRISMA 2020 guidelines, this study provides a systematic review of 115 articles published from 2022 to 2025. The review is structured around a multi-dimensional taxonomy of HGS variants and a comprehensive survey of seven major application domains, identifying engineering, machine learning, and medical optimization as the most prevalent areas. To enrich the depth of the review, this research incorporates a new quantitative performance evaluation using the CEC 2017 benchmark suite, where HGS is compared against nine state-of-the-art algorithms. The results demonstrate that HGS possesses robust global exploration capabilities, particularly when addressing complex composition landscapes, while also identifying specific areas for potential improvement in exploitation precision. Crucially, this study summarizes major improvement methods and establishes a prescriptive mapping that links inherent algorithmic limitations directly to specialized strategic enhancements such as chaotic mechanisim, opposition-based learning, and structural hybridizations. The survey findings confirm that HGS exhibits remarkable competitiveness and adaptability in solving diverse real-world optimization tasks. Despite its promising performance, HGS research remains relatively nascent. This review serves as a comprehensive resource for researchers by outlining potential research directions and providing insights into the properties of HGS variants and their suitability for various application domains.