A multistrategy improved hunger games search algorithm.

In this paper, we propose a multistrategy improved Hunger Games Search (MHGS) algorithm to address several inherent limitations of the original HGS, including imbalanced exploration and exploitation, insufficient population diversity, and premature convergence. The main contributions of MHGS are fourfold: (1) a phased position update framework that dynamically coordinates global exploration and local exploitation through three distinct search phases; (2) an enhanced reproduction operator inspired by biological reproductive patterns to preserve population diversity; (3) an adaptive boundary handling mechanism that redirects out-of-bounds individuals to promising regions, thus improving search efficiency; and (4) an elite dynamic oppositional learning strategy with self-adjusting coefficients to enhance the algorithm's ability to avoid local optima. These mechanisms work synergistically: the phased update balances global and local search, while the reproduction and boundary handling strategies jointly maintain solution diversity. The addition of oppositional learning further improves the robustness of the search process. Extensive evaluations on 23 benchmark functions, the CEC2017 test suite, and two engineering design problems demonstrate that MHGS achieves a 23.7% average improvement in accuracy compared to seven state-of-the-art algorithms (Wilcoxon rank-sum test, p < 0.05). Moreover, a binary variant, BMHGS_V3, using sigmoid transformation, attains an average classification accuracy of 92.3% on ten UCI datasets for feature selection tasks. The proposed MHGS algorithm provides a novel and effective framework for solving complex optimization problems, demonstrating significant theoretical and practical value in the field of computational intelligence.