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It combines fitness values to determine individual weights and updates them based on fitness value size, resulting in high adaptability and effective optimization. However, HGS faces issues like low convergence accuracy and susceptibility to local optima in complex optimization problems. To address these problems, an improved version called BDFXHGS is introduced. BDFXHGS incorporates a collaborative feeding strategy based on HGS\u2019s design advantages. Individuals approach others based on hunger degree, facilitating information exchange and resolving convergence and accuracy issues. BDFXHGS combines a disperse foraging strategy and a directional crossover strategy to enhance exploration and convergence speed. The paper conducts qualitative analysis and ablation experiments to examine the effectiveness of the strategies. Comparative experiments are performed using IEEE CEC 2017 benchmark functions to compare BDFXHGS with competitive algorithms, including previous champion algorithms in different dimensions. Additionally, BDFXHGS is evaluated on 25 constrained optimization problems from the IEEE CEC 2020 competition and five real engineering optimization problems. Experimental results show that BDFXHGS performs well on benchmarks and outperforms other algorithms in real-world applications.<\/jats:p>","DOI":"10.1093\/jcde\/qwae050","type":"journal-article","created":{"date-parts":[[2024,5,26]],"date-time":"2024-05-26T05:20:41Z","timestamp":1716700841000},"page":"280-307","source":"Crossref","is-referenced-by-count":2,"title":["Hunger games search algorithm based on stochastic individual information for engineering design optimization problems"],"prefix":"10.1093","volume":"11","author":[{"given":"Zhen","family":"Wang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Changchun Normal University , Changchun, Jilin 130032 , China"}]},{"given":"Dong","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Changchun Normal University , Changchun, Jilin 130032 , China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6938-9948","authenticated-orcid":false,"given":"Ali Asghar","family":"Heidari","sequence":"additional","affiliation":[{"name":"School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran , Tehran 15614 , Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7714-9693","authenticated-orcid":false,"given":"Huiling","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University , Wenzhou 325035 , China"}]}],"member":"286","published-online":{"date-parts":[[2024,5,25]]},"reference":[{"key":"2024061315251813100_bib1","doi-asserted-by":"crossref","first-page":"114788","DOI":"10.1016\/j.eswa.2021.114788","article-title":"Cooperative meta-heuristic algorithms for global optimization problems","volume":"176","author":"Abd\u00a0Elaziz","year":"2021","journal-title":"Expert Systems with 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