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 "slug": "hunger-games-search",
 "title": "Hunger games search",
 "updated": "2026-10-15",
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 "excerpt": "Hunger games search (HGS) is a population-based metaheuristic optimization algorithm introduced by Yutao Yang and colleagues in 2021, which uses adaptive hunger weights mimicking animal foraging to find the global optimum of single-objective functions.",
 "snippet": "Hunger games search (HGS) is a population-based metaheuristic optimization algorithm introduced by Yutao Yang and colleagues in 2021, which uses adaptive hunger weights mimicking animal foraging to find the global optimum of single-objective functions.",
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 "markdown": "# Hunger games search\n\nHunger games search (HGS) is a population-based metaheuristic (a general search framework for finding good solutions without problem-specific methods) algorithm that iteratively adjusts a set of candidate solutions to find the global optimum of a numerical, single-objective function. It belongs to the family of nature-inspired optimization methods: each candidate's movement is governed by adaptive \"hunger\" weights that mimic how animals forage and act under hunger-driven signals. The algorithm outputs the best-positioned agent found and its fitness value after a fixed number of iterations.\n\n| Key fact | Detail |\n|---|---|\n| Introduced | Yang, Chen, Heidari, and Gandomi, Expert Systems with Applications, 2021 <sup>[1](https://doi.org/10.1016/j.eswa.2021.114864)</sup> |\n| Problem class | Continuous, single-objective global optimization; discrete problems require a conversion step <sup>[2](https://www.nature.com/articles/s41598-025-16513-4)</sup> |\n| Core mechanism | Three-way \"game\" position update controlled by hunger weights \\( W_{1} \\), \\( W_{2} \\), a ranging controller \\( R \\), and the hunger factor \\( E \\) <sup>[2](https://www.nature.com/articles/s41598-025-16513-4)</sup> |\n| Standard parameters | Original HGS setting \\( l = 0.03 \\), hunger threshold \\( L_{\\mathrm{H}} = 100 \\); population size varies by experiment (a parameter study found \\( l = 0.08 \\) best for its benchmarks) <sup>[3](https://www.mdpi.com/2313-7673/8/5/441)</sup><sup> • </sup><sup>[4](https://opus.lib.uts.edu.au/bitstream/10453/149384/2/Binder1.pdf)</sup> |\n| Typical benchmarks | 23 classical functions and CEC2017 suites <sup>[2](https://www.nature.com/articles/s41598-025-16513-4)</sup><sup> • </sup><sup>[5](https://www.sciencedirect.com/science/article/abs/pii/S0378475421003426)</sup>; the CEC2020 suite appears in variant evaluations <sup>[3](https://www.mdpi.com/2313-7673/8/5/441)</sup><sup> • </sup><sup>[6](https://link.springer.com/content/pdf/10.1007/s00521-022-07916-9.pdf)</sup> |\n| Known weaknesses | Exploration/exploitation imbalance, premature convergence, sensitivity to \\( l \\) and \\( L_{\\mathrm{H}} \\) <sup>[2](https://www.nature.com/articles/s41598-025-16513-4)</sup><sup> • </sup><sup>[4](https://opus.lib.uts.edu.au/bitstream/10453/149384/2/Binder1.pdf)</sup> |\n\n## How it works\n\nHGS rests on the observation that animals make decisions, search dynamically, and act depending on the feeling of hunger; the introducing paper designed its hunger weights based on hunger-driven signals reported in the neuroscience literature (Betley et al., 2015).<sup>[1](https://doi.org/10.1016/j.eswa.2021.114864)</sup><sup> • </sup><sup>[7](https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0305653)</sup> Each individual carries a hunger degree, and the population's hunger values are converted into two adaptive weights, \\( W_{1} \\) and \\( W_{2} \\), that scale how strongly each agent is pulled toward the current best position.<sup>[2](https://www.nature.com/articles/s41598-025-16513-4)</sup>\n\nThe hunger factor E is defined through a hyperbolic secant of the fitness gap between an individual and the best fitness so far, \\( E = \\mathrm{sech}(\\lvert f(i) - f_{\\mathrm{best}} \\rvert) \\), so agents far from the best solution behave differently from those near it.<sup>[4](https://opus.lib.uts.edu.au/bitstream/10453/149384/2/Binder1.pdf)</sup> A ranging controller \\( R = 2 \\cdot r \\cdot h - h \\) with \\( h = 2 \\cdot (1 - t/T) \\) shrinks the activity range toward zero as the iteration count t approaches the maximum T, shifting the search from exploration toward exploitation.<sup>[4](https://opus.lib.uts.edu.au/bitstream/10453/149384/2/Binder1.pdf)</sup>\n\nThe position update is a three-way game rule <sup>[2](https://www.nature.com/articles/s41598-025-16513-4)</sup>:\n\n\\[ X(t+1) = \\begin{cases} X(t) \\cdot (1 + \\mathrm{randn}(1)), & r_{1} < l \\\\ W_{1} \\cdot X_{b} + R \\cdot W_{2} \\cdot \\lvert X_{b} - X(t) \\rvert, & r_{1} \\geq l,\\ r_{2} > E \\\\ W_{1} \\cdot X_{b} - R \\cdot W_{2} \\cdot \\lvert X_{b} - X(t) \\rvert, & r_{1} \\geq l,\\ r_{2} \\leq E \\end{cases} \\]<sup>[11](https://pmc.ncbi.nlm.nih.gov/articles/PMC11329106/)</sup>\n\nGame 1 lets an agent wander freely (exploration), while Games 2 and 3 move it toward or around the best agent \\( X_{b} \\) (exploitation). The threshold l decides how often free wandering occurs, and E decides which exploitation branch applies. The weights are computed from each individual's hunger value normalized by the population's hunger sum: \\( W_{1} \\) equals the normalized hunger times a random factor when the random draw is below the threshold, and 1 otherwise; \\( W_{2} \\) follows \\( W_{2}(i) = (1 - e^{-\\lvert \\mathrm{hungry}(i) - SHungry/N \\rvert}) \\cdot r_{5} \\cdot 2 \\).<sup>[2](https://www.nature.com/articles/s41598-025-16513-4)</sup><sup> • </sup><sup>[3](https://www.mdpi.com/2313-7673/8/5/441)</sup><sup> • </sup><sup>[4](https://opus.lib.uts.edu.au/bitstream/10453/149384/2/Binder1.pdf)</sup><sup> • </sup><sup>[12](https://www.nature.com/articles/s41598-025-98056-2)</sup> Hunger itself is updated by setting an individual's hunger to 0 when it holds the best fitness and otherwise incrementing it by a hunger sensation \\( H \\), which depends on the threshold \\( L_{\\mathrm{H}} \\).<sup>[3](https://www.mdpi.com/2313-7673/8/5/441)</sup>\n\n## How it is done\n\nOne HGS run takes the population size N, maximum iterations T, and dimension D as inputs, and returns the best agent \\( X_{b} \\) and its fitness \\( BF \\).<sup>[7](https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0305653)</sup> Each iteration proceeds as follows:\n\n1. Evaluate the fitness of every individual.\n2. Update each individual's hunger degree; set it to 0 for the best individual, otherwise increment it by H computed from the fitness gap and the threshold \\( L_{\\mathrm{H}} \\).<sup>[3](https://www.mdpi.com/2313-7673/8/5/441)</sup>\n3. Compute the hunger sum \\( SHungry \\) and the weights \\( W_{1} \\) and \\( W_{2} \\) for every individual.<sup>[7](https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0305653)</sup>\n4. Draw the random values \\( r_{1} \\) through \\( r_{6} \\) that realize the approach-food and hunger-role phases, apply the three-game position update, and evaluate the new positions.<sup>[2](https://www.nature.com/articles/s41598-025-16513-4)</sup><sup> • </sup><sup>[5](https://www.sciencedirect.com/science/article/abs/pii/S0378475421003426)</sup>\n5. After \\( T \\) iterations, output \\( X_{b} \\) and \\( BF \\).\n\nThe introducing paper's parameter study fixed the population at 100 and found \\( l = 0.08 \\) best on 23 benchmark functions with 30 runs each.<sup>[4](https://opus.lib.uts.edu.au/bitstream/10453/149384/2/Binder1.pdf)</sup> The hunger threshold \\( L_{\\mathrm{H}} \\) is commonly set to 100.<sup>[3](https://www.mdpi.com/2313-7673/8/5/441)</sup>\n\n## Origin\n\nHGS was reported by Yutao Yang and colleagues in \"Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts\", published in Expert Systems with Applications in 2021.<sup>[1](https://doi.org/10.1016/j.eswa.2021.114864)</sup> The paper framed the method against the no-free-lunch theorem, which states that no algorithm can solve all optimization problems as the best method, and released HGS as an open-source population-based standard tool.<sup>[8](https://www.sciencedirect.com/science/article/abs/pii/S0957417421003055)</sup> Its biological basis is the hunger-driven signaling work of Betley et al. (2015), which informed the design of the weights \\( W_{1} \\) and \\( W_{2} \\).<sup>[8](https://www.sciencedirect.com/science/article/abs/pii/S0957417421003055)</sup> The introducing paper benchmarked HGS against seven differential evolution-based methods (LSHADE, SPS_L_SHADE_EIG, LSHADE_cnEpSi, SHADE, SADE, MPEDE, and JDE) on many single-objective problems.<sup>[8](https://www.sciencedirect.com/science/article/abs/pii/S0957417421003055)</sup>\n\n## Variants\n\nBecause HGS is tailored for continuous optimization and is unsuitable for discrete problems without a conversion mechanism, several variants adapt it to other problem types or repair its weaknesses <sup>[2](https://www.nature.com/articles/s41598-025-16513-4)</sup>:\n\n- **mHGS** integrates fuzzy logic into the exploration phase to address weak local search, premature convergence, and poor exploration/exploitation balance.<sup>[6](https://link.springer.com/content/pdf/10.1007/s00521-022-07916-9.pdf)</sup>\n- **Chaotic HGS** applies ten chaotic maps to two of the six random values in three alternative scenarios; Scenario 2 showed more stable and faster convergence.<sup>[5](https://www.sciencedirect.com/science/article/abs/pii/S0378475421003426)</sup>\n- **RLHGS** is an enhanced version benchmarked on 23 functions and CEC2020 against eight state-of-the-art algorithms, and on welded beam, I-beam, and multiple disk clutch brake design problems.<sup>[3](https://www.mdpi.com/2313-7673/8/5/441)</sup>\n- **MHGS** adds a phased position update, an enhanced reproduction operator, adaptive boundary handling, and elite dynamic oppositional learning; its binary counterpart BMHGS_V3 uses sigmoid transformation for binary feature selection.<sup>[2](https://www.nature.com/articles/s41598-025-16513-4)</sup>\n- **ATHGS** combines adaptive weights with Tent chaos mapping.<sup>[7](https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0305653)</sup>\n- **AHGS** is an adaptive HGS for non-convex economic dispatch, which relative to HGS reduced run-to-run standard deviation by 57.06% to 99.52% and required lower total wall-clock time in all 21 test cases.<sup>[9](https://www.nature.com/articles/s41598-026-68731-z.pdf)</sup>\n\nFurther cataloged variants include quantum rotation gate and Nelder-Mead simplex enhancements, chaotic mappings with greedy selection and vertical crossover, non-homogeneous mutation for load frequency control, local escaping with [Brownian motion](https://www.edgepedia.org/brownian-motion), binary tau-based crossover, IHGS with cube mapping and refracted opposition-based learning, and the artificial bee bare-bone ABHGS for gene selection.<sup>[3](https://www.mdpi.com/2313-7673/8/5/441)</sup> A binary HGS was proposed for feature selection, and Al-Kaabi et al. introduced a multiobjective HGS for multiobjective optimal power flow.<sup>[2](https://www.nature.com/articles/s41598-025-16513-4)</sup>\n\n## Applications\n\nReported applications concentrate on engineering design and machine-learning tuning. HGS and its variants have been applied to welded beam, I-beam, multiple disk clutch brake, cantilever beam, tension/compression spring, and speed reducer design problems <sup>[3](https://www.mdpi.com/2313-7673/8/5/441)</sup><sup> • </sup><sup>[5](https://www.sciencedirect.com/science/article/abs/pii/S0378475421003426)</sup>, and to mass minimization of an automobile suspension arm, where HGS reached the best constrained solution compared with eight other optimizers.<sup>[10](https://www.degruyterbrill.com/document/doi/10.1515/mt-2022-0013/html?lang=en)</sup> In machine learning, HGS has tuned random vector functional link (RVFL) models <sup>[3](https://www.mdpi.com/2313-7673/8/5/441)</sup>, predicted ground vibration intensity in an HGS-ANN hybrid <sup>[3](https://www.mdpi.com/2313-7673/8/5/441)</sup>, and performed feature selection on medical and chemical datasets with dimensions up to 20,000 features.<sup>[6](https://link.springer.com/content/pdf/10.1007/s00521-022-07916-9.pdf)</sup>\n\n## Limitations and alternatives\n\nDocumented failure modes include imbalanced exploration and exploitation, insufficient population diversity, and premature convergence with a tendency to get stuck in local optima.<sup>[2](https://www.nature.com/articles/s41598-025-16513-4)</sup><sup> • </sup><sup>[3](https://www.mdpi.com/2313-7673/8/5/441)</sup> The parameters \\( l \\) and \\( L_{\\mathrm{H}} \\) affect convergence speed and accuracy, and the exploration/exploitation balance depends closely on them.<sup>[4](https://opus.lib.uts.edu.au/bitstream/10453/149384/2/Binder1.pdf)</sup> Benchmark studies use the 23 classical functions and the CEC2017 and CEC2020 suites, typically with Wilcoxon rank-sum tests and 30 independent runs.<sup>[2](https://www.nature.com/articles/s41598-025-16513-4)</sup><sup> • </sup><sup>[3](https://www.mdpi.com/2313-7673/8/5/441)</sup> MHGS reported a 23.7% average improvement in accuracy over seven state-of-the-art algorithms on 23 benchmark functions and CEC2017.<sup>[2](https://www.nature.com/articles/s41598-025-16513-4)</sup> The multistrategy improved HGS (MHGS) study evaluated HGS against PSO, GWO, WOA, AOA, SCSO, CDO, and AGWO on 23 benchmark functions and the CEC2017 suite, reporting HGS was outperformed with a 23.7% average accuracy improvement over seven state-of-the-art algorithms (Wilcoxon rank-sum test, p < 0.05); MIA-HGS also outperformed PSO and GWO across the board per the Wilcoxon sign-rank test.<sup>[2](https://www.nature.com/articles/s41598-025-16513-4)</sup>\n\nThe metaphor critique is substantial. Sorensen and colleagues argued that excessive reliance on biological analogies produces redundant algorithms that obscure mathematical novelty through terminological reinvention; Villalón and colleagues systematically demonstrated that many \"novel\" metaphor-driven methods, such as the gray wolf optimizer and [bat algorithm](https://www.edgepedia.org/bat-algorithm), are structurally equivalent to established approaches like PSO; and Velasco and colleagues found that 65% of recently proposed \"improved\" algorithms fail to address core limitations such as no-free-lunch theorem compliance.<sup>[2](https://www.nature.com/articles/s41598-025-16513-4)</sup> These critiques apply to the genre HGS belongs to, and readers should weigh reported benchmark wins against the possibility that the hunger mechanism repackages standard attraction-to-best and shrinking-range operators under new terminology.\n\n## References\n\n1. [Yutao Yang and colleagues (2021). Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Systems with Applications.](https://doi.org/10.1016/j.eswa.2021.114864)\n2. [A multistrategy improved hunger games search algorithm | Scientific Reports](https://www.nature.com/articles/s41598-025-16513-4)\n3. [An Enhanced Hunger Games Search Optimization with Application to Constrained Engineering Optimization Problems (Biomimetics, MDPI)](https://www.mdpi.com/2313-7673/8/5/441)\n4. [HGS thesis/book chapter with equation derivations and parameter analysis (UTS open repository)](https://opus.lib.uts.edu.au/bitstream/10453/149384/2/Binder1.pdf)\n5. [Chaotic hunger games search optimization algorithm for global optimization and engineering problems](https://www.sciencedirect.com/science/article/abs/pii/S0378475421003426)\n6. [Fuzzy-based hunger games search algorithm for global optimization and feature selection using medical data (Neural Computing and Applications, Springer)](https://link.springer.com/content/pdf/10.1007/s00521-022-07916-9.pdf)\n7. [Improve the Hunger Games search algorithm to optimize the GoogleNet model (PLOS One, 2024)](https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0305653)\n8. [Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts (Expert Systems with Applications)](https://www.sciencedirect.com/science/article/abs/pii/S0957417421003055)\n9. [A stability-oriented framework based on adaptive hunger games search for non-convex economic dispatch (Scientific Reports, 2026)](https://www.nature.com/articles/s41598-026-68731-z.pdf)\n10. [Hunger games search algorithm for global optimization of automobile suspension arm design (Materialwissenschaft und Werkstofftechnik / De Gruyter)](https://www.degruyterbrill.com/document/doi/10.1515/mt-2022-0013/html?lang=en)\n11. [pmc.ncbi.nlm.nih.gov](https://pmc.ncbi.nlm.nih.gov/articles/PMC11329106/)\n12. [Artificial intelligence-driven cybersecurity system for internet of things using self-attention deep learning and metaheuristic algorithms, Scientific Reports](https://www.nature.com/articles/s41598-025-98056-2)\n\n---\n*Topic: Encyclopedia › Physical world and mathematics › Mathematics and statistics*\n\n*Initially written Sep 29, 2026 · Edited: Oct 15, 2026*\n\n*Copyright 2026 EdgeChat AI, a subsidiary of Biostate AI.*\n\nLicense: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license\n",
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