{
  "abstract": "This paper proposes an Improved Hybrid Gaussian Particle Swarm Optimization (IHG-GPSO) algorithm for high-dimensional global optimization. Four synergistic mechanisms are integrated: a nonlinear cosine–sine inertia weight to smoothly transition from exploration to exploitation, a memoryless random experience learning term that retains the experience-guided optimization paradigm of global PSO (GPSO) and improved PSO (IPSO) while eliminating historical optimum storage, a best-directed differential experience term inspired by differential evolution that tightens the swarm around the current global best, and a stagnation-triggered Gaussian mutation to restore population diversity without perturbing elite solutions. The proposed algorithm is evaluated on 12 classical benchmark functions at 30, 50, and 100 dimensions under a budget of 105 fitness evaluations, and on the CEC2017 test suite with 29 functions at 10, 30, and 50 dimensions, against PSO, GPSO, IPSO, CLPSO, CMA-ES, and L-SHADE over 30 independent runs (20 independent runs for the classical functions at D=100). Wilcoxon rank-sum tests at α=0.05 show that IHG-GPSO is statistically superior or equivalent to PSO, GPSO, and IPSO on the 12 classical functions, with no case of statistical inferiority on that suite; comparisons with the contemporary optimizers CLPSO, CMA-ES, and L-SHADE show that IHG-GPSO outperforms PSO, GPSO, and IPSO on all 12 functions, while CLPSO retains an edge on selected highly multimodal functions (Rastrigin, Schwefel 2.26, Penalized 1) and CMA-ES/L-SHADE remain superior on selected unimodal cases. On CEC2017, IHG-GPSO is again the most accurate PSO-family method, attaining the best Friedman average rank among PSO, GPSO, and IPSO at all three dimensions (3.638, 3.862, and 3.621 at D = 10, 30, 50, respectively), while L-SHADE, CLPSO, and CMA-ES occupy the top of the overall ranking on most functions; statistically significant losses against them are confined to a subset of shifted and rotated problems. Convergence curves and boxplot analysis confirm faster convergence and higher stability than the PSO-family baselines, while the algorithm maintains O(TND) time complexity and O(ND) memory complexity, making it a lightweight and effective solver for high-dimensional optimization tasks.",
  "abstract_shown": true,
  "affiliations": [
    "School of Electrical and Information Engineering, North Minzu University, Yinchuan 750030, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Ruidong Wang",
      "orcid": "0000-0003-0910-9726"
    },
    {
      "affs": [
        0
      ],
      "name": "Chao Han",
      "orcid": "0009-0007-2835-8281"
    },
    {
      "affs": [
        0
      ],
      "name": "Yutao Ma"
    },
    {
      "affs": [
        0
      ],
      "name": "Zhibo Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6w7agfzpy718r.bib",
  "cited_by": 0,
  "doi": "10.3390/math14203733",
  "fulltext_url": null,
  "id": "6w7agfzpy718r",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/20/3733",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1702|Metaheuristic Optimization Algorithms Research"
    ]
  ],
  "openalex": "W7221775693",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/6w7agfzpy718r.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:29Z",
      "native_id": "10.3390/math14203733",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/20/3733"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2227-7390",
      "subfield": "general"
    }
  ],
  "title": "IHG-GPSO: An Improved Hybrid Gaussian Particle Swarm Optimization with Nonlinear Inertia Weight and Directed Differential Experience for Global Optimization",
  "title_text": "IHG-GPSO: An Improved Hybrid Gaussian Particle Swarm Optimization with Nonlinear Inertia Weight and Directed Differential Experience for Global Optimization",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:29Z",
  "url": "https://research.draconitic.com/paper/6w7agfzpy718r",
  "venue": "Mathematics",
  "versions": []
}