{
    "@context": "https://schema.org",
    "@type": "ScholarlyArticle",
    "identifier": {
        "jsc_id": 33,
        "DOI": "10.5281/zenodo.23266056",
        "DOI_url": "https://doi.org/10.5281/zenodo.23266056"
    },
    "title": "A Hybrid Metaheuristic Algorithm for Energy-Aware Task Scheduling",
    "abstract": "Energy use is now a primary constraint in heterogeneous computing systems: schedules that minimize the makespan of dependent tasks must also limit the power drawn by the processors that execute them. Energy-aware scheduling on heterogeneous processors is NP-hard, so exact methods do not scale. Single metaheuristics typically explore broadly without refining local structure, or refine a single solution without leaving poor regions of the search space. This paper proposes a hybrid metaheuristic that embeds simulated annealing as a local-refinement operator within a genetic algorithm and couples the search to a dynamic voltage-and-frequency-scaling energy model, so that voltage assignment is optimized together with task mapping. The scheduler minimizes a weighted objective that combines energy, makespan, and processor utilization; the method is specified formally and as reproducible pseudocode. On an illustrative synthetic benchmark of dependency graphs scheduled on heterogeneous processors, the proposed method is compared with a genetic algorithm, simulated annealing, particle swarm optimization, ant colony optimization, and a list-scheduling heuristic. The results indicate faster convergence and lower energy and makespan than these baselines, which supports combining memetic search with an explicit energy model for this class of scheduling problems.",
    "authors": [
        {
            "sequence": "first",
            "given": "Ana Paula Maia Tanajura",
            "family": "Ellefsen",
            "name": "Ana Paula Maia Tanajura Ellefsen",
            "affiliation": [
                {
                    "name": "Telenor, Snarøyveien 30, Oslo, Norway"
                }
            ],
            "email": "ana.ellefsen@dsv.com",
            "is_corresponding": true
        },
        {
            "sequence": "additional",
            "given": "Giorgio",
            "family": "Mossa",
            "name": "Giorgio Mossa",
            "affiliation": [
                {
                    "name": "Department of Mechanics, Mathematics and Management, Politecnico di Bari, Via Orabona, 4, 70125, Bari, Italy"
                }
            ],
            "email": "giorgio.mossa@poliba.it"
        }
    ],
    "keywords": [
        "Task scheduling",
        "Energy-aware computing",
        "Hybrid metaheuristic",
        "Genetic algorithm",
        "Simulated annealing"
    ],
    "section": "Research Article",
    "language": "en",
    "journal": {
        "name": "Journal of Smart Computing",
        "abbreviation": "JSC",
        "issn": "2979-8752",
        "publisher": "Center of Intelligent Operations and Technology Research, United Kingdom",
        "url": "https://ciotr.com/jsc/"
    },
    "issue": {
        "volume": 2,
        "number": 1,
        "year": 2026,
        "url": "https://ciotr.com/jsc/issue/8"
    },
    "pages": {
        "first": "43",
        "last": "52",
        "range": "43-52"
    },
    "dates": {
        "submitted": "2026-07-20",
        "published": "2026-04-25"
    },
    "urls": {
        "landing_page": "https://ciotr.com/jsc/article/33",
        "pdf": "https://ciotr.com/jsc/pdf/article-33.pdf",
        "metadata": "https://ciotr.com/jsc/article/33.json"
    },
    "license": {
        "name": "Creative Commons Attribution 4.0 International",
        "code": "CC BY 4.0",
        "url": "https://creativecommons.org/licenses/by/4.0/"
    },
    "citation": {
        "apa": "Ellefsen, A.P.M.T., & Mossa, G. (2026). A Hybrid Metaheuristic Algorithm for Energy-Aware Task Scheduling. Journal of Smart Computing, 2(1), 43-52. https://doi.org/10.5281/zenodo.23266056"
    }
}