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Comprehensive Empirical Benchmarking of Twelve Sorting Algorithms Across Comparison-Based, Non-Comparison-Based, and Hybrid Paradigms: A Multi-Dimensional Performance Model for Algorithm Selection at Practical Data Scales (n up to 100,000)

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  • Brenda M. Balala, MIT

    (Faculty, Computer Studies Department Notre Dame of Marbel University, Koronadal City, South Cotabato)

Abstract

This study extends the five-algorithm benchmark of Wibowo and Faisal [12] — which compared Heap, Shell, Merge, and Quick Sort against Python's built-in Timsort — to a twelve-algorithm framework spanning comparison-based, non-comparison-based, and hybrid/adaptive paradigms. Execution time (time.perf_counter()) and peak memory (tracemalloc) were measured across data sizes from 100 to 100,000 elements under random, ascending, and descending distributions, with stability and adaptivity empirically verified rather than only theoretically asserted. Results show that Counting Sort empirically breaks the Ω(n log n) comparison-sort lower bound under bounded key-range conditions, completing in 39.29 ms at n=100,000 versus 1,607.65–14,818.08 ms for the comparison-based algorithms tested (Mann-Whitney U test, p

Suggested Citation

  • Brenda M. Balala, MIT, 2026. "Comprehensive Empirical Benchmarking of Twelve Sorting Algorithms Across Comparison-Based, Non-Comparison-Based, and Hybrid Paradigms: A Multi-Dimensional Performance Model for Algorithm Selection at Practical Data Scales (n up to 100,000)," International Journal of Latest Technology in Engineering, Management & Applied Science, RSIS International, vol. 15(7), pages 803-822, August.
    • Handle: RePEc:bjf:ijltem:v:15:y:2026:i:7:a:72
    DOI: 10.51583/IJLTEMAS.2026.150700067
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