article · INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY · 2025

Parallel Metaheuristic Algorithms for Task Scheduling Problems

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Abstract

This study addresses the task-scheduling optimization challenges in parallel computing systems using a novel meta-heuristic framework. We analyze the differential evolution in task scheduling and propose an advanced shift-chain methodology to improve the cooperation between scheduling components. The proposed framework introduces a wait-ing time-based neighborhood exploration strategy for handling complex task dependencies, along with two parallel implementation approaches: basic matching vector (MV) parallelization and an event-driven strategy. The experi-mental results demonstrated superior solution quality and computational efficiency compared with existing methods, particularly in large scale problems. The modular design of this framework enables practical applications in moderncomputing environments.

Cite this paper

APA 7

Mao, S., & Edahiro, M. (2025). Parallel Metaheuristic Algorithms for Task Scheduling Problems. INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 25, 1–24. https://doi.org/10.24297/ijct.v25i.9709

MLA 9

Mao, Sirui, and Masato Edahiro. “Parallel Metaheuristic Algorithms for Task Scheduling Problems.” INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, vol. 25, 2025, pp. 1–24, https://doi.org/10.24297/ijct.v25i.9709.

Chicago

Mao, Sirui, and Masato Edahiro. 2025. “Parallel Metaheuristic Algorithms for Task Scheduling Problems.” INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 25: 1–24. https://doi.org/10.24297/ijct.v25i.9709.

Harvard

Mao, S. and Edahiro, M. (2025) ‘Parallel Metaheuristic Algorithms for Task Scheduling Problems’, INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 25, pp. 1–24. Available at: https://doi.org/10.24297/ijct.v25i.9709.

BibTeX

@article{mao2025parallel,
  title = {{Parallel Metaheuristic Algorithms for Task Scheduling Problems}},
  author = {Mao, Sirui and Edahiro, Masato},
  journal = {INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY},
  volume = {25},
  pages = {1--24},
  year = {2025},
  doi = {10.24297/ijct.v25i.9709}
}

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