Systematic Review of Application of Nature-Inspired Algorithms for Resource Optimization in Multi-Programmed Operating Systems

Year : 2025 | Volume : 12 | Issue : 02 | Page : 08 14
    By

    Yamuna Mundru,

  • Atti Manga Devi,

  • Manas Kumar Yogi,

  1. Assistant Professor, Department of Computer Science and Engineering – Artificial Intelligence and Machine Learning (CSE-AI&ML), Pragati Engineering College (A), Surampalem, Andhra Pradesh, India
  2. Assistant Professor, Information Technology Department, Pragati Engineering College (A), Surampalem, Andhra Pradesh, India
  3. Assistant Professor, Information Technology Department, Pragati Engineering College (A), Surampalem, Andhra Pradesh, India

Abstract

Multi-programmed operating systems are increasingly confronted with complex challenges in efficiently managing system resources, primarily due to the need to handle numerous concurrent processes with diverse and often conflicting resource demands. As these systems evolve, ensuring optimal performance across various dimensions, such as CPU scheduling, memory allocation, and load balancing, has become crucial. In this context, nature-inspired algorithms have emerged as promising solutions for enhancing resource optimization. These algorithms, which draw inspiration from natural processes such as evolution, swarm behaviour, and biological systems, offer flexible and adaptive mechanisms for tackling optimization problems in dynamic computing environments. This systematic review delves into the characteristics and functionalities of various nature-inspired algorithms, such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO), assessing their efficiency in resource management within multi-programmed operating systems. The review highlights that these algorithms can outperform traditional approaches by offering better adaptability and higher optimization accuracy. Notably, significant improvements are observed in areas like CPU task scheduling, effective memory usage, and balancing system load. However, the implementation of these algorithms is not without challenges. Issues such as high computational overhead, slower convergence rates, and difficulties in adapting to real-time system changes continue to pose limitations. Future research should focus on hybrid approaches and real-time adaptability to enhance system performance further.

Keywords: Nature-inspired algorithms, multi-programmed operating systems, resource optimization, CPU scheduling, memory management, load balancing

[This article belongs to Journal of Operating Systems Development & Trends ]

How to cite this article:
Yamuna Mundru, Atti Manga Devi, Manas Kumar Yogi. Systematic Review of Application of Nature-Inspired Algorithms for Resource Optimization in Multi-Programmed Operating Systems. Journal of Operating Systems Development & Trends. 2025; 12(02):08-14.
How to cite this URL:
Yamuna Mundru, Atti Manga Devi, Manas Kumar Yogi. Systematic Review of Application of Nature-Inspired Algorithms for Resource Optimization in Multi-Programmed Operating Systems. Journal of Operating Systems Development & Trends. 2025; 12(02):08-14. Available from: https://journals.stmjournals.com/joosdt/article=2025/view=236514


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Regular Issue Subscription Review Article
Volume 12
Issue 02
Received 27/05/2025
Accepted 30/05/2025
Published 23/07/2025
Publication Time 57 Days
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