Journal of Soft Computing and Information Technology

Journal of Soft Computing and Information Technology

Combining Bat and Ant Colony Metaheuristic Algorithms for Service Quality Preservation in Adaptive Mixed-Criticality Systems

Document Type : Persian Original Article

Authors
Farzaneh Gholami 1
Vahid Haghighatdoost 2
1 Computer Engineering Group, Computer Engineering Department,, Shahed University, Tehran, Iran
2 Faculty of Computer Engineering Group, Engineering Department,, Shahed University, Tehran, Iran
10.22034/jscit.2026.565981.2202
Abstract
Scheduling in uniprocessor real-time systems is a fundamental challenge to guarantee the timing constraints of tasks. Traditional methods such as Rate Monotonic and Earliest DeadLine First lack sufficient efficiency in complex systems. As an effective solution, Adaptive Mixed-Criticality (AMC) systems enable mode switches between different criticality levels, ensuring that high-criticality tasks do not miss their DeadLines, even under heavy workloads. However, determining the optimal priority assignment for tasks in these systems is an NP-Hard problem. In this paper, to overcome this challenge, a hybrid method based on the Bat Algorithm (BA) and Ant Colony Optimization (ACO) is proposed. In this approach, the output of the Bat Algorithm is utilized as the initial pheromone value for the Ant Colony algorithm to increase the convergence speed. Simulation results demonstrate that the proposed method, compared to baseline approaches and previous research in this field, increases the schedulability ratio of Low-criticality tasks by 91% and reduces the response time compared to machine learning algorithms, which indicates the higher efficiency of the hybrid algorithm in managing mixed-criticality systems.
Keywords
Adaptive Mixed-Criticality Systems
Fixed-Priority Scheduling
Bat Algorithm
Ant Colony Optimization
Response Time Analysis
[1] G. C. Buttazzo, Hard Real-Time Computing Systems: Predictable Scheduling Algorithms and Applications, New York: Springer, 2011.
[2] S. Vestal, " Preemptive Scheduling of Multi-Criticality Systems with Varying Degrees of Execution Time Assurance," in 28th IEEE International Real-Time Systems Symposium, 2007.
[3] S.K. Baruah , A.Burns ,R.I. Davis, "Response-Time Analisis For Mixed-Criticality Systems," in Proc. IEEE Real-Time Systems Symposium (RTSS), 2011.
[4] Marco Dorigo, Tomas Stutzle, Ant Colony Optimization, MIT Press, 2004.
[5] X.-S. Yang, "A New Metaheuristic Bat-Inspired Algorithm," in Springer Berlin Heidelberg, 2010.
[6] Burns, Alan; Davis, Robert I, "Schedulability Analysis for Adaptive Mixed-Criticality Systems with Arbitrary Deadlines and Semi-Clairvoyance," in IEEE Real-Time Systems Symposium (RTSS), 2020.
[7] J. Liu, W. Zheng, X. Li, and J. Chen, "Scheduling Mixed-Criticality Systems with Degraded Service," EEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 35, pp. 2022-2034, 2016.
[8] e. a. T. Mendes, "Deep Reinforcement Learning for Dynamic Scheduling of Mixed-Criticality Systems," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 43, pp. 450-463, 2024.
[9] A. Burns and R. I. Davis, "A Survey of Research on Mixed Criticality Systems," ACM Computing Surveys (CSUR), vol. 50, 2018.
[10] Alan burns, Robert I.Davis, "Mixed Criticality System - A Rewiew," Department of Computer Science, University of York, 2017.
[11] K. Deb, "An efficient constraint handling method for genetic algorithms," Computer methods in applied mechanics and engineering, vol. 186, no. 311-338, 2000.
Journal of Soft Computing and Information Technology
Volume 15, Issue 3
Autumn - October
Autumn 2026
Pages 87-98