Multi-objective quantum hybrid evolutionary algorithms for enhancing quality-of-service in internet of things

Area of Science:

• Computer Science
• Artificial Intelligence
• Network Engineering

Background:

• Optimizing Quality of Service (QoS) in the Internet of Things (IoT) is challenging due to device heterogeneity and resource constraints.
• Traditional multi-objective optimization algorithms struggle with slow convergence and local optima in complex IoT environments.

Purpose of the Study:

• To propose a novel quantum-inspired hybrid optimization algorithm for effective IoT service management.
• To enhance QoS parameters including energy efficiency, latency, convergence speed, and coverage cost in IoT applications.

Main Methods:

• Developed a hybrid algorithm integrating Multi-Objective Grey Wolf Optimization Algorithm (MOGWOA) and Multi-Objective Whale Optimization Algorithm (MOWOA).
• Incorporated quantum principles, such as quantum position and behavior, to improve exploration and exploitation capabilities.
• Conducted extensive simulations to evaluate the algorithm's performance against existing methods.

Main Results:

• The proposed quantum-inspired hybrid algorithm demonstrated improved convergence speed and avoidance of local optima.
• Achieved superior optimization results for energy efficiency and latency reduction in IoT applications.
• Validated enhanced performance in terms of convergence and coverage cost compared to traditional algorithms.

Conclusions:

• The novel quantum-inspired hybrid algorithm effectively addresses limitations of traditional methods for IoT QoS optimization.
• The integration of quantum mechanics significantly enhances the algorithm's efficiency and accuracy for complex IoT challenges.
• The proposed method offers a promising solution for improving overall IoT service management and performance.