AMIMSO: Adaptive Markov Inspired Mutation Swarm Optimization for Multi-Objective Task Scheduling in End-Edge-Cloud Environments

With the widespread adoption of end-edge-cloud computing architectures, modern distributed systems face the dual challenges of real-time data processing and resource optimization in task offloading and resource allocation. Efficient scheduling of diverse tasks presents a complex multi-objective optimization problem. To address this, this paper introduces a multi-objective optimization model that leverages the Lagrange multiplier method to balance task scheduling time, resource consumption, and task completion rates, achieving effective trade-offs among these objectives. Additionally, we propose an adaptive Markov inspired mutation swarm optimization algorithm(AMIMSO). By incorporating a Markov state transition mechanism, the algorithm dynamically adjusts inertia weights and learning factors based on the system's state, enabling adaptive switching between global exploration and local exploitation. This approach efficiently solves multi-objective optimization problems. Furthermore, the integration of mutation operations enhances the algorithm's capability to escape local optima, significantly improving global search efficiency and convergence speed. The experimental results based on simulation environment demonstrate that AMIMSO outperforms existing methods in terms of task completion rates, resource utilization efficiency, and system responsiveness, offering a robust and efficient solution for task scheduling and resource optimization in dynamic environments.