Adaptive Quantum Behaved Flower Pollination and Tabu Search Algorithm for Energy Efficient Task Management in Edge-Cloud Continuum

Abstract

Efficient task management in a dynamic and resource-constrained edge-cloud environments is essential for minimizing execution delays and reducing energy consumption. This paper presents a novel hybrid optimization algorithm, integrating an Adaptive Flower Pollination Algorithm (FPA) with Tabu Search, to address these challenges. The proposed approach introduces a diversity-based adaptive mechanism for global and local search and leverages Tabu Search to refine solutions and prevent convergence to suboptimal points. Extensive simulations demonstrate that the proposed algorithm outperforms state-of-the-art models in reducing delays and energy usage while balancing resource utilization in both edge and cloud environments. These results highlight the significant improvements achieved over baseline algorithms, providing an effective solution for task scheduling in edge-cloud systems.