A Hybrid of Bee Colony Optimization and Genetic Algorithm for Task Allocation in Multi-Core Systems to Minimize Makespan
TL;DR
A novel hybrid approach combining Bee Colony Optimization and Genetic Algorithm for efficient task scheduling in multi-core processor systems that leverages the global exploration capabilities of BCO and the exploitation strengths of GA to achieve optimal task-to-core assignments while minimizing makespan and balancing system load is proposed.
Abstract
Task scheduling in multi-core systems is a critical NP-hard optimization problem that significantly impacts system performance and resource utilization. This paper proposes a novel hybrid approach combining Bee Colony Optimization (BCO) and Genetic Algorithm (GA) for efficient task scheduling in multi-core processor systems. The hybrid BCO-GA algorithm leverages the global exploration capabilities of BCO and the exploitation strengths of GA to achieve optimal task-to-core assignments while minimizing makespan and balancing system load. The proposed approach incorporates adaptive parameter tuning, elite preservation strategies, and dynamic population management to enhance convergence speed and solution quality. Experimental evaluation using standard benchmark task graphs demonstrates that the hybrid BCO-GA algorithm achieves an average makespan reduction of 18.7% compared to standalone BCO, 15.3% compared to pure GA, and 23.4% compared to the Heterogeneous Earliest Finish Time (HEFT) algorithm. The results also show improved load balancing with a 21.5% reduction in load imbalance factor and 16.8% enhancement in processor utilization. The proposed hybrid approach demonstrates superior performance in handling various task graph characteristics including different Communication-to-Computation Ratios (CCR), task counts, and dependency structures, making it a robust solution for multi-core task scheduling problems.