A Hybrid Genetic Algorithm and Proximal Policy Optimization System for Efficient Multi-Agent Task Allocation

Efficient task allocation remains a fundamental challenge in multi-agent systems, particularly under resource constraints and large-scale deployments. Classical methods, including market-based mechanisms, centralized optimization techniques, and game-theoretic strategies, have been widely applied to address the multi-agent task allocation problem. While effective in small-to-medium-sized settings, these approaches often encounter limitations in terms of scalability, adaptability to dynamic environments, and computational efficiency as the problem size increases. To address these limitations, this study introduces a proximal policy optimization system augmented with a genetic algorithm (GAPPO) that integrates evolutionary search with deep reinforcement learning. GAPPO enables agents to develop energy-efficient task allocation strategies by perceiving environmental states and optimizing their actions through iterative policy updates. The genetic component promotes broader policy exploration beyond local optima, while the proximal policy optimization ensures update stability and sample efficiency. To evaluate the proposed GAPPO algorithm, extensive simulations are conducted across four scenarios, with the largest involving 50 tasks and 500 agents. The results demonstrate that GAPPO achieves superior performance compared to baseline methods, particularly in reducing task completion time. These findings highlight the algorithm’s robustness and efficiency in handling large-scale and computationally intensive coordination tasks.