A Multi-Strategy Harris Hawk Optimization Algorithm for Task Allocation in Wireless Sensor Networks for Power Systems

In power systems, wireless sensor networks (WSNs) play a crucial role in monitoring and managing power information, with task allocation efficiency directly impacting system performance and energy consumption. This paper introduces a multi-strategy Harris Hawk optimization algorithm (MSHHO) tailored for WSNs in power systems. Key innovations include a novel chaotic mapping strategy to enhance algorithmic convergence and global search capabilities by diversifying the population. Additionally, a nonlinear escape strategy improves the algorithm's ability to evade local optima, thereby enhancing search precision. Furthermore, an adaptive weighting strategy dynamically adjusts algorithm parameters during task allocation stages, further enhancing performance. Simulation experiments validate MSHHO's effectiveness across various network scales and task complexities. Comparative analyses against Simulated annealing algorithm (SA), Particle Swarm Optimization (PSO), and Harris Hawk Optimization Algorithm (HHO) demonstrate MSHHO's superior performance in reducing task completion time and energy consumption. This research offers a novel approach to optimize WSNs task allocation in power systems, holding significant theoretical and practical implications.