局部阴影条件下,光伏阵列的P-U曲线呈多峰状态,常规的最大功率点追踪MPPT(maximum power point tracking)算法容易陷入局部极值,无法及时精确地跟踪光伏发电系统的最大功率点,针对此问题提出1种基于改进蜣螂IDBO(improved dung beetle o...局部阴影条件下,光伏阵列的P-U曲线呈多峰状态,常规的最大功率点追踪MPPT(maximum power point tracking)算法容易陷入局部极值,无法及时精确地跟踪光伏发电系统的最大功率点,针对此问题提出1种基于改进蜣螂IDBO(improved dung beetle optimizer)算法的MPPT控制策略。首先对蜣螂种群的初始化进行针对性优化,并在位置更新过程中引入Levy飞行策略。通过在MATLAB/Simulink平台进行仿真验证及实物实验验证,证明了IDBO算法相较于传统算法,无论是在静态还是动态条件下,均能更快且更稳定地找到全局最大功率点。展开更多
This paper presents Dual Adaptive Neural Topology(Dual ANT),a distributed dual-network metaadaptive framework that enhances ant-colony-based multi-agent coordination with online introspection,adaptive parameter contro...This paper presents Dual Adaptive Neural Topology(Dual ANT),a distributed dual-network metaadaptive framework that enhances ant-colony-based multi-agent coordination with online introspection,adaptive parameter control,and privacy-preserving interactions.This approach improves standard Ant Colony Optimization(ACO)with two lightweight neural components:a forward network that estimates swarm efficiency in real time and an inverse network that converts these descriptors into parameter adaptations.To preserve the privacy of individual trajectories in shared pheromone maps,we introduce a locally differentially private pheromone update mechanism that adds calibrated noise to each agent’s pheromone deposit while preserving the efficacy of the global pheromone signal.The resulting systemenables agents to dynamically and autonomously adapt their coordination strategies under challenging and dynamic conditions,including varying obstacle layouts,uncertain target locations,and time-varying disturbances.Extensive simulations of large grid-based search tasks demonstrated that Dual ANT achieved faster convergence,higher robustness,and improved scalability compared to advanced baselines such asMulti-StrategyACO and Hierarchical ACO.The meta-adaptive feedback loop compensates for the performance degradation caused by privacy noise and prevents premature stagnation by triggering Levy flight exploration only when necessary.展开更多
文摘局部阴影条件下,光伏阵列的P-U曲线呈多峰状态,常规的最大功率点追踪MPPT(maximum power point tracking)算法容易陷入局部极值,无法及时精确地跟踪光伏发电系统的最大功率点,针对此问题提出1种基于改进蜣螂IDBO(improved dung beetle optimizer)算法的MPPT控制策略。首先对蜣螂种群的初始化进行针对性优化,并在位置更新过程中引入Levy飞行策略。通过在MATLAB/Simulink平台进行仿真验证及实物实验验证,证明了IDBO算法相较于传统算法,无论是在静态还是动态条件下,均能更快且更稳定地找到全局最大功率点。
基金funded by the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia,under project number NBU-FFR-2026-2441-02.
文摘This paper presents Dual Adaptive Neural Topology(Dual ANT),a distributed dual-network metaadaptive framework that enhances ant-colony-based multi-agent coordination with online introspection,adaptive parameter control,and privacy-preserving interactions.This approach improves standard Ant Colony Optimization(ACO)with two lightweight neural components:a forward network that estimates swarm efficiency in real time and an inverse network that converts these descriptors into parameter adaptations.To preserve the privacy of individual trajectories in shared pheromone maps,we introduce a locally differentially private pheromone update mechanism that adds calibrated noise to each agent’s pheromone deposit while preserving the efficacy of the global pheromone signal.The resulting systemenables agents to dynamically and autonomously adapt their coordination strategies under challenging and dynamic conditions,including varying obstacle layouts,uncertain target locations,and time-varying disturbances.Extensive simulations of large grid-based search tasks demonstrated that Dual ANT achieved faster convergence,higher robustness,and improved scalability compared to advanced baselines such asMulti-StrategyACO and Hierarchical ACO.The meta-adaptive feedback loop compensates for the performance degradation caused by privacy noise and prevents premature stagnation by triggering Levy flight exploration only when necessary.
