In industrial wireless networks,data transmitted from source to destination are highly repetitive.This often leads to the queuing of the data,and poor management of the queued data results in excessive delays,increased energy consumption,and packet loss.Therefore,a nature-inspired-based Dragonfly Interaction Optimization Algorithm(DMOA)is proposed for optimization of the queue delay in industrial wireless networks.The term“interaction”herein used is the characterization of the“flying movement”of the dragonfly towards damselflies(female dragonflies)for mating.As a result,interaction is represented as the flow of transmitted data packets,or traffic,from the source to the base station.This includes each and every feature of dragonfly movement as well as awareness of the rival dragonflies,predators,and damselflies for the desired optimization of the queue delay.These features are juxtaposed as noise and interference,which are further used in the calculation of industrial wireless metrics:latency,error rate(reliability),throughput,energy efficiency,and fairness for the optimization of the queue delay.Statistical analysis,convergence analysis,the Wilcoxon test,the Friedman test,and the classical as well as the 2014 IEEE Congress of Evolutionary Computation(CEC)on the benchmark functions are also used for the evaluation of DMOA in terms of its robustness and efficiency.The results demonstrate the robustness of the proposed algorithm for both classical and benchmarking functions of the IEEE CEC 2014.Furthermore,the accuracy and efficacy of DMOA were demonstrated by means of the convergence rate,Wilcoxon testing,and ANOVA.Moreover,fairness using Jain’s index in queue delay optimization in terms of throughput and latency,along with computational complexity,is also evaluated and compared with other algorithms.Simulation results show that DMOA exceeds other bio-inspired optimization algorithms in terms of fairness in queue delay management and average packet loss.The proposed algorithm is also evaluated for the con
To reveal the resonance suppression mechanism of the blood circulation in dragonfly wings,a numerical modeling method of dragonfly wings based on Voronoi diagrams is proposed,and the changes in mass,aerodynamic damping,and natural frequencies caused by blood circulation in veins are investigated.The equivalent mass of blood,boundary conditions,and aerodynamic damping are calculated theoretically.Modal analysis and harmonic response analysis of wing models with different blood circulation paths are performed using the finite-element method(FEM).The vibration reduction ratioδis introduced to compare the damping efficiency of different mass regions.Finally,a natural frequency testing device is constructed to measure the natural frequencies of dragonfly wings.The results indicate that the shape,mass,and natural frequencies of the dragonfly wing model constructed by numerical method agree well with reality.The mass distribution on the wing can be altered by blood circulation,thereby adjusting the natural frequencies and achieving resonance suppression.The highestδof 1.013 is observed in the C region when blood circulates solely in secondary veins,but it is still lower than theδof 1.017 when blood circulates in complete veins.The aerodynamic damping ratio(1.19–1.79%)should not be neglected in the vibration analysis of the beating wing.
为了提升热、电、气综合能源系统(Integrated Energy System,IES)的经济效益,文章以IES总成本最小构建了综合能源系统经济调度模型。采用逆混沌映射和非线性惯性权重对蜻蜓算法进行改进,使改进的蜻蜓算法(Improved Dragonfly Algorithm,IDA)具备更好地寻优性能。采用IDA算法对综合能源系统经济调度模型进行求解,并将求解结果与常用优化算法进行对比,结果表明,IDA算法在进行综合能源系统经济调度时的优化效果更好,在该调度方案下,各设备出力合理,IES总成本也达到了最小,实现了IES的经济调度。