适用于大规模系统的两阶段机组组合优化方法

提出一种两阶段优化方法以简单、快速、有效地求解大规模系统的机组组合问题。首先,针对传统PL法排序指标单一,不能全面评价机组运行费用的不足,引入可调发电机组煤耗、机组最大出力、机组启动成本三个指标,全面反映发电机组的经济性;其次,不同于传统PL法先安排机组启停满足旋转备用,再调整机组状态满足最小启停时间,同时考虑这两个约束,以更好地实现优先开启运行费用较小的机组;最后,在经济调度阶段提出一种考虑机组爬坡率的功率平衡调整策略,保证粒子的多样性的同时避免快速收敛到局部最优解,提高最终解的质量。将所提方法应用于10机组、20机组、40机组、60机组80机组以及100机组24时段6个系统进行测试,并与其它方法进行对比,数值结果表明,所提两阶段优化方法计算快速、收敛性良好、能够有效解决大规模系统机组组合问题。

考虑光伏不确定性的双层协同优化机组组合

为实现光伏系统高经济性、高环保性的运行,基于负荷与光伏发电的不确定性,设计了一种机组组合双层协同优化方法。第一层优化运用离散粒子群法,以系统污染排放最少为目标,求解最优机组启停状态问题;第二层优化运用改进粒子群法,以系统运行成本最小为目标,求解最优机组出力的问题。在机组组合问题上引入需求响应与光伏发电等手段,并考虑到用户负荷与需求侧的波动特性,建立了不确定性模型。以10机组系统为对象,通过MATLAB仿真验证表明,文章所提出的机组组合模型与优化方法能够使系统在允许存在预测误差的前提下,实现经济环保的运行。

Modified particle swarm optimization-based antenna tilt angle adjusting scheme for LTE coverage optimization

In order to solve the challenging coverage problem that the long term evolution（ LTE） networks are facing, a coverage optimization scheme by adjusting the antenna tilt angle（ ATA） of evolved Node B（ e NB） is proposed based on the modified particle swarm optimization（ MPSO） algorithm.The number of mobile stations（ MSs） served by e NBs, which is obtained based on the reference signal received power（RSRP） measured from the MS, is used as the metric for coverage optimization, and the coverage problem is optimized by maximizing the number of served MSs. In the MPSO algorithm, a swarm of particles known as the set of ATAs is available; the fitness function is defined as the total number of the served MSs; and the evolution velocity corresponds to the ATAs adjustment scale for each iteration cycle. Simulation results showthat compared with the fixed ATA, the number of served MSs by e NBs is significantly increased by 7. 2%, the quality of the received signal is considerably improved by 20 d Bm, and, particularly, the system throughput is also effectively increased by 55 Mbit / s.

Multi-path routing algorithm in WSN using an improvedparticle swarm optimization

To slove the problems of constrained energy and unbalanced load of wireless sensor network(WSN)nodes,a multipath load balancing routing algorithm based on neighborhood subspace cooperation is proposed.The algorithm adopts the improved particle swarm optimization(PSO)algorithm,takes the shortest distance and minimum energy consumption as optimization target and divides the nodes in one-hop neighborhood near the base station area into different regions.Furthermore,the algorithm designs a fitness function to find the best node in each region as a relay node and forward the data in parallel through the different paths of the relay nodes.The simulation results show that the proposed algorithm can reduce energy consumption and average end-to-end delay,balance network load and prolong network lifetime effectively.

Improved Particle Swarm Optimization for Solving Transient Nonlinear Inverse Heat Conduction Problem in Complex Structure

Accurately solving transient nonlinear inverse heat conduction problems in complex structures is of great importance to provide key parameters for modeling coupled heat transfer process and the structure’s optimization design.The finite element method in ABAQUS is employed to solve the direct transient nonlinear heat conduction problem.Improved particle swarm optimization(PSO)method is developed and used to solve the transient nonlinear inverse problem.To investigate the inverse performances,some numerical tests are provided.Boundary conditions at inaccessible surfaces of a scramjet combustor with the regenerative cooling system are inversely identified.The results show that the new methodology can accurately and efficiently determine the boundary conditions in the scramjet combustor with the regenerative cooling system.By solving the transient nonlinear inverse problem,the improved particle swarm optimization for solving the transient nonlinear inverse heat conduction problem in a complex structure is verified.

Antenna selection based on large-scale fading for distributed MIMO systems

An antenna selection algorithm based on large-scale fading between the transmitter and receiver is proposed for the uplink receive antenna selection in distributed multiple-input multiple-output(D-MIMO) systems. By utilizing the radio access units(RAU) selection based on large-scale fading,the proposed algorithm decreases enormously the computational complexity. Based on the characteristics of distributed systems,an improved particle swarm optimization(PSO) has been proposed for the antenna selection after the RAU selection. In order to apply the improved PSO algorithm better in antenna selection,a general form of channel capacity was transformed into a binary expression by analyzing the formula of channel capacity. The proposed algorithm can make full use of the advantages of D-MIMO systems,and achieve near-optimal performance in terms of channel capacity with low computational complexity.

Research on the Network Intrusion Detection System based on Modified Particle Swarm Optimization Algorithm

In this paper, we conduct research on the network intrusion detection system based on the modified particle swarm optimization algorithm. Computer interconnection ability put forward the higher requirements for the system reliability design, the need to ensure that the system can support various communication protocols to guarantee the reliability and security of the network. At the same time also require network system, the server or products have strong ability of fault tolerance and redundancy, better meet the needs of users, to ensure the safety of the information data and the good operation of the network system. For this target, we propose the novel paradigm for the enhancement of the modern computer network that is innovative.

Using improved particle swarm optimization to tune PID controllers in cooperative collision avoidance systems

The introduction ofproportional-integral-dorivative （PID） controllers into cooperative collision avoidance systems （CCASs） has been hindered by difficulties in their optimization and by a lack of study of their effects on vehicle driving stability, comfort, and fuel economy. In this paper, we propose a method to optimize PID controllers using an improved particle swarm optimization （PSO） algorithm, and to bettor manipulate cooperative collision avoidance with other vehicles. First, we use PRESCAN and MATLAB/Simulink to conduct a united simulation, which constructs a CCAS composed of a PID controller, maneuver strategy judging modules, and a path planning module. Then we apply the improved PSO algorithm to optimize the PID controller based on the dynamic vehicle data obtained. Finally, we perform a simulation test of performance before and after the optimization of the PID controller, in which vehicles equipped with a CCAS undertake deceleration driving and steering under the two states of low speed （≤50 km/h） and high speed （≥100 km/h） cruising. The results show that the PID controller optimized using the proposed method can achieve not only the basic functions of a CCAS, but also improvements in vehicle dynamic stability, riding comfort, and fuel economy.

Flight control for air-breathing hypersonic vehicles using linear quadratic regulator design based on stochastic robustness analysis

The flight dynamics model of air-breathing hypersonic vehicles （AHVs） is highly nonlinear and multivariable cou- pling, and includes inertial uncertainties and external disturbances that require strong, robust, and high-accuracy controllers. In this paper, we propose a linear-quadratic regulator （LQR） design method based on stochastic robustness analysis for the longitudinal dynamics of AHVs. First, input/output feedback linearization is used to design LQRs. Second, subject to various system parameter uncertainties, system robustness is characterized by the probability of stability and desired performance. Then, the mapping rela- tionship between system robustness and LQR parameters is established. Particularly, to maximize system robustness, a novel hybrid particle swarm optimization algorithm is proposed to search for the optimal LQR parameters. During the search iteration, a Chernoff bound algorithm is applied to determine the finite sample size of Monte Carlo evaluation with the given prohabilily levels. Finally, simulation results show that the optimization algorithm can effectively find the optimal solution to the LQR parameters.