融合正弦余弦和无限折叠迭代混沌映射的蝴蝶优化算法

针对蝴蝶优化算法求解精度不高、收敛速度较慢等问题,提出融合正弦余弦和无限折叠迭代混沌映射(ICMIC)的蝴蝶优化算法.首先,采用ICMIC映射对蝴蝶个体状态进行初始化,避免算法陷入局部最优.然后,在自身认知飞行部分引入正弦余弦算子,平衡算法的局部搜索能力与全局搜索能力.最后,通过改进依赖香味大小的幂指数调整吸收程度,获取更好的最优解.在8个基准函数上的实验表明,文中算法具有较优的全局搜索能力和求解鲁棒性,寻优精度较高,收敛速度较快.     

Study impact of various load models on DG placement and sizing using backtracking search algorithm

In this article, a meta-heuristic technique based on a backtracking search algorithm (BSA) is employed to produce solutions to ascertain distributed generators (DGs). The objective is established to reduce power loss and improve network voltage profile in radial distribution networks by determining optimal locations and sizes of the DGs. Power loss indices and bus voltages are engaged to explore the initial placement of DG installations. The study cares with the DG type injects active and reactive power. The proposed methodology takes into consideration four load models, and their impacts are addressed. The proposed BSA-based methodology is verified on two different test networks with different load models and the simulation results are compared to those reported in the recent literature. The study finds that the constant power load model among various load models is sufficed and viable to allocate DGs for network loss and voltage studies. The simulation results reveal the efficacy and robustness of the BSA in finding the optimal solution of DGs allocation.     

Group search optimizer based optimal location and capacity of distributed generations

This paper presents a novel efficient population-based heuristic approach for optimal location and capacity of distributed generations (DGs) in distribution networks, with the objectives of minimization of fuel cost, power loss reduction, and voltage profile improvement. The approach employs an improved group search optimizer (iGSO) proposed in this paper by incorporating particle swarm optimization (PSO) into group search optimizer (GSO) for optimal setting of DGs. The proposed approach is executed on a networked distribution system—the IEEE 14-bus test system for different objectives. The results are also compared to those that executed by basic GSO algorithm and PSO algorithm on the same test system. The results show the effectiveness and promising applications of the proposed approach in optimal location and capacity of DGs.     

Optimal Placement and Sizing of Distributed Generation Using Metaheuristic Algorithm

Power loss and voltage uncertainty are the major issues prevalently faced in the design of distribution systems. But such issues can be resolved through effective usage of networking reconfiguration that has a combination of Distributed Generation (DG) units from distribution networks. In this point of view, optimal placement and sizing of DGs are effective ways to boost the performance of power systems. The optimum allocation of DGs resolves various problems namely, power loss, voltage profile improvement, enhanced reliability, system stability, and performance. Several research works have been conducted to address the distribution system problems in terms of power loss, energy loss, voltage profile, and voltage stability depending upon optimal DG distribution. With this motivation, the current study designs a Chaotic Artificial Flora Optimization based on Optimal Placement and Sizing of DGs (CAFO-OPSDG) to enhance the voltage profiles and mitigate the power loss. Besides, the CAFO algorithm is derived from the incorporation of chaos theory concept into conventional artificial flora optimization AFO algorithm with an aim to enhance the global optimization abilities. The fitness function of CAFO-OPSDG algorithm involves voltage regulation, power loss minimization, and penalty cost. To consider the actual power system scenario, the penalty factor acts as an important element not only to minimize the total power loss but to increase the voltage profiles as well. The experimental validation of the CAFO-OPSDG algorithm was conducted against IEEE 33 Bus system and IEEE 69 Bus system. The outcomes were examined under various test scenarios. The results of the experiment established that the presented CAFO-OPSDG model is effective in terms of reducing the power loss and voltage deviation and boost-up the voltage profile for the specified system.     

一种新型Skew Tent映射的混沌混合优化算法

针对已有的混沌优化算法几乎都是利用Logistic映射作为混沌序列发生器,而该混沌序列的概率密度函数呈两头多、中间少的切比雪夫型的分布性质,不利于搜索的效率和能力,为此,首先构造一种新型混沌映射序列发生器—Skew Tent映射并结合迭代优化特点加以改进,然后分析了它的混沌特性．其次,将改进的混沌映射与Alopex启发算法相结合,充分发挥Alopex算法的快速搜索能力和混沌优化全局寻优的特性,提出一种混沌混合优化算法,提高了算法的收敛速度和有效搜索全局最优解．最后,仿真算例验证了该算法的有效性和Skew Tent混沌映射的应用前景．     

An Approach of Distributed Joint Optimization for Cluster-based Wireless Sensor Networks

Wireless sensor networks (WSNs) are energyconstrained, so energy saving is one of the most important issues in typical applications. The clustered WSN topology is considered in this paper. To achieve the balance of energy consumption and utility of network resources, we explicitly model and factor the effect of power and rate. A novel joint optimization model is proposed with the protection for cluster head. By the mean of a choice of two appropriate sub-utility functions, the distributed iterative algorithm is obtained. The convergence of the proposed iterative algorithm is proved analytically. We consider general dual decomposition method to realize variable separation and distributed computation, which is practical in large-scale sensor networks. Numerical results show that the proposed joint optimal algorithm converges to the optimal power allocation and rate transmission, and validate the performance in terms of prolonging of network lifetime and improvement of throughput.      

Optimal DG placement by multi-objective opposition based chaotic differential evolution for techno-economic analysis

Placement of optimally sized distributed generator (DG) units at optimal locations in the radial distribution networks can play a major role in improving the system performance. The maximum economic and technical benefits can be extracted by minimizing various objectives including yearly economic loss which includes installation, operation and maintenance cost, power loss as well as voltage deviation throughout the buses. The present problem is analysed considering these multi-objective frameworks and presents the best compromise solution or Pareto-optimal solution. Several equality and inequality constraints are also considered for the multi-objective optimization problem. In this paper, a novel multi-objective opposition based chaotic differential evolution (MOCDE) algorithm is proposed for solving the multi-objective problem in order to avoid premature convergence. Performance of population based meta-heuristic techniques largely depends on the proper selections of control parameters. It is reported that wrong parameters selection may lead to premature convergence and even stagnation. The proposed technique uses logistic mapping to generate chaotic sequence for control parameters. The proposed algorithm is implemented on IEEE-33 and IEEE-69 bus radial distribution systems for verifying its effectiveness. A comparative analysis with other modern multi-objective algorithms like NSGA-II, SPEA2 and MOPSO is also presented in this work. It is observed that the proposed algorithm can produce better results in terms of power loss and yearly economic loss minimization as well as improvement of voltage profile.     

A multiobjective approach for optimal placement and sizing of distributed generators and capacitors in distribution network

Both active and reactive power play important roles in power system transmission and distribution networks. While active power does the useful work, reactive power supports the voltage that necessitates control from system reliability aspect as deviation of voltage from nominal range may lead to inadvertent operation and premature failure of system components. Reactive power flow must also be controlled in the system to maximize the amount of real power that can be transferred across the power transmitting media. This paper proposes an approach to simultaneously minimize the real power loss and the net reactive power flow in the system when reinforced with distributed generators (DGs) and shunt capacitors (SCs). With the suggested method, the system performance, reliability and loading capacity can be increased by reduction of losses. A multiobjective evolutionary algorithm based on decomposition (MOEA/D) is adopted to select optimal sizes and locations of DGs and SCs in large scale distribution networks with objectives being minimizing system real and reactive power losses. MOEA/D is the process of decomposition of a multiobjective optimization problem into a number of scalar optimization subproblems and optimizing those concurrently. Case studies with standard IEEE 33-bus, 69-bus, 119-bus distribution networks and a practical 83-bus distribution network are performed. Output results of MOEA/D method are compared with similar past studies and notable improvement is observed.     

基于反传混沌粒子群训练的前馈神经网络研究

为了解决前馈神经网络训练收敛速度慢、易陷入局部极值及对初始权值依赖性强等缺点, 提出了一种基于反传的无限折叠迭代混沌粒子群优化(ICMICPSO)算法训练前馈神经网络(FNNs)参数。该方法在充分利用BP算法的误差反传信息和梯度信息的基础上, 引入了ICMIC混沌粒子群的概念, 将ICMIC粒子群(ICMICPS)作为全局搜索器, 梯度下降信息作为局部搜索器来调整网络的权值和阈值, 使得粒子能够在全局寻优的基础上对整个空间进行搜索。通过仿真实验与多种算法进行对比, 结果表明在训练和泛化能力上ICMICPSO-BPNN方法明显优于其他算法。     

基于改进量子差分进化的含分布式电源的配电网无功优化

差分进化算法（DE）已被证明为解决无功优化问题的有效方法.随着越来越多的分布式电源并网,对配电网潮流、电压均有一定改变,同时也影响了DE的鲁棒性和性能.本文在研究DE基础上,针对其收敛过早、局部搜索能力较差的缺陷,分析了量子计算思想和人工蜂群算法的优势,提出改进量子差分进化混合算法（IQDE）.通过量子编码思想提高了种群个体的多样性,人工蜂群算法的观察蜂加速进化操作和侦查蜂随机搜索操作分别提高了算法的局部搜索和全局搜索性能.建立以有功网损最小为目标的数学模型,将IQDE算法和DE算法分别用于14节点和30节点标准数据集进行大量仿真实验.实验结果表明,IQDE算法用更少的收敛时间、更小的种群规模便可以获得与DE算法相同甚至更佳的优化效果,并且可以很好的应用于解决难分布式电源的配电网无功优化问题.     

A new adaptive mutative scale chaos optimization algorithm and its application

Based on results of chaos characteristics comparing one-dimensional iterative chaotic self-map x = sin(2/x) with infinite collapses within the finite region[-1;1] to some representative iterative chaotic maps with finite collapses (e.g., Logistic map, Tent map, and Chebyshev map), a new adaptive mutative scale chaos optimization algorithm (AMSCOA) is proposed by using the chaos model x = in(2/x). In the optimization algorithm, in order to ensure its advantage of speed convergence and high precision in the seeking optimization process, some measures are taken: 1) the searching space of optimized variables is reduced continuously due to adaptive mutative scale method and the searching precision is enhanced accordingly; 2) the most circle time is regarded as its control guideline. The calculation examples about three testing functions reveal that the adaptive mutative scale chaos optimization algorithm has both high searching speed and precision.     

动态主用户流量模型的跨层优化设计

为适应主用户流量变化较快的场景,在不完美频谱感知的情况下最大化认知用户的吞吐量,提出了一种基于集中式Overlay认知无线网络中感知时间与资源分配跨层优化算法。将优化目标分解为信道分配和检测时间同功率分配联合优化两个子问题,通过子算法迭代,最终得到感知时间与资源分配的联合最优解。仿真结果表明,相对于仅考虑频谱感知或资源分配的单层优化算法,该算法可在兼顾公平的前提下使次用户吞吐量得到有效提升。     

基于CS-CPSO与SVM融合的WSNs入侵检测算法

为了提高基本粒子群优化(PSO)算法与支持向量机(SVM)融合的无线传感网络(WSNs)入侵检测算法的检测精度与收敛速度,提出了一种基于完全正弦映射混沌粒子群优化(cS-cPSO)算法与SVM融合的WSNs入侵检测算法(CS-CPSO-SVM).采用CS-CPSO算法优化SVM参数,不仅将正弦映射混沌搜索应用于粒子群算法中初始种群与局部最优解混沌扰动的产生,且将其用于惯性权重的优化以及随机常数和学习因子的产生,并用多个初始值分别迭代生成多条混沌轨道.以KDDCUP99数据集作为实验数据,经理论分析与仿真实验表明:该方法可以有效地检测入侵行为,并具有良好的检测精度与收敛速度.     

A new adaptive mutative scale chaos optimization algorithm and its application

Based on results of chaos characteristics comparing one-dimensional iterative chaotic self-map x = sin（2/x） with infinite collapses within the finite region[-1, 1] to some representative iterative chaotic maps with finite collapses （e.g., Logistic map, Tent map, and Chebyshev map）, a new adaptive mutative scale chaos optimization algorithm （AMSCOA） is proposed by using the chaos model x = sin（2/x）. In the optimization algorithm, in order to ensure its advantage of speed convergence and high precision in the seeking optimization process, some measures are taken： 1） the searching space of optimized variables is reduced continuously due to adaptive mutative scale method and the searching precision is enhanced accordingly; 2） the most circle time is regarded as its control guideline. The calculation examples about three testing functions reveal that the adaptive mutative scale chaos optimization algorithm has both high searching speed and precision.     

Krill herd algorithm for optimal location of distributed generator in radial distribution system

Distributed generator (DG) is recognized as a viable solution for controlling line losses, bus voltage, voltage stability, etc. and represents a new era for distribution systems. This paper focuses on developing an approach for placement of DG in order to minimize the active power loss and energy loss of distribution lines while maintaining bus voltage and voltage stability index within specified limits of a given power system. The optimization is carried out on the basis of optimal location and optimal size of DG. This paper developed a new, efficient and novel krill herd algorithm (KHA) method for solving the optimal DG allocation problem of distribution networks. To test the feasibility and effectiveness, the proposed KH algorithm is tested on standard 33-bus, 69-bus and 118-bus radial distribution networks. The simulation results indicate that installing DG in the optimal location can significantly reduce the power loss of distributed power system. Moreover, the numerical results, compared with other stochastic search algorithms like genetic algorithm (GA), particle swarm optimization (PSO), combined GA and PSO (GA/PSO) and loss sensitivity factor simulated annealing (LSFSA), show that KHA could find better quality solutions.     

Multilevel thresholding based on Chaotic Darwinian Particle Swarm Optimization for segmentation of satellite images

This paper proposes an improved variant of Darwinian Particle Swarm Optimization algorithm based on chaotic functions. Most of the evolutionary algorithms faces the problem of getting trapped in local optima in its search for global optimum solutions. This is highly influenced by the use of random sequences by different operators in these algorithms along their run. The proposed algorithm replaces random sequences by chaotic sequences mitigating the problem of premature convergence. Experiments were conducted to investigate the efficiency of 10 defined chaotic maps and the best one was chosen. Performance of the proposed Chaotic Darwinian Particle Swarm Optimization (CDPSO) algorithm is compared with chaotic variants of optimization algorithms like Cuckoo Search, Harmony Search, Differential Evolution and Particle Swarm Optimization exploiting the chosen optimal chaotic map. Various histogram thresholding measures like minimum cross entropy and Tsallis entropy were used as objective functions and implemented for satellite image segmentation scenario. The experimental results are validated qualitatively and quantitatively by evaluating the mean, standard deviation of the fitness values, PSNR, MSE, SSIM and the total time required for the execution of each optimization algorithm.     

基于改进混沌优化的多目标遗传算法

针对多目标遗传算法存在的缺陷,提出了基于改进混沌优化的多目标遗传算法．引入基于改Tent映射的自适应变尺度混沌优化方法细化搜索空间和高效寻优,结合非支配排序的群体分级机制和精英保留等多目标优化策略,保持种群多样性的同时保证了进化向Pareto优解集的方向进行．多目标测试函数的数值仿真和电力系统无功优化的算例分析表明了该算法的有效性和可行性．     

基于能量效率的认知无线网络联合优化算法

为提高认知无线网络能量有效性,提出一种基于能量效率的联合优化算法。在考虑主用户干扰容限的基础上构建了能量有效性模型,将优化目标分解为接入策略求解和功率优化问题,采用粒子群算法反复迭代,得到接入概率与功率分配的联合最优解。仿真结果表明,相对于不考虑功率优化或接入概率的传统优化方法,所提算法可使系统能量效率得到显著提升。     

An efficient chaotic water cycle algorithm for optimization tasks

Water cycle algorithm (WCA) is a new population-based meta-heuristic technique. It is originally inspired by idealized hydrological cycle observed in natural environment. The conventional WCA is capable to demonstrate a superior performance compared to other well-established techniques in solving constrained and also unconstrained problems. Similar to other meta-heuristics, premature convergence to local optima may still be happened in dealing with some specific optimization tasks. Similar to chaos in real water cycle behavior, this article incorporates chaotic patterns into stochastic processes of WCA to improve the performance of conventional algorithm and to mitigate its premature convergence problem. First, different chaotic signal functions along with various chaotic-enhanced WCA strategies (totally 39 meta-heuristics) are implemented, and the best signal is preferred as the most appropriate chaotic technique for modification of WCA. Second, the chaotic algorithm is employed to tackle various benchmark problems published in the specialized literature and also training of neural networks. The comparative statistical results of new technique vividly demonstrate that premature convergence problem is relieved significantly. Chaotic WCA with sinusoidal map and chaotic-enhanced operators not only can exploit high-quality solutions efficiently but can outperform WCA optimizer and other investigated algorithms.

     

具有组合变异策略的回溯搜索优化算法

针对回溯搜索优化算法收敛速度慢和易陷入局部最优的缺陷,提出了一种基于组合变异策略的改进回溯搜索优化算法。为了提高历史种群的多样性并扩大算法的搜索空间,在算法迭代过程中采用柯西种群生成策略,利用柯西分布尺度系数生成历史种群;引入基于混沌映射和伽玛分布的组合变异策略,在一定概率下对较差个体进行变异生成质量较好的个体;对新种群中越界个体采用越界处理策略,确保算法在预定的搜索空间内搜索。选取了11个标准测试函数,在低维和高维状态下进行数值仿真,并与3种表现良好的算法进行比较,结果表明该改进算法在收敛速度和收敛精度上有很大优势。