A hybrid self-adaptive particle swarm optimization and modified shuffled frog leaping algorithm for distribution feeder reconfiguration

One of the very important way to save the electrical energy in distribution system is network reconfiguration for loss reduction. This paper proposes a new hybrid evolutionary algorithm for solving the distribution feeder reconfiguration (DFR) problem. The proposed hybrid evolutionary algorithm is the combination of SAPSO (self-adaptive particle swarm optimization) and MSFLA (modified shuffled frog leaping algorithm), called SAPSO–MSFLA, which can find optimal configuration of distribution network. In the PSO algorithm, appropriate adjustment of the parameters is cumbersome and usually requires a lot of time and effort. Therefore, a self-adaptive framework is proposed to improve the robustness of the PSO, also in the modified shuffled frog leaping algorithm (MSFLA) to improve the performance of algorithm a new frog leaping rule is proposed to improve the local exploration of the SFLA. The main idea of integrating SAPSO and MSFLA is to use their advantages and avoid their disadvantages. The proposed algorithm is tested on two distribution test feeders. The results of simulation show that the proposed method is very powerful and guarantees to obtain the global optimization in minimum time.     

A hybrid evolutionary algorithm for distribution feeder reconfiguration

This paper presents a new method to reduce the distribution system loss by feeder reconfiguration.This new method combines self-adaptive particle swarm optimization(SAPSO) with shuffled frog-leaping algorithm(SFLA) in an attempt to find the global optimal solutions for the distribution feeder reconfiguration(DFR).In PSO algorithm,appropriate adjustment of the parameters is cumbersome and usually requires a lot of time and effort.Thus,a self-adaptive framework is proposed to improve the robustness of PSO.In ...     

A NEW HYBRID ALGORITHM FOR MULTI-OBJECTIVE DISTRIBUTION FEEDER RECONFIGURATION

This article proposes an efficient hybrid algorithm for multi-objective distribution feeder reconfiguration. The hybrid algorithm is based on the combination of discrete particle swarm optimization (DPSO), ant colony optimization (ACO), and fuzzy multi-objective approach called DPSO-ACO-F. The objective functions are to reduce real power losses, deviation of nodes voltage, the number of switching operations, and the balancing of the loads on the feeders. Since the objectives are not the same, it is not easy to solve the problem by traditional approaches that optimize a single objective. In the proposed algorithm, the objective functions are first modeled with fuzzy sets to calculate their imprecise nature and then the hybrid evolutionary algorithm is applied to determine the optimal solution. The feasibility of the proposed optimization algorithm is demonstrated and compared with the solutions obtained by other approaches over different distribution test systems.     

基于改进粒子群算法的配电网重构策略

针对有源配电网对安全可靠性的要求较高,而现有的配电网重构算法精度低、速度低的问题,提出了基于蛙跳分组思想的自适应惯性权重的全信息简化粒子群算法。首先,从降低网络有功功率损耗、提高电压稳定性、均衡馈线负荷三个角度考虑,建立配电网多目标数学模型;然后,通过基于Pareto支配原则,采用模糊隶属函数的标准化满意度将多目标转化为相同量纲、同一属性、相同数量级的单目标,弥补加权法带有主观性、量纲不统一的弊端;最后,为保证种群多样性,避免随机初始化产生大量不可行解,结合蚁群优化（ACO）算法随机生成树和改进粒子群算法制定出一种针对含分布式电源（DG）的多目标配电网重构策略。通过对含DG的IEEE33节点配电网系统仿真验证,实验结果表明,与标准粒子群优化（PSO）算法相比,该重构策略寻优效率提高了41.0%,与重构前相比,该重构策略降低配电网有功损耗41.47%,降低电压偏移指数57.0%,改善系统负荷均衡度31.25%。该重构策略有效提高了寻优精度,提高了寻优速度,从而提高了配电网运行的安全可靠性。     

一种混合粒子群算法及其在Job Shop问题中的应用

粒子群算法是一种新颖的演化计算技术,具有思想简单、容易实现的优点,被广泛应用于连续空间的优化。结合遗传算法的思想提出一种新的进化方式并用于Job Shop离散空间优化,进一步结合粒子群算法的群体多样性和禁忌搜索算法的集中搜索性提出一种粒子群算法和禁忌搜索算法的混合策略。用Job Shop问题作为测试基准,仿真试验显示混合粒子群算法是可行和有效的。     

OPTIMAL FEEDER RECONFIGURATION FOR LOSS REDUCTION BY ROBUST SEARCHING HYBRID DIFFERENTIAL EVOLUTION

This paper introduces a robust searching hybrid differential evolution (RSHDE) method to solve the optimal feeder reconfiguration for power loss reduction. The feeder reconfiguration of distribution systems is to recognize beneficially load transfers so that the objective function composed of power losses is minimized and the prescribed voltage limits are satisfied. Mathematically, the problem of this research is a nonlinear programming problem with integer variables. This paper presents a new approach, which uses the RSHDE algorithm with integer variables to solve the problem. Owing to handle the integer variables, the HDE may fail to find the initial search direction for large-scale integer system. This is because the HDE applies a random search at its initial stages. Therefore, two new schemes, the multidirection search scheme and the search space reduction scheme, are embeded into the HDE. These two schemes are used to enhance the search ability before performing the initialization step of the solution process. One three-feeder distribution system from the literature and one practical distribution network of Taiwan Power Company (TPC) are used to exemplify the performance of the proposed method. Moreover, the previous HDE, simulated annealing (SA) and genetic algorithms (GA) methods are also applied to the same example systems for the purpose of comparison. Numerical results show that the proposed method is better than the other methods.     

一种基于改进混合蛙跳的聚类算法

聚类分析是一种无监督的模式识别方式,它是数据挖掘中的重要技术之一。给出了一种基于改进混合蛙跳算法的聚类分析方法,该方法结合了K—均值算法和改进混合蛙跳算法各自的优点,引入了K—均值操作,再用改进混合蛙跳算法进行优化,很大程度上提高了该算法的局部搜索能力和收敛速度。通过仿真对基于改进混合蛙跳的聚类方法与其他已有的聚类方法进行了比较,验证了所提出算法的优越性。     

Network reconfiguration of unbalanced distribution networks using fuzzy-firefly algorithm

This paper addresses a method to optimize the unbalanced distribution networks (UDNs) for keeping up the voltage profile with respect to the consequence of solving the multi-objective reconfiguration using the Firefly algorithm in a fuzzy domain with a load flow method proposed in this paper. The objectives to be minimized are the total network power losses, the deviation of bus voltage and load equalizing in the feeders with network reconfiguration. Every goal is moved into the fuzzy domain utilizing its membership function and fuzzified independently. The proposed method for network reconfiguration has been implemented in 25-node and 19-node UDNs. The outcomes obtained by the suggested method of these two unbalanced networks have been compared with that of obtained by Genetic algorithm (GA), ABC algorithm, PSO algorithm and GA-PSO algorithm using the same objective function. The juxtaposition of the proposed method with the available methods is also presented.     

Time-series forecasting using a system of ordinary differential equations

This paper presents a hybrid evolutionary method for identifying a system of ordinary differential equations (ODEs) to predict the small-time scale traffic measurements data. We used the tree-structure based evolutionary algorithm to evolve the architecture and a particle swarm optimization (PSO) algorithm to fine tune the parameters of the additive tree models for the system of ordinary differential equations. We also illustrate some experimental comparisons with genetic programming, gene expression programming and a feedforward neural network optimized using PSO algorithm. Experimental results reveal that the proposed method is feasible and efficient for forecasting the small-scale traffic measurements data.     

An efficient evolutionary optimization algorithm for multiobjective distribution feeder reconfiguration

In this paper, a Multi-objective Modified Honey Bee Mating Optimization (MMHBMO) evolutionary algorithm is proposed to solve the multi-objective Distribution Feeder Reconfiguration (DFR). The real power loss, the number of the switching operations and the deviation of the voltage at each node are considered as the objective functions. Conventional algorithms for solving the multiobjective optimization problems convert the multiple objectives into a single objective using a vector of the user-predefined weights. This paper presents a new MHBMO algorithm for the DFR problem. In the proposed algorithm an external repository is utilized to save non-dominated solutions found during the search process. A fuzzy clustering technique is used to control the size of the repository within the limits because of the objective functions are not the same. The proposed algorithm is tested on a distribution test feeder.     

A new hybrid particle swarm and simulated annealing stochastic optimization method

A novel hybrid particle swarm and simulated annealing stochastic optimization method is proposed. The proposed hybrid method uses both PSO and SA in sequence and integrates the merits of good exploration capability of PSO and good local search properties of SA. Numerical simulation has been performed for selection of near optimum parameters of the method. The performance of this hybrid optimization technique was evaluated by comparing optimization results of thirty benchmark functions of different dimensions with those obtained by other numerical methods considering three criteria. These criteria were stability, average trial function evaluations for successful runs and the total average trial function evaluations considering both successful and failed runs. Design of laminated composite materials with required effective stiffness properties and minimum weight design of a three-bar truss are addressed as typical applications of the proposed algorithm in various types of optimization problems. In general, the proposed hybrid PSO-SA algorithm demonstrates improved performance in solution of these problems compared to other evolutionary methods The results of this research show that the proposed algorithm can reliably and effectively be used for various optimization problems.     

Particle swarm optimization with genetic recombination: a hybrid evolutionary algorithm

This article presents a hybrid evolutionary algorithm (HEA) based on particle swarm optimization (PSO) and a real-coded genetic algorithm (GA). In the HEA, PSO is used to update the solution, and a genetic recombination operator is added to produce offspring individuals based on the parents, which are selected in proportion to their relative fitness. Through the recombination, new offspring enter the population, and individuals with poor fitness are eliminated. The performance of the proposed hybrid algorithm is compared with those of the original PSO and GA, and the impact of the recombination probability on the performance of the HEA is also analyzed. Various simulations of multivariable functions and neural network optimizations are carried out, showing that the proposed approach gives a superior performance to the canonical means, as well as a good balance between exploration and exploitation.     

Implementing a diffusion model optimized by a hybrid evolutionary algorithm to forecast notebook shipments

Taiwan computer firms need to forecast trends in notebook shipments. The Bass diffusion model has been successfully applied to describe the empirical adoption curve for many new products and technological innovations. In order to improve the parameter estimates, a hybrid evolutionary algorithm, which couples genetic algorithms (GAs) with particle swarm optimization (PSO), is proposed. This hybrid approach can produce more accurate estimates of the parameters for the Bass diffusion model. In addition, the price index plays an important role in the notebook market. Thus, the modified diffusion model is proposed to investigate the forecasting performance for notebook shipments. The results illustrate that a hybrid approach outperforms other methods such as nonlinear algorithm, GA and PSO in terms of mean absolute percentage error.     

自适应混合变异的蛙跳算法

蛙跳算法是一种受自然界生物现象启发产生的群体进化算法,计算速度快,寻优能力强,但局部搜索能力较弱,容易陷入早熟收敛。针对其缺点,结合高斯变异和柯西变异的优点,提出了一种改进的混合蛙跳算法。改进后的算法收敛速度加快,在一定程度上避免陷入局部最优,提高了蛙跳算法解决复杂函数问题的能力。实验验证了其有效性。     

平面正交各向异性体材料参数识别的新方法

为有效确定平面正交各向异性体的材料参数,提出一种基于比例边界有限元法(Scaled Boundary Finite Element Method,SBFEM)和混合粒子群算法的识别方法.该方法以测量位移与SBFEM计算相应的位移之差的平方和最小为基础,采用粒子群优化（Particle Swarm Optimization, PSO)算法全局搜索材料参数.为加快收敛速度和提高反演识别精度,在PSO算法中引入自然选择的机制.采用SBFEM进行正分析问题计算时,只需对计算域边界进行数值离散,大大减少计算量.相对于边界元法,SBFEM不需要基本解.数值算例表明所提出的方法有效.     

Modified shuffled frog leaping algorithm with genetic algorithm crossover for solving economic load dispatch problem with valve-point effect

This paper addresses a hybrid solution methodology involving modified shuffled frog leaping algorithm (MSFLA) with genetic algorithm (GA) crossover for the economic load dispatch problem of generating units considering the valve-point effects. The MSFLA uses a more dynamic and less stochastic approach to problem solving than classical non-traditional algorithms, such as genetic algorithm, and evolutionary programming. The potentiality of MSFLA includes its simple structure, ease of use, convergence property, quality of solution, and robustness. In order to overcome the defects of shuffled frog leaping algorithm (SFLA), such as slow searching speed in the late evolution and getting trapped easily into local iteration, MSFLA with GA cross-over is put forward in this paper. MSFLA with GA cross-over produces better possibilities of getting the best result in much less global as well as local iteration as one has strong local search capability while the other is good at global search. This paper proposes a new approach for solving economic load dispatch problems with valve-point effect where the cost function of the generating units exhibits non-convex characteristics, as the valve-point effects are modeled and imposed as rectified sinusoid components. The combined methodology and its variants are validated for the following four test systems: IEEE standard 30 bus test system, a practical Eastern Indian power grid system of 203 buses, 264 lines, and 23 generators, and 13 and 40 thermal units systems whose incremental fuel cost function take into account the valve-point loading effects. The results are quite promising and effective compared with several benchmark methods.     

基于混合微粒群算法的说话人识别

为了解决传统高斯混合模型(GMM)对初值敏感,在实际训练中极易得到局部最优参数的问题,提出了一种采用微粒群算法优化GMM参数的新方法。该方法将最大似然估计融入到微粒群算法迭代过程中,形成了新的混合算法。它利用微粒群算法的全局优化性及最大似然估计的局部寻优性求解高斯混合模型的参数,以提高参数精度。说话人辨认实验表明,与传统的方法相比,新方法可以得到更优的模型参数,使得系统的识别率进一步提高。     

A new ARIMA-based neuro-fuzzy approach and swarm intelligence for time series forecasting

Time series forecasting is an important and widely interesting topic in the research of system modeling. We propose a new computational intelligence approach to the problem of time series forecasting, using a neuro-fuzzy system (NFS) with auto-regressive integrated moving average (ARIMA) models and a novel hybrid learning method. The proposed intelligent system is denoted as the NFS–ARIMA model, which is used as an adaptive nonlinear predictor to the forecasting problem. For the NFS–ARIMA, the focus is on the design of fuzzy If-Then rules, where ARIMA models are embedded in the consequent parts of If-Then rules. For the hybrid learning method, the well-known particle swarm optimization (PSO) algorithm and the recursive least-squares estimator (RLSE) are combined together in a hybrid way so that they can update the free parameters of NFS–ARIMA efficiently. The PSO is used to update the If-part parameters of the proposed predictor, and the RLSE is used to adapt the Then-part parameters. With the hybrid PSO–RLSE learning method, the NFS–ARIMA predictor may converge in fast learning pace with admirable performance. Three examples are used to test the proposed approach for forecasting ability. The results by the proposed approach are compared to other approaches. The performance comparison shows that the proposed approach performs appreciably better than the compared approaches. Through the experimental results, the proposed approach has shown excellent prediction performance.     

改进的SAMPSO的软件测试数据自动生成

针对软件测试数据的自动生成提出了一种简化的自适应变异的粒子群算法（SAMPSO）。该算法在运行过程中根据群体适应度方差以及当前最优解的大小来确定当前最佳粒子的变异概率,变异操作增强了粒子群优化算法前期全局搜索能力,去掉了粒子群优化（PSO）算法中进化方程的粒子速度项,仅由粒子位置控制进化过程,避免了由粒子速度项引起的粒子发散而导致后期收敛变慢和精度低问题。实验结果表明该算法在测试数据的自动生成上优于基本的粒子群算法,提高了效率。     

一种改进的粒子群优化算法

作为群体智能的代表性方法之一,粒子群优化算法(PSO)通过粒子间的竞争和协作以实现在复杂搜索空间中寻找全局最优点。提出了一种改进的粒子群优化算法(MPSO),该算法以广泛学习粒子群优化算法(CLPSO)的思想为基础,主要引入了选择墙的概念。同时在参数的设置中结合高斯分布的概念,以提高算法的收敛性。实验结果表明,改进后的粒子群算法防止陷入局部最优的能力有了明显的增强。同时,算法使高维优化问题中全局最优解相对搜索空间位置的鲁棒性得到了明显提高。