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Short-term load forecasting based on modified particle swarm optimizer and fuzzy neural network model

SHI Biao~,LI Yu-xia~,YU Xin-hua~,YAN Wang~,

系统工程理论与实践 , 2010,
Abstract: To improve short-term load forecasting accuracy,a modified particle swarm optimizer(MPSO) and fuzzy neural network(FNN) hybrid optimization algorithm is proposed.In which the FNN is trained by MPSO to implement the optimization of FNN parameters.The short-term load-forecasting model is established based on the modified particle swarm optimizer and fuzzy neural network hybrid optimization algorithm.In load forecasting such factors impacting loads as meteorology,weather and date types are comprehensively cons...
Short-term load forecast based on modified particle swarm optimizer and back propagation neural network model

SHI Biao,LI Yu-xia,YU Xin-hua,YAN Wang,

计算机应用 , 2009,
Abstract: Aiming at improving the power short-term forecast accuracy and speed, the Modified Particle Swarm Optimizer (MPSO) algorithm was presented. The forecast model was set up by combining with the Back Propagation (BP) neural network to form Modified Particle Swarm Optimizer and Back Propagation (MPSO-BP) neural network algorithm, and then the neural network was trained by using the MPSO-BP algorithm. It can automatically determine the parameters of the neural network from the sample data. The power short-term forecast model based on the MPSO-BP neural network was formed with considering weather, date and other factors. The experimental results show that the MPSO-BP algorithm improves the BP neural network generalization capacity, and the convergence of method is faster and forecast accuracy is more accurate than that of the traditional BP neural network. Therefore, the model can be used to forecast the short-term load of the power system.
An Immune Cooperative Particle Swarm Optimization Algorithm for Fault-Tolerant Routing Optimization in Heterogeneous Wireless Sensor Networks
Yifan Hu,Yongsheng Ding,Kuangrong Hao
Mathematical Problems in Engineering , 2012, DOI: 10.1155/2012/743728
Abstract: The fault-tolerant routing problem is important consideration in the design of heterogeneous wireless sensor networks (H-WSNs) applications, and has recently been attracting growing research interests. In order to maintain disjoint communication paths from source sensors to the macronodes, we present a hybrid routing scheme and model, in which multiple paths are calculated and maintained in advance, and alternate paths are created once the previous routing is broken. Then, we propose an immune cooperative particle swarm optimization algorithm (ICPSOA) in the model to provide the fast routing recovery and reconstruct the network topology for path failure in H-WSNs. In the ICPSOA, mutation direction of the particle is determined by multi-swarm evolution equation, and its diversity is improved by immune mechanism, which can enhance the capacity of global search and improve the converging rate of the algorithm. Then we validate this theoretical model with simulation results. The results indicate that the ICPSOA-based fault-tolerant routing protocol outperforms several other protocols due to its capability of fast routing recovery mechanism, reliable communications, and prolonging the lifetime of WSNs.
Acceleration Factor Harmonious Particle Swarm Optimizer
Jie Chen,Feng Pan,Tao Cai,

国际自动化与计算杂志 , 2006,
Abstract: A Particle Swarm Optimizer (PSO) exhibits good performance for optimization problems, although it cannot guarantee convergence to a global, or even local minimum. However, there are some adjustable parameters, and restrictive conditions, which can affect the performance of the algorithm. In this paper, the sufficient conditions for the asymptotic stability of an acceleration factor and inertia weight are deduced, the value of the inertia weight ω is enhanced to (-1, 1).Furthermore a new adaptive PSO algorithm - Acceleration Factor Harmonious PSO (AFHPSO) is proposed, and is proved to be a global search algorithm. AFHPSO is used for the parameter design of a fuzzy controller for a linear motor driving servo system. The performance of the nonlinear model for the servo system demonstrates the effectiveness of the optimized fuzzy controller and AFHPSO.
Parameter Estimation of Conditional Random Fields Model By Improved Particle Swarm Optimizer  [cached]
Zengfa Dou,Lin Gao
Journal of Computers , 2011, DOI: 10.4304/jcp.6.8.1628-1633
Abstract: A new parameter estimation algorithm based on improved particle swarm optimizer is proposed to improve the precision and recall rate of conditional random fields model. Aggregation degree of particle swarm is utilized to control particle swarm optimizer’s early local convergence, the relative change ratio of log-likelihood between iterations is employed to end its iterations, and the inertia factor and learning factor are set as linear variables to control the searching scope. We evaluate our method on GENIA, GENETAG and private library. The experiment results prove our method outperforms traditional parameter estimation method on precision and recall.
Adaptive Network Selection Scheme for Overlaid Heterogeneous Networks with Time-Varying Load  [PDF]
Shiquan Piao,Hyunchul Jo,Jungmyun Kim,Huisup Cho
Advances in Mathematical and Computational Methods , 2011, DOI: 10.5729
Abstract: In this paper, an adaptive network selection scheme for a wireless communication systemwith overlaid cellular network structure is described. Most network selection schemes for overlaidcellular network divide the calls to into two groups, according to their speed or mean dwell time.After comparing user speed or mean dwell time with a given threshold, these schemes allocate anew or handoff call to a selected micro-cell or macro-cell. The proposed scheme not only considersuser speed, but also takes the residual capacity of the micro-cell and macro-cell into account todecide a suitable network. The given user speed threshold in the proposed scheme is not fixed;instead, it is adaptively changed for a time on either the user speed, or on the residual capacity ofthe micro-cell and macro-cell. We used computer simulations to investigate and compare ourpropose scheme with the static threshold scheme.
Separability Detection Cooperative Particle Swarm Optimizer based on Covariance Matrix Adaptation
Sheng-Fuu Lin,Yi-Chang Cheng,Jyun-Wei Chang,Pei-Chia Hung
International Journal of Advanced Computer Sciences and Applications , 2012,
Abstract: The particle swarm optimizer (PSO) is a population-based optimization technique that can be widely utilized to many applications. The cooperative particle swarm optimization (CPSO) applies cooperative behavior to improve the PSO on finding the global optimum in a high-dimensional space. This is achieved by employing multiple swarms to partition the search space. However, independent changes made by different swarms on correlated variables will deteriorate the performance of the algorithm. This paper proposes a separability detection approach based on covariance matrix adaptation to find non-separable variables so that they can previously be placed into the same swarm to address the difficulty that the original CPSO encounters
New Particle Swarm Optimizer with Sigmoid Increasing Inertia Weight  [PDF]
Reza Firsandaya Malik,Tharek Abdul Rahman,Siti Zaiton Mohd. Hashim,Razali Ngah
International Journal of Computer Science and Security , 2007,
Abstract: The inertia weight of particle swarm optimization (PSO) is a mechanism to control the exploration and exploitation abilities of the swarm and as mechanism to eliminate the need for velocity clamping. The present paper proposes a new PSO optimizer with sigmoid increasing inertia weight. Four standard non-linear benchmark functions are used to confirm its validity. The comparison has been simulated with sigmoid decreasing and linearly increasing inertia weight. From experiments, it shows that PSO with increasing inertia weight gives better performance with quick convergence capability and aggressive movement narrowing towards the solution region.
Adaptive Parameters for a Modified Comprehensive Learning Particle Swarm Optimizer  [PDF]
Yu-Jun Zheng,Hai-Feng Ling,Qiu Guan
Mathematical Problems in Engineering , 2012, DOI: 10.1155/2012/207318
Abstract: Particle swarm optimization (PSO) is a stochastic optimization method sensitive to parameter settings. The paper presents a modification on the comprehensive learning particle swarm optimizer (CLPSO), which is one of the best performing PSO algorithms. The proposed method introduces a self-adaptive mechanism that dynamically changes the values of key parameters including inertia weight and acceleration coefficient based on evolutionary information of individual particles and the swarm during the search. Numerical experiments demonstrate that our approach with adaptive parameters can provide comparable improvement in performance of solving global optimization problems. 1. Introduction The complexity of many real-world problems has made exact solution methods impractical to solve them within a reasonable amount of time and thus gives rise to various types of nonexact metaheuristic approaches [1–3]. In particular, swarm intelligence methods, which simulate a population of simple individuals evolving their solutions by interacting with one another and with the environment, have shown promising performance on many difficult problems and have become a very active research area in recent years [4–11]. Among these methods, particle swarm optimization (PSO), initially proposed by Kennedy and Eberhart [4], is a population-based global optimization technique that involves algorithmic mechanisms similar to social behavior of bird flocking. The method enables a number of individual solutions, called particles, to move through the solution space and towards the most promising area for optimal solution(s) by stochastic search. Consider a -dimensional optimization problem as follows: In the -dimensional search space, each particle of the swarm is associated with a position vector and a velocity vector , which are iteratively adjusted by learning from a local best found by the particle itself and a current global best found by the whole swarm: where and are two acceleration constants reflecting the weighting of “cognitive” and “social” learning, respectively, and and are two distinct random numbers in . It is recommended that since it on average makes the weights for cognitive and social parts both to be 1. To achieve a better balance between the exploration (global search) and exploitation (local search), Shi and Eberhart [12] introduce a parameter named inertia weight to control velocity, which is currently the most widely used form of velocity update equation in PSO algorithms: Empirical studies have shown that a large inertia weight facilitates exploration and a
Common model analysis and improvement of particle swarm optimizer

Feng PAN,Jie CHEN,Minggang GAN,Guanghui WANG,Tao CAI,

控制理论与应用 , 2007,
Abstract: Particle swarm optimizer (PSO), a new evolutionary computation algorithm, exhibits good performance for optimization problems, although PSO can not guarantee convergence of a global minimum, even a local minimum. However, there are some adjustable parameters and restrictive conditions which can affect performance of the algorithm. In this paper, the algorithm are analyzed as a time-varying dynamic system, and the sufficient conditions for asymptotic stability of acceleration factors, increment of acceleration factors and inertia weight are deduced. The value of the inertia weight is enhanced to (?1, 1). Based on the deduced principle of acceleration factors, a new adaptive PSO algorithmharmonious PSO (HPSO) is proposed. Furthermore it is proved that HPSO is a global search algorithm. In the experiments, HPSO are used to the model identification of a linear motor driving servo system. An Akaike information criteria based fitness function is designed and the algorithms can not only estimate the parameters, but also determine the order of the model simultaneously. The results demonstrate the effectiveness of HPSO.
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