APSO based automated planning in Constructive Simulation

Network reconfiguration for loss reduction in a distribution system is a very important way to save the electrical energy. This article proposes a hybrid evolutionary algorithm to solve the distribution feeder reconfiguration problem. The algorithm combines a fuzzy adaptive particle swarm optimization with a differential evolution algorithm, called the fuzzy adaptive particle swarm optimization–differential evolution. The fuzzy adaptive particle swarm optimization includes two parts. The first part is fuzzy adaptive binary particle swarm optimization, which determines the status of tie switches (open or closed), and the second part is fuzzy adaptive discrete particle swarm optimization, which determines the sectionalizing switch number. In the proposed algorithm, due to the results of binary particle swarm optimization and discrete particle swarm optimization algorithms that highly depend on the values of their parameters (such as the inertia weight and learning factors), a fuzzy system is employed to adaptively adjust the parameters during the search process. The differential evolution algorithm is combined with the fuzzy adaptive particle swarm optimization algorithm to improve its performance. The proposed algorithm is tested on two distribution test feeders. The results of simulation show that the proposed method is very powerful and is guaranteed to obtain the global optimization.

Aiming at the control variables of reactive power optimization are discrete, and some parameters in the standard particle swarm optimization (PSO) algorithm need to be predefined by test, so the algorithm's practicability is restricted. For these reasons, an adaptive particle swarm optimization (APSO) algorithm is proposed by the authors. APSO introduces the self-adaptive tuning strategy and boundary constraint conditions can find the global optimal solution and solve the discrete variables. The reactive power optimization results of the standard IEEE-30-bus power system show that APSO is efficient than standard PSO. The global convergence accuracy and convergence stability is obviously improved compared with that of PSO.

This paper presents a two-level learning method for designing an optimal Radial Basis Function Network (RBFN) using Adaptive Velocity Update Relaxation Particle Swarm Optimization algorithm (AVURPSO) and Orthogonal Least Squares algorithm (OLS) called as OLS-AVURPSO method. The novelty is to develop an AVURPSO algorithm to form the hybrid OLS-AVURPSO method for designing an optimal RBFN. The proposed method at the upper level finds the global optimum of the spread factor parameter using AVURPSO while at the lower level automatically constructs the RBFN using OLS algorithm. Simulation results confirm that the RBFN is superior to Multilayered Perceptron Network (MLPN) in terms of network size and computing time. To demonstrate the effectiveness of proposed OLS-AVURPSO in the design of RBFN, the Mackey-Glass Chaotic Time-Series as an example is modeled by both MLPN and RBFN.

Particle swarm optimization (PSO) is a kind of evolutionary algorithm to find optimal (or near optimal) solutions for numerical and qualitative problems. In this paper, a new variation on the traditional PSO algorithm, called adaptive particle swarm optimization (APSO), has been proposed, employing adaptive behavior to significantly improve the performance of the original algorithm. Every particle chooses its inertial factor according to the fitness of itself and the optimal particle in the presented algorithm. Finally, Traveling salesman problem (TSP) is applied to show the effectiveness of the proposed PSO. Simulation results show that the new algorithm has advantage of global convergence property and can effectively alleviate the problem of premature convergence.

In this article we describe a novel Adaptive particle swarm optimization (APSO) algorithm based on population diversity information. It is presented to solve the precocious convergence problem of particle swarm optimization algorithm. The APSO algorithm uses the information of the population diversity to adjust nonlinearly inertia weight. Velocity mutation factor and position interchange factor are both introduced and the global performance is clearly improved. The APSO algorithm is applied to optimization of parameters in the optimal model based on support vector machine (SVM). SVM is a popular classification method with many diverse applications. A novel Adaptive particle swarm optimization (APSO) based approach for parameter determination and feature selection of the SVM, termed APSO+SVM is developed. The illustrating example shows that the classification accuracy of APSO+SVM is higher than other traditional methods of classification, so using APSO+SVM method to classify is feasible and effective.

To solve the problems of slow convergence and low computational precision of blind source separation(BSS) based on traditional particle swarm optimization(PSO), a novel approach-based adaptive particle swarm optimization for real-time blind source separation is proposed, in which the observations are linear convolutive mixtures of statistically independent speech sources. It combines the independent component analysis algorithm and the adaptive particle swarm optimization, takes the negentropy of mixtures as a target function, and adaptively adjusts the inertia weight factor according to the negentropy of separated signals, and optimizes the separation matrix so as to makes each signal component independent. The simulation and experimental results on mixed speech signal showed an improved separation and a superior convergence rate of the proposed algorithm over that of PSO. Moreover, this algorithm can converge to a much lower cost value than that of PSO.

Particle swarm optimization (PSO) algorithm is a promising swarm intelligence optimization technology. It has been applied to a variety of complex optimization problems due to its outstanding global search ability. However, it suffers from premature convergence and slow convergence rate. Motivated by adaptive moment estimation (Adam) method, which is computationally efficient, little memory-required and also appropriate for non-stationary objectives, a hybrid algorithm combining adaptive PSO with a modified Adam method (AdamPSO) is proposed in this paper. Adaptive particle swarm optimization (APSO) is first used to perform stochastic and rough search. In the solution space obtained by APSO, Adam method is then used to perform further search, which may establish a new solution space. Depending on the fitness value of particles, the position of each particle switches alternately between APSO and Adam. The experimental results on six well-known benchmark functions show that our proposed algorithm gets better convergence performance compared to other five classical PSOs.

It is very hard to measure some process variables directly in actual industrial processes, so a soft senor model using adaptive particle swarm optimization (PSO) optimization based multi-kernel ELM with error feedback is proposed in this paper. Firstly, multi-kernel ELM is constructed by adding gaussian and polynomial kernel function to ameliorate the overfitting problem in traditional ELM. Secondly, we propose an adaptive PSO (APSO) for ameliorating the low efficiency problem in the later period of PSO method by adding mutation operator. When given parameter reaches a threshold, the mutation operator adaptively adjusts the position of the particle. Also, the proportion of two kernel functions and the kernel parameters in training process are obtained by APSO. In each iteration, the training error is back propagated to the hidden layer as the co-outputs of hidden layer for further improving the accuracy and stability of the model. Finally, a simulation experiment on the purified terephthalic acid (PTA) solvent system is made to verify the modeling accuracy and optimized performances. The evaluation result demonstrates that the proposed method can provide higher accuracy and a more reliable soft senor model compared with other method.

Aiming at the trend of “S” growth in annual electricity consumption in Ningxia, the gray linear regression combined model (GLM) load forecasting method is adopted, which can improve the lack of exponential growth trend in linear regression model and the lack of linear factors in the GM(1,1) model. However, the traditional GLM model has theoretical defects in the process of solving parameters, and there are limitations in the application range. Therefore, this paper introduces an adaptive particle swarm optimization algorithm which is more efficient than the standard particle swarm optimization algorithm, and combines it with the gray linear regression combination model. The adaptive particle swarm optimization algorithm is used to solve the parameters of the GLM model, then carry out residual correction. Adaptive particle swarm optimization gray linear regression combination model (APSO-GLM) is proposed. The example analysis shows that the model overcomes the defects of the GM(1,1) model and the linear regression model. Compared with the single model, the prediction accuracy is higher and it has a wider application range than the traditional GLM model.

An adaptive dynamic multi-swarm particle swarm optimization with stagnation detection and spatial exclusion for solving continuous optimization problems

The electrical energy harnessing rate is significantly increased from the solar PV system, since earth surfaces received a huge amount of solar irradiation which is converted into electrical energy using solar PV systems (SPV). However, SPV has nonlinear characteristics between voltage and current under partially shaded conditions. Therefore, tracing MPP under partially shaded conditions is very difficult. Moreover, partial shading is caused by a hotspot on the solar panel which causes power loss. It reduced the efficiency of SPV and the life span of solar PV. Hence, the research work proposed two soft computing-based MPP tracking techniques such as adaptive cuckoo search optimization (ACSO) and adaptive particle swarm optimization (APSO) for SPV under PSC. For analysis, two types of partially shaded patterns have been used. The effectiveness of MPPT is estimated by the tracing speed, oscillation near MPP, and maximum power extraction from SPV. The simulation found that the MPP tracing speed of ACSO is comparable to that of APSO. Also, APSO is extracting higher power that of APSO. In addition, both MPPTs offer very less oscillation near MPP.KeywordsACSOAPSOConventional converterDuty cycleSolar PVShading condition

Surface roughness is one of the main characteristics of a work piece in the quality control process. Several parameters such as cutting tool material and geometry, cutting parameters, work piece material properties, machine tool and coolant type affect the surface quality. An important task of process planners is the proper selection of three main cutting parameters: cutting speed, feed rate, and depth of cut in order to have not only low surface roughness, but also to perform the process within a reasonable amount of time. In this paper, using full factorial experiment design, the multiple regression equation for the surface roughness in the climb milling process of DIN 1.4021 martensitic stainless steel has been obtained and then used as one of the objective functions in the Multi-objective Improved Self- Adaptive Particle Swarm Optimization (MISAPSO) algorithm. This algorithm has been used to obtain cutting parameters to achieve low surface roughness simultaneously with a high material removal rate. The relatively new algorithm MISAPSO developed with some changes in the common particle swarm optimization (PSO) technique, has been used in multi-objective optimization of machining processes and was shown to be able to help the process planners in selecting cutting parameters.

Multiobjective band selection approach via an adaptive particle swarm optimizer for remote sensing hyperspectral images

Fuzzy C-Means (FCM) algorithm is one of the most popular fuzzy clustering techniques. However, it is easily trapped in local optima. Particle swarm optimization (PSO) is a stochastic global optimization model, which is used in many optimization problems. In this paper, a hybrid clustering algorithm, called HAPF, based on adaptive PSO (APSO) and FCM is proposed, in order to take advantage of the merits of both APSO and FCM. In HAPF the state of swarm aggregation is divided into three situations: strong, loose, and medium, respectively representing swarm's exploitation phrase, exploration phrase, and a balance between the two phrases. In addition, the interval of population diversity measured by the variance of population fitness is partitioned into three sections. After mapping the relationship between the swarm aggregation situations and the value of population diversity, three inertia weight groups are dynamically adjusted accordingly to the real-time state of population diversity. Experimental results show that the proposed HAPF is able to escape local optima and find better optima than other seven well-known clustering algorithms.

A new and efficient methodology for optimal reactive power and voltage control of distribution networks with distributed generators based on fuzzy adaptive hybrid particle swarm optimisation (PSO) is proposed. The objective is to minimise comprehensive cost, consisting of power loss and operation cost of transformers and capacitors, and subject to constraints such as minimum and maximum reactive power limits of distributed generators, maximum deviation of bus voltages and maximum allowable daily switching operation number. PSO is used to solve the corresponding mixed integer non‐linear programming problem and the hybrid PSO (HPSO) method, consisting of three PSO variants, is presented. In order to mitigate the local convergence problem, fuzzy adaptive inference is used to improve the searching process and the final fuzzy adaptive inference‐based HPSO is proposed. The proposed algorithm is implemented in VC++ 6.0 program language and the corresponding numerical experiments are finished on the modified version of the IEEE 33‐node distribution system with two newly installed distributed generators and eight newly installed capacitors banks. The numerical results prove that the proposed method can search a more promising control schedule of all transformers, all capacitors and all distributed generators with less time consumption, compared with other listed artificial intelligent methods.