Self-adaptive Particle Swarm Optimization Research Articles

An Improved Adaboost Method for Face Detection

Existing Adaboost methods for face detection based on particle swarm optimization (PSO) do not consider that PSO suffers from easily trapping in local optimum and slow convergence speed. This paper presents an improved Adaboost method for face detection to solve this problem. In this method, self-adaptive escape PSO (AEPSO) is introduced into conventional Adaboost face detection, meanwhile, Haar-Like rectangular features are represented by particles, so that features selection and classifiers construction could be resolved by using AEPSO. Results of simulation based on Matlab indicate the improved method obtains better detection performance.

Repulsive Self-Adaptive Acceleration Particle Swarm Optimization Approach

Abstract Adaptive Particle Swarm Optimization (PSO) variants have become popular in recent years. The main idea of these adaptive PSO variants is that they adaptively change their search behavior during the optimization process based on information gathered during the run. Adaptive PSO variants have shown to be able to solve a wide range of difficult optimization problems efficiently and effectively. In this paper we propose a Repulsive Self-adaptive Acceleration PSO (RSAPSO) variant that adaptively optimizes the velocity weights of every particle at every iteration. The velocity weights include the acceleration constants as well as the inertia weight that are responsible for the balance between exploration and exploitation. Our proposed RSAPSO variant optimizes the velocity weights that are then used to search for the optimal solution of the problem (e.g., benchmark function). We compare RSAPSO to four known adaptive PSO variants (decreasing weight PSO, time-varying acceleration coefficients PSO, guaranteed convergence PSO, and attractive and repulsive PSO) on twenty benchmark problems. The results show that RSAPSO achives better results compared to the known PSO variants on difficult optimization problems that require large numbers of function evaluations.

Self-adaptive mix of particle swarm methodologies for constrained optimization

In recent years, many different variants of the particle swarm optimizer (PSO) for solving optimization problems have been proposed. However, PSO has an inherent drawback in handling constrained problems, mainly because of its complexity and dependency on parameters. Furthermore, one PSO variant may perform well for some test problems but not obtain good results for others. In this paper, our purpose is to develop a new PSO algorithm that can efficiently solve a variety of constrained optimization problems. It considers a mix of different PSO variants each of which evolves with a different number of individuals from the current population. In each generation, the algorithm assigns more individuals to the better-performing variants and fewer to the worse-performing ones. Also, a new PSO variant is developed for use in the proposed algorithm to maintain a better balance between its local and global PSO versions. A new methodology for adapting PSO parameters is presented and the proposed self-adaptive PSO algorithm tested and analyzed on two sets of test problems, namely the CEC2006 and CEC2010 constrained optimization problems. Based on the results, the proposed algorithm shows significantly better performance than the same global and local PSO variants as well as other-state-of-the-art algorithms. Although, based on our analysis, it cannot guarantee an optimal solution for any unknown problem, it is expected to be able to solve a wide variety of practical problems.

Prediction of CO2 Solubility in Polymers by Radial Basis Function Artificial Neural Network Based on Chaotic Self‐adaptive Particle Swarm Optimization and Fuzzy Clustering Method

AbstractTo replace costly and time‐consuming experimentation in laboratory, a novel solubility prediction model based on chaos theory, self‐adaptive particle swarm optimization (PSO), fuzzy c‐means clustering method, and radial basis function artificial neural network (RBF ANN) is proposed to predict CO2 solubility in polymers, hereafter called CSPSO‐FC RBF ANN. The premature convergence problem is overcome by modifying the conventional PSO using chaos theory and self‐adaptive inertia weight factor. Fuzzy c‐means clustering method is used to tune the hidden centers and radial basis function spreads. The modified PSO algorithm is employed to optimize the RBF ANN connection weights. Then, the proposed CSPSO‐FC RBF ANN is used to investigate solubility of CO2 in polystyrene (PS), polypropylene (PP), poly(butylene succinate) (PBS) and poly(butylene succinate‐co‐adipate) (PBSA), respectively. Results indicate that CSPSO‐FC RBF ANN is an effective method for gas solubility in polymers. In addition, compared with conventional RBF ANN and PSO ANN, CSPSO‐FC RBF ANN shows better performance. The values of average relative deviation (ARD), squared correlation coefficient (R2) and standard deviation (SD) are 0.1071, 0.9973 and 0.0108, respectively. Statistical data demonstrate that CSPSO‐FC RBF ANN has excellent prediction capability and high‐accuracy, and the correlation between prediction values and experimental data is good.

Fault Diagnosis of Grounding Grid Based on Self-Adaptive Particle Swarm Optimization

To make effective fault diagnosis of grounding grid , a new method using Self-Adaptive Particle Swarm Optimization (SAPSO) is proposed. Firstly, the grounding grid can be handled as a resistive network to establish fault diagnosis equations. Then the objective function based on minimum energy principle is added to lower the ill-condition of diagnostic equation. Next, according to optimization techniques, a new method of SAPSO is proposed to solve the corrosion diagnosis equations. The method takes advantage of the high global searching ability of SAPSO to obtain the optimal solution to the diagnosis model. By means of the analysis of the simulation, the correctness and reliability of the method have been verified.

Self-Adaptive Particle Swarm Optimization Algorithm with Mutation Operation Based on K-Means

Adaptive Particle Swarm Optimization algorithm with mutation operation based on K-means is proposed in this paper, this algorithm Combined the local searching optimization ability of K-means with the gobal searching optimization ability of Particle Swarm Optimization, the algorithm self-adaptively adjusted inertia weight according to fitness variance of population. Mutation operation was peocessed for the poor performative particle in population. The results showed that the algorithm had solved the poblems of slow convergence speed of traditional Particle Swarm Optimization algorithm and easy falling into the local optimum of K-Means, and more effectively improved clustering quality.

Optimal short-term hydro-thermal scheduling using quasi-oppositional teaching learning based optimization

This paper presents a new approach for solving short-term hydrothermal scheduling (HTS) using an integrated algorithm based on teaching learning based optimization (TLBO) and oppositional based learning (OBL). The practical hydrothermal system is highly complex and possesses nonlinear relationship of the problem variables, cascading nature of hydro reservoirs, water transport delay and scheduling time linkage that make the problem of optimization difficult using standard optimization methods. To overcome these problems, the proposed quasi-oppositional teaching learning based optimization (QOTLBO) is employed. To show its efficiency and robustness, the proposed QOTLBO algorithm is applied on two test systems. Numerical results of QOTLBO are compared with those obtained by two phase neural network, augmented Lagrange method, particle swarm optimization (PSO), improved self-adaptive PSO (ISAPSO), improved PSO (IPSO), differential evolution (DE), modified DE (MDE), fuzzy based evolutionary programming (Fuzzy EP), clonal selection algorithm (CSA) and TLBO approaches. The simulation results reveal that the proposed algorithm appears to be the best in terms of convergence speed, solution time and minimum cost when compared with other established methods. This method is considered to be a promising alternative approach for solving the short-term HTS problems in practical power system.

Application and Comparison of PSO, its Variants and HDE Techniques to Emission/Economic Dispatch

The conventional particle swarm optimization (PSO) algorithm known for its global searching capabilities has been successfully applied to emission/economic dispatch (EED) problems. In this paper, the applicability of PSO and its four variants, namely, self-adaptive PSO, dispersed PSO, chaotic PSO and new PSO to emission/economic dispatch problems has been presented. Furthermore, the employability of hybrid differential evolution (HDE) technique to EED problems is also proved here. Two numerical examples are tested—one having 9 units, and limits on SO2 and NO x emissions and the other having 6 units with NO x emission and B-loss co-efficients. Results obtained are compared with those reported in literature. The comparison of the results shows that the conventional PSO, its variants and HDE algorithm converge to optimal or near optimal solutions in the two examples tested in this paper. But the convergence time in HDE is far greater when compared with PSO and its variants.

Study on Chaotic Particle Swarm Optimization Algorithm in Solution of Logistics Scheduling Problem

Study the basic theory and process of chaos particle swarm optimization (PSO) algorithm, improve the basic PSO algorithm by introducing the self-adaptive inertia weighting factor method. Construct the mathematical model of basic logistics scheduling to complete the simulation analysis experiments. Experiment results show that self-adaptive chaos particle swarm optimization algorithm is effective and feasible to solve the logistics scheduling model problem.

Prediction of gas solubility in polymers by back propagation artificial neural network based on self-adaptive particle swarm optimization algorithm and chaos theory

A novel prediction method based on chaos theory, self-adaptive particle swarm optimization (PSO) algorithm, and back propagation artificial neural network (BP ANN) is proposed to predict gas solubility in polymers, hereafter called CSPSO BP ANN. The premature convergence problem of CSPSO BP ANN is overcome by modifying the conventional PSO algorithm using chaos theory and self-adaptive inertia weight factor. Modified PSO algorithm is used to optimize the BP ANN connection weights. Then, the proposed CSPSO BP ANN (two input nodes consisting of temperature and pressure; one output node consisting of gas solubility in polymers) is used to investigate solubility of CO2 in polystyrene, N2 in polystyrene, and CO2 in polypropylene, respectively. Results indicate that CSPSO BP ANN is an effective prediction method for gas solubility in polymers. Moreover, compared with conventional BP ANN and PSO ANN, CSPSO BP ANN shows better performance. The values of average relative deviation (ARD), squared correlation coefficient (R2) and standard deviation (SD) are 0.1275, 0.9963, and 0.0116, respectively. Statistical data demonstrate that CSPSO BP ANN has excellent prediction capability and high accuracy, and the correlation between predicted and experimental data is good.

Optimal Surge Arrester Parameter Estimation Using a PSO-Based Multiobjective Approach

In this paper, an improved self-adaptive particle swarm optimization (ISAPSO) algorithm is developed for estimating the best set of surge arrester model parameters. The purpose is to minimize the relative error between the calculated and manufacturer's measured residual voltage peak values for lightning, switching, and steep-front impulses. An objective function, which generalizes the model parameters for all impulse current types with different peak levels, is proposed. This objective function is the summation of three subfunctions that in each of them one type of impulse current with different current peak levels is considered. An efficient multiobjective approach is also applied along with the presented ISAPSO method. The proposed algorithm finds different sets of Pareto-optimal nondominated solutions for the objective functions. A fuzzy clustering technique is used to restrain the size of the repository within the desired limit. The proposed method is applied for a 150-kV TRIDELTA metal-oxide surge arrester in order to demonstrate its accuracy and effectiveness.

Constrained Problem Optimization Based on Self-adaptive Particle Swarm Optimization with Novel Penalty Mechanisms

Penalty mechanism is widely used for dealing with constrained optimization problems, in this paper novelty mechanism combined with self-adaptive particle swarm optimization is brought up. Based on traditional H, J and P strategy, improved approaches are adopted for coping with constrained problems. On the other hand, in order to improve PSO’s exploratory capacity, turbulence operations are used. Compared with traditional PSO, self-adaptive parameters are applied to improve PSO’s expanding capacity. Finally several benchmark functions testify the high performance of the brought up algorithm complexities with GA, and their algorithm complexity and time complexities are analyzed in detail.

Profil clinique et étiologique des pleurésies sérofibrineuses, hospitalisées au service de pneumologie de l’hôpital Ibn Nafis de Marrakech de 2005 à 2009

Parameter estimation for hydrological models is a challenging task, which has received significant attention by the scientific community. This paper presents a master–slave swarms shuffling evolution algorithm based on self-adaptive particle swarm optimization (MSSE-SPSO), which combines a particle swarm optimization with self-adaptive, hierarchical and multi-swarms shuffling evolution strategies. By comparison with particle swarm optimization (PSO) and a master–slave swarms shuffling evolution algorithm based on particle swarm optimization (MSSE-PSO), MSSE-SPSO is also applied to identify HIMS hydrological model to demonstrate the feasibility of calibrating hydrological model. The results show that MSSE-SPSO remarkably improves the calculation accuracy and is an effective approach to calibrate hydrological model.

Solubility Prediction of Gases in Polymers based on Chaotic Self-adaptive Particle Swarm Optimization Artificial Neural Networks

Solubility Prediction of Gases in Polymers based on Chaotic Self-adaptive Particle Swarm Optimization Artificial Neural Networks

An Improved Self-Adaptive PSO Algorithm with Detection Function for Multimodal Function Optimization Problems

This paper presented an improved self-adaptive particle swarm optimization (IDPSO) algorithm with detection function to solve multimodal function optimization problems. To overcome the premature convergence of PSO in a short time, the evolution direction of each particle is redirected dynamically by tuning the three parameters of IDPSO in the evolution process. Numerical results on several benchmark functions indicate that the IDPSO strategy outperformed other variants of PSO.

Automatic calibration a hydrological model using a master–slave swarms shuffling evolution algorithm based on self-adaptive particle swarm optimization

Parameter estimation for hydrological models is a challenging task, which has received significant attention by the scientific community. This paper presents a master–slave swarms shuffling evolution algorithm based on self-adaptive particle swarm optimization (MSSE-SPSO), which combines a particle swarm optimization with self-adaptive, hierarchical and multi-swarms shuffling evolution strategies. By comparison with particle swarm optimization (PSO) and a master–slave swarms shuffling evolution algorithm based on particle swarm optimization (MSSE-PSO), MSSE-SPSO is also applied to identify HIMS hydrological model to demonstrate the feasibility of calibrating hydrological model. The results show that MSSE-SPSO remarkably improves the calculation accuracy and is an effective approach to calibrate hydrological model.

A New Blocking Method with Dynamic Self-Adaptive PSO Applied in Grounding Grid Corrosion Diagnosis

To make Power system safe and stable, corrosion of grounding grid has become one of the most threatened phenomena and problems. With the object of reducing work force and diagnosing the corrosion situations more quickly and exactly, a new method has been proposed based on blocking way with dynamic self-adaptive PSO. First, irregular grids are supposed to be separated into several regular blocks. Then the diagnosis equations will be constructed so that the original ill conditions can be reduced and the solution accuracy will be increased as well. Accordingly, the problem of solving equations should be changed into optimizing solution of problems. In this paper, a new method called Dynamic Self-Adaptive Particle Swarm Optimization (DSAPSO) is proposed, which utilizes an average rule to estimate the whole swarm to divide it into three parts of different conditions, which are called excellent part, self-adaptive part and also worse part. Such a standard principle is based on the evaluation of fitness. Aiming at different groups, their speeds and positions will be updated with different parameters. And also a self-adaptive method has been added into regulation of parameter. Furthermore, such a dynamic method will do favor in increasing the global searching ability and avoiding from calculating complexity. Eventually, the effectiveness and accuracy of the method is verified by the application in a power station in Jilin Province.

Image reconstruction for an electrical capacitance tomography system based on a least-squares support vector machine and a self-adaptive particle swarm optimization algorithm

The task of image reconstruction for an electrical capacitance tomography (ECT) system is to determine the permittivity distribution and hence the phase distribution in a pipeline by measuring the electrical capacitances between sets of electrodes placed around its periphery. In view of the nonlinear relationship between the permittivity distribution and capacitances and the limited number of independent capacitance measurements, image reconstruction for ECT is a nonlinear and ill-posed inverse problem. To solve this problem, a new image reconstruction method for ECT based on a least-squares support vector machine (LS-SVM) combined with a self-adaptive particle swarm optimization (PSO) algorithm is presented. Regarded as a special small sample theory, the SVM avoids the issues appearing in artificial neural network methods such as difficult determination of a network structure, over-learning and under-learning. However, the SVM performs differently with different parameters. As a relatively new population-based evolutionary optimization technique, PSO is adopted to realize parameters’ effective selection with the advantages of global optimization and rapid convergence. This paper builds up a 12-electrode ECT system and a pneumatic conveying platform to verify this image reconstruction algorithm. Experimental results indicate that the algorithm has good generalization ability and high-image reconstruction quality.

An improved self-adaptive PSO technique for short-term hydrothermal scheduling

This paper presents an improved self-adaptive particle swarm optimization algorithm (ISAPSO) to solve hydrothermal scheduling (HS) problem. To overcome the premature convergence of particle swarm optimization (PSO), the evolution direction of each particle is redirected dynamically by adjusting the two sensitive parameters of PSO in the evolution process. Moreover, a new strategy is proposed to handle the various constraints of HS problem in this paper. The results solved by this proposed strategy can strictly satisfy the constraints of HS problem. Finally, the feasibility and effectiveness of proposed ISAPSO algorithm is validated by a test system containing four hydro plants and an equivalent thermal plant. The results demonstrate that the proposed ISAPSO can get a better solution in both robustness and accuracy while compared with the other methods reported in this literature.

Design and implementation of adaptive power-aware virtual machine provisioner (APA-VMP) using swarm intelligence

Cloud computing aims at providing dynamic leasing of server capabilities as scalable, virtualized services to end users. Our work focuses on the Infrastructure as a Service (IaaS) model where custom Virtual Machines (VM) are launched in appropriate servers available in a data center. The cloud data center taken into consideration is heterogeneous and large scale in nature. Such a resource pool is basically characterized by high resource dynamics caused by non-linear variation in the availability of processing elements, memory size, storage capacity, bandwidth and power drawn resulting from the sporadic nature of workload. Apart from the said resource dynamics, our proposed work also considers the processor transitions to various sleep states and their corresponding wake up latencies that are inherent in contemporary enterprise servers. The primary objective of the proposed metascheduler is to map efficiently a set of VM instances onto a set of servers from a highly dynamic resource pool by fulfilling resource requirements of maximum number of workloads. As the cloud data centers are overprovisioned to meet the unexpected workload surges, huge power consumption has become one of the major issues of concern. We have proposed a novel metascheduler called Adaptive Power-Aware Virtual Machine Provisioner (APA-VMP) that schedules the workload in such a way that the total incremental power drawn by the server pool is minimum without compromising the performance objectives. The APA-VMP makes use of swarm intelligence methodology to detect and track the changing optimal target servers for VM placement very efficiently. The scenario was experimented by novel Self-adaptive Particle Swarm Optimization (SAPSO) for VM provisioning, which makes best possible use of the power saving states of idle servers and instantaneous workload on the operational servers. It is evident from the results that there is a significant reduction in the power numbers against the existing strategies.