Extended Particle Swarm Optimization Research Articles

An extended PSO algorithm for cold-chain vehicle routing problem with independent loading and minimum fuel volume

With the increasing complexity of the distribution environment, customers usually propose higher requirements, such as independent loading of local and foreign cold-chain items in the event of an emergency. Moreover, minimum fuel volume plays an important role in the process of transportation with different speeds and different kinds of vehicles. In this paper, we present a new mathematical model to characterize cold-chain vehicle routing optimization with independent loading of local and foreign items and minimum fuel volume. To address the above mathematical model, an extended particle swarm optimization (PSO) algorithm is proposed by combining original PSO with 2-opt optimization to improve diversity and reduce convergence speed. Six sets of experiments are set to verify the practical performance and stability of the extended PSO algorithm based on three standard datasets of C201, R201, and RC201 from Solomon.

Retracted: Optimization of Correlation Filters Using Extended Particle Swarm Optimization Technique.

[This retracts the article DOI: 10.1155/2021/6321860.].

A Novel Motor Structure with Extended Particle Swarm Optimization for Space Robot Control.

This paper studies motor structures and optimization methods for space robots, proposing an optimized stepped rotor bearingless switched reluctance motor (BLSRM) to solve the poor self-starting ability and significant torque fluctuation issues in traditional BLSRMs. Firstly, the advantages and disadvantages of the 12/14 hybrid stator pole type BLSRM were analyzed, and a stepped rotor BLSRM structure was designed. Secondly, the particle swarm optimization (PSO) algorithm was improved and combined with finite element analysis for motor structure parameter optimization. Subsequently, a performance analysis of the original and new motors was conducted using finite element analysis software, and the results showed that the stepped rotor BLSRM had an improved self-starting ability and significantly reduced torque fluctuation, verifying the effectiveness of the proposed motor structure and optimization method.

Hybrid Extend Particle Swarm Optimization (EPSO) model for Enhancing the performance of MANET Routing Protocols

The routing protocols in MANETs are designed to provide efficient and reliable communication in a highly dynamic and resource-constrained environment. It is very efficient and requires low computational and memory resources compared to most routing protocols. Therefore, mobility and the number of nodes significantly impact the performance and reliability of routing protocols. This paper proposes a hybrid extended particle swarm optimization (EPSO) model to improve the performance of MANET routing protocols. It determines the optimal mobility and the number of hubs and nodes that satisfy the best possible version of MANET. MANET requires a robust routing algorithm that can adapt to a network that arbitrarily changes its topology at any time. The proposed model in the NS2 simulator proves the model's validity in improving the performance of MANET. The proposed model sets the general parameters of routing protocols and achieves high performance with fewer discarded packets and low delay when sending and receiving over MANET. The MANET sent 167 packets in the proposed model, and the number of discarded packets was less than 1%.

Event-Triggered Online Scheduling for Industrial-Integrated Energy System

Sound scheduling and allocation of multi-energy media is of paramount significance for reducing energy consumption and improving operation efficiency in an industrial integrated energy system of a steel industrial park, and the online optimization solution of various scheduling events can be regarded as the prerequisite for such challenging tasks. Thus, a novel granular-driven extended particle swarm optimization with meta-cognitive component in terms of what-to-learn, how-to-learn, and when-to-learn, termed as MC-GEPSO, is proposed in this study, which were realized by the observation, learning and selection of operation mode described by granularity. Furthermore, an adaptive interval type-2 fuzzy systembased on autonomous fuzzy rule learning mechanism is reported for achieving the selection of granularity described by equipment operation performance, and the multiple-objective optimizations of online-scheduling can be solved by GEPSO. The performance of the proposed MC-GEPSO is experimentally validated by a number of industry study cases, where the proposed approach outperforms manual scheduling in aspect of operation cost, operation efficiency, and carbon emissions.

Multiple targets video tracking based on extended kalman filter in combination with particle swarm optimization for intelligent applications

Multiple targets tracking is a major issue in the intelligent applications. Numerous methods have been presented for the multiple targets tracking to capture the targets trajectory in a video sequence in order to increase intelligence and reduce human error. In this paper, a method is proposed based on combining the Extended Kalman Filter (EKF) and Particle Swarm Optimization (PSO) to construct an intermediate tracker and track targets more accurately. The EKF solves targets collision problem, and PSO reduces the covariance of measured noise. Finally, the Joint Probabilistic Data Association (JPDA) filter is used to reduce the number of multiple hypotheses and create a one-to-one correspondence between targets and measurements. To detect targets, frames subtracting along with background modeling and canny edge detector are used. To reduce running time of the proposed method, number of video frames per second (fps) is reduced from 30 to 10 and the sampling rate is also reduced. Despite of this reduction, simulation reults of the proposed method show the multiple targets tracking with 98% accuracy at an acceptable running time compared to the similar methods. In addition, by using the proposed method, the number of assignment states is reduced in the targets tracking process. Overall, the proposed method not only can be used in the intelligent applications, but also in the video compression applications as well.

Consensus Reaching With Minimum Cost of Informed Individuals and Time Constraints in Large-Scale Group Decision-Making

Consensus reaching process (CRP) is important and present in a wide range of application areas. In practical CRP, the managers (e.g., enterprise) often hire some informed individuals (e.g., persuaders) to promote the efficiency of consensus reaching. This article proposes a CRP with minimum cost of informed individuals and time constraint in large-scale group decision-making (LSGDM) with bounded confidence effects. The consensus model with bounded confidence effects (CBC model) is formulated. Then, desirable properties of the CBC model are discussed to facilitate its resolution. Next, an extended particle swarm optimization algorithm is designed to solve the CBC model. Finally, a numerical analysis, a comparison analysis, and a simulation analysis are provided to illustrate the feasibility and effectiveness of the proposed approach.

Blockchain-Based Educational Management and Secure Software-Defined Networking in Smart Communities

As network multimedia technology has advanced rapidly, video communication has made enormous strides and has been used in many educational industries. The use of educational video communication is a simple, effective, and convenient method of communication. A fast and energy-efficient blockchain clustering algorithm is proposed to pick the desired cluster head in software-defined networking (SDN). This research suggests using an adaptive resource allocation method and secure communication networks to improve the quality of instructional video transmission. To better educational management resources (EMR), this research suggests using a particle swarm algorithm (PSA). Using the PSA-EMR, an extended particle swarm optimization model suggests a low-complexity solution for adaptive central and distributed resource allocation in communication networks. The method improves the productivity and accuracy of the teaching team. Finally, a cost-effective digital framework for collecting and transmitting high-quality instructional content was developed to conclude the research.

An Identification Algorithm of Relay Protection Parameter for Flexible High-Voltage Direct Current Transmission Lines Under Synchronous Sampling

Relay protection device is a protection device when the flexible DC transmission line is long and leads to line failure. Therefore, in order to ensure the reliable identification of relay protection parameters, an identification algorithm of relay protection parameter for flexible DC transmission line under synchronous sampling is proposed. By calculating the relay protection parameters based on synchronous sampling, the algorithm obtains the positive sequence parameters and zero sequence parameters of flexible DC transmission line’s relay protection, and uses the matrix beam algorithm to identify the positive sequence parameters and zero sequence parameters, so as to obtain the subsynchronous oscillation modal parameters. In order to improve the identification effect, the extended particle swarm optimization algorithm is used to optimize the identification results. The test results show that the algorithm has the feasibility of relay protection parameter identification and the calculation function of positive sequence parameters and zero sequence parameters of relay protection device. It can reliably identify the current change and the change results of positive sequence parameters and zero sequence parameters with minimal identification error.

OCR for Arabic character recognition with advanced principal component analysis based on feature extraction and fuzzy-KNN based classification

Offline character recognition has become a highly important study field for various pattern recognition applications in recent years. Several handwritten character recognition systems have been suggested, with the complexity of these systems varying depending on the recognizing units' writing styles. In reality, identifying letters or numerals is far simpler than recognizing cursive sentences or lines of text. As a result, early handwriting recognition algorithms could only distinguish a few characters with limited vocabularies. Nowadays Arabic handwritten character recognition is very important as it is very difficult to identify. The cursive writing and variety of styles make this recognition more complex. This paper presents an automated model for ACR. This ACR is constructed from four phases: Preprocessing, Segmentation, Feature Extraction, and Classification. In this research article, we compare the advanced EPSO EKNN Algorithm with the earlier DBN Algorithm and also suggest a new method having higher Accuracy. In this research article, the "Enhanced K-Nearest Neighbor" (EKNN) classification model was used to identify and classify or simply recognize the particular Arabic character, and the "Extended Particle Swarm Optimization" (EPSO) methodology was introduced to select the best feature from feature extraction to comply with Arabic-Character Classification.

Extended particle swarm optimization for feature selection of high‐dimensional biomedical data

AbstractThis paper proposes a novel Extended Particle Swarm Optimization model (EPSO) that potentially enhances the search process of PSO for optimization problem. Evidently, gene expression profiles are significantly important measurement factor in molecular biology that is used in medical diagnosis of cancer types. The challenge to certain classification methodologies for gene expression profiles lies in the thousands of features recorded for each sample. A modified Wrapper feature selection model is applied with the aim of addressing the gene classification challenge by replacing its randomness approach with EPSO and PSO, respectively. EPSO is initializing the random size of the population and dividing them into two groups in order to promote the exploration and reduce the probability of falling in stagnation. Experimentally, EPSO has required less processing time to select the optimal features (average of 62.14 s) than PSO (average of 95.72 s). Furthermore, EPSO accuracy has provided better classification results (start from 54% to 100%) than PSO (start from 52% to 96%).

Optimization of Correlation Filters Using Extended Particle Swarm Optimization Technique.

In the past few decades, the field of image processing has seen a rapid advancement in the correlation filters, which serves as a very promising tool for object detection and recognition. Mostly, complex filter equations are used for deriving the correlation filters, leading to a filter solution in a closed loop. Selection of optimal tradeoff (OT) parameters is crucial for the effectiveness of correlation filters. This paper proposes extended particle swarm optimization (EPSO) technique for the optimal selection of OT parameters. The optimal solution is proposed based on two cost functions. The best result for each target is obtained by applying the optimization technique separately. The obtained results are compared with the conventional particle swarm optimization method for various test images belonging from different state-of-the-art datasets. The obtained results depict the performance of filters improved significantly using the proposed optimization method.

Renewable energy systems based on micro-hydro and solar photovoltaic for rural areas: A case study in Yogyakarta, Indonesia

This paper presents renewable energy systems based on micro-hydro and solar photovoltaic for rural areas, with a case study in Yogyakarta, Indonesia. The Special Region of Yogyakarta, located on the island of Java, Indonesia, has a high potential for the development of renewable energy resources, especially hydropower and solar power. Many rural areas in Yogyakarta lack a supply of electricity. In this study, data on the potential for hydropower and solar power in rural regions of Yogyakarta are processed to determine the best capacity of hydroelectric and solar power plants. The extended particle swarm optimization (PSO) technique has been used to ensure optimal capacity optimization of this hybrid systems. The final result of this study is the most optimal of hydropower and solar power generation capacity based on the calculation of cost of capital, grid sales, cost of energy, and net present value.

Optimizing resource scheduling based on extended particle swarm optimization in fog computing environments

AbstractCloud computing (CC) allows on‐demand networks to access central computer resources, such as servers, databases, storage, and network services. While clouds can handle enormous amounts of data, they still encounter problems due to insufficient cloud resources. Therefore, another computing model, called fog computing, was introduced. However, the inefficient scheduling of user tasks in fog computing can cause more delays than that in CC. To address the issues of resource utilization, response time, and latency, optimal and efficient techniques are required for the scheduling strategies. In this study, we developed an extended particle swarm optimization (EPSO) algorithm with an extra gradient method to optimize the task scheduling problem in cloud‐fog environments. Our primary aim is to improve the efficiency of resources and minimize the time taken to complete tasks. We conducted extensive experiments on the iFogSim simulator in terms of makespan and total cost. We compared the performance of the proposed EPSO method with that of other traditional techniques, such as ideal PSO and modified PSO; the results demonstrated that EPSO achieved a makespan of 342.53 s. Thus, it can be concluded that the performance of the proposed method is comparable to that of other approaches.

Adaptive prescribed‐time disturbance observer using nonsingular terminal sliding mode control: Extended Kalman filter and particle swarm optimization

In this study, adaptive prescribed finite-time stabilisation of uncertain single-input and single-output non-linear systems is considered in the presence of unknown states, unknown parameters, external load disturbance, and non-symmetric input saturation. A prescribed finite time disturbance observer is designed to approximate the unmeasured external disturbance. Also, a non-singular prescribed finite time terminal sliding mode control is proposed for the closed-loop control of the system with the non-symmetric input saturation. Extended Kalman filter algorithm is employed for the real-time estimations of the states and unknown parameters of the system. Moreover, a particle swarm optimisation algorithm is used to obtain the design parameters of the proposed disturbance observer and controller. To show the performance of designed control scheme, the proposed approach is employed to guarantee prescribed finite time stabilisation of non-linear vibration of a non-local strain gradient nanobeam. The Galerkin projection method is used to reduce the non-dimensional form of the governing non-linear partial differential equation of Euler–Bernoulli nanobeam to the ordinary differential equation. Finally, numerical simulations are performed to illustrate the effectiveness and performance of the developed adaptive control scheme for the vibration control of nanobeam in comparison with the conventional sliding mode control.

Pseudoextended Bouc–Wen Model and Adaptive Control Design With Applications to Smart Actuators

Smart material-based actuators are known to exhibit the hysteresis behavior, and most of them are rate-dependent, i.e., the level of hysteresis depends closely on the rate of input excitation frequency. This behavior is undesirable and has to be eliminated in order to facilitate high-precision applications. Therefore, there is a need for the comprehensive and robust approach to model and control the hysteresis effects. This paper proposes a new model modification to the phenomenological class of the hysteresis model. In this paper, the special case of the Bouc–Wen model is used as the basis for developing the modified one, and its establishment is realized in the discrete-time domain. Through the identification method called “extended particle swarm optimization,” it is verified that the proposed model is able to capture the dynamic and hysteretic behavior of a class of smart material-based actuators with a relatively good accuracy. Then, a robust adaptive control is synthesized based on the proposed model in order to eliminate the hysteresis effects. It is shown that the formulated adaptive controller guarantees the stability of the closed-loop control system. Experimental results show that the proposed control scheme mitigates the hysteresis effects efficiently.

Forecasting Crude Oil Price Using EEMD and RVM with Adaptive PSO-Based Kernels

Crude oil, as one of the most important energy sources in the world, plays a crucial role in global economic events. An accurate prediction for crude oil price is an interesting and challenging task for enterprises, governments, investors, and researchers. To cope with this issue, in this paper, we proposed a method integrating ensemble empirical mode decomposition (EEMD), adaptive particle swarm optimization (APSO), and relevance vector machine (RVM)—namely, EEMD-APSO-RVM—to predict crude oil price based on the “decomposition and ensemble” framework. Specifically, the raw time series of crude oil price were firstly decomposed into several intrinsic mode functions (IMFs) and one residue by EEMD. Then, RVM with combined kernels was applied to predict target value for the residue and each IMF individually. To improve the prediction performance of each component, an extended particle swarm optimization (PSO) was utilized to simultaneously optimize the weights and parameters of single kernels for the combined kernel of RVM. Finally, simple addition was used to aggregate all the predicted results of components into an ensemble result as the final result. Extensive experiments were conducted on the crude oil spot price of the West Texas Intermediate (WTI) to illustrate and evaluate the proposed method. The experimental results are superior to those by several state-of-the-art benchmark methods in terms of root mean squared error (RMSE), mean absolute percent error (MAPE), and directional statistic (Dstat), showing that the proposed EEMD-APSO-RVM is promising for forecasting crude oil price.

Nonlinear System Identification Using Clustering Algorithm Based on Kernel Method and Particle Swarm Optimization

Many clustering algorithms have been proposed in literature to identify the parameters involved in the Takagi–Sugeno fuzzy model, we can quote as an example the Fuzzy C-Means algorithm (FCM), the Possibilistic C-Means algorithm (PCM), the Allied Fuzzy C-Means algorithm (AFCM), the NEPCM algorithm and the KNEPCM algorithm. The main drawback of these algorithms is the sensitivity to initialization and the convergence to a local optimum of the objective function. In order to overcome these problems, the particle swarm optimization is proposed. Indeed, the particle swarm optimization is a global optimization technique. Thus, the incorporation of local research capacity of the KNEPCM algorithm and the global optimization ability of the PSO algorithm can solve these problems. In this paper, a new clustering algorithm called KNEPCM-PSO is proposed. This algorithm is a combination between Kernel New Extended Possibilistic C-Means algorithm (KNEPCM) and Particle Swarm Optimization (PSO). The effectiveness of this algorithm is tested on nonlinear systems and on an electro-hydraulic system.

Performance Improvement of Radial Distribution Network with Distributed Generation Integration Using Extended Particle Swarm Optimization Algorithm

This paper proposes the performance improvement of radial distribution network with distributed generation (DG) integration using extended particle swarm optimization (PSO) algorithm. High-performance distribution network is a network that has a low power loss, better voltage profile, and loading balance among feeders. The effort to improve the performance of the distribution network is network configuration optimization. The optimization has become an important issue with the presence of DG in distribution networks. In this study, network configuration optimization is based on an extended PSO algorithm. The methodology has been tested in two models of IEEE radial distribution networks. The results showed that the optimal configuration of the distribution network is able to reduce power loss and to improve the voltage profile of the network significantly.

Two-echelon logistics distribution region partitioning problem based on a hybrid particle swarm optimization–genetic algorithm

Two-echelon logistics distribution region partitioning is a critical step to optimize two or multi-echelon logistics distribution network, and it aims to assign distribution unit to a certain logistics facility (i.e. logistic center and distribution center). Given the partitioned regions, vehicle routing problem can be further developed and solved. This paper established a model to minimize the total cost of the two-echelon logistics distribution network. A hybrid algorithm named as the Extended Particle Swarm Optimization and Genetic Algorithm (EPSO–GA) is proposed to tackle the model formulation. A two-dimensional particle encoding method is adopted to generate the initial population of particles. EPSO–GA combines the merits of Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA) with both global and local search capability. By updating the inertia weight and exchanging best-fit solutions and worst-fit solutions between PSO and GA, EPSO–GA algorithm is able to converge to an optimal solution with a reasonable design of termination and iteration rules. The computation results from a case study in Guiyang city, China, reveal that EPSO–GA algorithm is superior to the other three algorithms, Hybrid Particle Swarm Optimization (HPSO), GA, and Ant Colony Optimization (ACO), in terms of the partitioning schemes, the total cost and number of iterations. By comparing with the exact method, the proposed approach demonstrates its capability to optimize a small scale two-echelon logistics distribution network. The proposed approach can be readily implemented in practice to assist the logistics operators reduce operational costs and improve customer service. In addition, the proposed approach is of great potential to apply in other research domains.