Enhanced Particle Swarm Optimization Algorithm Research Articles

Multilevel thresholding with divergence measure and improved particle swarm optimization algorithm for crack image segmentation

Crack formation is a common phenomenon in engineering structures, which can cause serious damage to the safety and health of these structures. An important method of ensuring the safety and health of engineered structures is the prompt detection of cracks. Image threshold segmentation based on machine vision is a crucial technology for crack detection. Threshold segmentation can separate the crack area from the background, providing convenience for more accurate measurement and evaluation of the crack condition and location. The segmentation of cracks in complex scenes is a challenging task, and this goal can be achieved by means of multilevel thresholding. The arithmetic-geometric divergence combines the advantages of the arithmetic mean and the geometric mean in probability measures, enabling a more precise capture of the local features of an image in image processing. In this paper, a multilevel thresholding method for crack image segmentation based on the minimum arithmetic-geometric divergence is proposed. To address the issue of time complexity in multilevel thresholding, an enhanced particle swarm optimization algorithm with local stochastic perturbation is proposed. In crack detection, the thresholding criterion function based on the minimum arithmetic-geometric divergence can adaptively determine the thresholds according to the distribution characteristics of pixel values in the image. The proposed enhanced particle swarm optimization algorithm can increase the diversity of candidate solutions and enhance the global convergence performance of the algorithm. The proposed method for crack image segmentation is compared with seven state-of-the-art multilevel thresholding methods based on several metrics, including RMSE, PSNR, SSIM, FSIM, and computation time. The experimental results show that the proposed method outperforms several competing methods in terms of these metrics.

An Enhanced Particle Swarm Optimization Algorithm via Adaptive Dynamic Inertia Weight and Acceleration Coefficients

The particle swarm optimization (PSO) algorithm counts among the most popular metaheuristic algorithms based on swarm intelligence. Since the publication of the first article on this optimization technique, researchers have developed many PSO variants with some improvement in its performance. The PSO optimization performance hinges on its ability to achieve a good exploration-exploitation balance. The most common method that helps to improve exploration-exploitation balance is modifying the PSO three controlling parameters, namely the inertia weight and two acceleration coefficients. In this paper a PSO variant that combines adaptive dynamic inertia weight and adaptive dynamic acceleration coefficients to enhance the exploration-exploitation balance of the PSO is proposed. The enhanced PSO algorithm called Adaptive Dynamic Inertia Weight and Acceleration Coefficient Optimization (ADIWACO) algorithm is tested on seven well-known standard test functions comprising four unimodal and three multimodal ones. The performance of the PSO is then compared with that of the standard PSO (SPSO) and four existing PSO variants. The experimental results comprising optimum value, runtime, mean value, standard deviation and convergence rate, and confirmed by the results of ranking statistics and Wilcoxon signed rank test conducted on the experimental results, indicate significantly better performance by the proposed PSO algorithm.

PsoResNet: An improved PSO-based residual network search algorithm

Neural Architecture Search (NAS) methods are widely employed to address the time-consuming and costly challenges associated with manual operation and design of deep convolutional neural networks (DCNNs). Nonetheless, prevailing methods still encounter several pressing obstacles, including limited network architecture design, excessively lengthy search periods, and insufficient utilization of the search space. In light of these concerns, this study proposes an optimization strategy for residual networks that leverages an enhanced Particle swarm optimization algorithm. Primarily, low-complexity residual architecture block is employed as the foundational unit for architecture exploration, facilitating a more diverse investigation into network architectures while minimizing parameters. Additionally, we employ a depth initialization strategy to confine the search space within a reasonable range, thereby mitigating unnecessary particle exploration. Lastly, we present a novel approach for computing particle differences and updating velocity mechanisms to enhance the exploration of updated trajectories. This method significantly contributes to the improved utilization of the search space and the augmentation of particle diversity. Moreover, we constructed a crime-dataset comprising 13 classes to assess the effectiveness of the proposed algorithm. Experimental results demonstrate that our algorithm can design lightweight networks with superior classification performance on both benchmark datasets and the crime-dataset.

Multi-objective Collaborative Optimization Method for Intelligent Distribution Network Operation and Maintenance

Most of the conventional multi-objective coordination methods for active distribution network operation are designed based on the principle of weighted random algorithm, which has limited application scope and cannot simplify the algorithm and reduce the complexity of the solution process, resulting in poor stability of active distribution network lines and potential faults. Based on this, this algorithm is introduced to carry out multi-objective collaborative optimization for intelligent distribution network operation. Firstly, the node types for the intelligent distribution network are defined and the power flow of the distribution network is analyzed. Secondly, a multi-objective configuration model for the lower level of the operation is constructed to maximize the distribution network investment and operation benefit index. A multi-objective collaborative optimization algorithm based on an enhanced particle swarm optimization algorithm is designed to reduce the operation cost of the distribution network and enhance the security and stability of the distribution network system during operation. The simulation test results show that after the application of the new method, the intelligent distribution network voltage stability index Vm is always less than 1, and the line stability has been significantly improved.

A multimaterial sensor with Janus characteristics based on enhanced particle swarm optimization algorithm

This paper presents an enhanced particle swarm optimization (EPSO) algorithm for optimizing the parameters of a layered photonic structure. By dynamically adjusting the thickness and refractive index of each dielectric layer, the phase and absorption properties of the transmitted electromagnetic waves (EWs) can be modified, effectively achieving high absorption rates by incorporating defects formed by the Kerr nonlinear material. The proposed EPSO algorithm facilitates the identification of the global optimal solution, resulting in the formation of sharp absorption peaks with values exceeding 0.9 for incident EWs from various directions. Leveraging the Janus properties, the EW exhibits distinct physical characteristics when incident from the front and back directions, enabling the creation of sensors tailored for thickness, angle, and pressure sensing. The forward angle sensing demonstrates a high sensitivity (S) of 0.32 nm/degree, a quality factor (Q) of 543.9, and a figure of merit (FOM) of 0.32 degree−1. Both pressure and thickness can be sensed in both forward and backward directions, with S of 5.46 nm/GPa and 10.24 nm/GPa, mean Q of 563.9 and 316.7, and FOM of 5.5 GPa−1 and 3.3 GPa−1, respectively. Thus, it can be stated that the proposed EPSO holds promising prospects for optical sensor design.

Reactive Power Optimization of Distributed PVConnected to Three-Phase Unbalanced Distribution Network

This study presents an approach to optimize reactive power in three-phase unbalanced distribution networks using an enhanced particle swarm optimization algorithm. The study analyzes the characteristics of reactive power optimization and establishes a reactive power optimization model for the distribution network using a multi-objective optimization method. Inequality constraints are utilized to determine the reactive power optimization model constraints for three-phase unbalanced distribution networks. The study applies an improved particle swarm optimization algorithm to solve the reactive power model and achieve optimization. The experimental results demonstrate that the proposed method considerably reduces the unused power of the optimized power network within 0-500ms and achieves a maximum reactive power optimization time of fewer than 8.2s, thereby enhancing the efficiency of reactive power optimization in distribution networks.

An optimal management architecture based on digital twin for smart solar-based islands incorporating deep learning and modified particle swarm optimization

Smart island (SI) energy management is a type of energy management system used to ensure that energy is used efficiently on islands. It is designed to reduce energy consumption and costs while also improving the sustainability of the island. Smart island energy management systems use a combination of technologies such as solar, wind, and other renewable energy sources, energy storage systems, smart meters, and advanced analytics to monitor and manage energy usage. The system can be used to provide islanders with real-time energy usage data, allowing them to make informed decisions about their energy use. Using the effects on the environment and economics of the SI as a basis for fully modeling the optimum performance of the SI in grid-connected operations, the feasibility and balance of the SI power grid are ensured. In addition, taking into account the impact of interruptible loads (IL) on SI operation costs, a power shift for IL can be performed using storage batteries in a digital twin environment. A new ecology-driven optimization algorithm has been developed that continuously adjusts migration rates with habitat suitability indexes of normalized individuals and adds a differential perturbation to the migration operator of the migration process. The enhanced particle swarm optimization algorithm has been implemented as the SI optimization dispatching algorithm. In order to have a precise prediction of the renewable energy sources, their output power is predicted using the recurrent neural network (RNN) deep learning model. Based on the simulation outcomes, it is evident that the suggested power dispatching model could greatly decrease the overall price of the system by implementing the advanced and effective algorithm and model presented in the study.

An Enhanced PSO Algorithm for Scheduling Workflow Tasks in Cloud Computing

This paper proposes an enhanced Particle Swarm Optimization (PSO) algorithm in order to deal with the issue that the time and cost of the PSO algorithm is quite high when scheduling workflow tasks in a cloud computing environment. To reduce particle dimensions and ensure initial particle quality, intensive tasks are combined when scheduling workflow tasks. Next, the particle initialization is optimized to ensure better initial particle quality and reduced search space. Then, a suitable self-adaptive function is integrated to determine the best direction of the particles. The experiments show that the proposed enhanced PSO algorithm has better convergence speed and better performance in the execution of workflow tasks.

Particle Swarm Optimized Compact, Low Loss 3-dB Power Splitter Enabled by Silicon Columns in Silicon-on-Insulator

We demonstrate a 3-dB power splitter optimized by an enhanced particle swarm optimization algorithm based on a curved directional coupler, with a set of silicon columns introduced into the coupling region. The proposed device exhibits compact size, low loss and low wavelength dependence in the O-band. We employ the particle swarm optimization algorithm to engineer the dispersion by designing the radius of the silicon columns automatically. The demonstrated 3-dB power splitter enabled by silicon columns in silicon-on-insulator can achieve multiple performance metrics simultaneously according to our simulation results, with a compact footprint as small as 11.9 µm, low excess loss as low as 0.04 dB and broad 3-dB operational bandwidth of 60 nm with transmission fluctuations within 0.05 in the wavelength range from 1270 to 1330 nm. This work pioneers the silicon columns in the coupling region and adopts an enhanced particle swarm optimization algorithm to optimize device properties, providing significant potential for application in large-scale PICs as well as offering a new degree of freedom in the design of power splitters.

Effective Prostate Cancer Detection using Enhanced Particle Swarm Optimization Algorithm with Random Forest on the Microarray Data

Prostate Cancer (PC) is the leading cause of mortality among males, therefore an effective system is required for identifying the sensitive bio-markers for early recognition. The objective of the research is to find the potential bio-markers for characterizing the dissimilar types of PC. In this article, the PC-related genes are acquired from the Gene Expression Omnibus (GEO) database. Then, gene selection is accomplished using enhanced Particle Swarm Optimization (PSO) to select the active genes, which are related to the PC. In the enhanced PSO algorithm, the interval-newton approach is included to keep the search space adaptive by varying the swarm diversity that helps to perform the local search significantly. The selected active genes are fed to the random forest classifier for the classification of PC (high and low-risk). As seen in the experimental investigation, the proposed model achieved an overall classification accuracy of 96.71%, which is better compared to the traditional models like naïve Bayes, support vector machine and neural network.

Joint Optimization of Multi-User Partial Offloading Strategy and Resource Allocation Strategy in D2D-Enabled MEC

With the emergence of more and more computing-intensive and latency-sensitive applications, insufficient computing power and energy of user devices has become a common phenomenon. Mobile edge computing (MEC) is an effective solution to this phenomenon. MEC improves task execution efficiency by offloading some tasks to edge servers for execution. In this paper, we consider a device-to-device technology (D2D)-enabled MEC network communication model, and study the subtask offloading strategy and the transmitting power allocation strategy of users. The objective function is to minimize the weighted sum of the average completion delay and average energy consumption of users, which is a mixed integer nonlinear problem. We first propose an enhanced particle swarm optimization algorithm (EPSO) to optimize the transmit power allocation strategy. Then, we utilize the Genetic Algorithm (GA) to optimize the subtask offloading strategy. Finally, we propose an alternate optimization algorithm (EPSO-GA) to jointly optimize the transmit power allocation strategy and the subtask offloading strategy. The simulation results show that the EPSO-GA outperforms other comparative algorithms in terms of the average completion delay, average energy consumption, and average cost. In addition, no matter how the weight coefficients of delay and energy consumption change, the average cost of the EPSO-GA is the least.

A Multi-Objective Optimization Approach Based on an Enhanced Particle Swarm Optimization Algorithm With Evolutionary Game Theory

A Multi-Objective Optimization Approach Based on an Enhanced Particle Swarm Optimization Algorithm With Evolutionary Game Theory

A Monte Carlo Enhanced PSO Algorithm for Optimization Design of Heliostat Field

Tower solar thermal power generation technology is a new low-carbon and environmentally friendly clean energy technology. In this paper, based on Monte Carlo algorithm model and particle swarm optimization PSO algorithm, the optimization model of heliostatic mirror field is constructed, which is of great practical significance for effectively improving the collection and conversion efficiency of solar energy and promoting the sustainable development of clean energy.

An enhanced PSO algorithm to configure a responsive-resilient supply chain network considering environmental issues: a case study of the oxygen concentrator device.

In recent years, the hyper-competitive marketplace has led to a drastic enhancement in the importance of the supply chain problem. Hence, the attention of managers and researchers has been attracted to one of the most crucial problems in the supply chain management area called the supply chain network design problem. In this regard, this research attempts to design an integrated forward and backward logistics network by proposing a multi-objective mathematical model. The suggested model aims at minimizing the environmental impacts and the costs while maximizing the resilience and responsiveness of the supply chain. Since uncertainty is a major issue in the supply chain problem, the present paper studies the research problem under the mixed uncertainty and utilizes the robust possibilistic stochastic method to cope with the uncertainty. On the other side, since configuring a supply chain is known as an NP-Hard problem, this research develops an enhanced particle swarm optimization algorithm to obtain optimal/near-optimal solutions in a reasonable time. Based on the achieved results, the developed algorithm can obtain high-quality solutions (i.e. solutions with zero or a very small gap from the optimal solution) in a reasonable amount of time. The achieved results demonstrate the negative impact of the enhancement of the demand on environmental damages and the total cost. Also, according to the outputs, by increasing the service level, the total cost and environmental impacts have increased by 41% and 10%, respectively. On the other hand, the results show that increasing the disrupted capacity parameters has led to a 17% increase in the total costs and a 7% increase in carbon emissions.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00521-022-07739-8.

Applying to aerodynamic optimization an enhanced particle swarm optimization algorithm based on parallel exchange

The particle swarm optimization (PSO) algorithm is easy to implement and can obtain high-quality solutions to optimization problems. It is widely applied to nonlinear and difficult problems such as aerodynamic optimization. However, to solve multi-modal problems, it easily falls into locally optimal values, showing that its robustness is poor. In order to improve the robustness of the PSO algorithm, an enhanced particle swarm optimization algorithm based on parallel exchange (EPSOBPE) is proposed. The algorithm enhances the optimization capability and its robustness through the parallel evolution of the cuckoo search algorithm (CSA), PSO population, hierarchical exchange operation and reinforcement learning strategy. Therefore, the algorithm has both the global search capability of the CSA and the local capability of the PSO algorithm, thus making the EPSOBPE very robust. Functional test results show that the EPSOBPE has stronger robustness and adaptability to different problems than other intelligent optimization algorithms. Moreover, the EPSOBPE is applied to the aerodynamic optimization design of the RAE2822 airfoil and the M6 wing. Compared with other algorithms, the EPSOBPE is more robust, and its optimization capability is better.

Multi‐objective economic/emission optimal energy management system for scheduling micro‐grid integrated virtual power plant

AbstractDue to rapid socioeconomic growth and current environmental concerns, reducing global greenhouse gas emissions is a key step toward sustainable development. In recent years, some researchers have begun to adopt new grid management technologies such as virtual power plants (VPP) that allow them to improve energy management and cost and reduce gas emissions. The integration of distributed energy resources (DER), such as solar photovoltaic and wind power, combined with micro‐turbines and energy storage systems and with the support of VPP intelligence, will contribute immensely to improving micro‐grids (MGs) energy performance and reducing gas emissions. In this paper, an expert multi‐objective feasibility enhanced particle swarm optimization algorithm is adopted for the optimal scheduling of energy management system to reduce the total operating cost and net emission simultaneously. The proposed algorithm is tested on an Algerian reel building‐MG integrated VPP with DERs and combined cooling, heat, and power generation. Finally, different cases are simulated for a 24‐h time and discussed to demonstrate the validity of the proposed VPP energy system and the effectiveness of the adopted algorithm to reduce gas emissions and the total operating cost.

MAXIMUM POWER TRACKING OF A SOLAR SYSTEM USING IMPROVED PARTICLE SWARM OPTIMIZATION ALGORITHM

For photovoltaic systems to be more efficient, maximum power point (MPP) tracking of the solar arrays is necessary. The output power of solar arrays is influenced by two factors: temperature and solar irradiation. It is also possible that a traditional MPP tracker would operate incorrectly because to the mismatch effect produced by partial shadow. There are many maximum values for the solar array power–current characteristics when it is partly shaded. For PV systems in partly shadowed conditions, this research provides an MPPT approach with enhanced particle swarm optimization algorithm.

Innovative Hyperspectral Image Classification Approach Using Optimized CNN and ELM

In order to effectively extract features and improve classification accuracy for hyperspectral remote sensing images (HRSIs), the advantages of enhanced particle swarm optimization (PSO) algorithm, convolutional neural network (CNN), and extreme learning machine (ELM) are fully utilized to propose an innovative classification method of HRSIs (IPCEHRIC) in this paper. In the IPCEHRIC, an enhanced PSO algorithm (CWLPSO) is developed by improving learning factor and inertia weight to improve the global optimization performance, which is employed to optimize the parameters of the CNN in order to construct an optimized CNN model for effectively extracting the deep features of HRSIs. Then, a feature matrix is constructed and the ELM with strong generalization ability and fast learning ability is employed to realize the accurate classification of HRSIs. Pavia University data and actual HRSIs after Jiuzhaigou M7.0 earthquake are applied to test and prove the effectiveness of the IPCEHRIC. The experiment results show that the optimized CNN can effectively extract the deep features from HRSIs, and the IPCEHRIC can accurately classify the HRSIs after Jiuzhaigou M7.0 earthquake to obtain the villages, bareland, grassland, trees, water, and rocks. Therefore, the IPCEHRIC takes on stronger generalization, faster learning ability, and higher classification accuracy.

A Hybrid Approach for Task Scheduling Based Particle Swarm and Chaotic Strategies in Cloud Computing Environment

This paper presents a hybrid approach based discrete Particle Swarm Optimization (PSO) and chaotic strategies for solving multi-objective task scheduling problem in cloud computing. The main purpose is to allocate the summited tasks to the available resources in the cloud environment with minimum makespan (i.e. schedule length) and processing cost while maximizing resource utilization without violating Service Level Agreement (SLA) among users and cloud providers. The main challenges faced by Particle Swarm Optimization (PSO) when used to solve scheduling problems are premature convergence and trapping into local optimum. This paper presents an enhanced Particle Swarm Optimization algorithm hybridized with Chaotic Map strategies. The proposed approach is called Enhanced Particle Swarm Optimization based Chaotic Strategies (EPSOCHO) algorithm. Our proposed approach suggests two Chaotic Map strategies: sinusoidal iterator and Lorenz attractor to enhanced PSO algorithm in order to get good convergence and diversity for optimizing the task scheduling in cloud computing. The proposed approach is simulated and implemented in Cloudsim simulator. The performance of the proposed approach is compared with the standard PSO algorithm, the improved PSO algorithm with Longest job to fastest processor (LJFP-PSO), and the improved PSO algorithm with minimum completion time (MCT-PSO) using different sizes of tasks and various benchmark datasets. The results clearly demonstrate the efficiency of the proposed approach in terms of makespan, processing cost and resources utilization.

A Student Grouping Method for Massive Online Collaborative Learning

As a brand-new learning strategy, cooperative learning can greatly improve the quality of classroom teaching and the teamwork and learning efficiency of students. The conventional student grouping models for cooperative learning take less consideration of the complementarity of students’ knowledge structures and learning interests, so they are not quite suitable for online teaching, which is provided for a huge number of students and has a large scale of learning resources and great freedom of choice. To this end, this paper studies the student grouping method for massive online collaborative learning. Firstly, the problem of identifying the student’s knowledge state was described and characterized, and a diagnosis model of students’ knowledge state simulating the cooperative learning process was constructed based on the gated recurrent neural network. The creation of an online collaborative learning scenario that considers both the knowledge state and interests of students was regarded as an NP-hard problem, and then, the enhanced particle swarm optimization algorithm was adopted to realize the grouping of students for massive online collaborative learning. The experimental results verified the effectiveness of the model constructed in this paper.