Hybrid Particle Swarm Optimization Algorithm Research Articles

Hybrid Particle Swarm and Gray Wolf Optimization Algorithm for IoT Intrusion Detection System

Hybrid Particle Swarm and Gray Wolf Optimization Algorithm for IoT Intrusion Detection System

Hybrid particle swarm optimization algorithm for scheduling flexible assembly systems with blocking and deadlock constraints

This paper focuses on the scheduling problem of flexible assembly systems (FASs) without intermediate buffers. The main characteristic of the problem is that as no intermediate buffer exists between consecutive machines, blocking and deadlock constraints must be considered. Petri nets are used to model the considered FASs, and a novel hybrid particle swarm optimization (HPSO) algorithm is proposed to minimize the makespan. The proposed algorithm is the combination of the discrete PSO, particle repairing algorithm, particle improvement strategy, and local search method. First, each candidate solution for the problem is encoded as a permutation with repetition of part numbers, and can be uniquely decoded into a sequence of transitions. To ensure the feasibility of solutions, a repairing algorithm is developed, in which a deadlock avoidance policy is used. Then, a particle improvement policy is proposed to improve the performance of particles. Meanwhile, a local search method is designed and incorporated into HPSO to improve its search ability. Experiments are conducted to verify the effectiveness of the particle improvement policy and local search method. Comparisons between HPSO and ten other algorithms are performed. The comparison results and analysis show that our proposed algorithm can find feasible solutions for all tested instances, and is superior to other scheduling algorithms in terms of finding better solutions and performance stability.

Comparison of ultrasonic attenuation models for small droplets measurement based on numerical simulation and experiment

Ultrasonic method has developed rapidly in particle measurement. Suitable ultrasonic attenuation theoretical models are very important for measuring different kinds of particles. In this paper, the ultrasonic measurement technique for small droplets in air is studied based on simulation and experiment, and the applicability of some theoretical models is discussed. In simulation method, both pressure acoustic module and thermoviscous acoustic module are compared, and a method of randomly placing small droplets is proposed. Based on the thermoviscous acoustic module, four ultrasonic attenuation theoretical models including ECAH model, McClements model, Hemar model and Qian model are compared. The mechanism of ultrasonic attenuation under different ultrasonic frequency, droplet radius and concentration is analyzed. And the results show that ECAH model and McClements model are closer to the simulation results, but ECAH model is the most consistent with the simulation data. Furthermore, the flowing experiment is carried out to measure the ultrasonic attenuation coefficient of small droplets in air. The experimental results are inverted using above four theoretical models and hybrid particle swarm optimization algorithm to obtain the droplet radius and concentration. Compared with the results of optical photographing method, the error of ECAH model and McClements model is the smallest within ± 10%. Based on the simulation results and experimental results, ECAH model is the most suitable theoretical model for ultrasonic attenuation method to inverse droplet radius and concentration for small droplets in air measurement at low concentrations, and McClements model has a very close performance to ECAH model.

The impact of local search strategies on chaotic hybrid firefly particle swarm optimization algorithm in flow-shop scheduling

A flow-shop production environment is a widely used production setting in the industry, especially preferred when the variety of products and processes is limited. The constant development of technology and the increasing competition in the market compel researchers to seek ways to increase efficiency. Therefore, scheduling remains an important area of study for researchers. When the size of the problem increases, particularly in real-life contexts, it becomes almost impossible to work out a feasible solution to scheduling problems through current computer technologies. In such cases, solutions are often developed using heuristic or meta-heuristic methods. However, the performance of these methods may vary according to the problem or the components of the suggested method of solution.This study aimed to investigate the effect of local search strategies on the solution performance of hybrid firefly and particle swarm optimization algorithm, developed using chaotic local search. We used four different solution methods: the triple local search strategy consisting of insertion, swap and inversion, which were used frequently in earlier studies, and the individual use of each of these three strategies. In this study, the average relative difference values calculated using insertion, swap, inversion and triple strategy were found to be 3.03, 1.27, 2.54 and 1.20, respectively. As a result of the analyses carried out to compare the results of the solutions obtained in the flow-shop scheduling problems of Taillard, it was found that better results were obtained using the triple strategy. These findings can be a point of reference for future research.

Bi-objective grey wolf optimization algorithm combined Levy flight mechanism for the FMC green scheduling problem

As more and more enterprises have laid great emphasis on eco-friendly manufacturing processes while enhancing agility, this paper focuses on the green scheduling problem of the Flexible Manufacturing Cell with material handling robots (FMC-R). In the FMC, each job is characterized by several operations, processing machines, and machining time. Robots at one straight track transfer jobs to the related machines. Distinguished from most of the FMC scheduling problems, this paper considers not only the operation sequencing of jobs but also the robots’ transportation processes. A bi-objective mathematical model is established aiming to minimize the total makespan and the total energy consumption of the FMC system simultaneously. Due to the NP-hard nature of the problem, a Levy Flight and Weighted Distance-updated Multi-objective Grey Wolf Algorithm (LWMOGWO) is proposed. It combines the Levy flight with Multi-objective Grey Wolf Algorithm (MOGWO). A weighted distance updating mechanism is integrated to calculate the positions of individuals. A local neighbourhood search strategy is adopted, which makes it possible to take full advantage of the three leading wolves in population. The performance of the LWMOGWO algorithm is evaluated through groups of experiments by comparing with multiple hybrid meta-heuristic algorithms, hybrid particle swarm optimization (NGPSO) algorithm, an improved grey wolf optimization (IGWO) algorithm, MOGWO, NSGA-II (Non-dominated sorting genetic algorithm), and PLMEAPS. Results reveal the proposed LWMOGWO algorithm has a higher quality of solutions in solving the scheduling of the FMC-R problem.

Reliability Analysis and Modeling of Green Computing Based Software Systems

Background: Software industries are growing very fast to develop new solutions and ease people’s life. Software reliability has been considered as a critical factor in today’s growing digital world. Software reliability models are one of the most generally used mathematical tools for estimation of software reliability. These reliability models can be applied on the development of sustainable and green computing-based software’s having their constrained development environments. Objective: This paper proposes a new reliability estimation model for green IT environment based software systems. Methods: In this paper, a new failure rate behavior-based model centered on green software development life cycle process has been developed. This model integrates a new modulation factor for incorporating changing needs in each phase of green software development methodology. Parameter estimation for proposed model has been done using hybrid Particle Swarm Optimization and Gravitational Search Algorithm. The proposed model has been tested on real-world datasets. Results: Experimental results are showing the enhanced capability of proposed model in simulating real green software development environment. Using GC-1 and GC-2 dataset, the proposed model is about 60.05% which is more significant than other models. Conclusion: This paper proposed a new failure rate model for softwares that have been developed under green IT environment.

New failure rate model for iterative software development life cycle process

Software reliability models are one of the most generally used mathematical tool for estimation of reliability, failure rate and number of remaining faults in the software. Existing software reliability models are designed to follow waterfall software development life cycle process. These existing models do not take advantage of iterative software development process. In this paper, a new failure rate model centered on iterative software development life cycle process has been developed. It aims to integrate a new modulation factor for incorporating varying needs in each phase of iterative software development process. It comprises imperfect debugging with the possibility of fault introduction and removal of multiple faults in an interval as iterative development of the software proceeds. The proposed model has been validated on twelve iterations of Eclipse software failure dataset and nine iterations of Java Development toolkit (JDT) software failure dataset. Parameter estimation for the proposed model has been done by hybrid particle swarm optimization and gravitational search algorithm. Experimental results in-terms of goodness-of-fit shows that proposed model has outperformed Jelinski Moranda, Shick Wolverton, Goel Okummotto Imperfect debugging, GS Mahapatra, Modified Shick Wolverton in 83.33% of iterations for eclipse dataset and 77.77% of iterations for JDT dataset.

Hybrid Butterfly Optimization and Particle Swarm Optimization Algorithm for Video Transmission in VANET

Hybrid Butterfly Optimization and Particle Swarm Optimization Algorithm for Video Transmission in VANET

Convergence Rate For Low-Pass Infinite Impulse Response Digital Filter

The hybrid optimization technique is used to design a low-pass infinite impulse response (IIR) digital filter, to improve the coefficients of adaptive IIR digital filter, thus ensure stability. In this paper, the newly technique of hybrid particle swarm optimization (PSO) algorithm is proposed. This technique is a dynamic and static topology with a PSO algorithm, which called dynamic and static PSO (DS-PSO) algorithm. In the simulation, the low-pass IIR digital filter 8th order is designed. The fitness function problem is discussed based on values of the ripple of the passband, a ripple of the stopband, and a transition band. Thus, the proposed algorithm results are compared with previous studies results. From results comparison, results have shown the convergence rate with the DS-PSO algorithm outperformed the convergence rates using fuzzy gravitational search algorithm (FGSA), gravitational search algorithm (GSA), and differential evolution (DE) with mean values of 55.21%, 57.80%, and 70.40%, respectively.

Hybrid Wolf Pack Algorithm and Particle Swarm Optimization Algorithm for Breast Cancer Diagnosis

Hybrid Wolf Pack Algorithm and Particle Swarm Optimization Algorithm for Breast Cancer Diagnosis

Hybrid particle swarm optimization algorithm for solving the clustered vehicle routing problem

This paper considers a variant of the classical capacitated vehicle routing problem called clustered vehicle routing problem (CluVRP). In CluVRP, customers are grouped into different clusters. A vehicle visiting a cluster cannot leave the cluster until all customers in the same cluster have been served. Each cluster and customer have to be served only once. A new hybrid metaheuristic, combining the particle swarm optimization (PSO) and variable neighborhood search (VNS) for the specific problem, is proposed to solve the CluVRP. In the hybrid PSO, the basic PSO principle ensures the solution diversity and VNS ensures solution intensity to bring the solution to the local optima. Extensive computational experiments have been performed on numerous benchmark instances with various sizes obtained from the CluVRP literature to evaluate the performance of the proposed hybrid PSO. The obtained results of the proposed algorithm are compared with the results found in the literature to validate the effectiveness of the proposed hybrid PSO. The proposed algorithm is proven to be superior to the state-of-the-art algorithms on the CluVRP. The proposed algorithm obtains 138 new best-known solutions among the 293 benchmark instances.

Parameter Estimation for the One-Term (Multiterm) Fractional-Order SEIAR Models of Norovirus Outbreak

In the paper, we use the Caputo fractional derivative to consider general single-term and multiterm fractional-order SEIAR models for the outbreak of Norovirus. Then, the inverse problem about parameter estimation for these fractional-order SEIAR models of the Norovirus outbreak is studied firstly. To provide the numerical solution of the single-term (or multiterm) fractional-order nonlinear differential equation, the GMMP scheme and Newton method are introduced. Then, we make use of the modified hybrid Nelder-Mead simplex search and particle swarm optimization (MH-NMSS-PSO) algorithm to obtain the fractional orders and parameters for these fractional-order SEIAR models of Norovirus outbreak. To guarantee the correctness and effectiveness of the methods, the data of a 2007 Norovirus outbreak in a middle school in one city is used as the real data to solve the inverse problem of the parameter estimation. With the new parameters, all numerical studies illustrate that the numerical solutions fit very well with the real data, which reveals that the single-term and multiterm fractional-order SEIAR models of Norovirus outbreak all can predict the number of the infectious people accurately. And it also shows that the GMMP scheme and the MH-NMSS-PSO method are efficient and valid for estimating the parameters of the single-term (or multiterm) fractional-order nonlinear equations. Then, we research the impact of changes in each parameter on the amount of infected humans I t when the remaining parameters are unchanged. All results of numerical simulation reveal that the single-term and multiterm fractional-order SEIAR model of Norovirus can provide better results than other models. And we also study the effect of the isolation on different days. The conclusion is obtained that the earlier the isolation is taken, the less the infected people are. Hence, for a fractional-order application in the SEIAR model of Norovirus outbreak, we establish the effective parameter estimation methods.

Kriging-based optimization of functionally graded structures

This work presents an efficient methodology for the optimum design of functionally graded structures using a Kriging-based approach. The method combines an adaptive Kriging framework with a hybrid particle swarm optimization (PSO) algorithm to improve the computational efficiency of the optimization process. In this approach, the surrogate model is used to replace the high-fidelity structural responses obtained by a NURBS-based isogeometric analysis. In addition, the impact of key factors on surrogate modelling, as the correlation function, the infill criterion used to update the surrogate model, and the constraint handling is assessed for accuracy, efficiency, and robustness. The design variables are related to the volume fraction distribution and the thickness. Displacement, fundamental frequency, buckling load, mass, and ceramic volume fraction are used as objective functions or constraints. The effectiveness and accuracy of the proposed algorithm are illustrated through a set of numerical examples. Results show a significant reduction in the computational effort over the conventional approach.

Identifying the Locations of Atmospheric Pollution Point Source by Using a Hybrid Particle Swarm Optimization

This research aims to improve the particle swarm optimization (PSO) algorithm by combining a multidimensional search with a line search to determine the location of the air pollution point sources and their respective emission rates. Both multidimensional search and line search do not require the derivative of the cost function. By exploring a symmetric property of search domain, this innovative search tool incorporating a multidimensional search and line search in the PSO is referred to as the hybrid PSO (HPSO). Measuring the pollutant concentration emanating from the pollution point sources through the aid of sensors represents the first stage in the process of evaluating the efficiency of HPSO. The summation of the square of the differences between the observed concentration and the concentration that is theoretically expected (inverse Gaussian plume model or numerical estimations) is used as a cost function. All experiments in this research are therefore conducted using the HPSO sensing technique. To effectively identify air pollution point sources as well as calculate emission rates, optimum positioning of sensors must also be determined. Moreover, the frame of discussion of this research also involves a detailed comparison of the results obtained by the PSO algorithm, the GA (genetic algorithm) and the HPSO algorithm in terms of single pollutant location detection, respectively. In the case of multiple sources, only the findings based on PSO and HPSO algorithms are taken into consideration. This research eventually verifies and confirms that the HPSO does offer substantially better performance in the measuring of pollutant locations as well as emission rates of the air pollution point sources than the original PSO.

Damage detection in steel plates using feed-forward neural network coupled with hybrid particle swarm optimization and gravitational search algorithm

Damage detection in steel plates using feed-forward neural network coupled with hybrid particle swarm optimization and gravitational search algorithm

Hybrid feature selection method based on particle swarm optimization and adaptive local search method

Machine learning has been expansively examined with data classification as the most popularly researched subject. The accurateness of prediction is impacted by the data provided to the classification algorithm. Meanwhile, utilizing a large amount of data may incur costs especially in data collection and preprocessing. Studies on feature selection were mainly to establish techniques that can decrease the number of utilized features (attributes) in classification, also using data that generate accurate prediction is important. Hence, a particle swarm optimization (PSO) algorithm is suggested in the current article for selecting the ideal set of features. PSO algorithm showed to be superior in different domains in exploring the search space and local search algorithms are good in exploiting the search regions. Thus, we propose the hybridized PSO algorithm with an adaptive local search technique which works based on the current PSO search state and used for accepting the candidate solution. Having this combination balances the local intensification as well as the global diversification of the searching process. Hence, the suggested algorithm surpasses the original PSO algorithm and other comparable approaches, in terms of performance.

Using Hybrid Artificial Neural Network and Particle Swarm Optimization Algorithm for Modeling and Optimization of Welding Process

This study addresses a hybrid procedure used for modeling and optimization of gas tungsten arc (GTA) welding process of AL5052 alloy. There are different process input parameters among which welding current ([Formula: see text], frequency ([Formula: see text], welding speed ([Formula: see text], and gap ([Formula: see text] are the most important ones considered in GTA welding process. Furthermore, heat affected zone (HAZ) is considered as the most important quality measure of the process. To gather the required data for the modeling and optimization purpose, design of experiments (DOE) approach has been used. Image processing technique is used to take accurate measurements of HAZ values. In order to determine the relationship between process input variables and output measures, artificial neural networks (ANNs) have been used. Then, the trained ANNs have been used to find the optimal value of the outputs using particle swarm optimization (PSO) algorithm. Experiments have been done to verify the optimal levels of the input parameters. Verification results demonstrate that the proposed ANN-PSO procedure is quite efficient in modeling and optimization (with about 4% error) of GTA welding process.

Research on Optimal Operation of Electricity Heat Hydrogen System Based on Evaluation of New Energy Consumption Potential

Under the background of the “double high” power system, the electricity heat hydrogen system (EHHS) plays a significant role in the process of energy decarbonization. In order to meet the different optimization objectives of the system under different new energy consumption states, a new energy consumption potential assessment and optimized operation method based on intuitionistic fuzzy rough set theory is proposed. By using the intuitionistic fuzzy rough set theory, the continuous attribute data is divided into different levels and the results of its membership and non-membership are gotten at different levels. The membership results of real-time consumption data are matched with the rule sets, and then the system consumption state judgment result is obtained. In this article, the system consumption situation is divided into five states, and compared with the traditional division method, so the system state can be described more comprehensively. At the same time, the fuzzy set is used to deal with the ambiguity of the boundary between each state. The intuition theory is used to solve the problem of the uncertainty of the consumption state, and then the accurate judgment can be realized. In response to different consumption states, an optimal scheduling model is established in which a hydrogen heat energy system (HHES) is involved to meet different requirements, and a hybrid particle swarm optimization algorithm is used to solve the model. Adopting the IEEE-30 bus system as the network structure of EHHS in the simulation, the analysis shows that the dynamic state division method based on intuitionistic fuzzy rough set theory can better be used to judge the system state according to real-time variable factors. The system optimization based on the consumption state division has the advantages of improving the operating economy and increasing the consumption of new energy.

Radar Working State Recognition Based on Improved HPSO-BP

In this paper, a recognition model based on the improved hybrid particle swarm optimisation (HPSO) optimised backpropagation network (BP) is proposed to improve the efficiency of radar working state recognition. First, the model improves the HPSO algorithm through the nonlinear decreasing inertia weight by adding the deceleration factor and asynchronous learning factor. Then, the BP neural network’s initial weights and thresholds are optimised to overcome the shortcomings of slow convergence rate and falling into local optima. In the simulation experiment, improved HPSO-BP recognition models were established based on the datasets for three radar types, and these models were subsequently compared to other recognition models. The results reveal that the improved HPSO-BP recognition model has better prediction accuracy and convergence rate. The recognition accuracy of different radar types exceeded 97%, which demonstrates the feasibility and generalisation of the model applied to radar working state recognition.

Hybrid Dragonfly and Particle Swarm Optimization Algorithm for Congestion Management

Hybrid Dragonfly and Particle Swarm Optimization Algorithm for Congestion Management