Modified Particle Swarm Algorithm Research Articles

Performance evaluation of linear antenna array using quasi opposition modified particle swarm algorithm

Linear antenna arrays find extensive application in the communication systems of the future, including IoT, 5 G, and beamforming technologies. However, sustaining subsidiary lobes while keeping a tight beamwidth remains a challenge. In this paper, an enhanced version of Artificial Hummingbird Algorithm (AHOA) is presented. AHOA is a kind of particle swarm algorithm based on the unique flying abilities and clever foraging techniques of hummingbirds seen in nature. In this research, a hybridization of AHOA and quasi opposition based learning is presented for linear antenna array applications. The quasi opposition learning based artificial hummingbird method has been developed to produce more accurate outcomes for further complicated tasks and is named as Quasi Opposition Based Artificial Hummingbird Algorithm. The approach is evaluated across various communication needs of the linear array, and the results are compared with those obtained from other conventional methods. In comparison to the other approach, the proposed strategy delivers the lowest subsidiary lobes along with the narrow beamwidth without any grating lobes. Thus, the proposed approach is capable of managing diverse linear array applications without sacrificing beamwidth or subsidiary lobes levels.

A Modified Particle Swarm Algorithm for the Multi-Objective Optimization of Wind/Photovoltaic/Diesel/Storage Microgrids

Microgrids have been widely used due to their advantages, such as flexibility and cleanliness. This study adopts the hierarchical control method for microgrids containing multiple energy sources, i.e., photovoltaic (PV), wind, diesel, and storage, and carries out multi-objective optimization in the tertiary control, i.e., optimizing the economic cost, environmental cost, and the degree of energy utilization of microgrids. As the traditional multi-objective particle swarm algorithm is prone to local convergence, this study introduces variable inertia weight and learning factors to obtain a modified particle swarm algorithm, which is more advantageous in multi-objective optimization. Compared to the traditional particle swarm algorithm, the modified particle swarm algorithm increased the photovoltaic absorbed rate from 0.7724 to 0.8683 and the wind energy absorbed rate from 0.6064 to 0.7158 in one day, which resulted in an increase in energy utilization by 14.89%, and a reduction in financial environmental costs from RMB 135,870 to RMB 132,230. The simulation of the optimization effect of the conventional particle swarm algorithm and the modified particle swarm algorithm on the microgrid were carried out, respectively, in MATLAB, which verifies the advantage of the modified particle swarm algorithm on the optimization of microgrids. Then, the optimization results, i.e., the data of the power scheduling process of the four power sources, were made into a table and imported into the microgrid model in Simulink. The simulation results indicated that the microgrid was able to output stable voltage, current, and frequency. Finally, the changes in microgrids affected by the external environment were further investigated from the aspects of the market environment and natural environment. Moreover, we verified the presence of a contradiction between the optimization of the microgrid economy and environmental protection. Thus, microgrids need to adjust their optimization focus according to the natural conditions in which they are located.

An Optimized Cluster Head Selection and Secured Wireless Sensor Network Using MRSA

Currently, Wireless Sensor Networks (WSNs) play a vital role in remote monitoring and sharing of information in an infrastructure. Even though WSNs have numerous advantages, their usage is reduced by node's lifetime and data loss. The aforementioned problems in WSNs were overcome by using energy efficient protocols by various researchers. But most of the authors focused on either trust model or energy efficient model, or analyzed both models using separate algorithms. However, this leads to repeated process and to high energy consumption. Here, the cluster head is selected by using the swarm intelligence algorithm and LEACH protocol. The hybrid ant colony and modified Particle swarm algorithm are used to select the cluster heads. The CH selection depends on the two parameters, namely residual energy and number of successful transmissions of node. The node's trust is assessed using these two parameters (transmission nodes and residual energy), which also assist in ensuring the safety of the data and energy. The proposed system uses a digital signature algorithm known as the modified RSA algorithm to achieve network security when performing cryptography on data. The performance obtained by the proposed method was compared to other existing techniques in terms of throughput, energy consumption, packet delivery ratio, and network lifetime.

Research on Congestion Mitigation of Coordinated Control for Connected Vehicles on Pick/Drop Stand Road

Pick/drop stand road (PDSR) is necessary for specific functions, but it very easily generates congestion and thus results in a less safe and comfortable ride for passengers. The Internet of Vehicles has offered new ideas and technical means to solve the problem. First, this study divides the PDSR into different functional zones and constructs the coordinated control environment of connected vehicles (CVs). A car-following coordinated control strategy (CF-CCS) and a lane-changing coordinated control strategy (LC-CCS) are proposed based on a mixed platoon of CVs and human-driven vehicles. For CF-CCS, this study offers a model for car-following coordinated platoon control based on terminal state prediction of the leading vehicle, with platoon safety and passengers’ comfort as optimization objectives, and solves it using a modified particle swarm algorithm. For LC-CCS, a model for lane-changing coordinated control of platoon is proposed, and a numerical approach is utilized to solve it. It is based on finding the best area for lane-changing location while considering the safety of lane changing and the efficiency of vehicle traffic on the PDSR. Last, VISSIM and MATLAB simulations are conducted to prove that the proposed two strategies can effectively ease congestion, significantly increase the speed of traffic flow on PDSR, and provide passengers with safer and more comfortable rides.

Optimization-based vibration suppression method of deterministic blisks by nonlinear coatings with mistuning identification

Vibration problems of rotor blisks have been investigated in the past several years. To suppress the vibration level, hard coatings have been employed on blisks in several works. The best way to improve the damping performance of coatings is optimization. However, previous optimization works only focus on tuned simplified blisks and optimized the mechanical parameters and thickness of coatings, which may be not applicable to complicated blisks with inevitable mistuning. In this work, an optimization-based vibration suppression method to optimize the coated pattern incorporating the coating material nonlinearity and substrate mistuning is presented with consideration of the possible application on deterministic complicated blisks, which contains mistuning identification of uncoated blisks and vibration suppression optimization of deterministic mistuned blisks. To improve the computational efficiency, an improved nonlinear reduced order model is presented to obtain the forced responses with consideration of the coating material nonlinearity. The mistuning pattern of uncoated blisks is identified by a particle swarm algorithm based on experimental and numerical results. To avoid large damping mistuning, the optimization model to minimize the forced responses of the deterministic mistuned blisk with the identified mistuning pattern is presented to optimize the coated pattern with an assumption that the coating volume of each blade is exactly the same, which is solved by a modified particle swarm algorithm. Several validations and vibration suppression optimization analyses are performed. The results indicate that the present reduced order model can improve the computational efficiency. The forced responses are reduced significantly by using the presented optimization-based vibration suppression method, but the coating material nonlinearity and different excitations have important effects on the optimum coated pattern.

Low Carbon Electric Vehicle Charging Coordination in Coupled Transportation and Power Networks

As a part of the global decarbonization agenda, the electrification of the transport sector involving the large-scale integration of electric vehicles (EV) constitutes one of the key initiatives. However, the upstream generation carbon emissions associated with EV charging demand are still accountable which should not be overlooked. In this context, efficient coordination of EV flows and their charging demand in the coupled transportation and power networks promise significant decarbonization potential. Nevertheless, such potential can only be realized under adequate incentive mechanisms. In this article, a novel low carbon EV charging coordination approach in coupled transportation and power network is proposed and formulated as a bi-level optimization. In the upper level, an AC optimal power flow problem is formulated and solved to determine the optimal operation for power system. Then the carbon emission flow tracing is performed to compute rational locational-differentiated price signals. Given price-based incentives, the lower level employs traffic user equilibrium to describe the distribution of EV path flow and charging demands taking into account the uncertain traffic condition, and the aggregate charging demands are fed back to the upper level. The bi-level problem is solved iteratively through a modified particle swarm algorithm with enhanced convergence properties. Case studies demonstrate the effectiveness of the proposed coordination method in effectively mitigating the global carbon emission of the coupled networks.

Signal timing optimization of shifted left-turn intersections

A signal timing optimization method based on shifted left turn lane intersections is proposed to increase the efficiency of shifted left turn lane junctions. In previous studies, vehicle delays were often the only concern but vehicle exhaust emissions were ignored, which could easily aggravate environmental pollution. A multi-objective optimization model was developed in this paper for intersection signal timing from a vehicle and environmental perspective, using vehicle delay, capacity and exhaust emissions as indicators. A modified particle swarm algorithm was used to resolve the impact of a shifted left turn lane on vehicles at a selected junction in Shanghai. The proposed scheme can reduce vehicle delay by 24.58%, increase traffic capacity by 17.09% and reduce tail gas emission by 34.68% in peak period compared with before improvement. In low peak period, the delay, exhaust emission and traffic capacity are reduced by 49.04%, 41.68% and 17.09% compared with those before improvement. Using this optimization scheme can improve vehicle delay to a certain extent, improve vehicle traffic capacity and reduce pollution caused by exhaust emission to the environment. It also verifies the validity of the model and provides some theoretical basis for setting the signal timing for shifted left-turn lane intersections.

Significance of Parallel Computing on the Performance of Digital Image Correlation Algorithms in MATLAB

Digital Image Correlation (DIC) is a powerful tool used to evaluate displacements and deformations in a non-intrusive manner. By comparing two images, one from the undeformed reference states of the sample and the other from the deformed target state, the relative displacement between the two states is determined. DIC is well-known and often used for post-processing analysis of in-plane displacements and deformation of the specimen. Increasing the analysis speed to enable real-time DIC analysis will be beneficial and expand the scope of this method. Here we tested several combinations of the most common DIC methods in combination with different parallelization approaches in MATLAB and evaluated their performance to determine whether the real-time analysis is possible with these methods. The effects of computing with different hardware settings were also analyzed and discussed. We found that implementation problems can reduce the efficiency of a theoretically superior algorithm, such that it becomes practically slower than a sub-optimal algorithm. The Newton–Raphson algorithm in combination with a modified particle swarm algorithm in parallel image computation was found to be most effective. This is contrary to theory, suggesting that the inverse-compositional Gauss–Newton algorithm is superior. As expected, the brute force search algorithm is the least efficient method. We also found that the correct choice of parallelization tasks is critical in attaining improvements in computing speed. A poorly chosen parallelization approach with high parallel overhead leads to inferior performance. Finally, irrespective of the computing mode, the correct choice of combinations of integer-pixel and sub-pixel search algorithms is critical for efficient analysis. The real-time analysis using DIC will be difficult on computers with standard computing capabilities, even if parallelization is implemented, so the suggested solution would be to use graphics processing unit (GPU) acceleration.

Modified particle swarm algorithm for scheduling agricultural products

Industries tend to manufacture higher quality products with a low cost due to the competitive environmental loop and dynamic customers’ demand. The integration of intelligent manufacturing planning to agricultural operations and products will permit a spike in efficiency of yield, especially in the regions of agriculturally based economies. Though, to gain a vivid exploitation, the old-fashioned agriculture products manufacturing planning and scheduling techniques needs to be revised, especially the methodology in job shop planning must be augmented with efficient operational sequential scheduling. Cellular Manufacturing Systems (CMS) inclined to possess a higher complexity than the traditional manufacturing systems. Three major problems are coping in CMS domain: cell formation, product family selection, and product scheduling. This work deals with the problem of product scheduling in the CMS environment. A mixed integer linear programming mathematical model is introduced for the conflicting performance measures i.e. minimization of work in process (WIP) and maximization of average machine cell utilization. Since the current problem is considered as NP-hard problem, so a modified particle swarm optimization (MPSO) algorithm is proposed to find the optimum scheduling under the given constraint model of conflicting objectives. In the proposed MPSO, basic PSO is integrated with NEH heuristic to achieve better optimal sequence in less computation time. The obtained results are compared with other hybrid PSO algorithms with seed solutions from Gupta and Palmer heuristics. Furthermore, results are also compared against meta-heuristics such as genetic algorithm (GA), standard PSO, and artificial bee colony (ABC) algorithm. It shows that the proposed MPSO algorithm performs better than the existing compared algorithms. A real time case scenario of agriculture-based manufacturing industry is solved to validate the proposed planning algorithm.

Modified particle swarm algorithm for the optimal water allocation of reservoir

Abstract At present, meta-heuristic algorithms are the most popular methods for the optimization of the operations of reservoirs. In order to avoid inappropriate solutions, i.e. spills occurring when the reservoir is not full, a modified method is proposed that can adjust the trajectories of the particles, using the particle swarm algorithm, according to the operation rule of the reservoir. The method was tested in a case study, and was compared to two commonly used methods for generating particle position vectors. These included the direct method, which uses water supply and water spills as the iteration variables, and the indirect method, which uses water storages (water levels) as the iteration variables. The results showed that the three methods could achieve similar solutions at the 75% probability of exceedance. There was no difference in the convergence speeds or the final objective function values of the three models. However, at the 50% probability of exceedance, the modified method produced results that followed the operation rule of the reservoir, whereas the other two methods could lead to inappropriate water spills. This new method may provide a reference for other meta-heuristic algorithms in models of the optimal operation of reservoirs.

A multi-layer approach to DN 50 electric valve fault diagnosis using shallow-deep intelligent models

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.

Transformer Fault Diagnosis Based on RapidMiner and Modified ELM Algorithm

For transformer fault diagnosis, the three ratio method lacks of encoding, and artificial intelligence methods lack of anti-interference ability.Thus, a new method of transformer fault diagnosis based on RapidMiner and modified particle swarm optimization Extreme Learning Machine (RM-MPSO-ELM) is proposed. Firstly, RapidMiner picks out the most relevant input variables of the transformer fault. then, using the modified particle swarm algorithm to optimize the parameters for Extreme Learning Machine. Finally, using the ELM to identify the potential of transformer fault, the diagnostic performance of IEC three ratio method, support vector machine (SVM) method and different combinations of ELM algorithm are also compared. The results show that the proposed method achieves higher diagnosis precision.

Study of League Championship Algorithm Efficiency for Global Optimization Problem

The article objective is to study a new League Championship Algorithm (LCA) algorithm efficiency by its comparing with the efficiency of the Particle Swarm optimization (PSO) algorithm.The article presents a brief description of the terms used in the League Championship algorithm, describes the basic rules of the algorithm, on the basis of which the iterative process for solving the global optimization problem is built.Gives a detailed description of the League Championship algorithm, which comprises a flowchart of the algorithm, as well as a formalization of all its main steps.Depicts an exhaustive description of the software developed to implement the League Championship algorithm to solve global optimization problems.Briefly describes the modified particle swarm algorithm. Presents the values of all free parameters of the algorithm and the algorithm modifications, which make it different from the classical version, as well.The main part of the article shows the results of a great deal of computational experiments using two abovementioned algorithms. All the performance criteria, used for assessment of the algorithms efficiency, are given.Computational experiments were performed using the spherical function, as well as the Rosenbrock, Rastrigin, and Ackley functions. The results of the experiments are summarized in Tables, and also illustrated in Figures. Experiments were performed for the vector dimension of the variable parameters that is equal to 2, 4, 8, 16, 32, and 64.An analysis of the results of computational experiments involves a full assessment of the efficiency of the League Championship algorithm, and also provides an answer about expediency for further algorithm development.It is shown that the League Championship algorithm presented in the article has a high development potential and needs further work for its study.

Echo decomposition of full-waveform LiDAR based on a digital implicit model and a particle swarm optimization.

In the waveform decomposition of full-waveform LiDAR, the Gaussian model (GSM) and the generalized Gaussian model (GGSM) are widely used to extract echoes from return waveforms. However, those models have explicit functions that follow specific distribution shapes, so they are suitable only for decomposing echo waveforms with similar shapes. This paper introduces a digital implicit model (DIM) and presents a universal decomposition method for the full-waveform LiDAR. In this method, the decomposition model is considered to be an implicit function, associated with a digital template waveform library, whose optimization is implemented by a modified particle swarm algorithm. The template waveform library is a customized fingerprint for any special full-waveform LiDAR, so the DIM can deal effectively with infinite echoes with arbitrary shapes. A full-waveform LiDAR system with asymmetric echo distribution is designed to compare the decomposition performances among the GSM, GGSM, and DIM. Experimental results show that, when decomposing the return waveform containing a single echo, the normalized sum of squares due to fitting error (SSE) of the DIM can be 60 times lower than the GSM and the GGSM. By comparing the estimation accuracies of the amplitude, the FWHM and the location of the echo component, the DIM has the best decomposition performance and the best ranging accuracy (subcentimeter level) among the three models; when decomposing the return waveform containing three overlapping echoes, the normalized SSE of the DIM can be 28 times lower than the GSM and 12 times lower than the GGSM. By comparing the estimation accuracies of the amplitude, FWHM, and location of echoes components, the DIM has the best decomposition performance and best ranging accuracy (centimeter level) among the three models.

Modelling and optimization of injection molding process for PBT/PET parts using modified particle swarm algorithm

In the present study, a systematic methodology has been presented to determine optimal injection molding conditions for minimizing warpage and shrinkage in a thin wall relay part using modified particle swarm algorithm (MPSO). Polybutylene terephthalate (PBT) and polyethylene terephthalate (PET) have been injected in thin wall relay component under different processing parameters: melt temperature, packing pressure and packing time. Further, Taguchi’s L9 (32) orthogonal array hasbeen used for conducting simulation analysis to consider the interaction effects of the above parameters. A predictive mathematical model for shrinkage and warpage has been developed in terms of the above process parameters using regression model. ANOVA analysis has been performed to establish statistical significance among the injection molding parameters and the developed model. The developed model has been further optimized using a newly developed modified particle swarm optimization (MPSO) algorithm and the process parameters values have been obtained for minimized shrinkage and warpage. Furthermore, the predicted values of the shrinkage and warpage using MPSO algorithm have been reduced by approximately 30% as compared to the initial simulation values making more adequate parts.

A Research Optimization of CMOS Analog Circuits using Modified Particle Swarm Algorithm

In this paper for proficiently dealing with three of the most generally utilized fundamental circuits, for example complementaryl metal-oxide semiconductor (CMOS) operational enhancer circuits A Metaheuristic search Algorithm called Modified Particle swarm Algorithm (MPSO) is appeared as a mechanical get-together. The MPSO estimation begins the framework by making some principal enthusiastic happy plans and using condition based for precariousness of these methodologies towards and outwards the best methodology. Promptly, the transistor estimations of the above-said circuits are refreshed utilizing MPSO to improve the structure focal motivations behind the circuit by rapidly lessening the district required by the transistors in a circuit utilizing MATLAB. By then the parameters are gotten are approved utilizing CADENCE. Here three operational intensifiers are anticipated model Two-plan operational intensifier, Folded course operational speaker and Telescopic operational enhancer. Ideally organizing clear circuits, the extension results and mix plots exhibit the consistency of adjusted molecule swarm improvement estimation over obtained figuring and molecule swarm algorithm

Optimization of tooth modifications for spur and helical gears using an adaptive multi-objective swarm algorithm

Metaheuristic methods have proved to be suitable for solving complex multi-criteria optimization problems. In this paper, a modified particle swarm algorithm has been implemented in order to improve the quasi-static behavior of a power transmission gearbox, thus optimizing various objectives such as the maximum contact pressure on the gear flanks, the root-mean-square of the loaded transmission error signal, the tooth bending stress, and/or the pressure-speed factor. For narrow-faced spur gears, the comparison between optimal solutions found by the algorithm and the so-called master curve shows quite good agreements. The chosen form of the profile modifications, linear or quadratic, is then discussed. Finally, the robustness of the optimal solutions is tested to guarantee their efficiency against variable shaft misalignments.

Multimodal control parameter optimization for aircraft longitudinal automatic landing via the hybrid particle swarm-BFGS algorithm

The airborne automatic landing system needs to provide both accurate trajectory tracking and constant sink rate. In this article, a hybrid optimizer combining a modified particle swarm algorithm with the damped Broyden–Fletcher–Goldfarb–Shanno (BFGS) method is presented to not only solve the parameter tuning problem for the automatic landing system design, but also explore the hidden properties of the multimodal optimization problem. In doing so, the quasi-random sequence-based particle initialization method is adopted to let the particles cover the problem domain more evenly. Then, the damped BFGS-based searching method is used to calculate the local minimum of each particle. A three-point searching method is adopted as a substitution method if the damped BFGS method fails. By using the modified niching method, each particle is updated and converges to an estimated local minimum point. Finally, a range of optimized solutions is analysed and presented to the engineer for the automatic landing system design. A series of simulations are carried out on a test bed based on a 6 degrees of freedom (DoF) non-linear model of the F/A-18. Comparative results verify the effectiveness of the proposed method.

Warpage and Shrinkage Analysis and Optimization of Rapid Tooling Molded thin Wall Component Using Modified Particle Swarm Algorithm

This paper presents a systematic methodology to determine optimal injection molding conditions for minimum warpage and shrinkage in a thin wall relay part using modified particle swarm optimization algorithm (MPSO). Polybutylene terephthalate (PBT) and polyethylene terephthalate (PET) were injected in a thin wall relay component for different processing parameters: melt temperature, packing pressure and packing time. Further, Taguchi’s L9 (3[Formula: see text] orthogonal array is used for conducting simulation analysis to consider the interaction effects of the above parameters. A predictive mathematical model for shrinkage and warpage is developed in terms of the above process parameters using regression analysis. ANOVA analysis is performed to establish statistical significance within the injection molding parameters. The analytical model is further optimized using a newly developed MPSO algorithm and the process parameters values are predicted for minimizing shrinkage and warpage. The predicted values of shrinkage and warpage using MPSO algorithm are improved by approximately 30% as compared to the initial simulation values and comparable to previous literature results.

Optimizing the High-Level Maintenance Planning Problem of the Electric Multiple Unit Train Using a Modified Particle Swarm Optimization Algorithm

Electric multiple unit (EMU) trains’ high-level maintenance planning is a discrete problem in mathematics. The high-level maintenance process of the EMU trains consumes plenty of time. When the process is undertaken during peak periods of the passenger flow, the transportation demand may not be fully satisfied due to the insufficient supply of trains. In contrast, if the process is undergone in advance, extra costs will be incurred. Based on the practical requirements of high-level maintenance, a 0–1 programming model is proposed. To simplify the description of the model, candidate sets of delivery dates, i.e., time windows, are generated according to the historical data and maintenance regulations. The constraints of the model include maintenance regulations, the passenger transportation demand, and capacities of workshop. The objective function is to minimize the mileage losses of all EMU trains. Moreover, a modified particle swarm algorithm is developed for solving the problem. Finally, a real-world case study of Shanghai Railway is conducted to demonstrate the proposed method. Computational results indicate that the (approximate) optimal solution can be obtained successfully by our method and the proposed method significantly reduces the solution time to 500 s.