Particle Swarm Optimization Search Research Articles

Optimal design of an off-grid hybrid renewable energy system considering generation and load uncertainty: the case of Zanjan city, Iran

In this paper, designing a hybrid stand-alone photovoltaic/wind energy system with battery storage (PV/WT/Batt) is presented to minimize the total cost of the hybrid system and considering reliability constraints for Zanjan city in Iran country considering generation and load uncertainties. The total cost includes the cost of the system components and load losses cost and also deficit power probability-hourly interruption probability index, which is defined as the reliability constraint. The optimal size of the system components is found using an improved crow search algorithm based on mutation and crossover operators of the genetic algorithm to prevent premature convergence. The optimal designing of the hybrid system is implemented considering the uncertainty of photovoltaic and wind resources as well as the load demand of the system based on the probability distribution functions of uncertain parameters using Monte Carlo simulation. The simulations are implemented in two deterministic and probabilistic scenarios. In the deterministic scenario, the optimal design of various combinations of the hybrid system is investigated in view of the system cost and deficit power probability probability-hourly interruption probability index based on the improved crow search algorithm with and without the cost of load loss. The results of the deterministic scenario showed that the system cost is increased considering the cost of load loss, but the system load is supplied with higher reliability. Also, the deterministic results cleared the superiority of the improved crow search algorithm compared with conventional crow search algorithm and particle swarm optimization methods. Moreover, the results of the system designing showed that considering the uncertainty, the system cost increases, and the reliability is significantly improved. The results cleared that the system cost in deterministic and probabilistic designing is obtained 1.0108 M$ and 1.210 M$, respectively for load supply equal to 153.87 MWh. So, due to the inherent nature of the uncertainty in the renewable resources power generation as well as continuous load changes, a probabilistic approach of the hybrid system designing is necessary to know the accurate cost and reliability.Article highlightsEffective performance of the proposed method in system designing with lowest cost and higher reliability.Achieving to a more reliable hybrid energy system with considering cost of load losses in designing.Increasing the system the cost and improving the reliability considering uncertainties of generation and load.

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 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.

Optimized adaptive noise canceller for denoising cardiovascular signal using SOS algorithm

Cardiovascular or electrocardiogram signals (ECG) are contaminated by various artefacts while recording which in turn degrades the quality of the vital information present in the signal. Hence, noise removal from ECG signal is an important matter of concern. In this paper, swarm intelligence techniques for the optimization purpose of adaptive filters/noise canceller (ANC) are utilized in the biomedical signal processing field. The analysis of results for de-noising ECG signals through adaptive filtration is presented using the symbiotic organisms search (SOS), particle swarm optimization (PSO) and harmony search (HS) algorithms which are applied to estimate and adjust the parameters of the adaptive filter in such a way to keep a close track of spontaneous variations of non–stationary signals in a faithful manner. From the simulation results, it has been observed that the ANC filter designed with the SOS technique achieves significant improvement in fidelity parameters, such as signal to noise ratio (SNR), mean square error (MSE), maximum error (ME), mean difference (MD), peak reconstruction error (PRE), normalized root mean squared error (NRMSE) and normalized root mean error (NRME) when compared with other reported techniques in the literature as well as the benchmark algorithms, namely PSO and HS. The SOS based ANC technique results in nearly 7 dB improvement in output SNR as compared to the recently reported ANC filter based on bacteria foraging optimization (BFO) algorithm and also provides a fair reduction of more than 90% in NRMSE and NRME when compared to other state-of-art denoising techniques. Furthermore, the ANC filter using the SOS technique enhances the correlation between the pure and reconstructed ECG signals.

Optimization of dynamic properties for laminated multiphase nanocomposite sandwich conical shell in thermal and magnetic conditions

This paper extends an optimization procedure to obtain the optimal dynamic properties of laminated sandwich multiphase nanocomposite truncated conical shell under magneto-hygro-thermal conditions. Based on principle of Hamilton, the equations of motion are obtained and solved by differential quadrature method and Bolotin's methods for obtaining the dynamic stability region. Based on particle swarm optimization and harmony search algorithms, a novel hybrid optimization method basis HS and PSO is proposed to enhance the performance and convergence of optimum dynamic conditions in this problem. By applying the hybrid optimization algorithm namely as HS-PSO, the volume percent of CNT and carbon fiber, number of laminas, cone semi vertex angle and moisture changes are optimized and the effects of magnetic field and temperature are shown on the dynamic stability of system. The result illustrates that proposed PSO-HS method with same conditions by other optimization methods as harmony memory size (number of particles) of 5 and total iterations of 100 shows the superior convergence performance compare to HS and PSO algorithms.

Mixed fleet based green clustered logistics problem under carbon emission cap

Sustainable transportation is an ever-demanding matter for cities and societies in light of minimizing global CO2 emissions. This paper introduces the mixed fleet based green clustered logistics problem (MFGCLP) under CO2 emission cap to deal with the sustainable development effort of the transportation industry. The mixed fleet consists of hydrogen vehicles and conventional vehicles. In the proposed distribution problem, customers are clustered in different segments based on similar characteristics. The customers belonging to a cluster must be served by the same vehicle before it visits customers from a different cluster or before it returns to the depot. The CO2 emission of the vehicles is realistically considered as a function of traveled distance, speed, and on-board cargo load. The problem also includes time windows for customers and maximum tour length for the routes. A new hybrid metaheuristic, combining particle swarm optimization (PSO) and neighborhood search, is proposed to solve the problem. Extensive computational experiments have been performed on newly generated problem instances, and benchmark problem instances adopted from the literature. The proposed hybrid PSO proved to be superior to the state-of-the-art algorithms available in the literature.

A novel quantum inspired hybrid metaheuristic for dispatch of power system including solar photovoltaic generation

ABSTRACT This manuscript proposes a novel metaheuristic, which is an amalgamation of the quantum concept and the gravitational search particle swarm optimization technique. The inclusion of quantum concepts in the algorithm enhances its capability, as in quantum space there is no restriction on the movement of particle and the solution can be obtained with smaller population and faster convergence. An adaptive contraction expansion factor is also introduced which ensures better exploration of the algorithm. The technique is applied to solve the combined economic emission dispatch of a hybrid solar thermal unit operating in New Delhi, India for full and reduced solar radiation. To verify the effectiveness of the proposed technique, it is also applied to solve the combined economic emission dispatch of a 10 unit, 2000 MW system. It is observed that the proposed approach provides a fuel cost saving of up to 1300$ when compared with other referred techniques.

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.

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

An Effective Method for Parameter Estimation of Solar PV Cell Using Grey-Wolf Optimization Technique

In the field of renewable energy, the extraction of parameters for solar photovoltaic (PV) cells is a widely studied area of research. Parameter extraction of solar PV cell is a highly non-linear complex optimization problem. In this research work, the authors have explored grey wolf optimization (GWO) algorithm to estimate the optimized value of the unknown parameters of a PV cell. The simulation results have been compared with five different pre-existing optimization algorithms: gravitational search algorithm (GSA), a hybrid of particle swarm optimization and gravitational search algorithm (PSOGSA), sine cosine (SCA), chicken swarm optimization (CSO) and cultural algorithm (CA). Furthermore, a comparison with the algorithms existing in the literature is also carried out. The comparative results comprehensively demonstrate that GWO outperforms the existing optimization algorithms in terms of root mean square error (RMSE) and the rate of convergence. Furthermore, the statistical results validate and indicate that GWO algorithm is better than other algorithms in terms of average accuracy and robustness. An extensive comparison of electrical performance parameters: maximum current, voltage, power, and fill factor (FF) has been carried out for both PV model.

Investigation on magnetostrictive behaviour of a converter transformer influenced by dominant harmonics: AFEMandANNbased approach

The introduction of harmonics to the HVDC system is imminent, which affects the components of the power system from varied perspectives. The present work emphasises analysing the behaviour of the converter transformer concerning the harmonics in the input. The process involves designing a stepped configuration of a 240-MVA converter transformer using the finite element method (FEM). The designed model is analysed as per its structural dynamics to obtain its vibrational response for different ranges over the frequency spectrum. Furthermore, the module of acoustics and thermal analysis is coupled to the vibrational model. The coupled model serves as an efficient tool in analysing the impact of different orders of harmonics on the vibrational, acoustic and thermal performance of the core of the converter transformer. The computational effort taken in solving the coupled field problem is simplified by designing a neural network model and the prediction efficacy of the network is enhanced by using a Hybrid of Particle Swarm Optimisation and Gravitational Search Algorithm (HPSOGWO) as tuner of the internal parameters of the neural network. The significance of the designed model is also examined against other well-versed hybrid algorithms in the literature in terms of various statistical indices.

Improvement of Firefly Algorithm using Particle Swarm Optimization and Gravitational Search Algorithm

Improvement of Firefly Algorithm using Particle Swarm Optimization and Gravitational Search Algorithm

Constriction coefficient based particle swarm optimization and gravitational search algorithm for multilevel image thresholding

AbstractImage segmentation is one of the pivotal steps in image processing. Actually, it deals with the partitioning of the image into different classes based on pixel intensities. This work introduces a new image segmentation method based on the constriction coefficient‐based particle swarm optimization and gravitational search algorithm (CPSOGSA). The random samples of the image act as searcher agents of the CPSOGSA algorithm. The optimal number of thresholds is determined using Kapur's entropy method. The effectiveness and applicability of CPSOGSA in image segmentation is accomplished by applying it to five standard images from the USC‐SIPI image database, namely Aeroplane, Cameraman, Clock, Lena, and Pirate. Various performance metrics are employed to investigate the simulation outcomes, including optimal thresholds, standard deviation, MSE (mean square error), run time analysis, PSNR (peak signal to noise ratio), best fitness value calculation, convergence maps, segmented image graphs, and box plot analysis. Moreover, image accuracy is benchmarked by utilizing SSIM (structural similarity index measure) and FSIM (feature similarity index measure) metrics. Also, a pairwise non‐parametric signed Wilcoxon rank‐sum test is utilized for statistical verification of simulation results. In addition, the experimental outcomes of CPSOGSA are compared with eight different algorithms including standard PSO, classical GSA, PSOGSA, SCA (sine cosine algorithm), SSA (salp swarm algorithm), GWO (grey wolf optimizer), MFO (moth flame optimizer), and ABC (artificial bee colony). The simulation results clearly indicate that the hybrid CPSOGSA has successfully provided the best SSIM, FSIM, and threshold values to the benchmark images.

New identification method for computer numerical control geometric errors

To address the problems of the low accuracy of geometric error identification and incomplete identification results of the linear axis detection of computer numerical control (CNC) machine tools, a new 21-item geometric error identification method based on double ball-bar measurement was proposed. The model between the double ball-bar reading and the geometric error term in each plane was obtained according to the three-plane arc trajectory measurement. The mathematical model of geometric error components of CNC machine tools is established, and the error fitting coefficients are solved through the beetle antennae search particle swarm optimization (BAS–PSO) algorithm, in which 21 geometric errors, including roll angle errors, were identified. Experiments were performed to compare the optimization effect of the BAS–PSO and PSO and BAS and genetic particle swarm optimization (GA–PSO) algorithms. Experimental results show that the PSO algorithm is trapped in the local optimum, and the BAS–PSO is superior to the other three algorithms in terms of convergence speed and stability, has higher identification accuracy, has better optimization performance, and is suitable for identifying the geometric error coefficient of CNC machine tools. The accuracy and validity of the identification results are verified by the comparison with the results of the individual geometric errors detected through laser interferometer experiments. The identification accuracy of the double ball-bar is below 2.7 µm. The proposed identification method is inexpensive, has a short processing time, is easy to operate, and possesses a reference value for the identification and compensation of the linear axes of machine tools.

Enhanced harmony search algorithm for sustainable ecological operation of cascade hydropower reservoirs in river ecosystem

With the merits of superior performance and easy implementation, the harmony search (HS), a famous population-based evolutionary method, has been widely adopted to resolve global optimization problems in practice. However, the standard HS method still suffers from the defects of premature convergence and local stagnation in the complex multireservoir operation problem. Thus, this study develops an enhanced harmony search (EHS) method to improve the HS’s search ability and convergence rate, where adaptive parameter adjustment strategy is used to enhance the global search performance of the swarm, while the elite-learning evolutionary mode is used to improve the converge trajectory of the population. To verify its practicability, EHS is applied to solve numerical optimization and multireservoir operation problems. The results show that EHS can produce better results than several existing methods in different cases. For instance, the mean objective of EHS is improved by about 23.9%, 28.7% and 26.8% compared with particle swarm optimization, differential evolution and gravitational search algorithm in 1998–1999 typical runoff case. Hence, an effective optimizer is developed for sustainable ecological operation of cascade hydropower reservoirs in river ecosystem.

OPTIMASI BIAYA PONDASI BORED PILE DENGAN METODE METAHEURISTIK

Foundation is part of the building structure to transfer the load from upper structure to soil layer. A deep foundation is used when the hard soil layer is located far below the soil surface. To design an optimal and economical deep foundation that still meet the safety requirements, Ultimate Limit State (ULS), Serviceability Limit State (SLS), and economic are required. Previous studies indicate that metaheuristic method can be used as an alternative to solve existing optimization problems. Therefore, this study uses metaheuristic methods which are Particle Swarm Optimization (PSO) and Symbiotic Organism Search (SOS) to solve the optimization of deep foundation. In this study, optimization was carried out for case studies of four and nine story buildings which used bored pile foundation. The result showed that metaheuristic method was able to find the optimal deep foundation dimensions.

The Supply Chain Optimization of Ready Mix Concrete at PT X in Surabaya Using the Metaheuristic Method

Infrastructure development is followed by an increase in demand for ready-mix concrete (RMC). Some failures in delivering RMC on time would delay construction performance or the truck would need to wait at the construction site, so the delivered concrete became useless if it exceeded the concrete setting time limit. This research optimizes the supply chain of ready mix concrete based on real-time data from a batching plant in Surabaya by using the metaheuristic method. The metaheuristic method used in this research are Particle Swarm Optimization (PSO) and Symbiotic Organisms Search (SOS), and also compares the performance of both metaheuristic algorithms, because both algorithms have proven effective in solving various optimization problems. The objective function of this research is to minimize the total waiting times of mixer trucks. The results showed that the SOS algorithm has better performance than the PSO algorithm, which gets lower total waiting times of 100,863 hours.

An Efficient Optimization Algorithm for Modular Product Design

Modularity concepts play an important role in the process of developing new complex products. Modularization involves dividing a product into a set of modules - each of which consisting of a set of components - that are interdependent in the same cluster and independent between clusters. During this process, a product can be represented using a Design Structure Matrix (DSM). A DSM acts as a tool for system analysis to provide clear visualization of product elements. In addition, DSM, shows the interactions between these product elements. This paper aims to propose an efficient optimization algorithm that dynamically divides a DSM into an optimal number and size of clusters in a way that minimizes total coordination cost; the interactions inside clusters (modules) and interactions between clusters. Given problem complexity, five metaheuristic optimization algorithms are proposed and tested to solve it; these algorithms are used to determine: (1) the optimal clusters’ number within a DSM, and (2) the optimal components assignment clusters to minimize the total coordination cost. The five used metaheuristics are: Cuckoo Search, Modified Cuckoo Search, Particle Swarm Optimization, Simulated Annealing, and Gravitational Search Algorithm. Eighty problems with different properties are generated and used to examine the proposed algorithms for effectiveness and efficiency. Extensive comparisons are conducted and analyzed. Cuckoo Search is outperforming the other four algorithms.

A novel hybrid gravitational search particle swarm optimization algorithm

Particle Swarm Optimization (PSO) algorithm is a member of the swarm computational family and widely used for solving nonlinear optimization problems. But, it tends to suffer from premature stagnation, trapped in the local minimum and loses exploration capability as the iteration progresses. On the contrary, Gravitational Search Algorithm (GSA) is proficient for searching global optimum, however, its drawback is its slow searching speed in the final phase. To overcome these problems in this paper a novel Hybrid Gravitational Search Particle Swarm Optimization Algorithm (HGSPSO) is presented. The key concept behind the proposed method is to merge the local search ability of GSA with the capability for social thinking (gbest) of PSO. To examine the effectiveness of these methods in solving the abovementioned issues of slow convergence rate and trapping in local minima five standard and some modern CEC benchmark functions are used to ensure the efficacy of the presented method. Additionally, a DNA sequence problem is also solved to confirm the proficiency of the proposed method. Different parameters such as Hairpin, Continuity, H-measure, and Similarity are employed as objective functions. A hierarchal approach was used to solve this multi-objective problem where a single objective function is first obtained through a weighted sum method and the results were then empirically validated. The proposed algorithm has demonstrated an extraordinary performance per solution stability and convergence.

PSO+LOA: hybrid constrained optimization for scheduling scientific workflows in the cloud

Cloud computing provides a cost-effective deploying environment for hosting and executing workflows as its elasticity, scalability and pay-per-use model. Scientific applications are normally compute- or resource-intensive, and how to run them in the cloud while both meeting QoS of users and guaranteeing the benefits of cloud service providers (CSPs) is still a challenge and depends mainly on workflow scheduling. In this article, we propose a hybrid optimization approach, PSO+LOA, i.e., a combination of particle swarm optimization (PSO) and lion optimization algorithm (LOA) for scheduling workflows in the cloud to minimize the total execution time under budget constraints. The main contributions of our work are: (1) A Euclidean distance (ED) aware particle reposition strategy is defined for two close particles, so as to separate them away from each other, hence enhancing the capability of escaping from local optima. (2) To improve the search and convergence efficiency of original PSO, we modify the velocity update equation by introducing adaptive parameters. (3) Inspired by the multiple-swarm co-evolutionary mechanism of LOA, we integrate PSO with LOA to make a good balance between exploration and exploitation during the whole optimization process. Extensive experiments are conducted over well-known scientific workflows with different sizes and types through WorkflowSim. The experimental results demonstrate that in most cases, PSO+LOA outperforms the existing algorithms in the extent of budget constraint satisfiability, solution quality, i.e., it can generate much better solutions which meet the needs of different budget constraints, especially for large-scale applications, such as the average relative deviation index for PSO+LOA and genetic algorithm (GA) are 0.03% and 0.20%, respectively.