Improved Hybrid Particle Swarm Optimization Research Articles

Dynamic Reconfiguration Method of Photovoltaic Array Based on Improved HPSO Combined with Coefficient of Variation

In order to address the issue of power loss resulting from partial shadow and enhance the efficiency of photovoltaic power generation, the photovoltaic array reconfiguration technology is being increasingly utilized in photovoltaic power generation systems. This paper proposes a reconfiguration method based on improved hybrid particle swarm optimization (HPSO) for the photovoltaic array of TCT (total-cross-tied) structure. The motivation behind this method is to get the best reconfiguration scheme in a simple and efficient manner. The ultimate goal is to enhance the output power of the array, save energy, and improve its overall efficiency. The improved HPSO introduces the concept of hybridization in genetic algorithms and adopts a nonlinear decreasing weight method to balance the local search and global search ability of the algorithm and prevent it from falling into the local optimal solution. The objective function used is the variation coefficient of the row current without the weight factor. This approach saves time and balances the row current of the array by altering the electrical connection of the component. In the 4 × 3 array, the improved HPSO is compared with the Zig-Zag method. In the 9 × 9 array, the improved HPSO is compared with the CS (competence square) method and the improved SuDoKu method. The simulation results show that the power enhancement percentage of the improved HPSO is between 6.39% and 28.26%, and the power curve tends to single peak characteristics. The improved HPSO has a smaller mismatch loss and a higher fill factor in the five shadow modes, which can effectively improve the output power, and it is convenient to track the maximum power point later.

Hybrid Particle Swarm Optimization for High-Dimensional Latin Hypercube Design Problem

Latin Hypercube Design (LHD) is widely used in computer simulation to solve large-scale, complex, nonlinear problems. The high-dimensional LHD (HLHD) problem is one of the crucial issues and has been a large concern in the long run. This paper proposes an improved Hybrid Particle Swarm Optimization (IHPSO) algorithm to find the near-optimal HLHD by increasing the particle evolution speed and strengthening the local search. In the proposed algorithm, firstly, the diversity of the population is ensured through comprehensive learning. Secondly, the Minimum Point Distance (MPD) method is adopted to solve the oscillation problem of the PSO algorithm. Thirdly, the Ranked Ordered Value (ROV) rule is used to realize the discretization of the PSO algorithm. Finally, local and global searches are executed to find the near-optimal HLHD. The comparisons show the superiority of the proposed method compared with the existing algorithms in obtaining the near-optimal HLHD.

Performance assessment of steel cantilever beams based on connection behaviour using DIC technique and improved hybrid PSO algorithm

Cantilever beams can have various behaviour depending on the details of the connection. Partially-fixed connections have a different performance from conventionally-fixed connections, in which the exact information is not available. In this paper, using the finite element model updating (FEMU) of a steel cantilever beam, the support conditions of a partially-fixed connection and the behaviour of the beam are investigated. The FEMU is utilized as an appropriate technique for structural health monitoring, which requires the use of data acquisition methods from the structure. In this research, instead of using discrete sensors, displacement and strain information are continuously measured from the beam surface using the digital image correlation (DIC) method. The difference between the initial model and the data extracted from the DIC is determined by a cost function. A hybrid method called Powell particle swarm optimization is used to minimize the cost function. This method includes particle swarm optimization (PSO) as a global search technique and Powell optimization as a local search technique; the hybrid method achieves better performance by combining the two methods. A dynamic inertia weight is presented to improve the performance of the PSO algorithm. Abaqus software and Python programming have been used to carry out the FEMU process. Finally, the amount of rotation and displacement of support are calculated, and the dependency of beam behaviour on each of the variables is investigated. This method yields acceptable convergence of the results and can be extended to study other structural components.

Improved swarm-wavelet based extreme learning machine for myoelectric pattern recognition

Myoelectric signal generated by muscles is one of the bio-signals which is used by humans to control equipments. To achieve this purpose, a good myoelectric pattern recognition (M-PR) is required. The applied classifier and extracted feature sets greatly affect the success of M-PR. This paper proposes a hybrid and fast classifier, extreme learning machine (ELM) which is enhanced by improved hybrid particle swarm optimization with wavelet mutation (improved swarm-wavelet). ELM is, in essence, a single-hidden layer feed-forward neural network that keeps off iterative learning to save the training time. In addition to improving the actual performance of M-PR, we also evaluate the optimization of ELM using improved swarm-wavelet in this paper. The swarm-wavelet is improved by using the particle refresh and applying velocity improvement to avoid trapped in local minima. In ELM, the improved swarm-wavelet is used to find the most suitable parameters to increase the classification accuracy. Furthermore, this paper provides comparisons of improved swarm-wavelet-ELM, swarm-wavelet-ELM and standard swarm-ELM. The experimental results show that the improved swarm-wavelet-ELM, our proposed method, is the most accurate classifier with mean accuracy of 99.6%.

Design of Permanent Magnet Synchronous Motor Servo System Based on Improved Particle Swarm Optimization

In this article, an improved hybrid particle swarm optimization (IHPSO) algorithm is proposed to solve the optimization problem of controller parameters in the design of a permanent magnet synchronous motor (PMSM) servo system. The proposed algorithm presents the directional mutation operation to the particles, which fixes the position of some particular particles so as to enhance the searching ability to some remote regions. In order to cooperate with directional mutation operation, the updating formula of particles velocity is ulteriorly improved. Then, the proposed IHPSO algorithm is adopted to optimize parameters of the designed controller. A simulation and an experimental platform of the PMSM servo system are designed using a biological intelligence controller based on hormone regulation for the speed control and feedforward compensation for the position controller, where IHPSO is applied to the parameter optimization for speed and position controllers, which validate the effectiveness of the IHPSO algorithm and the designed control system.

Customer Segmentation Using K-Means Clustering and the Hybrid Particle Swarm Optimization Algorithm

AbstractIn a competitive market, it is of great significance to divide customer groups to develop customer-centered personalized products. In this paper, we propose a customer segmentation method based on the K-means algorithm and the improved particle swarm optimization (PSO) algorithm. As the PSO algorithm easily falls into local extremum, the improved hybrid particle swarm optimization (IHPSO) algorithm is proposed to improve optimization accuracy. The full factorial design is used to determine the optimal parameter combination; the roulette operator is used to select excellent particles; then, the selected particles are crossed according to their adaptive crossover probabilities; when the population falls into a local optimum, the particles are mutated according to their adaptive mutation probabilities. Aimed at the K-means’ sensitivity to selecting the initial cluster centers, IHPSO is used to optimize the cluster centers (IHPSO-KM). We compare IHPSO with the PSO, LDWPSO, GA, GA-PSO and ALPSO algorithms on nine benchmark functions. We also conduct comparative experiments to compare IHPSO-KM with several conventional and state-of-the-art approaches on five UCI datasets. All results show that the two proposed methods outperform existing models. Finally, IHPSO-KM is applied in customer segmentation. The experimental results also prove the rationality and applicability of IHPSO-KM for customer segmentation.

Optimize batch size combination using improved hybrid particle swarm optimization

Batch processing is one way to improve business process activities. This research aims to evaluate batch processing performance that minimizes its cycle time with a design batch processing scenario. Meta-heuristics approach such as Improved-Hybrid Particle Swarm Optimization (IH-PSO) gives new batch size combination, resulting with lowest cycle time. Initial scenario, a batch size combination of 2,2,2,2,2,1 produces a cycle time of 3050.75 minutes, while the IH-PSO is providing a new batch size combination of 4,4,4,5,4,1 indicating a cycle time of 3003.65 minutes. This approach has proven to be more efficient than the traditional approach to finding the optimum batching size.

A Novel Particle Swarm Optimization with Genetic Operator and Its Application to TSP

To solve some problems of particle swarm optimization, such as the premature convergence and falling into a sub-optimal solution easily, we introduce the probability initialization strategy and genetic operator into the particle swarm optimization algorithm. Based on the hybrid strategies, we propose a improved hybrid particle swarm optimization, namely IHPSO, for solving the traveling salesman problem. In the IHPSO algorithm, the probability strategy is utilized into population initialization. It can save much more computing resources during the iteration procedure of the algorithm. Furthermore, genetic operators, including two kinds of crossover operator and a directional mutation operator, are used for improving the algorithm’s convergence accuracy and population diversity. At last, the proposed method is benchmarked on 9 benchmark problems in TSPLIB and the results are compared with 4 competitors. From the results, it is observed that the proposed approach significantly outperforms others on most the 9 datasets.

A Novel Particle Swarm Optimization With Genetic Operator and Its Application to TSP

To solve some problems of particle swarm optimization, such as the premature convergence and falling into a sub-optimal solution easily, we introduce the probability initialization strategy and genetic operator into the particle swarm optimization algorithm. Based on the hybrid strategies, we propose a improved hybrid particle swarm optimization, namely IHPSO, for solving the traveling salesman problem. In the IHPSO algorithm, the probability strategy is utilized into population initialization. It can save much more computing resources during the iteration procedure of the algorithm. Furthermore, genetic operators, including two kinds of crossover operator and a directional mutation operator, are used for improving the algorithm’s convergence accuracy and population diversity. At last, the proposed method is benchmarked on 9 benchmark problems in TSPLIB and the results are compared with 4 competitors. From the results, it is observed that the proposed approach significantly outperforms others on most the 9 datasets.

An Energy-Efficient Evolutionary Clustering Technique for Disaster Management in IoT Networks.

Wireless Sensor Networks (WSNs) are key elements of Internet of Things (IoT) networks which provide sensing and wireless connectivity. Disaster management in smart cities is classified as a safety-critical application. Thus, it is important to ensure system availability by increasing the lifetime of WSNs. Clustering is one of the routing techniques that benefits energy efficiency in WSNs. This paper provides an evolutionary clustering and routing method which is capable of managing the energy consumption of nodes while considering the characteristics of a disaster area. The proposed method consists of two phases. First, we present a model with improved hybrid Particle Swarm Optimization (PSO) and Harmony Search Algorithm (HSA) for cluster head (CH) selection. Second, we design a PSO-based multi-hop routing system with enhanced tree encoding and a modified data packet format. The simulation results for disaster scenarios prove the efficiency of the proposed method in comparison with the state-of-the-art approaches in terms of the overall residual energy, number of live nodes, network coverage, and the packet delivery ratio.

A Novel Self-Healing Strategy for Distribution Network with Distributed Generators Considering Uncertain Power-Quality Constraints

Self-healing of smart distribution networks with distributed generators, which are usually operated as independent islands after fault, can improve power-supply reliability. As a hot research topic, a self-healing scheme is usually treated as the output of a nonlinear optimizuoation model. However, existing strategies have two main shortcomings. The first, high-optimization dimension, results in low-optimization efficiency. The second, the effects of power-quality issues, which are more serious on islands and may further threaten the safe operation of islands, is usually neglected. To quickly obtain a reliable self-healing scheme, a novel strategy is proposed. As the first step, the distribution network after a fault occurrence can be divided into several trouble-free subnets via the connectivity analysis; each subnet is called an initial island. Further, for each initial island, a two-step optimization model of self-healing, which consists of load-shedding optimization and network reconfiguration optimization, is proposed to obtain the self-healing strategy with lower searching space as well as higher solving efficiency. In detail, in load-shedding optimization, by means of heuristic differential evolution algorithm, larger total recovery capacity is achieved by considering the droop characteristic of distributed generators (DGs) within the permissible change in frequency. In network-reconfiguration optimization, based on the improved hybrid particle swarm optimization algorithm, a comprehensive set of power-quality constraints, including constraint of change in frequency, uncertain constraints of node voltage total harmonic distortion (THD), and negative sequence components of DGs, is developed to guarantee the reliability of each island. To evaluate whether the constraints are satisfied during the optimization procedure, an improved flexible power-flow algorithm is developed to calculate the power flow of each island under change in frequency. Further, 2m+1-point estimate method is employed for uncertainty analyses of the distributions of harmonic and negative sequence components caused by the uncertainty of corresponding sources. Finally, via a 94-node practical distribution network, the effectiveness and advantages of the proposed strategy in safety, recovery capacity, and optimization efficiency are verified.

An improved hybrid particle swarm optimization for multi-objective flexible job-shop scheduling problem

Purpose With the continuous upgrading of the production mode of the manufacturing system, the characteristics of multi-variety, small batch and mixed fluidization are presented, and the production environment becomes more and more complex. To improve the efficiency of solving multi-objective flexible job shop scheduling problem (FJSP), an improved hybrid particle swarm optimization algorithm (IH-PSO) is proposed. Design/methodology/approach After reviewing literatures on FJSP, an IH-PSO algorithm for solving FJSP is developed. First, IH-PSO algorithm draws on the crossover and mutation operations of genetic algorithm (GA) algorithm and proposes a new method for updating particles, which makes the offspring particles inherit the superior characteristics of the parent particles. Second, based on the improved simulated annealing (SA) algorithm, the method of updating the individual best particles expands the search scope of the domain and solves the problem of being easily trapped in local optimum. Finally, analytic hierarchy process (AHP) is used in this paper to solve the optimal solution satisfying multi-objective optimization. Findings Through the benchmark experiment and the production example experiment, it is verified that the proposed algorithm has the advantages of high quality of solution and fast speed of convergence. Research limitations/implications This method does not consider the unforeseen events that occur during the process of scheduling and cause the disruption of normal production scheduling activities, such as machine breakdown. Practical implications IH-PSO algorithm combines PSO algorithm with GA and SA algorithms. This algorithm retains the advantage of fast convergence speed of traditional PSO algorithm and has the characteristic of inheriting excellent genes. In addition, the improved SA algorithm is used to solve the problem of falling into local optimum. Social implications This research provides an efficient scheduling method for solving the FJSP problem. Originality/value This research proposes an IH-PSO algorithm to solve the FJSP more efficiently and meet the needs of multi-objective optimization.

An Improved Hybrid Particle Swarm Optimization and Tabu Search Algorithm for Expansion Planning of Large Dimension Electric Distribution Network

Optimal expansion of medium-voltage power networks is a common issue in electrical distribution planning. Minimizing the total cost of the objective function with technical constraints make it a combinatorial problem which should be solved by powerful optimization algorithms. In this paper, a new improved hybrid Tabu search/particle swarm optimization algorithm is proposed to optimize the electric expansion planning. The proposed method is analyzed both mathematically and experimentally and it is applied to three different electric distribution networks as case studies. Numerical results and comparisons are presented and show the efficiency of the proposed algorithm. As a result, the proposed algorithm is more powerful than the other algorithms, especially in larger dimension networks.

Synthesis of New Reconfigurable Limited Size FSS Structures Using an Improved Hybrid Particle Swarm Optimization

This paper describes a new design methodology for reconfigurable printed circuits with limited size, using an improved hybrid particle swarm optimization (HPSO) algorithm, reducing the search space by the definition of negative zones (NZ), regions where the swarm of particles should not travel. The proposed design methodology (HPSO-NZ) is used in the development of reconfigurable frequency selective surfaces (RFSSs), restricted to a limited overall size, resulting in entirely new frequency selective surface (FSS) geometries. Two FSS prototypes are designed, fabricated, and measured for comparison purpose. A good agreement is observed between simulation and measurements results, confirming the efficiency and accuracy of the HPSO-NZ algorithm. Also, the performance of the HPSO-NZ algorithm is compared to the ones of genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, showing good consistency results

Evaluation strategy of particle swarm optimization and it’s application in pumping station system optimal operation

To deal with the high energy consumption issue of existing pumping station operation modes, an optimization model ensuring the flow rate and safety demands of pumping station and aiming at the minimum operating power of the system, is established based on basic Particle Swarm Optimization (PSO), which can provide theoretical support for the optimal operation of pumping station. When solving high dimension problems, PSO is easy to get into local optimum and has a slow convergence rate at the later stage of iteration. In order to overcome the shortcomings mentioned above, the Halton sequence is used to generate the initial population randomly, at the same time the inertia weight reducing along the opening downward parabola and Simulated Annealing(SA) are employed, too. The Improved Hybrid Particle Swarm Optimization (IHPSO) is implemented in Matlab. And it is applied to calculate the optimization schemes of the pumping station system of the South-to-North Water Transfer Project in China. The results show that the convergence and stability of the IHPSO are better than the PSO, and IHPSO can effectively solve pumping station optimization operation with multiple variables. The power of the optimum scheme based on IHPSO is about 7.68% less than that based on PSO, and it is decreased about 11.51% compared with the designed scheme.

Fault Diagnosis of Analog Circuits Based on IH-PSO Optimized Support Vector Machine

Because of its excellent small sample learning abilities and simple network structure, support vector machine (SVM) is widely applied in various pattern recognition fields, e.g., face recognition, scene classification, fault diagnosis, etc. Due to the complexity and diversity of analog circuit faults, the diagnosis accuracy and stability of SVM classifier optimized by traditional particle swarm optimization (PSO) are unsatisfactory. Therefore, this paper proposes an improved hybrid particle swarm optimization (IH-PSO) algorithm to optimize SVM, which is applied in the fault diagnosis of analog circuits. Compared with the traditional PSO algorithm, the proposed IH-PSO mainly has three improvements, namely, the opposition-based learning population initialization, the nonlinear time-varying inertia weight, and the new position updating strategy with a spiral convergence mechanism. The performance of the proposed IH-PSO algorithm is verified by 12 commonly used benchmark functions and experimental results show that the proposed IH-PSO algorithm overcomes the deficiencies of the traditional PSO algorithm, such as slow convergence speed and trapping into local optimums. In addition, to further verify the performance of IH-PSO algorithm, the IH-PSO optimized SVM is applied to solve analog circuits fault diagnosis problems. Extensive experiments are carried out and results indicate that the proposed method has better performances both in diagnosis accuracy and stability compared with that of the traditional method.

Hub Covering Location Problem Considering Queuing and Capacity Constraints

In this paper, a hub covering location problem is considered. Hubs, which are the most congested part of a network, are modeled as M/M/C queuing system and located in placeswhere the entrance flows are more than a predetermined value.A fuzzy constraint is considered in order to limit the transportation time between all origin-destination pairs in the network.On modeling, a nonlinear mathematical program is presented.Then, the nonlinear constraints are convertedto linear ones.Due to the computational complexity of the problem,genetic algorithm (GA),particle swarm optimization (PSO)based heuristics, and improved hybrid PSO are developedto solve the problem. Since the performance of the given heuristics is affected by the corresponding parameters of each, Taguchi method is appliedin order to tune the parameters. Finally,the efficiency ofthe proposed heuristicsis studied while designing a number of test problems with different sizes.The computational results indicated the greater efficiency of the heuristic GA compared to the other methods for solving the problem

Day-ahead unit commitment method considering time sequence feature of wind power forecast error

The variable and intermittent nature of large-scale wind power integration makes day-ahead unit commitment (UC) decision-making difficult. This paper establishes a novel and effective UC model with wind power integration by optimizing the utilization of the forecast error and reserve decision. First, considering the temporal feature of the UC model, a time sequence segment-fitting method (TSFM) for the wind power forecast error is presented, in which the non-parametric fitting method is used to address the ‘fat-tail’ effect of error distribution. Second, according to the probability intervals of the forecast error and characteristic of the reserve, a new reserve decision method is proposed to define three classes of reserve strategies and optimize the capacity for each type of reserve. Third, a UC model with time-varying confidence levels is established by introducing conditional value at risk (CVaR) and chance-constrained programming (CCP), and is linked with the TSFM. This novel model can balance the costs of fuel, various reserves, load shedding risk, and wind curtailment risk, which can improve the economy of the power grid operation. Finally, an improved hybrid particle swarm optimization algorithm with a heuristic searching strategy is applied to solve this multivariate mixed integer non-linear programming problem. The simulation results verify the effectiveness and practicality of the model proposed.

The resource allocation model for multi-process instances based on particle swarm optimization

Resource allocation in process management focuses on how to maximize process performance via proper resource allocation since the quality of resource allocation determines process outcome. In order to improve resource allocation, this paper proposes a resource allocation method, which is based on the improved hybrid particle swarm optimization (PSO) in the multi-process instance environment. Meanwhile, a new resource allocation model is put forward, which can optimize the resource allocation problem reasonably. Furthermore, some improvements are made to streamline the effectiveness of the method, so as to enhance resource scheduling results. In the end, experiments are conducted to demonstrate the effectiveness of the proposed method.

A practical parameter determination strategy based on improved hybrid PSO algorithm for higher-order sliding mode control of air-breathing hypersonic vehicles

A hybrid particle swarm optimization (PSO) algorithm for longitudinal dynamic models of air-breathing hypersonic flight vehicles (HFV) is proposed and applied to determine design parameters for a higher-order sliding model controller (HOSMC) while considering the effects of parameter uncertainty on trajectory tracking control. The input and output linearization of air-breathing HFV longitudinal dynamic models were achieved using the feedback linearization approach. Also, an HOSMC was designed for air-breathing HFV trajectory tracking control, and the design parameters were determined based on stochastic robustness analysis and hybrid PSO algorithm. Simulations revealed that the HOSMC design parameters can be optimized effectively and easily using the parameter determination strategy based on an improved hybrid PSO algorithm. The proposed HOSMC was used to stabilized the trajectory tracking of air-breathing HFV and the controller proposed exhibited great robustness.