General Particle Swarm Optimization Research Articles

On PID Controllers for a Complex-Order Fractional Model of an Automotive Injection System

Recent studies have shown that complex-order fractional operators allow for compact modeling with simpler structures and less parameters than models based on integer-order operators and even non-integer real-order operators. This work verifies the usefulness and potential of a complex-order fractional model when used to set parameters of a PID controller regulating the common-rail pressure in the injection system of a compressed natural gas engine. Parameters are derived by a general particle swarm optimization that considers a cost function weighting the performance and robustness measures. Simulation results show the effectiveness of the complex-order fractional model-based PID controller through the response to step references, the rejection of disturbances, the low sensitivity to variations of the injection system parameters.

BN-GEPSO: Learning Bayesian Network Structure Using Generalized Particle Swarm Optimization

At present Bayesian Networks (BN) are being used widely for demonstrating uncertain knowledge in many disciplines, including biology, computer science, risk analysis, service quality analysis, and business. But they suffer from the problem that when the nodes and edges increase, the structure learning difficulty increases and algorithms become inefficient. To solve this problem, heuristic optimization algorithms are used, which tend to find a near-optimal answer rather than an exact one, with particle swarm optimization (PSO) being one of them. PSO is a swarm intelligence-based algorithm having basic inspiration from flocks of birds (how they search for food). PSO is employed widely because it is easier to code, converges quickly, and can be parallelized easily. We use a recently proposed version of PSO called generalized particle swarm optimization (GEPSO) to learn bayesian network structure. We construct an initial directed acyclic graph (DAG) by using the max-min parent’s children (MMPC) algorithm and cross relative average entropy. This DAG is used to create a population for the GEPSO optimization procedure. Moreover, we propose a velocity update procedure to increase the efficiency of the algorithmic search process. Results of the experiments show that as the complexity of the dataset increases, our algorithm Bayesian network generalized particle swarm optimization (BN-GEPSO) outperforms the PSO algorithm in terms of the Bayesian information criterion (BIC) score.

New efficient Hadoop scheduler: Generalized particle swarm optimization and simulated annealing‐dominant resource fairness

SummaryDigital growth during the Corona pandemic has generated massive data. The Hadoop in big data has to be more efficient in resource handling and job scheduling. This article proposes the improved job scheduler which is more efficient in fair job scheduling even with the heterogeneous resources. The faster job execution depends upon the localization too. The nearer the slots are, the faster is the execution. So, this article proposes a hybrid metaheuristic algorithm with fair scheduling and data locality as the two objectives in job scheduling. The dominant resource fairness policy in Hadoop YARN is updated by hybrid generalized particle swarm optimization and simulated annealing for minimum locality and maximum fairness in scheduling. The algorithm is tested on various workloads for heterogeneous resources.

Intelligent power management based on multi-objective cost function for plug-in biogas hybrid vehicles under uncertain driving conditions

This paper presents a new intelligent power management strategy based on multi-objective cost function for plug-in biogas hybrid vehicles (PBHVs). This strategy consists of long-term power management and a short-term controller. The long-term power management depends on an improved generalized particle swarm optimization algorithm (IGPSO) to obtain the globally optimal values of motor and biogas engine torques. To reduce the computation time, five-mode rule-based control is used, where the IGPSO estimates the optimal values for the motor and engine torques in a hybrid mode depending on a multi-objective cost function. This cost function aims to reduce fuel consumption and the drawn current from the battery and takes into consideration the battery ageing. The short-term controller is designed using an interval type-2 Takagi–Sugeno-Kang (IT2TSK) fuzzy algorithm, which depends on human experts to overcome the uncertainties of the driving conditions. Lyapunov stability theory for the online controller is proved. The proposed technique improves the energy consumption compared to other techniques. The simulation results using real data for the engine, motor and battery illustrate the feasibility and effectiveness of the proposed approach with comparative results.

A new power management strategy for plug-in hybrid electric vehicles based on an intelligent controller integrated with CIGPSO algorithm

This paper proposes an improved power management strategy for plug-in hybrid electric vehicles (PHEVs). This strategy consists of a long-term power management approach and a short-term intelligent controller. In the long-term power management, a chaotic improved generalized particle swarm optimization technique (CIGPSO) is used to optimize the motor and diesel engine torques. In order to reduce the computation time, a five-mode rule-based control system is employed, where the CIGPSO estimates the optimal values of the motor and engine torques in a hybrid mode, which manages the power between the motor and engine in accordance with a multi objective cost function. This cost function reduces fuel usage as well as the drawn current from the battery with taking into account the process of the battery aging. Moreover, the CIGPSO is able to obtain the state of charge (SoC) curve of the battery during the charging and discharging of the battery throughout the trip. The short-term controller is designed using an interval type-2 Takagi-Sugeno-Kang fuzzy (IT2TSK) algorithm which depends on human experts to overcome the uncertainties of the diverse driving conditions. Lyapunov stability theory for the online controller is achieved. The proposed strategy reduces the energy consumption compared to other strategies such as generalized PSO and improved multi-objective PSO algorithms. The simulation results are performed using real data for the engine, motor, and battery to demonstrate the flexibility, viability and effectiveness of the proposed approach with comparative results. Index terms—Plug-in hybrid electric vehicle, power management strategy, chaotic improved generalized particle swarm optimization algorithm, rule-based control, interval type-2 Takagi-Sugeno-Kang fuzzy algorithm.

Size optimization design of members for shear wall high-rise buildings

Structural optimization can lead to efficient designs for high-rise concrete buildings. However, the high computational cost of the structural analysis step hinders its application in practical cases. With regards to this problem, a novel hybrid optimization framework is developed in this study. Discrete variables, code-stipulated constraints and other construction requirements under multiple loading scenarios are considered. The hybrid optimization framework contains two steps: in the first step, a computationally efficient approximation of the optimization formulation is constructed using a combined response surface methodology (RSM). Combining all the design constraints, three sub-optimization problems are proposed based on the relaxation model of the original problem to update the response surface model in the near-optimum region. When the response surface model has sufficient accuracy in the near-optimum region, in the second step, a discrete Particle Swarm Optimization (PSO) technique is applied to search for the optimal solution utilizing the response surface model. By integrating the commercially available ETABS, a dedicated optimization software with an independent interface is developed and details for practical software development are included in this paper. Three examples of high-rise concrete building design are presented to demonstrate the effectiveness of the proposed optimization framework, and the results are compared to those of a general PSO algorithm. It is shown that, compared to the conventional design approach, a 14.3% reduction in material usage is achieved by the hybrid optimization framework. Additionally, the proposed framework significantly improves the computational efficiency compared to PSO. This methodology can therefore be used by engineers to optimize member sizes for high-rise buildings so that stable and sustainable designs can be achieved.

An ANN-PSO-Based Method for Optimizing Agricultural Tractors in Field Operation for Emission Reduction

Aiming at the serious problem of agricultural tractor emission pollution, especially the limitation of nitrogen dioxide (NOx) and soot emissions, we took an agricultural diesel engine as the research object, and a diesel engine combustion chamber model was established for both simulated calculations and experimental verification analysis. The in-cylinder pressure and heat release obtained from the combustion chamber model simulation calculations were within 6% error of the experimental data. The overall trend of change was basically consistent. The established model can simulate the working conditions of the experimental engine relatively well. An artificial neural network (ANN) was also established as an agent model based on the indentation rate, tab depth, and combustion chamber depth as the input, and NOx and soot as the output. The decision coefficients of the ANN model were R2 = 0.95 and 0.93, with corresponding Mean Relative Error (MRE) values of 10.13 and 8.18%, respectively, which are within the generally required interval, indicating that the obtained ANN model has good adaptability and accuracy. On the basis of the general particle swarm optimization (PSO) algorithm, an improved PSO algorithm was proposed, in which the inertia factor is continuously adjusted with the help of the skip line function in the optimization process so that the inertia factor adapts to different rates and adjusts the magnitude of the corresponding values in different periods. The improved PSO algorithm was used to optimize the optimal input parameter matching of the agent model to form a new combustion chamber structure, which was imported into CONVERGE CFD software for emission simulation and comparison with the emissions of the original combustion chamber. It was found that the NOx reduction was about 1.21 g·(kW·h)−1, and the soot reduction was about 0.06 g·(kW·h)−1 with the new combustion chamber structure. The ANN + PSO optimization method proved to be effective in reducing the NOx and soot emissions of diesel engine pollutants, and it may also provide a reference and ideas for the design and development of relevant agricultural engine combustion chamber systems.

An Improved Particle Swarm Optimization with Multiple Strategies for PID Control System Design

In this paper, an improved particle swarm optimization (PSO) with multiple subpopulations is developed for PID control system designs. The original single population needs to be divided into several subpopulations, and each subpopulation then tackles a corresponding performance index of the system. Under this proposed structure, several PID controllers can be simultaneously designed to meet different performance indexes when the algorithm is executed only one time. It is a great improvement because the general PSO algorithm with a single population can only deal with one performance index. To demonstrate the feasibility of the proposed scheme, a complicated chemical nonlinear process called the continuously stirred tank reactor (CSTR) is illustrated. Three different kinds of control operations are simulated including the step response control, set-point tracking control, and unstable equilibrium point control. For each control case five different performance indexes are assigned to guide the PID controller design combined with the nonlinear CSTR system. Simulation results will sufficiently confirm the superiority of the proposed algorithm.

Optimal Control of Hybrid Photovoltaic-Thermometric Generator System Using GEPSO

Recently the concern about energy consumption across the globe has become more severe due to global warming. One essential way to address this problem is to maximize the efficiency of existing renewable energy resources and effectively eliminate their power losses. The previous studies on energy harvesting of photovoltaic (PV) modules try to cope with this problem using gradient-based control techniques and pay little attention to the significant loss of solar energy in the form of waste heat. To reconcile these waste-heat problems, this paper investigates hybrid photovoltaic-thermoelectric generation (PV-TEG) systems. We implement the generalized particle swarm optimization (GEPSO) technique to maximize the power of PV systems under dynamic conditions by utilizing the waste heat to produce electricity through embedding the thermoelectric generator (TEG) with the PV module. The removal of waste heat increases the efficiency of PV systems and also adds significant electrical power. As a control method, the proposed GEPSO can maximize the output power. Simulations confirm that GEPSO outperforms some state-of-the-art methods, e.g., the perturb and observe (PO), cuckoo search (CS), incremental conductance (INC), and particle swarm optimization (PSO), in terms of accuracy and tracking speed.

A multi-objective optimization algorithm based on subgroup stratified coarse-grained model and its application

Low efficiency and the tendency to fall into local optimum are the major obstacles to web service composition optimization. In this paper, we propose a multi-objective optimization algorithm based on the subgroup stratified coarse-grained model to improve the performance of web service composition optimization. Compared to the general particle swarm optimization algorithms, the proposed algorithm improves population structure, dynamically adjusts evolution strategy and increases the local extremum's perturbation. We demonstrate a solution to the constrained multi-objective web service composition optimization problem based on the proposed algorithm. Theoretical analysis and experimental results show that it improves the performance of web service composition optimization.

GEPSO: A new generalized particle swarm optimization algorithm

Particle Swarm Optimization (PSO) algorithm is a nature-inspired meta-heuristic that has been utilized as a powerful optimization tool in a wide range of applications since its inception in 1995. Due to the flexibility of its parameters and concepts, PSO has appeared in many variants, probably more than any other meta-heuristic algorithm. This paper introduces the Generalized Particle Swarm Optimization (GEPSO) algorithm as a new version of the PSO algorithm for continuous space optimization, which enriches the original PSO by incorporating two new terms into the velocity updating equation. These terms aim to deepen the interrelations of particles and their knowledge sharing, increase variety in the swarm, and provide a better search in unexplored areas of the search space. Moreover, a novel procedure is utilized for dynamic updating of the particles’ inertia weights, which controls the convergence of the swarm towards a solution. Also, since parameters of heuristic and meta-heuristic algorithms have a significant influence on their performance, a comprehensive guideline for parameter tuning of the GEPSO is developed. The computational results of solving numerous well-known benchmark functions by the GEPSO, original PSO, Repulsive PSO (REPSO), PSO with Passive Congregation (PSOPC), Negative PSO (NPSO), Deterministic PSO (DPSO), and Line Search-Based Derivative-Free PSO (LS-DF-PSO) approaches showed that the GEPSO outperformed the compared methods in terms of mean and standard deviation of fitness function values and runtimes.

CDS-Based Topology Control in FANETs via Power and Position Optimization

In a flying ad hoc network (FANET), reliable maintenance of topology confronts a huge challenge due to the highly time-varying connectivity between flying nodes. Aiming at mitigating this issue, this letter proposes a connected dominating set (CDS) based topology control mechanism iteratively for the FANETs by solving a jointed optimization problem on adjusting the node's transmission power and position during the construction of CDS. In particular, a well-tailored group of the topology constructing and maintaining algorithms are developed with a time-discretized approach. Simulation results show that the proposed mechanism has a better performance with respects to overheads and network stability than a general particle swarm optimization (PSO) algorithm, especially in a typical swarm pattern for unmanned aerial vehicle (UAV) team.

Control of double-loop permanent magnet synchronous motor drives by optimized fractional and distributed-order PID controllers

This paper investigates fractional- and distributed-order proportional-integral-derivative (FOPID and DOPID) controllers for permanent magnet synchronous motors (PMSM). The design of FOPID controllers requires the setting of five parameters, i.e. the gains of the three different actions and the non-integer orders of integration and differentiation. An efficient generalized particle swarm optimization (GPSO) sets the parameters in two steps: firstly for an inner loop, secondly for an outer loop. The DOPID controller is characterized by seven different orders of integration or differentiation. In this second case, the tuning is also performed by GPSO in two steps. In both cases, the feedback transfer function of the inner loop is a non-integer order plant for the outer loop, then the outer controller should be of fractional order to obtain the best performance/robustness indexes, as shown by simulation.

Wavelet analysis for spectral inversion of seismic reflection data

In the current work we present a seismic inversion alternative based on the spectral inversion method developed by Puyear and Castagna (2008). We incorporate the wavelet analysis to synthesize singularities of interest and obtain a detailed analysis of seismic windows. The methodology is a suitable alternative to constrained seismic inversion for determination of the reflection coefficients as functions of frequency. Moreover, the forward problem of seismic data as a function of frequency is non-linear; therefore, in order to solve the inverse problem, global optimization techniques are applied: Iterative Solution of Generalized Inversion, Genetic Algorithms and Particle Swarm Optimization. The most reliable results were obtained based on the fact that the wavelet synthesis facilitates getting the first approximation of the events retaining the characteristic amplitude and establishing the initial domain model for the optimization algorithms being only slightly slower than the deterministic deconvolution technique. We show the results of the developed methodology on synthetic and real seismic reflection data.

A precise mathematical model for geometric modeling of wire rope strands structure

Based on the Frenet frame, this paper proposes a general mathematical spiral model with an arbitrary smooth space curve as the center path, which can accurately build complex skeleton lines of wire rope strands. From the aspect of geometry, all the wires are spatial cylinders and must meet the actual geometric requirements: 1. The center cylinder is tangent or separated from the spiral cylinder; 2. The adjacent spiral cylinders do not overlap each other. For requirement 1, Costello’ conclusion is referenced and extended universally to suit an arbitrary smooth space central curve case with rigorous proofs. For requirement 2, the overlapping problem is described as obtaining the minimum distance between the two adjacent spatial path curves, which is deduced by a novel cross section method (SCM) with rigorous proofs and solved by the General Particle Swarm Optimization (PSO) algorithm. Based on the above models, the geometric modeling of wire rope strands procedure is proposed and implemented on the platforms of MATLAB and SolidWorks. Validations are conducted through geometric graphical representations, compared with those from some previous researches. For the simple straight strand case, when the number of spiral cylinders and spiral radius are given, the critical relationship between the ratio of spiral wire radius to spiral radius and the spiral angle is firstly obtained, which can be a precise dimension design reference of simple straight strand for eliminating initial geometric overlap. Further, to show the advance, some precise graphical examples of complex wire rope strands like independent wire rope core (IWRC) and multilayered rope are presented. The wire rope strands geometric modeling method proposed in this paper is precise enough averting initial geometric overlap between the wires for the benefit of subsequent mechanical computation accuracy and efficiency.

IMPROVED PARTICLE SWARM OPTIMIZATION FOR NON-LINEAR PROGRAMMING PROBLEM WITH BARRIER METHOD

The Non-Linear Programming Problems (NLPP) are computationally hard to solve as compared to the Linear Programming Problems (LPP). To solve NLPP, the available methods are Lagrangian Multipliers, Sub gradient method, Karush-Kuhn-Tucker conditions, Penalty and Barrier method etc. In this paper, we are applying Barrier method to convert the NLPP with equality constraint to an NLPP without constraint. We use the improved version of famous Particle Swarm Optimization (PSO) method to obtain the solution of NLPP without constraint. SCILAB programming language is used to evaluate the solution on sample problems. The results of sample problems are compared on Improved PSO and general PSO.

Realizing the empirical mode decomposition by the adaptive stochastic resonance in a new periodical model and its application in bearing fault diagnosis

We investigate a multi-frequency signal that cannot be decomposed by empirical mode decomposition directly. Moreover, this kind of signal in the noisy background cannot be decomposed successfully by the traditional stochastic resonance with bistable system yet. We propose a new method which using the empirical mode decomposition combined the adaptive stochastic resonance in a new periodical model to solve this problem. The results show that the proposed method decomposes the multi-frequency signal perfectly. Meanwhile, the general scale transformation and random particle swarm optimization algorithm are used to help obtain a better result in the process of optimization. Through using this new method, the simulation results are satisfactory. More importantly, this new method also shows good performance in the application of bearing fault diagnosis.

Pareto optimal train scheduling for urban rail transit using generalized particle swarm optimization

In urban rail transport, train timetable plays a crucial role, whose quality determines the whole system’s performance to a large extent. In practical urban rail operation, two contradictive aspects—service quality and operation cost—should be considered during train scheduling. A good train timetable should achieve considerable service quality with as little operation cost as possible. Previously, many studies have been conducted specific to urban rail train scheduling, although most of them do not put enough emphasis on its multi-objective nature. In this article, therefore, Pareto optimal urban rail train scheduling which can give more instruction to practical operation is studied. First, referring to some existing studies, the problem is reasonably defined, which takes time-dependent origin–destination demand as the input and aims at minimizing the passengers’ total travel time and the number of used train stocks. Then, an efficient iteration algorithm and a valid train stock assignment procedure are designed to calculate the passengers’ total travel time and required train stock number, respectively. On that basis, the studied problem is reasonably formulated as a bi-objective optimization model and a Pareto-based particle swarm optimization procedure is designed to solve it. Finally, with two different scaled urban rail lines, the whole methodology is illustrated and the algorithm is tested.

Optimization of Reactive Power Algorithm in Distribution Network Containing Wind Farm

A new strategy on reactive power optimization for power grid with wind farm integration is proposed. The mathematical model is built by using the optimal scenario analysis method and the modified particle swarm optimization algorithm (MPSO). The method about how to obtain the optimal scenarios is discussed. The optimal scenarios position for wind power is deduced based on the Wasserstein distance metric, and the occurrence probability is also studied simultaneously. In order to avoid falling into local optimum, the self-adapting mutagenic factor and mutation probability are designed in MPSO. Simulation examples show the effectiveness of MPSO. The mutagenic factor takes effect when the objective function value tends to be constant. Power loss and voltage stability margin are considered in the objective function. In initial phase of MPSO, the general particle swarm optimization algorithm (PSO) is processed, and this can guarantee the rapid convergence for the optimization procedure. After some iterations, the mutagenic factor begins to have an impact to ensure the global optimum can be obtained. The IEEE 69-bus distribution system is used to the experiment. In the experiment, the optimal scenarios position and scenario occurrence probability are worked out. Test results show that the new strategy is effective.

A multi-strategy improved particle swarm optimization algorithm and its application to identifying uncorrelated multi-source load in the frequency domain

As a type of indirect method, the traditional frequency approach to load identification establishes a systems frequency response function (FRF) and calculates loads using its inverse and the responses. Based on the established FRF method, a novel identification approach that transforms the inverse problem into a forward single-objective optimization model is proposed. Furthermore, a multi-strategy improved particle swarm optimization algorithm (MsiPSO) for identifying uncorrelated multi-source load in the frequency domain is also proposed. Depending on the specific application, MsiPSO initializes the swarm based on domain knowledge. It applies asymmetric and nonlinear strategies to adaptively set the control parameters and genetic operators to strengthen the diversity of the population and avoid local optima. In the experiments, MsiPSO is compared with the general particle swarm optimization (PSO) algorithm and some well-performing variants. A simulated model is then defined to validate the load recognition accuracy of the proposed approach. The experimental results show that MsiPSO is competitive with other methods in terms of convergence, stability, and precision. It has a higher recognition accuracy and is faster than traditional load identification methods and standard PSO.