Constrained Particle Swarm Optimization Research Articles

Distributed three-dimensional cooperative guidance via receding horizon control

The paper presents a new three-dimensional (3D) cooperative guidance approach by the receding horizon control (RHC) technique. The objective is to coordinate the impact time of a group of interceptor missiles against the stationary target. The framework of a distributed RHC scheme is developed, in which each interceptor missile is assigned its own finite-horizon optimal control problem (FHOCP) and only shares the information with its neighbors. The solution of the local FHOCP is obtained by the constrained particle swarm optimization (PSO) method that is integrated into the distributed RHC framework with enhanced equality and inequality constraints. The numerical simulations show that the proposed guidance approach is feasible to implement the cooperative engagement with satisfied accuracy of target capture. Finally, the computation efficiency of the distributed RHC scheme is discussed in consideration of the PSO parameters, control update period and prediction horizon.

Multiobjective Reliable Cloud Storage with Its Particle Swarm Optimization Algorithm

Information abounds in all fields of the real life, which is often recorded as digital data in computer systems and treated as a kind of increasingly important resource. Its increasing volume growth causes great difficulties in both storage and analysis. The massive data storage in cloud environments has significant impacts on the quality of service (QoS) of the systems, which is becoming an increasingly challenging problem. In this paper, we propose a multiobjective optimization model for the reliable data storage in clouds through considering both cost and reliability of the storage service simultaneously. In the proposed model, the total cost is analyzed to be composed of storage space occupation cost, data migration cost, and communication cost. According to the analysis of the storage process, the transmission reliability, equipment stability, and software reliability are taken into account in the storage reliability evaluation. To solve the proposed multiobjective model, a Constrained Multiobjective Particle Swarm Optimization (CMPSO) algorithm is designed. At last, experiments are designed to validate the proposed model and its solution PSO algorithm. In the experiments, the proposed model is tested in cooperation with 3 storage strategies. Experimental results show that the proposed model is positive and effective. The experimental results also demonstrate that the proposed model can perform much better in alliance with proper file splitting methods.

Structure Prediction of Atoms Adsorbed on Two-Dimensional Layer Materials: Method and Applications

The adsorption of atoms is one of the efficient approaches for functionalizing two-dimensional (2D) layer materials with desirable properties. The structural knowledge of atoms adsorbed on 2D layer materials is crucial for understanding their functional performance. Here we propose a versatile method for predicting the structures of atoms adsorbed on 2D materials via the swarm-intelligence-based CALYPSO structure-prediction method. Several techniques are implemented to improve the efficiency of structure searching, including fixed adsorption sites, constraints of symmetry and distance during structure generation, and the constrained particle swarm-optimization algorithm for structure evolution. The method is successfully applied to investigate the well-studied systems of hydrogenated and oxidized graphene. The energetically most stable structures of single-sided hydrogenated graphene are predicted for different contents of hydrogen; altering the hydrogen content appears to effectively tune the band gap. A...

Sizing and energy management of a medium hybrid electric boat

Fuel consumption is viewed as a fundamental problem in boats which are to operate on sea for a long time without need to refueling. In this paper, hybridization of a conventional medium-sized boat to reduce fuel consumption is considered. For this purpose, the optimal sizes for the main components, electric motor, internal combustion engine, and battery and generator are calculated via a constrained particle swarm optimization algorithm which considers accelerating and speeding expectations as the constraints. Furthermore, a new fuzzy-thermostat controller whose parameters are tuned via optimization is proposed to manage the energy flow between the main components. To estimate the fuel consumption, a backward–forward simulation approach based on the quasi-static maps of the diesel engine and electric motor is employed. Moreover, the boat resistance forces are calculated for a typical driving cycle at both displacement and planning modes. Simulation results reveal that hybridization of the conventional boat leads to a 40 % reduction in fuel consumption without violating the performance constraints.

Integration and comparison of different genomic data for outcome prediction in cancer.

BackgroundIn cancer, large-scale technologies such as next-generation sequencing and microarrays have produced a wide number of genomic features such as DNA copy number alterations (CNA), mRNA expression (EXPR), microRNA expression (MIRNA), and DNA somatic mutations (MUT), among others. Several analyses of a specific type of these genomic data have generated many prognostic biomarkers in cancer. However, it is uncertain which of these data is more powerful and whether the best data-type is cancer-type dependent.Therefore, our purpose is to characterize the prognostic power of models obtained from different genomic data types, cancer types, and algorithms. For this, we compared the prognostic power using the concordance and prognostic index of models obtained from EXPR, MIRNA, CNA, MUT data and their integration for ovarian serous cystadenocarcinoma (OV), multiform glioblastoma (GBM), lung adenocarcinoma (LUAD), and breast cancer (BRCA) datasets from The Cancer Genome Atlas repository. We used three different algorithms for prognostic model selection based on constrained particle swarm optimization (CPSO), network feature selection (NFS), and least absolute shrinkage and selection operator (LASSO).ResultsThe integration of the four genomic data produced models having slightly higher performance than any single genomic data. From the genomic data types, we observed better prediction using EXPR closely followed by MIRNA and CNA depending on the cancer type and method. We observed higher concordance index in BRCA, followed by LUAD, OV, and GBM. We observed very similar results between LASSO and CPSO but smaller values in NFS. Importantly, we observed that model predictions highly concur between algorithms but are highly discordant between data types, which seems to be dependent on the censoring rate of the dataset.ConclusionsGene expression (mRNA) generated higher performances, which is marginally improved when other type of genomic data is considered. The level of concordance in prognosis generated from different genomic data types seems to be dependent on censoring rate.Electronic supplementary materialThe online version of this article (doi:10.1186/s13040-015-0065-1) contains supplementary material, which is available to authorized users.

Trajectory planning of free-floating space robot using Particle Swarm Optimization (PSO)

This paper investigates the application of Particle Swarm Optimization (PSO) strategy to trajectory planning of the kinematically redundant space robot in free-floating mode. Due to the path dependent dynamic singularities, the volume of available workspace of the space robot is limited and enormous joint velocities are required when such singularities are met. In order to overcome this effect, the direct kinematics equations in conjunction with PSO are employed for trajectory planning of free-floating space robot. The joint trajectories are parametrized with the Bézier curve to simplify the calculation. Constrained PSO scheme with adaptive inertia weight is implemented to find the optimal solution of joint trajectories while specific objectives and imposed constraints are satisfied. The proposed method is not sensitive to the singularity issue due to the application of forward kinematic equations. Simulation results are presented for trajectory planning of 7 degree-of-freedom (DOF) redundant manipulator mounted on a free-floating spacecraft and demonstrate the effectiveness of the proposed method.

Minimising losses of SEIG using constrained particle swarm optimisation considering voltage regulation

Induction generators are widely used in various applications since they offer distinct advantages over conventional synchronous machines, like a simplified design, installation at lower capital cost and substantial savings in operation and maintenance expenses. The wind turbine induction generator systems are proposed to supply isolated loads under widely varying conditions. These conditions are the wind speed and load variations. Under these varying conditions, there will be some changes in the terminal generated voltage. The terminal voltage can be regulated by adapting the value of excitation capacitance required for the induction generator. This paper presents a constrained particle swarm optimisation technique for minimising the power losses of self–excited induction generator with terminal voltage control under operating conditions by selecting the suitable capacitance required for generator excitation. Testing of the proposed technique over conventional particle swarm optimisation technique is performed. Results signify the supremacy of the proposed technique over conventional particle swarm optimisation technique.

Constrained NMF-based high-dimension adaptive particle swarm optimization algorithm for endmember extraction from a hyper spectral remote sensing image

Constrained NMF-based high-dimension adaptive particle swarm optimization algorithm for endmember extraction from a hyper spectral remote sensing image

Application of Constrained Multi-Objective Hybrid Quantum Particle Swarm optimization for Improving Performance of an Ironless Permanent Magnet Linear Motor

This study presents an ironless permanent magnet linear brushless motor (PMLBM) with three objective functions: maximal thrust force, minimal temperature, and minimal volume. Using response surface methodology (RSM), this study presents a mathematical predictive model with constraints using the penalty functions concept for each objective function. The design variables in this study include magnetic width, magnetic height, magnetic pitch, air-gap, c oil width, coil height, and coil diameter. This study uses an elitist hybrid quantum behavior particle swarm optimization algorithm with mutation to solve this multi-objective optimization problem (EMOHQPSO). This elitist mechanism with crowding distance sorting improves the number and diversity of the solutions. Results show that the proposed approach is superior to the non-dominated sorting genetic algorithm (NSGA II) and multi-objective particle swarm optimization (MOPSO), respectively, on the 3D graph Pareto-optimal front. Compared to the initial motor, the thrust force increased by 6.27%, the thrust density increased by 14.9%, and the temperature and volume decreased by 14.03% and 6.25% respectively. These results confirm the satisfactory performance of the proposed solutions.

H∞ optimization of dynamic vibration absorber variant for vibration control of damped linear systems

This study focuses on the H∞ optimal design of a dynamic vibration absorber (DVA) variant for suppressing high-amplitude vibrations of damped primary systems. Unlike traditional DVA configurations, the damping element in this type of DVA is connected directly to the ground instead of the primary mass. First, a thorough graphical analysis of the variations in the maximum amplitude magnification factor depending on two design parameters, natural frequency and absorber damping ratios, is performed. The results of this analysis clearly show that any fixed-points-theory-based conventional method could provide, at best, only locally but not globally optimal parameters. Second, for directly handling the H∞ optimization for its optimal design, a novel meta-heuristic search engine, called the diversity-guided cyclic-network-topology-based constrained particle swarm optimization (Div-CNT-CPSO), is developed. The variant DVA system developed using the proposed Div-CNT-CPSO scheme is compared with those reported in the literature. The results of this comparison verified that the proposed system is better than the existing methods for suppressing the steady-state vibration amplitude of a controlled primary system.

Shaped beam array synthesis using particle swarm optimisation method with mutual coupling compensation and wideband feeding network

This work presents the design, simulation and implementation of a linear array antenna with a fully constrained cosecant squared radiation pattern and a novel wideband feeding network. This antenna is constructed of wideband printed dipole antennas as the array elements in which the mutual coupling effect is properly compensated. For this purpose, a constrained particle swarm optimisation (PSO) algorithm is used as the synthesis method which is able to deal with multiple constraints in the desired radiation pattern. The excitations obtained from the PSO algorithm are applied to the design of a wideband feeding network which is able to deliver stable phase shifts and output powers to the array elements over the desired bandwidth. Finally, the simulation results are confirmed by the measurement results.

Work cycle optimization problem of manipulator with revolute joints

In this work the problem of motion modeling and work cycle optimization of manipulator with revolute joints has been considered. The motion equations of the manipulator elements under any spatial work cycle conditions have been formulated. The formulation has been completed by using the classic vector mechanics and Lagrange equations of second kind. The equations of motion of the system have been obtained using commercial software. The chosen motion model for each considered actuator is point-to-point motion model with quasi-trapezoid velocity profile. Additionally, the problem of optimization of a particular work cycle has been presented. The optimization objective has been chosen as minimization of loads (torques) in actuators. The objective function has been formulated using performance indexes and the design variables are rated velocity value and initial time value of work cycle in each considered actuator. The formulated optimization problem has been solved using constrained Multi-Objective Particle Swarm Optimization algorithm. A numerical computation has been completed using specially performed software and results of the computation have been attached to the paperwork.

Low-thrust trajectory design with constrained particle swarm optimization

In this paper, combined with the direct approach, particle swarm optimization (PSO) is applied to low-thrust trajectory optimization problems. A double-loop trajectory optimization algorithm is developed. The outer loop of this algorithm is a modified PSO optimizer, which can deal with constrained optimization problems and avoid premature convergence. The function of the outer loop is generating a series of time histories of control, called particles, and driving the particles toward the optimal solution. The direct approach (fourth-order Runge–Kutta shooting/parallel shooting method) is adopted as the inner loop algorithm, whose main task is to correct the particles provided by the outer loop and ensure that all the constraints are satisfied. This algorithm has the global search feature of the PSO and the relative large radius of convergence of the direct approach. Its efficiency is substantiated by solving a fixed-time fuel-optimal transfer problem from an asteroid to the Earth. Furthermore, this algorithm can be considered to be a universal low-thrust optimizer, and it can easily be used to solve more complex trajectory optimization problems such as multi-swingby problem and multidisciplinary design optimization (MDO) problems.

Fixed structure constrained preview control design using enhanced PSO approach

This paper proposes the design of fixed structure preview controller with multiple objectives in constrained environment using hybrid technique based on co-evolutionary particle swarm optimisation and marriage in honey bees optimisation algorithm. The paper, also, presents a comparative evaluation of the commonly used constraint - handling approaches in evolutionary algorithms with the proposed hybrid multi-objective constrained co-evolutionary particle swarm optimisation MOCC-PSO procedure. The available procedures and the proposed algorithm are evaluated and verified using MATLAB platform for engineering design problems, namely autonomous control of under-water vehicle and 2-DOF helicopter. The results validate the ability of the algorithm in terms of the quality of solution obtained in the constrained environment and the ease to implement the non-classical objectives and constraints.

Planning of Renewable DGs for Distribution Network Considering Load Model: A Multi-objective Approach

Renewable distributed generation (DG) has become one of the key drivers of fast changing energy scenario around the world. A clear strategy is ardently essential to accelerate growth of grid interactive renewable DGs. This paper proposes a constrained multi objective particle swarm optimization based planning of renewable DGs for distribution network. The proposed method tries to find the best trade-off between benefit to economic ratio, voltage stability factor and network security index and then generate optimal location, types and size of renewable DGs for distribution network. Effectiveness of the proposed methodology is shown on a typical 28-bus Indian distribution network. Test results depict that proper selection of locations, types and sizes of DGs would be helpful to maximize economical benefit, improve security and enhance voltage stability of the distribution network. The study also illustrates the effect of load modelling in planning of renewable DGs for distribution network.

Shape Optimization of Airship Based on Constrained Particle Swarm Optimization

This paper presents an optimized process of the shape of airships using a Constrained Particle Swarm Optimization (CPSO) algorithm from a parametric multidisciplinary perspective. The optimizational model is considered from the multidisciplinary approach such as the pneumatic, the structure and the strength of the airship, rather than the single disciplinary previously. Moreover, CPSO is first proposed to optimize the airship shape by optimizing the positions of the slenderness ratio in the continuous space. CPSO, as an evolutionary computation method, improves Particle Swarm Optimization (PSO) and adds a constraint condition so that it has the characteristics of random starting, parallel search and strong memorability. Our method, proved by experiment, has the great advantages of the fast convergence speed, the less parameter Settings and the easy achievement etc. The proposed pipeline is tested on a real case study and the comprehensive experimental results are shown to demonstrate the effectiveness of the presented approach.

A novel approach to combine features for salient object detection using constrained particle swarm optimization

Despite significant amount of research works, the best available visual attention models still lag far behind human performance in predicting salient object. In this paper, we present a novel approach to detect a salient object which involves two phases. In the first phase, three features such as multi-scale contrast, center-surround histogram and color spatial distribution are obtained as described in Liu et al. model. Constrained Particle Swarm Optimization is used in the second phase to determine an optimal weight vector to combine these features to obtain saliency map to distinguish a salient object from the image background. To achieve this, we defined a simple fitness function which highlights a salient object region with well-defined boundary and effectively suppresses the background regions in an image. The performance is evaluated both qualitatively and quantitatively on a publicly available dataset. Experimental results demonstrate that the proposed model outperforms existing state-of-the-art methods in terms of precision, recall, F -measure and area under curve.

Placement of wind and solar based DGs in distribution system for power loss minimization and voltage stability improvement

Proper placement of Distributed Generation (DG) in distribution system is still very challenging issue for obtaining their maximum potential benefits. This paper proposes a new constrained multi-objective Particle Swarm Optimization (PSO) based Wind Turbine Generation Unit (WTGU) and photovoltaic (PV) array placement approach for power loss reduction and voltage stability improvement of radial distribution system. The paper reflects the effectiveness of WTGU and PV array performance models in DG placement problem formulation. Wind and solar based DGs are operated in different active and reactive power mode and tested on 12-bus, 15-bus, 33-bus and 69-bus radial distribution system. Obtained results are compared with other DG placement technique and proposed method is found to be more effective in terms of voltage stability enhancement and power loss minimization. A novel Voltage Stability Factor (VSF) has been proposed in this paper which can quantify voltage stability levels of buses in the system. Comparing with other voltage stability index and power stability index, developed VSF has emerged as more simple and efficient tool.

Model Reference based Tuning of PID Controller using Bode's Ideal Transfer Function and Constrained Particle Swarm Optimization

A new method for designing PID Controllers using Bode’s ideal transfer function and constrained Particle Swarm Optimization (PSO) is proposed in this paper. Bode’s ideal transfer function is introduced using fractional calculus and Carlsson’s approximation is used for converting the transfer function from fractional to integer domain. The PID controller is designed by minimizing a hybrid objective function using PSO. Simulation examples confirming the effectiveness of the resulting controller are also discussed in detail and a performance comparison, highlighting the enhanced capability of PSO over other conventional mathematical optimization approaches, is also made in the paper.

Extension of the constrained particle swarm optimization algorithm to optimal operation of multi-reservoirs system

Three constrained versions of Particle Swarm Optimization (PSO) algorithm are presented in this paper for the efficient optimal operation of multi-reservoir systems using storage/release volumes as decision variables of the problem. Proposed algorithms are based on identifying and excluding the infeasible region of the search space before and during the search which has already proposed and used by the author for the operation of single reservoirs [5]. In the first version named Partially Constrained Particle Swarm Optimization I (PCPSO1), the sequential nature of the solution building procedure of PSO is used to explicitly enforce the release/storage constraints of the problem during solution construction. For this, the continuity equation is used at each period of the operation of each reservoir to define a new set of bounds for decision variable of the next period which satisfy release/storage constraints of the problem. Particles of the swarm are, therefore, forced to fly in the feasible region of the search space except for some rare cases. In the second version named Partially Constrained Particle Swarm Optimization Two (PCPSO2), the periods of the operations for upstream reservoirs, reservoirs with known inflow, are treated in a reverse order prior to the PCPSO1 search to define new set of bounds for storage volumes such that PCPSO1 algorithm is not given any chance of producing infeasible operations regarding upstream reservoirs. In the third version, the PCPSO2 search is augmented with a mechanism similar to that used in PCPSO2 for all downstream reservoirs by which all the infeasible operation of the downstream dams are also excluded from the search process and, hence, the name of Fully Constrained Particle Swarm Optimization (FCPSO) algorithm. Proposed methods are used to solve two benchmark problems of hydropower operations of multi-reservoir system namely Four and Ten reservoir systems and the results are presented and compared with those of the conventional unconstrained PSO and other methods in the literature. The methods are shown to be very effective in locating optimal or near optimal solutions and efficient in terms of the convergence characteristics of the resulting algorithms. Proposed algorithms are also shown to be relatively insensitive to the swarm size and initial swarm compared to the original algorithm.