Two-stage Particle Swarm Optimization Research Articles

Feature-Weighting-Based Prediction of Drought Occurrence via Two-Stage Particle Swarm Optimization

Drought directly affects environmental sustainability. Predicting the drought at the earliest opportunity will help to execute drought mitigation plans. Several drought indices are used to predict the severity of drought across different geographical regions. The two main drought indices used in India for meteorological drought are the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI). This work is a study to find the ability of above mentioned indices to predict meteorological drought for the state of Tamil Nadu using 62 years of data. The prediction results are evaluated using the performance metrics of precision, recall, f1 score, Matthews correlation coefficient, and accuracy. The dataset is severely imbalanced due to the low number of drought incidence years. Hence there exists a tug of war between precision and recall, so for improving it without affecting one another, a multi-objective optimization process is applied. The prediction performance is improved by using the filter-global-supervised feature weighting and wrapper-global-supervised feature weighting techniques. In the filter-based feature weighting approach, the information gain measure and Pearson correlation coefficient are used as feature weights. For the wrapper-based feature weighting approach, two-stage particle swarm optimization (PSO) is designed to calculate the weights of the features, and the random forest is used as the classifier. This two-stage PSO constructs the best population set for individual objectives and then searches around it to find the best particle so that the multiple contradicting objectives will converge into the best solution easier. When compared to classification without feature weighting, two-stage PSO feature weighting achieves a 45% improvement in precision. However, only a moderate improvement in recall is obtained. According to the findings, SPI3 and SPEI12 should be given more weightage in metrological drought prediction.

Optimization of Multi-Port Empty Container Repositioning under Uncertain Environments

Empty container repositioning has always been an important problem faced by ports. In this paper, we study the multi-port empty container repositioning problem with the uncertainty of the demand and supply of empty containers in ports. First, we established an optimization model for empty container reposition strategies and calculated every port’s proportion, depending on its own empty container storage, on the operation of other ports and on the leasing company, in meeting the demand. Next, we established an optimization model for the empty container non-repositioning strategy, calculated the same proportions and compared their performance under two strategies. A two-stage particle swarm optimization (TS-PSO) algorithm was designed to solve the two models, and an example was used to verify the effectiveness of the algorithm and the rationality of the two models. Our research showed that the total cost of empty containers, storage costs, lease costs and the optimal storage of empty containers in ports under the repositioning strategy are all lower than those of the non-repositioning strategy. The proportion of the empty container demand met by inventory under the repositioning strategy is higher than that under the non-repositioning strategy, while the proportion of the empty container demand met by leasing is lower. The costs of leasing cost and repositioning empty containers are higher than those under the non-repositioning strategy. The increase in transportation time between ports per empty container mainly leads to the increase in the reposition costs of all ports. The increase in storage fees chiefly leads to the rising storage costs of all ports. The increase in leasing fees leads to the rising leasing costs of all ports. The paper provides a method for ports to decide the upper limit of empty container storage and to optimize their repositioning strategy.

Hybrid differential evolution particle swarm optimization (DEA-DCWPSO) for resource-constrained multi-project scheduling problem

In this paper, we studied the resource constrained project scheduling problem, and the research object is extended to the multi-project environment. On the basis of multi-project priority evaluation, with the goal of minimizing the weighted duration of multi-project, a multi-project schedule planning model is constructed. Through reasonable scheduling of multiple parallel projects sharing resources under resource constraints, it provides a decision-making basis for project managers to allocate resources reasonably under resource constraints to meet the duration requirements of each project and to shorten the weighted total duration of multiple projects as much as possible. A two-stage hybrid differential evolution particle swarm optimization algorithm is used to solve the model. In the first stage, differential evolution algorithm is used to produce new individuals, and in the second stage, particle swarm optimization algorithm uses a new speed update formula. In the first stage, in order to ensure that the optimal individual will not be destroyed by crossover and mutation, and to maintain the convergence of differential evolution algorithm, we try to introduce Elitist Retention (ER) strategy into differential evolution algorithm. In the second stage, we use a kind of particle swarm optimization algorithm with dynamically changing inertia weight. Through the dynamic changing inertia weight, the global search and local search ability of the algorithm can be adjusted flexibly. The case verification shows that the hybrid differential evolution particle swarm optimization algorithm can be used to solve the RCMPSP model more effectively.

Accelerated Identification Algorithms for Exponential Nonlinear Models: Two-Stage Method and Particle Swarm Optimization Method

Accelerated Identification Algorithms for Exponential Nonlinear Models: Two-Stage Method and Particle Swarm Optimization Method

A three-dimensional slope stability analysis method based on finite element method stress analysis

A new three-dimensional slope stability analysis method based on stress calculation by the finite element method is proposed. This method conforms to the uniform assumption of the sliding direction of the rigid limit equilibrium method and can easily be used calculate the three-dimensional sliding direction. By using this method, both the local and global safety factors of the slip surface can be calculated according to the integral mean value theorem. Additionally, an improved ellipsoidal slip surface construction method is proposed for roughly searching for the minimum safety factor and the critical slip surface. A two-stage particle swarm optimization is used to search for the critical slip surface more accurately. Two numerical examples of fixed slip surfaces are provided to confirm the effectiveness of the proposed method and its applicability to nonspherical slip surfaces. The influence of the slip surface mesh grid size, boundary conditions, Poisson's ratio, and dilatancy angle on the calculation results is revealed. Another two numerical examples of searching for the critical slip surface are then provided, and the results of the proposed method are compared with the results of the strength reduction method to confirm the validity of the proposed method.

Accelerated Two-Stage Particle Swarm Optimization for Clustering Not-Well-Separated Data

Cluster analysis is a data mining technique that has been widely used to exploit useful information in a great amount of data. Because of their evaluation mechanism based on an intracluster distance (ICD) function, traditional single-objective clustering algorithms are not appropriate for not-well-separated data. Specifically, they may easily result in the drop of the optimal solution accuracy on their late stages of search when dealing with the latter. To overcome the problem, in this paper a novel index reflecting the similarity of data within a cluster is presented and called intracluster cohesion (ICC). However, if a multiobjective method is used to cluster with ICD and ICC as the specified objectives, its clustering accuracy may depend on one’s experience. Motivated by these, we propose an accelerated two-stage particle swarm optimization (ATPSO) in which ${K}$ -means is utilized to accelerate particles’ convergence during the population initialization. Its clustering process consists of two stages. First, the main objective of minimizing ICD is to execute preliminary clustering; second, ICC is optimized to promote the clustering accuracy. Extensive experiments with the help of 17 open-source clustering sets in various geometric distributions are conducted. The results show that ATPSO outperforms PSO, ${K}$ -means PSO (KPSO), chaotic PSO (CPSO), and accelerated CPSO in terms of accuracy, and its efficiency is approximate to that of KPSO. Its convergence trend indicates that the adoption of the proposed ICC contributes to the clustering accuracy. Remarkably, compared with the Pareto-based multiobjective PSO, ATPSO can detect clusters more accurately and quickly through the proposed two-stage search.

Layout optimization of crushing station in open-pit mine based on two-stage fusion particle swarm algorithm

In the production process of open-pit mines, transportation costs account for 45–60% of the total cost. Therefore, a reasonable and economic transportation system can effectively improve production efficiency. In this article, optimization of the layout of the fixed crushing station in the in-pit crusher and conveyor system of open-pit mining is studied. First, the entire ore body is discretized into multiple ore blocks. By calculating the transportation distance between each block and the crushing station, an optimized model for minimizing the total transportation work is established. Finally, combining the advantages of particle swarm optimization (PSO) and quantum PSO (QPSO), a two-stage fusion particle swarm optimization algorithm (TSF-PSO) is designed to solve the model, and the effectiveness of the model is verified by actual cases. Experimental data show that, compared with the PSO and QPSO algorithms, the solution efficiency of the TSF-PSO algorithm is improved by 2.83% and 1.48%, respectively.

A Two-Stage Particle Swarm Optimization Algorithm for Wireless Sensor Nodes Localization in Concave Regions

At present, range-free localization algorithm is the mainstream of node localization method, which has made tremendous achievements. However, there are few algorithms that can be used in concave regions, and the existing algorithms have defects such as hop distance error, excessive time complexity and so on. To solve these problems, this paper proposes a two-stage PSO (Particle Swarm Optimization) algorithm for wireless sensor nodes localization in “concave regions”. In the first stage, it proposes a method of distance measuring based on similar path search and intersection ratio, and completes the initial localization of unknown nodes based on maximum likelihood estimation. In the second stage, the improved PSO algorithm is used to optimize the initial localization results in the previous stage. The experimental result shows that the localization error of this algorithm is always within 10% and the execution time is maintained at about 20 s when the communication radius and beacon node ratio is changing. Therefore, the algorithm can obtain high localization accuracy in wireless sensor network with “concave regions”, requiring low computing power for nodes, and energy consumption. Given this, it can greatly extend the service life of sensor nodes.

A Siting and Sizing Optimization Approach for PV–Battery–Diesel Hybrid Systems

This paper proposes a new method to determine the sizing and siting of diesel generators (DGs), photovoltaic (PV) solar panels, and batteries for off-grid systems. In this work, the objective is to reduce the total system cost while fulfilling the load demand and maintaining the power quality of the system, among other constraints. This is achieved by employing a two-stage particle swarm optimization algorithm; the first for siting and sizing of the system and the second for its scheduling. Three case studies, namely only DGs, DGs + PV, and DGs + PV + batteries systems, based on the energy requirement of an Indonesian island are presented. In addition, real solar irradiance and temperature data are utilized together with a measured load profile. The results reveal that a system that relies only on DGs is not able to keep the system voltage within the tolerable range (from 0.95 to 1.05 p.u.), while yielding a lifetime cost of 104 million USD. The DGs + PV system requires a lower lifetime cost, 67 million USD. Nevertheless, the voltage of the system reaches values lower than 0.95 p.u. for certain nodes during night-time as the PV are not able to provide power. Finally, the DGs + PV + batteries system yields the lowest cost, 64 million USD, and is able to keep the values of the voltage within the desired range, as batteries can provide power when PV are not operating.

Maximum Power Point Tracking for Cascaded PV-Converter Modules Using Two-Stage Particle Swarm Optimization

The paper presents a novel two-stage particle swarm optimization (PSO) for the maximum power point tracking (MPPT) control of a PV system consisting of cascaded PV-converter modules, under partial shading conditions (PSCs). In this scheme, the grouping method of the shuffled frog leaping algorithm (SFLA) is incorporated with the basic PSO algorithm, ensuring fast and accurate searching of the global extremum. An adaptive speed factor is also introduced to improve its convergence speed. A PWM algorithm enabling permuted switching of the PV sources is applied. The method enables this PV system to achieve the maximum power generation for any number of PV and converter modules. Simulation studies of the proposed MPPT scheme are performed on a system having two chained PV buck-converter modules and a dc-ac H-bridge connected at its terminals for supplying an AC load. The results show that this type of PV system allows each module to achieve the maximum power generation according its illumination level without affecting the others, and the proposed new control method gives significantly higher power output compared with the conventional P&O and PSO methods.

A Two-Stage Particle Swarm Optimization Algorithm for MPPT of Partially Shaded PV Arrays

ABSTRACTThe power-voltage (P-V) characteristic curves of a PV array are nonlinear and have multiple peaks under partially shaded conditions (PSCs). This paper proposes a novel maximum power point tracking (MPPT) method for a PV system with reduced steady-state oscillation based on a two-stage particle swarm optimization (PSO) algorithm. The grouping method of the shuffled frog leaping algorithm (SFLA) is incorporated in the basic PSO algorithm (PSO-SFLA), ensuring fast and accurate searching of the global extremum. An adaptive speed factor is also introduced into the improved PSO to further enhance its convergence speed. Test results show that the proposed method converges in less than half the time taken by the conventional PSO method, and the power is improved by 33% under the worst PSCs, which confirms the superiority of the proposed method over the standard PSO algorithm in terms of tracking speed and steady-state oscillations under different PSCs.

Two stage particle swarm optimization to solve the flexible job shop predictive scheduling problem considering possible machine breakdowns

In real-world industrial environments, unplanned events and unforeseen incidents can happen at any time. Scheduling under uncertainty allows these unexpected disruptions to be taken into account. This work presents the study of the flexible job shop scheduling problems (FJSP) under machine breakdowns. The objective is to solve the problem such that the lowest makespan is obtained and also robust and stable schedules are guaranteed. A two-stage particle swarm optimization (2S-PSO) is proposed to solve the problem assuming that there is only one breakdown. Various benchmark data taken from the literature, varying from Partial FJSP to Total FJSP, are tested. Computational results prove that the developed algorithm is effective and efficient enough compared to literature approaches providing better robustness and stability. Statistical analyses are given to confirm this performance.

Generalized Unified Power Flow Controller for Optimal Reactive Power Dispatch by Considering Practical Constraints

Objectives: One of the drawbacks of the power system network, i.e. Optimum Reactive Power Dispatch (ORPD) is optimized. Due to this, system transmission power losses and bus voltage magnitudes are optimized Methods/Statistical Analysis: A unique optimization rule, Uniformly Distributed Two-stage Particle Swarm Optimization (UDTPSO) are enforced in conjunction with the traditional Particle Swarm Optimization (PSO).The power injection model for the Generalized Unified Power Flow Controller (GUPFC) is used to enhance the power flow in a power system network. Findings: The proposed technique has fast convergence rate in less number of iterations which validates the effectiveness of UDTPSO. The study is tested on a standard IEEE-30 bus system and the results obtained with UDTPSO are valid with the existing PSO. Applications: Effective utilization of a Flexible Alternating Current Transmission system (FACTS) device called Generalized Unified Power Flow Controller (UPFC) for power flow control which will improve existing transmission capability.

Analyzing the Effect of Multi-fuel and Practical Constraints on Realistic Economic Load Dispatch using Novel Two-stage PSO

In power system restructuring, pricing the electrical power plays a vital role in cost allocation between suppliers and consumers. In optimal power dispatch problem, not only the cost of active power generation but also the costs of reactive power generated by the generators should be considered to increase the effectiveness of the problem. As the characteristics of reactive power cost curve are similar to that of active power cost curve, a nonconvex reactive power cost function is formulated. In this paper, a more realistic multi-fuel total cost objective is formulated by considering active and reactive power costs of generators. The formulated cost function is optimized by satisfying equality, in-equality and practical constraints using the proposed uniform distributed two-stage particle swarm optimization. The proposed algorithm is a combination of uniform distribution of control variables (to start the iterative process with good initial value) and two-stage initialization processes (to obtain best final value in less number of iterations) can enhance the effectiveness of convergence characteristics. Obtained results for the considered standard test functions and electrical systems indicate the effectiveness of the proposed algorithm and can obtain efficient solution when compared to existing methods. Hence, the proposed method is a promising method and can be easily applied to optimize the power system objectives.

Construction of Nonlinear Droop Relations to Optimize Islanded Microgrid Operation

In this paper, nonlinear droop relations are suggested to optimize the operation of islanded microgrids. By using nonlinear droop, many aspects of the microgrid operation can be optimized, in addition to the inherent advantages of the droop control. In the proposed method, the droop relations are allowed to have any nonlinear shape as long as that shape satisfies certain characteristics required for the stability and proper microgrid operation. The procedure of constructing the nonlinear droop relations that minimize the operating cost of the microgrid and share the reactive power effectively among the sources is investigated and explained in detail. The selected droop structure is a combination of integer and fractional power functions whose parameters are selected using a two-stage particle swarm optimization algorithm. The effectiveness of the proposed method is verified through simulation and experimental studies.

Economic Concerns in Optimal Power Dispatch in the Presence of a Generalized Unified Power Flow Controller under Practical Constraints

In power system restructuring, pricing the electrical power plays a vital role in cost allocation between suppliers and consumers. In optimal power dispatch problem, not only the cost of active power generation but also the costs of reactive power generated by the generators, shunt capacitors, transmission losses and device investment costs should be considered. In this paper, a more realistic multi-fuel total cost objective is formulated by considering all the above mentioned costs. As the characteristics of reactive power cost curve are similar to that of active power cost curve, a non-convex reactive power cost function is formulated. The formulated cost function is optimized by satisfying equality, in-equality and practical constraints and also device limits using the proposed uniform distributed two-stage particle swarm optimization. In this paper, power injection model of generalized unified power flow controller (GUPFC) including converter switching losses is presented. The proposed objective functions are optimized in the presence of UPFC and GUPFC and finally suitable device is identified for the standard IEEE-30 bus test system.

Analysis and effect of multi-fuel and practical constraints on economic load dispatch in the presence of Unified Power Flow Controller using UDTPSO

Abstract This paper presents an attempt to analyze the effect of multi-fuel and practical constraints on economic load dispatch problem using a novel uniform distributed two-stage particle swarm optimization (UDTPSO) algorithm without and with unified power flow controller (UPFC) while satisfying equality, inequality, practical constraints such as ramp-rate and prohibited operating zone (POZ) limits and device operating limits. A Novel severity function is formulated based on the transmission line overloads and bus voltage violations to identify an optimal location to install UPFC. A multi-objective optimization problem is solved for multi-fuel non-convex cost and transmission power loss objectives. Obtained results for considered standard test functions and electrical systems indicate the effectiveness of the proposed algorithm and can obtain efficient solution when compared to existing methods. Hence, the proposed method is a promising method and can be easily applied to optimize the power system objectives.

Two-stage Particle Swarm Optimization Algorithm for the Time Dependent Alternative Vehicle Routing Problem

This study considered a Time Dependent Alternative Vehicle Routing Problem (TDAVRP) in a multi-graph network (TDAVRP) and was formulated into a Mixed Integer Programming model. Due to its NP nature, an algorithm based on Particle Swarm Optimization (PSO) with local improvement was developed to speed up the solution procedure. By using different sets of Solomon’s benchmark problems and continuous travel time functions, the accuracy and efficiency of the two-stage PSO were evaluated. The computational results showed that the proposed algorithm is capable of deriving optimal or near optimal solutions in a short period of time when the size of the problems are small and is able to obtain feasible solutions within a reasonable time when solving the large problems which cannot be solved by ILOG CPLEX. In addition, Sensitivity Analysis was conducted to evaluate the performances of the parameters. The results indicated that the number of customers is a sensitive parameter and will influence the required number of vehicles, value of violations and percentage of alternative edges in the solution sets.

Forecasting the HS300 Stock Index with Radial Basis Function Neural Network Using Two-Stage Particle Swarm Optimization

This paper introduces a hybrid radial basis function neural network (RBFNN) using a two-stage particle swarm optimization. In stage one, we initially demonstrate how the PSO learning method simultaneously determines the optimal number of hidden neurons, centres and widths of the radial basis function. Afterwards we apply the least mean squares (LMS) method to calculate the weight between the hidden layer and output layer in stage two. Then we propose a novel fitness function and make forecast simulation of HS 300 stock index. It proves to improve the performance of RBFNN.

Construction of quasi‐cyclic low‐density parity‐check codes with low encoding complexity

SUMMARYLow encoding complexity is very important for quasi‐cyclic low‐density parity‐check (QC‐LDPC) codes used in wireless communication systems. In this paper, a new scheme is presented to construct QC‐LDPC codes with low encoding complexity. This scheme is called two‐stage particle swarm optimization (TS‐PSO) algorithm, in which both the threshold and girth distribution of QC‐LDPC codes are considered. The proposed scheme is composed of two stages. In the first stage, we construct a binary base matrix of QC‐LDPC code with the best threshold. The matrix is constructed by combining a binary PSO algorithm and the protograph extrinsic information transfer (PEXIT) method. In the second stage, we search an exponent matrix of the QC‐LDPC code with the best girth distribution. This exponent matrix is based on the base matrix obtained in the first stage. Consequently, the parity‐check matrix of the QC‐LDPC code with the best threshold and best girth distribution are constructed. Furthermore, bit error rate performances are compared for the QC‐LDPC codes constructed by proposed scheme, the QC‐LDPC code in 802.16e standard, and the QC‐LDPC code in Tam's study. Simulation results show that the QC‐LDPC codes proposed in this study are superior to both the 802.16e code and the Tam code on the additive white Gaussian noise (AWGN) and Rayleigh channels. Moreover, proposed scheme is easily implemented, and is flexible and effective for constructing QC‐LDPC codes with low encoding complexity. Copyright © 2012 John Wiley & Sons, Ltd.