Discrete Particle Swarm Optimization Research Articles

Reconfiguration of distribution network using discrete particle swarm optimization to reduce voltage fluctuations

Reconfiguration of the distribution network (DN) is the changing of status of normally open and close switches for a specific purpose. In this paper, a situation is studied in which a fluctuating load (such as a welder) violates the network power quality (PQ) standards. Therefore, in order to mitigate the emission of the generated flicker, an optimization problem is developed for improving the PQ of the network comprising voltage flicker level and voltage profile in addition to the network power loss as a traditional objective of the DN reconfiguration. The optimization procedure is performed by changing the network configuration using tie and sectionalizing switches. Discrete particle swarm optimization is utilized to optimize the network configuration. The proposed method is evaluated in two DNs. IEEE standard 69-bus DN and 95-bus practical Iranian DN are considered as case study networks. The results show the effectiveness of the proposed method for improving the network PQ, especially in case of voltage flicker. The presented studies and procedure will guide the utility engineers in making decisions associated with the choice of reasonable mitigation technique to overcome the identified PQ problems.

Finding Community of Brain Networks Based on Neighbor Index and DPSO with Dynamic Crossover

Background: : The brain networks can provide us an effective way to analyze brain function and brain disease detection. In brain networks, there exist some import neural unit modules, which contain meaningful biological insights. Objective:: Therefore, we need to find the optimal neural unit modules effectively and efficiently. Method:: In this study, we propose a novel algorithm to find community modules of brain networks by combining Neighbor Index and Discrete Particle Swarm Optimization (DPSO) with dynamic crossover, abbreviated as NIDPSO. The differences between this study and the existing ones lie in that NIDPSO is proposed first to find community modules of brain networks, and dose not need to predefine and preestimate the number of communities in advance. Results: : We generate a neighbor index table to alleviate and eliminate ineffective searches and design a novel coding by which we can determine the community without computing the distances amongst vertices in brain networks. Furthermore, dynamic crossover and mutation operators are designed to modify NIDPSO so as to alleviate the drawback of premature convergence in DPSO. Conclusion: The numerical results performing on several resting-state functional MRI brain networks demonstrate that NIDPSO outperforms or is comparable with other competing methods in terms of modularity, coverage and conductance metrics.

Z-Shaped Transfer Functions for Binary Particle Swarm Optimization Algorithm.

Particle swarm optimization (PSO) algorithm is a swarm intelligent searching algorithm based on population that simulates the social behavior of birds, bees, or fish groups. The discrete binary particle swarm optimization (BPSO) algorithm maps the continuous search space to a binary space through a new transfer function, and the update process is designed to switch the position of the particles between 0 and 1 in the binary search space. Aiming at the existed BPSO algorithms which are easy to fall into the local optimum, a new Z-shaped probability transfer function is proposed to map the continuous search space to a binary space. By adopting nine typical benchmark functions, the proposed Z-probability transfer function and the V-shaped and S-shaped transfer functions are used to carry out the performance simulation experiments. The results show that the proposed Z-shaped probability transfer function improves the convergence speed and optimization accuracy of the BPSO algorithm.

Analysis of network topology and deployment mode of 5G wireless access network

Aiming at the characteristics of high-density and random deployment of 5G cellular networks, through efficient network-level deployment methods, the network can quickly and efficiently adapt to large-scale dynamic changes in user traffic. Aiming at the need for 5G systems to meet the requirements of high-speed mobile environment communications, this paper deeply studies the factors that affect communication quality in mobile scenarios and their relationships. Based on this, around the content transmission and distribution of information, a 5G mobile communication network architecture based on content-centric network technology is proposed. To meet the deployment requirements of cross-domain VNF (Virtual Network Function) during the deployment phase, in order to solve the problem that the existing cross-domain deployment algorithms do not take into account node computing resources and link bandwidth resources, this paper proposes a DPSO-K (Discrete Particle Swarm Optimization—Kruskal) 5G cross-domain virtual network function deployment method. Compared with the traditional method, the overall cost is reduced, and the cost is least affected by the number of data fields. In different practical scenarios, the resource reduction gain of the strategy is evaluated. Simulation results verify the impact of different system parameters on the energy efficiency of the network. The results show that this method has obvious advantages in optimizing the energy spectrum efficiency of randomly deployed networks.

A new strategy for packets scheduling in cognitive radio internet of things

The usage of Cognitive Radio Networks (CRNs) to establish Internet of Things (IoT) smart Networks is an effective and economical solution to the IoT spectrum scarcity problem. CRNs is merged with the IoT technology to be named Cognitive Radio Internet of Things (CRIoT). Spectrum sharing/scheduling in CRIoT has not attracted a lot of attention as it should do. This paper proposes a new packets' scheduling strategy for CRIoT. It is a centralized time slotted algorithm that uses discrete permutation particle swarm optimization for scheduling packets among predicted primary network empty time slots. An objective function is formulated to maximize the fairness index among fog nodes, minimize the packets' queuing delay and minimize the number of dropped packetsat each time interval. In order to evaluate the proposed protocol, it is implemented along with another protocol named spectrum auction. Using the fairness index, the average queuing delay and the number of dropped packets as evaluation metrics, simulation results proved that the proposed protocol outperforms the spectrum auction protocol. It also has a lower time and space complexity.

A coalition-structure’s generation method for solving cooperative computing problems in edge computing environments

Coalition-structure’s generation methods are usually employed to solve team allocation optimization problems or cooperative computing scheduling problems in the case of multitasking concurrency. In edge computing environments, affected by such factors as a large number of edge nodes, weak computing power, multiple optimization objectives and multiple constraints, the traditional methods can hardly guarantee the optimization speed and the optimal solution’s quality when solving similar problems. Based on the advantages of cooperative game algorithms and heuristic algorithms, we propose a coalition-structure’s generation method suitable for edge computing environments in this paper. Firstly, we introduce the concept of bargaining set and remove the impossible coalition-structures by judging the no-bargain coalition to narrow the strategic space. Secondly, for increasing the optimization speed and the optimal solution’s quality, we improve the inertia weight computing method and the particle state determination method of the primary discrete particle swarm, propose M-ary discrete particle swarm optimization (MDPSO). Finally, we design a series of contrast experiments and verify that this method boasts obvious advantages in optimization speed, the optimal solution’s quality, stability, and other aspects.

Task Allocation Mechanism for Cable Real-Time Online Monitoring Business Based on Edge Computing

Edge computing has a promising application prospect in the field of Internet of Things, especially cable real-time online monitoring business in a smart grid. However, for the relatively limited resources and capabilities of the edge nodes, such as computing resource and storage resource, it is hard to highly and comprehensively satisfy the high real-time requirements of cable online monitoring tasks. To solve this problem, effectively dynamic task allocation is needed on the basis of efficient utilization and optimization of resource and capabilities of the edge nodes. In this article, a task allocation mechanism for cable real-time online monitoring business based on edge computing is proposed. First, considering the linear distribution characteristics of the cable, the statuses of edge nodes, the processing overhead of tasks, and the scheduling strategy of delay-sensitive tasks, we establish a task allocation model based on edge computing. Second, a task allocation strategy based on improved discrete particle swarm optimization is proposed. In our strategy, we focus on the task queuing problem in edge nodes and the optimized task allocation problem among edge nodes. Simulation results show that the task allocation mechanism proposed in this article can effectively reduce the average delay of cable real-time online monitoring businesses, and further improve the security and reliability of the smart grid.

A Method of Searching for Optimal Coalition Structure for Solving Resource Scheduling Problem of Overall Load Balancing in Edge Computing Environments

The cooperative computing resource scheduling problem in the distributed computing environment is usually transformed into the problem about how to find the optimal coalition structure. However, affected by factors such as high node density and large number of concurrent tasks in edge computing environments, it is difficult to formulate resource scheduling schemes for overall load balancing in a large strategic space by traditional methods. In order to solve this problem, we proposed a method of searching for optimal coalition structure based on discrete particle swarm optimization (DPSO). Firstly, we converted the resource scheduling problem of edge computing into an optimization problem model, constructed a new structure of coalition structure to express the resource scheduling scheme, and gave key elements such as optimization objectives, decision variables, and constraint conditions. Secondly, we improved DPSO and designed M-ary discrete particle swarm optimization (MDPSO): improved particle state determination method for adapting to the new expression of the coalition structure, and proposed a power series-based inertia weight computing method to improve the optimization speed and the optimal solution quality. Finally, we simulated real application scenarios and designed comparative experiments on the optimization speed, the optimal solution quality, stability, etc., which verifies that the method proposed in this paper has obvious advantages in solving the resource scheduling problem of overall load balancing in edge computing.

Multiobjective Optimization of Safety Risk of Prefabricated Building Construction considering Risk Correlation

The analysis of the overall risk level of the system and the reduction of risk control costs are the key links to avoid the safety risks of prefabricated construction. In this paper, a dual-objective optimization model is constructed with the objective of minimizing system security risk loss and control cost to minimize the level of construction safety risks for prefabricated building while minimizing cost consumption. This paper introduces risk correlation into the optimization model based on the 2-additive fuzzy measure and solves it with discrete multiobjective particle swarm optimization (discrete-MOPSO). The example shows that considering the correlation of risks in different situations can better reflect the interaction relationship between the risk factors of building construction safety and improve the rationality of the optimization model.

Canopy SIF synergize with total spectral reflectance optimized by the MDBPSO algorithm to monitor wheat stripe rust

<p indent=0mm>In order to extract the characteristic parameters sensitive to wheat stripe rust from total spectral reflectance and improve the operation efficiency and estimation accuracy of the wheat stripe rust remote sensing detection model, this paper improved the traditional discrete binary particle swarm optimization (DBPSO) algorithm from two aspects: inertia weight and particle update method. The modified discrete binary particle swarm optimization (MDBPSO) algorithm was used to select the characteristic parameters for the severity of wheat stripe rust from the total spectral reflectance. The selected characteristic variables were combined with canopy solar-induced chlorophyll fluorescence (SIF) data as independent variables to construct wheat stripe rust estimation model. Random forest (RF) algorithm and back propagation (BP) neural network algorithm were used as model construction method to compare and analyze the accuracy of the feature parameter construction model extracted by the correlation coefficient (CC) analysis method and the DBPSO algorithm. The MDBPSO algorithm had faster convergence speed and higher optimization accuracy than the DBPSO algorithm. The number of iterations before and after the improvement was reduced from 395 to 156. The optimal fitness function (OFV) value decreased from 0.145 to 0.127. When the MDBPSO algorithm was used to select feature variables, the accuracy of the models constructed by the two methods of RF and BP neural networks was higher than that by CC analysis and DBPSO. The validation set determination coefficient (<italic>R</italic>²_V) between the predicted disease index (DI) value and the measured DI value of the RF algorithm was 9% and 3% higher than that of the CC analysis method and the DBPSO algorithm, the validation set root mean square error (RMSE_V) was reduced by 28% and 11%, respectively. The <italic>R</italic>²_V between the predicted disease index (DI) value and the measured DI value of the BP neural network algorithm was 13% and 6% higher than that of the CC analysis method and DBPSO algorithm, respectively, and the RMSE_V was reduced by 21% and 10% respectively. The MDBPSO algorithm can improve the remote sensing detection accuracy of wheat stripe rust. Among the three feature selection algorithms of MDBPSO, DBPSO, and CC analysis, the accuracy of the model constructed by the RF algorithm was higher than that by the BP neural network algorithm. The <italic>R</italic>²_V between the predicted DI value and the measured DI value of the RF model was at least 7% higher than that of BP neural network algorithm, with an average increase of 9%; RMSE_V had reduced by at least 15%, with an average reduction of 20%. The MDBPSO-RF model for wheat stripe rust remote sensing detection constructed by RF method using the characteristic parameters selected by the MDBPSO algorithm as independent variables is an appropriate model for wheat stripe rust remote sensing detection. The research results provide new ideas for further realizing large-area high-precision remote sensing monitoring of crop health.

Fuel-Efficient on-Orbit Service Vehicle Allocation Based on an Improved Discrete Particle Swarm Optimization Algorithm

Given the limited fuel capacity of an on-orbit service vehicle (OSV), proper OSV allocation to satellites during each service mission is critical for economic fuel consumption. This allocation problem can be formulated as an optimization problem with many continuous and discrete design variables of wide domains. This problem can be effectively handled through the proposed approach that combines the tabu search with the discrete particle swarm optimization algorithm (DPSO-TS). First of all, Pontryagin’s minimum principle and genetic algorithm (GA) are exploited to find the most fuel-efficient transfer trajectory. This fuel efficiency maximization can then serve as the performance index of the OSV allocation optimization model problem. In particular, the maximization of the minimum residual fuel over individual OSVs is proposed as a performance index for OSV allocation optimization. The optimization problem is numerically solved through the proposed DPSO-TS algorithm. Finally, the simulation results demonstrate that the DPSO-TS algorithm has a higher accuracy compared to the DPSO, the DPSO-PDM and the DPSO-CSA algorithms in the premise that these four algorithms have the basically same computational time. The DPSO-TS algorithm can effectively solve the OSV allocation optimization problem.

Reliable Data Transmission Model for Mobile Ad Hoc Network Using Signcryption Technique

In recent years, the need for high security with reliability in the wireless network has tremendously been increased. To provide high security in reliable networks, mobile ad hoc networks (MANETs) play a top role, like open network boundary, distributed network, and fast and quick implementation. By expanding the technology, the MANET faces a number of security challenges due to self-configuration and maintenance capabilities. Besides, traditional security solutions for wired networks are ineffective and inefficient because of the nature of highly dynamic and resource-constrained MANETs. In this paper, the researchers focus on improving reliable data transmission with high security in the MANET using an optimization technique. In the proposed MANET system, the nodes are clustered by utilizing an energy-efficient routing protocol. Then, the modified discrete particle swarm optimization is used to select the optimal cluster head. A secured routing protocol and a signcryption model can be used to improve the transmission security of the reliable MANET. The signcryption algorithm encrypts the digital signature, which can enhance the overall efficiency and confidentiality. The security-based analysis is performed on the basis of packet delivery ratio, energy consumption, network lifetime, and throughput. Finally, the result demonstrates that the MANET with optimization techniques achieves a high transmission rate and improves the reliable data security.

A new particle swarm intelligence-based graph partitioning technique for image segmentation

The advances in the image processing area demand for improvement in image segmentation methods. Effect of light and noise being ignored in image segmentation while tracing the objects of interest in addition to this texture is also one of the most important factors for analyzing an image automatically. Among the diverse segmentation methods, graph-based techniques are widespread because of their capabilities of generating accurate segmentation structures. In this paper, we have proposed a novel technique by using discrete particle swarm optimization and multilevel partitioning for segmentation of an image. The developed technique has lesser complexity, better efficiency and gives improved results than other methods.

An Approach for Streaming Data Feature Extraction Based on Discrete Cosine Transform and Particle Swarm Optimization

Incremental feature extraction algorithms are designed to analyze large-scale data streams. Many of them suffer from high computational cost, time complexity, and data dependency, which adversely affects the processing of the data stream. With this motivation, this paper presents a novel incremental feature extraction approach based on the Discrete Cosine Transform (DCT) for the data stream. The proposed approach is separated into initial and sequential phases, and each phase uses a fixed-size windowing technique for processing the current samples. The initial phase is performed only on the first window to construct the initial model as a baseline. In this phase, normalization and DCT are applied to each sample in the window. Subsequently, the efficient feature subset is determined by a particle swarm optimization-based method. With the construction of the initial model, the sequential phase begins. The normalization and DCT processes are likewise applied to each sample. Afterward, the feature subset is selected according to the initial model. Finally, the k-nearest neighbor classifier is employed for classification. The approach is tested on the well-known streaming data sets and compared with state-of-the-art incremental feature extraction algorithms. The experimental studies demonstrate the proposed approach’s success in terms of recognition accuracy and learning time.

An early fault detection method for induced draft fans based on MSET with informative memory matrix selection

Early fault detection of induced draft (ID) fans is very important to improve the reliability by providing predictive maintenance and reducing unscheduled shutdowns. This study proposed an early fault detection method for ID fans based on MSET with informative memory matrix selection. Firstly, to obtain an informative memory matrix, the discrete particle swarm optimization (DPSO) was utilized to search samples with large condition information. An accurate MSET model was then developed based on the memory matrix to produce predictions of the feature variables. Finally, a similarity index that represents the health status of the equipment was defined to give warnings of early faults. An application to detect the early faults of an ID fan in a coal-fired power plant was demonstrated to illustrate the effectiveness of proposed method.

State encoding for low power in FSM using non-oscillating self-adaptive particle swarm optimization (NOS-SAPSO)

In this paper, states encoding for the low power design in the Finite State Machine has been presented. Power reduction is considered in the dynamic portion of power consumption where switching activity between states decides the majority factor of power dissipation. Switching activity can be minimized up to a great extent with the optimal assignment of state encoding. Hence, a non-oscillatory variant of discrete self-adaptive particle swarm optimization has proposed to obtain the optimal encoding of states. The proposed method provides the diversity-based self-adaptive parametrization of inertia weight, cognition and social constant along with the best solution evolved ever to direct the progress for the population. The proposed method has delivered the non-oscillatory faster convergence with a high rate of success in delivering a global solution. It is found that the proposed method has surpassed various variants of the existing form of particle swarm optimization over the benchmark problem of the finite state machine.

Surface based variable thickness slicing modeling for laser metal deposition

Laser metal deposition (LMD) has been a promising additive manufacturing technology widely used in mold rapid manufacturing. In order to improve the capacity of LMD for complex curved surface structures, a surface based variable thickness slicing (S-VTS) model is proposed to adaptively generate the LMD process based on the geometric characteristics of structures. Two deposition strategies in scanning and overlapping directions are designed to enable variable thickness of each cladding layer by dynamically adjusting the scanning speed and overlapping rate. To improve the surface quality and forming efficiency, the discrete particle swarm optimization (DPSO) algorithm is adopt to optimize the process parameters of S-VTS model, including the number of cladding layer, scanning pass, and segment per pass. Several experiments are conducted to form the cuboid samples with wavy and freeform surface and verify the feasibility of the S-VTS model. The results demonstrate that under the open-loop control condition, the geometric accuracy, surface quality, and efficiency of the proposed method is improved in comparison with uniform thickness slicing (UTS) deposition. Moreover, heterogeneous microstructure is always generated by the S-VTS method in terms of grain size and growth direction.

A worker-selection incentive mechanism for optimizing platform-centric mobile crowdsourcing systems

With the development of Mobile Crowd Sensing Networks (MCSN), more and more Mobile Crowdsourcing applications emerge. The mobile sensing technologies and theories have become the research hotspot. How to design an effective incentive mechanism to maximize social welfare is the key research in mobile crowdsourcing. This paper proposes an online auction algorithm through combining multi-attribute auction and reverse auction to select the candidate set of crowd workers dynamically. In order to further optimize the utility of platform-centric mobile crowdsourcing, the candidate set of crowd workers is optimized through improving the Discrete Particle Swarm Optimization (DPSO), which can determine the winner set of crowd workers. Through comparison experiments on simulation dataset and real dataset, the effectiveness and performance of the proposed worker-selection incentive mechanism are verified. From experimental results, it can be inferred that the proposed worker-selection incentive mechanism can inspire users to participate in crowd tasks and maximize the utility of mobile crowdsourcing systems effectively.

Collaborative Optimal Operation of AC/DC Hybrid Microgrid with High Proportion of Renewable Energy

Complexity of collaborative optimal dispatching and uncertainty of system are enhanced with the increase ratio of renewable energy in AC/DC hybrid microgrid. The prediction error of the output of renewable energy as a fuzzy variable is treated in this paper. Then a fuzzy chance constrained collaborative optimal model of AC/DC hybrid microgrid considering economy and flexibility is established. In view of the problems that both uncertain programming and mixed integer programming are contained in the model, where conventional algorithms have difficulty in solving it, the thought of fuzzy simulation into the neural network algorithm is introduced in this paper and the uncertainty of the source side by training the neuron to simulate the uncertainty is solved. Finally, the discrete binary particle swarm optimization to solve the integer programming is introduced. Based on the field data of a certain AC/DC hybrid microgrid demonstration project in Zhejiang Province, the effectiveness of the model and algorithm are verified.

Railway Safety Risk Assessment and Control Optimization Method Based on FTA-FPN: A Case Study of Chinese High-Speed Railway Station

In order to make safety risk assessment more accurately and more reasonably for high-speed railway station in China, this paper analyzes risk factors of fault tree and transfers the fault tree of risk accident into fuzzy petri net and then builds the FPN-FTA model by combining the dynamic weighting fuzzy petri net (FPN) and fault tree analysis (FTA) based on the latter. This paper simulates the FTA-FPN model with Stateflow of Matlab software. Then, it builds up a bi-objective risk control model, making the minimum safety risk level and minimum necessary cost as the objectives, and it designs discrete particle swarm optimization algorithm to solve the risk control model. Finally, this paper selects stampede accident of Shijiazhuang high-speed railway station as an example in case study for assessing stampede risk level and gets the risk control schemes for this station. The results verify the feasibility and validity of the model and algorithm.