Parallel Particle Swarm Optimization Research Articles

Hybrid optimization for virtual machine migration of utilizing healthcare text in the cloud

Cloud computing as the modern technology that generates, processing, storing, and sharing of medical data has evolved significantly. The health industry has made a lot of progress in transforming its data management activities, from regular storage to the digitalization of health care data. Cloud computing impacting based on lowering of costs, availability of resources, and power. moreover patient has the right or ownership of data in-the-cloud virtualization technology. Keeping the data of the patient in the cloud also facilitates interoperability between the various sectors of the health-care sector-pharmacy, insurance, and payments. Cloud offers virtual hardware, runtime settings, and facilities for those with a credit card. Cloud infrastructure has become a common term for reference to various devices, resources, and concepts. The proposed framework provides a simulated migration approach that is complex and energy-intensive. By activating idle physical machinery mode, this mechanism reduces the power to conserve electricity. This study suggests a modern cloud-based Health care services paradigm for optimizing VM migration utilizing Parallel Particle Swarm Optimization (PPSO). To measure the performance of our VMs model, a new model for health care service is also provided. The findings reveal that, in the overall deployment period, the new model approaches 60% of the state-of-the-art implementations. Furthermore, device performance is increased by 6.2% for demanded data in real-time. Furthermore, the accuracy of the smart hybrid model of resource utilization is 96.8%. In all associated activities, the suggested model is 67% better than other referred versions.

Simultaneous implementation of sludge dewatering and solid biofuel production by microwave torrefaction

Sludge dewatering is a matter of great concern to reduce the volume of sludge, stabilize its organic components, and achieve resource utilization. This study investigates sludge dewatering by microwave torrefaction along with the production of sludge solid biofuel at 480–800 W combined with durations of 5–25 min. Proximate analysis, calorific value analysis, thermogravimetric analysis, and scanning electron microscopy observations are employed to evaluate the dewatering degree, fuel properties, and energy efficiency of the torrefaction process. The independent parallel reaction (IPR) model and particle swarm optimization (PSO) analysis are also adopted for sludge pyrolysis kinetics calculation. The results show that microwave torrefaction is efficient for sludge dewatering with a short duration. The produced sludge solid biofuel is similar to stone-like coal, and can be used for civil or industrial boilers after flotation or just co-firing with briquette. The ash content of sludge solid biofuel shows a declining trend and the surface characteristics change from smooth to rough and fluffy with increasing the torrefaction severity. The bio-oil is mainly composed of phenols, siloxanes, and cholesterol. In addition, hydrogen is detected in the torgas. Furthermore, it is found that lower torrefaction power with a shorter duration yields a higher energy efficiency of the torrefaction process.

Floorplanning for Area Optimization Using Parallel Particle Swarm Optimization and Sequence Pair

In the integrated circuit (IC) designing floorplanning is an important phase in the process of obtaining the layout of the circuit to be designed. The floorplanning determines the performance, size, yield, and reliability of VLSI ICs. The obtained results in this step are necessary for the other consecutive process of the chip designing. VLSI floorplanning from the computational point of view is a non-polynomial hard (NP-hard problem), and hence cannot be efficiently solved by the classical optimization techniques. In this paper, we have proposed a metaheuristic approach to address the problem by using the parallel particle swarm optimization (P-PSO) technique. The P-PSO uses a new greedy operation on the sequence pair (SP) to explore the search space to find an optimal solution. Experimental results on the Microelectronic Centre of North Carolina and Gigascale Systems Research Center benchmark shows that the applied parallel PSO (P-PSO) may be used to produce an optimal solution.

Parallel computing and swarm intelligence based artificial intelligence model for multi-step-ahead hydrological time series prediction

Accurate future runoff prediction information is of great importance for the planning and management of both water resource and electric power systems. As employed to address the hydrological forecasting problem, artificial neural network (ANN) exhibits strong generalization and flexibility, but usually suffers from some shortcomings in practice, like unsatisfying learning efficiency and local convergence. The goal of this research is to develop a parallel computing and swarm intelligence based artificial neural network for multi-step-ahead hydrological time series prediction. Specially, the connection weights and biases of the ANN model are incrementally optimized via a parallelized particle swarm optimization obeying the Fork/Join framework, where the large-scale swarm is divided into a series of small and independent subswarms that will search for the optimal solution in the feasible space at the same time. The proposed method is driven to forecast the runoff time series of two real-world hydrological stations in China. The simulations indicate that the proposed method betters the conventional forecasting methods with respect to various indexes in different cases. For instance, in the 2-step-ahead case, the proposed method betters ANN with about 6.4 % improvement in the Nash-Sutcliffe efficiency value during the testing phase. Hence, the main contribution of this research is the utilization of swarm intelligence algorithm and high-performance parallel computing technique to improve the artificial intelligence model’s performances in time series forecasting.

Generation-Level Parallelism for Evolutionary Computation: A Pipeline-Based Parallel Particle Swarm Optimization.

Due to the population-based and iterative-based characteristics of evolutionary computation (EC) algorithms, parallel techniques have been widely used to speed up the EC algorithms. However, the parallelism usually performs in the population level where multiple populations (or subpopulations) run in parallel or in the individual level where the individuals are distributed to multiple resources. That is, different populations or different individuals can be executed simultaneously to reduce running time. However, the research into generation-level parallelism for EC algorithms has seldom been reported. In this article, we propose a new paradigm of the parallel EC algorithm by making the first attempt to parallelize the algorithm in the generation level. This idea is inspired by the industrial pipeline technique. Specifically, a kind of EC algorithm called local version particle swarm optimization (PSO) is adopted to implement a pipeline-based parallel PSO (PPPSO, i.e., P3SO). Due to the generation-level parallelism in P3SO, when some particles still perform their evolutionary operations in the current generation, some other particles can simultaneously go to the next generation to carry out the new evolutionary operations, or even go to further next generation(s). The experimental results show that the problem-solving ability of P3SO is not affected while the evolutionary speed has been substantially accelerated in a significant fashion. Therefore, generation-level parallelism is possible in EC algorithms and may have significant potential applications in time-consumption optimization problems.

Optimal Location and Sizing of Distributed Generators in DC Networks Using a Hybrid Method Based on Parallel PBIL and PSO

This paper addresses the problem of the locating and sizing of distributed generators (DGs) in direct current (DC) grids and proposes a hybrid methodology based on a parallel version of the Population-Based Incremental Learning (PPBIL) algorithm and the Particle Swarm Optimization (PSO) method. The objective function of the method is based on the reduction of the power loss by using a master-slave structure and the consideration of the set of restrictions associated with DC grids in a distributed generation environment. In such a structure, the master stage (PPBIL) finds the location of the generators and the slave stage (PSO) finds the corresponding sizes. For the purpose of comparison, eight additional hybrid methods were formed by using two additional location methods and two additional sizing methods, and this helped in the evaluation of the effectiveness of the proposed solution. Such an evaluation is illustrated with the electrical test systems composed of 10, 21 and 69 buses and simulated on the software, MATLAB. Finally, the results of the simulation demonstrated that the PPBIL–PSO method obtains the best balance between the reduction of power loss and the processing time.

RFID Reader Anticollision Based on Distributed Parallel Particle Swarm Optimization

The deployment of a very large number of readers in a limited space may increase the probability of collision among radio-frequency identification (RFID) readers and reduce the dependability and controllability of Internet-of-Things (IoT) systems. Intelligent computing technologies can be used to realize intelligent management by scheduling resources to circumvent collision issues. In this article, an improved RFID reader anticollision model is constructed by modifying the measure index, introducing a constraint function, and simultaneously considering collisions among readers and between readers and tags. The dense deployment of large numbers of readers increases the number of variables to be encoded, resulting in a high-dimensional problem that cannot be effectively and efficiently solved by traditional algorithms. Accordingly, distributed parallel cooperative co-evolution particle swarm optimization (DPCCPSO) is proposed. The inertia weight and learning factors are adjusted during evolution, and an improved grouping strategy is presented. Moreover, various combinations of random number generation functions are tested. For improved efficiency, DPCCPSO is implemented with distributed parallelism. Experimental verification shows that the proposed novel algorithm exhibits superior performance to existing state-of-the-art algorithms, particularly when numerous RFID readers are deployed.

A Parallel Particle Swarm Optimisation for Selecting Optimal Virtual Machine on Cloud Environment

Cloud computing has a significant role in healthcare services, especially in medical applications. In cloud computing, the best choice of virtual machines (Virtual_Ms) has an essential role in the quality improvement of cloud computing by minimising the execution time of medical queries from stakeholders and maximising utilisation of medicinal resources. Besides, the best choice of Virtual_Ms assists the stakeholders to reduce the total execution time of medical requests through turnaround time and maximise CPU utilisation and waiting time. For that, this paper introduces an optimisation model for medical applications using two distinct intelligent algorithms: genetic algorithm (GA) and parallel particle swarm optimisation (PPSO). In addition, a set of experiments was conducted to provide a competitive study between those two algorithms regarding the execution time, the data processing speed, and the system efficiency. The PPSO algorithm was implemented using the MATLAB tool. The results showed that the PPSO algorithm gives accurate outcomes better than the GA in terms of the execution time of medical queries and efficiency by 3.02% and 37.7%, respectively. Also, the PPSO algorithm has been implemented on the CloudSim package. The results displayed that the PPSO algorithm gives accurate outcomes better than default CloudSim in terms of final implementation time of medicinal queries by 33.3%. Finally, the proposed model outperformed the state-of-the-art methods in the literature review by a range from 13% to 67%.

Designing a data-driven leagile sustainable closed-loop supply chain network

ABSTRACT Nowadays, there is a great deal of interest in applying sustainability concepts for logistics and supply chain management. This paper proposes a new multi objective model in the area of closed loop supply chain problem integrated with lot sizing by considering lean, agility and sustainability factors simultaneously. In this regard, responsiveness, environmental, social and economic aspects are regarded in the model in addition to the capacity and service-level constraints. Most importantly, strategic and operational backup decisions are developed to increase the resiliency of the system against disruption of the facilities and routes simultaneously. In the following, a new hybrid metaheuristic algorithm comprised a parallel Multi-Objective Particle Swarm Optimization (PMOPSO) algorithm and a multi objective social engineering optimizer (MOSEO) is developed to deal with large size problems efficiency. To ensure about the effectiveness of the proposed hybrid algorithm, the results of this algorithm are compared with a Non-dominated Sorting Genetic Algorithm (NSGA-II).

Improved parallel reactive hybrid particle swarm optimization using improved neighborhood schedule generation method for the integrated framework of optimal production scheduling and operational planning of an energy plant in a factory

AbstractThis study proposes improved parallel reactive hybrid particle swarm optimization (IPRHPSO) using an improved neighborhood schedule generation method for the integrated framework of optimal production scheduling and operational planning of an energy plant in a factory. Conventionally, in an energy plant, fixed loads of various tertiary energies have been utilized to solve the optimal operational planning of an energy plant so far. Additionally, in production lines, only the minimization of production time has been yet considered. Therefore, the secondary energy cost of a factory cannot be reduced accurately. However, in this study, the loads of various tertiary energies are calculated according to the candidates of production scheduling and the optimal operational planning of an energy plant is determined using the tertiary energies. This can explicitly reduce the secondary energy cost of a factory. The proposed method was applied to 10 jobs and machine JSPs each. Accordingly, it was verified that it can minimize the secondary energy cost and production time simultaneously, and realize fast computation through parallel computation using IPRHPSO.

An energy management system for optimal operation of BSS in DC distributed generation environments based on a parallel PSO algorithm

Abstract This paper proposes an energy management system (EMS) for the day-ahead dispatch of battery storage systems (BSS) under a distributed generation environment for direct current (DC) networks, with the main objective of reducing the cost of the energy purchased to the utility grid. This approach considers the state-of-charge (SOC) of the BSS and the production variation of the renewable generators, in particular of wind and photovoltaic technologies, and the variations in the power consumption and energy costs. The proposed EMS uses a master-slave strategy formed by a parallel implementation of the particle swarm optimizer (PPSO) and a multi-period power flow method based on successive approximations (SA), with the aim of achieving the optimal daily operation of the BSS. The objective function selected for the optimization was the reduction of the energy purchasing costs, also including the power balance, devices capabilities and voltage regulation. The effectiveness of the EMS is evaluated in a test system of 21 buses, comparing the solution quality and speed with three optimization techniques published in literature: a black hole optimizer, a continuous genetic algorithm with matrix structure, and a traditional Chu & Beasley genetic algorithm. In addition, two simulation scenarios were used to identify the optimal final SOC conditions for the BSS. Finally, the results show that the proposed EMS provides the best trade-off between quality solution and speed. The simulations are conducted in MATLAB software using sequential quadratic programming.

Short-term stochastic optimization of a hydro-wind-photovoltaic hybrid system under multiple uncertainties

With the increasing emphasis on environmental problems and climate change, renewable energy sources have been developed globally to push modern power systems towards sustainability. However, the weather-dependent and non-dispatchable features of renewable energy sources often hinder their integration into power grids and also pose a challenge for peak load regulation. Recently, the complementary operation of multi-energy hybrid systems has been attracting increasing attention as a promising way to overcome the mismatch between renewable energy supply and varying load demand. Multi-energy systems should be operated considering multiple uncertainties since a deterministic method only captures a fixed snapshot of a constantly changing system. In this study, the obtained short-term peak shaving operation of a hydro-wind-photovoltaic hybrid system is developed as a stochastic programming model. The uncertainties of renewable energy production and load demand are thoroughly simulated in the form of synthetic ensemble forecasts and scenario trees. To enhance the computational efficiency, a parallel particle swarm optimization algorithm is developed to solve the stochastic peak shaving model, in which a novel encoding scheme and parallel computing strategy are used. The proposed framework is applied to a hydro-wind-photovoltaic hybrid system of the East China Power Grid. The results of three numerical experiments indicate that the framework can achieve satisfactory peak shaving performance of the power system and enable decision makers to examine the robustness of operational decisions. In addition, it is acceptable for decision makers that joint complementary operation of the hybrid system greatly enhances the peak shaving capacity (with the performance metrics being improved by 95.7%, 96.4% and 30.5%) at the cost of 0.11% loss of total power generation.

Symbiotic Organism Search Algorithm with Multi-Group Quantum-Behavior Communication Scheme Applied in Wireless Sensor Networks

The symbiotic organism search (SOS) algorithm is a promising meta-heuristic evolutionary algorithm. Its excellent quality of global optimization solution has aroused the interest of many researchers. In this work, we not only applied the strategy of multi-group communication and quantum behavior to the SOS algorithm, but also formed a novel global optimization algorithm called the MQSOS algorithm. It has speed and convergence ability and plays a good role in solving practical problems with multiple arguments. We also compared MQSOS with other intelligent algorithms under the CEC2013 large-scale optimization test suite, such as particle swarm optimization (PSO), parallel PSO (PPSO), adaptive PSO (APSO), QUasi-Affine TRansformation Evolutionary (QUATRE), and oppositional SOS (OSOS). The experimental results show that MQSOS algorithm had better performance than the other intelligent algorithms. In addition, we combined and optimized the DV-hop algorithm for node localization in wireless sensor networks, and also improved the DV-hop localization algorithm to achieve higher localization accuracy than some existing algorithms.

Maximizing the Profit for Industrial Customers of Providing Operation Services in Electric Power Systems via a Parallel Particle Swarm Optimization Algorithm

This work was supported in part by the Primeros Proyectos de Investigacion under Grant PAID-06-18, in part by the Vicerrectorado de Investigacion, Innovacion y Transferencia de la Universitat Politecnica de Valencia (UPV) Valencia-Spain, Generalitat Valenciana through the Research Project under Grant AICO/2019/001, in part by the Spanish Administration under Grant FPU2016/00962, in part by the AEI/10.13039/501100011033 (Ministerio de Ciencia, Innovacion y Universidades, Spanish Government) through the Research Projects under Grant ENE-2016-78509-C3-1-P and Grant RED2018-102618-T, and in part by the EU FEDER Funds.

Quasi-Affine Transformation Evolutionary Algorithm With Communication Schemes for Application of RSSI in Wireless Sensor Networks

QUasi-Affine TRansformation Evolutionary algorithm (QUATRE) is a new optimization algorithm based on population for complex multiple real parameter optimization problems in real world. In this paper, a novel multi-group multi-choice communication strategy algorithm for QUasi-Affine TRansformation Evolutionary (MM-QUATRE) algorithm is proposed to solve the disadvantage that the original QUATRE is always easily to fall into local optimization in the strategy of updating bad nodes with multiple groups and multiple choices. We compared it with other intelligent algorithms, the most advanced PSO variant, parallel PSO (P-PSO) variant, native QUATRE and parallel QUATRE (P-PSO) under CEC2013 large-scale optimization test suite. Thus, the performance of MM-QUATRE was verified. The conclusion that the MM-QUATRE algorithm is superior to other intelligent algorithms is proved by the experimental results. In addition, the application results of MM-QUATRE algorithm (MM-QUATRE-RSSI) based on RSSI in WSN node localization were analyzed and studied. The results appear that this method has higher localization accuracy than other similar algorithms.

A Parallel Multi-Verse Optimizer for Application in Multilevel Image Segmentation

Multi-version optimizer (MVO) inspired by the multi-verse theory is a new optimization algorithm for challenging multiple parameter optimization problems in the real world. In this paper, a novel parallel multi-verse optimizer (PMVO) with the communication strategy is proposed. The parallel mechanism is implemented to randomly divide the initial solutions into several groups, and share the information of different groups after each fixed iteration. This can significantly promote the cooperation individual of MVO algorithm, and reduce the deficiencies that the original MVO is premature convergence, search stagnation and easily trap into local optimal search space. To confirm the performance of the proposed scheme, the PMVO algorithm was compared with the other well-known optimization algorithms, such as gray wolf optimizer (GWO), particle swarm optimization (PSO), multi-version optimizer (MVO), and parallel particle swarm optimization (PPSO) under CEC2013 test suite. The experimental results prove that the PMVO is superior to the other compared algorithms. In addition, PMVO is also applied to solve complex multilevel image segmentation problems based on minimum cross entropy thresholding. The application results appear that the proposed PMVO algorithm can achieve higher quality image segmentation compared to other similar algorithms.

Research on Integrated Optimization Design Method of High-Efficiency Motor Propeller System for UAVs With Multi-States

With the popularity of electric multi-rotor unmanned aerial vehicles (EMRUAV), the problem of low efficiency of the motor propeller system (MPS) becomes serious especially for EMRUAVs which have multiple working states. To solve this problem, an integrated optimization design method for high-efficiency MPS is proposed using an improved parallel particle swarm optimization and differential evolution (PSODE) hybrid algorithm. First, the brushless motor as well as the variable-speed and the adjustable-pitch propellers is modelled. Then considering the characteristics of the motor and the propeller sufficiently, an integrated optimization method for variable-speed and adjustable-pitch MPS is proposed by utilizing the improved PSODE. Specially, the self-organizing feature map theory is added to substitute the traditional “winner-take-all” method in the interactive study of the improved PSODE. Next, the optimization of a small electric-powered tilt quad rotor (TQR) which has four states: hovering, cruising, transition and maximum speed is carried out. At last, the wind-tunnel experiments are implemented to verify the feasibility and effectiveness of the proposed optimization method. The results indicate that the adjustable-pitch MPS is more suitable for EMRUAVs which have multiple working states, and that the improved PSODE has faster convergence speed and better global search ability than other optimization methods.

Multiobjective Complex Network Clustering Based on Dynamical Decomposition Particle Swarm Optimization

Clustering is a basic tool applied to complex networks. However, the clustering of complex networks is often based on a single objective function, which can obtain insufficient clustering effects. To address the insufficiencies of single objective complex network clustering, multiobjective complex network clustering was proposed. In this article, to improve multiobjective complex network clustering, we prove the superiority of dynamic decomposition mathematically and propose a parallel discrete particle swarm optimization algorithm based on dynamic decomposition (DDDPSO). First, solutions are obtained at different levels by optimizing the objective functions of parallel subpopulations. Second, the decomposition space is divided dynamically by the reference vector of dynamic decomposition. Particle swarms are used to search for optimal solutions in the partitioned dynamic spaces. Finally, the individuals in the particle swarm are sorted according to the obtained solutions to obtain individuals with good convergence and diversity. We conduct comparisons with many state-of-the-art algorithms on many widely used test datasets to test the DDDPSO. The experimental results show the effectiveness of the proposed approach for complex network clustering.

Association rules mining in parallel conditional tree based on grid computing inspired partition algorithm

Association rules have important applications in many fields, however, with the explosive growth of information technology in recent years, the mining efficiency of association rules has become a very serious problem. The parallel multi-swarm PSO frequent pattern (PMSPF) algorithm creatively combines the particle swarm optimisation (PSO) algorithm with the frequent pattern-growth (FP-growth) algorithm to greatly improve the mining efficiency of association rules. However, under the computing environment of the Spark cluster, the calculation load is not balanced. Therefore, large amount of data may lead to problems like memory overflow. In this paper, parallel conditional frequent pattern (PCFP) tree algorithm is proposed on the basis of PMSPF. First of all, through data grouping, the problem of too large a data volume to construct FP-tree is solved. Then, through parallel strategy of the condition tree, parallel computing is implemented. The experimental results show that although PCFP algorithm generates certain data redundancy in the process of data grouping, the efficiency of the algorithm is significantly higher than that of the PMSPF algorithm and traditional parallel frequent pattern (PFP) algorithm.

Analysis of Parallel Genetic Algorithm and Parallel Particle Swarm Optimization Algorithm UAV Path Planning on Controller Area Network

Path planning of the unmanned aerial vehicle (UAV) is one of the complex optimization problems due to the model complexity and a high number of constraints. Evolutionary algorithms are a conventional method to solve complex optimization problems with multiple constraints. However, they are time-consuming algorithms which make their implementation difficult. UAV path planning is usually a real-time problem that is needed to solve in limited time steps. Parallelization is one of the most effective methods for reducing the computation time of evolutionary algorithms. In the industrial applications, usually there is a communication platform between devices which can be used for the implementation of the parallelization process. For example, controller area network (CAN) is a protocol used for reliable communication in aerospace systems. Due to the development of this protocol on UAVs and its characteristics, it can be used for parallel optimization of path planning problem. In this paper, an asynchronous distributed multi-master parallel genetic algorithm and a parallel particle swarm optimization algorithm are implemented on CAN bus to improve the speed and performance of the UAV path planning.