Parallel Particle Swarm Optimization Research Articles

Simultaneous scheduling of jobs and transporters in a hybrid flow shop with collision-free transporter routing: A novel parallel heuristic

A computationally potent two-stage parallel heuristic (TSPH) and a mixed integer linear programming (MILP) are suggested to address the simultaneous scheduling of jobs and transporters in a hybrid flow shop system, wherein multiple transporters, stage omission, transporter eligibility, machine eligibility, and collision-free transporter routing are assumed. To the best of our knowledge, the significance of transporter collision-free routing has not been spotlighted in the literature on flow shop systems. Collision-free routing of transporters is a requisite technical attribute as transport means may collide on routes and break down the whole system. Our MILP and TSPH assume collision-free routing to impede the issue. Being equipped with parallel computing, TSPH can deal with large problems in a considerably short time. To support, TSPH is analogized against the suggested MILP, two-step MILP (TSMILP), and an efficient parallel meta-heuristic (i.e., parallel particle swarm optimization and genetic algorithm (PPSOGA)) that outperformed many literarily prominent meta-heuristics in the literature. The benchmark results uncover that TSPH outclasses both TSMILP and PPSOGA in the quality of solutions. Eventually, utilizing the convergence plot and Nemenyi’s post-hoc procedure for Friedman’s test, it is revealed that the outcomes of TSPH are remarkably more desirable than those of TSMILP and PPSOGA.

Reconstruction of internal heat source in biological tissue using parallel particle swarm optimization

With the rapid development of engineering thermophysics, researches on human biological heat transfer phenomena has gradually shifted from qualitative to quantitative. The heat distribution information of human lesions is of great value for disease analysis, diagnosis, and treatment. It is a typical inverse problem of heat conduction that deriving the distribution of internal heat sources from the temperature distribution on the body surface. Differing from traditional numerical methods for solving heat conduction, this paper transforms such an inverse problem of bio-heat transfer into a direct one, thereby avoiding complex boundary conditions and regularization processes. To noninvasively reconstruct the internal heat source and its corresponding 3D temperature field in biological tissue, the parallel particle swarm optimization (PPSO) is used in the simulation module, where the position P(x, y, z) of point heat source in biological tissue and its corresponding temperature T are set as the optimization variables. Under a certain optimized sample, one can obtain the simulated temperature distributing on the surface of the module, then subtract the simulated temperature from the measured temperature of the same surface which was measured using a thermal infrared imager. If the absolute value of the difference is smaller, it indicates that the current sample is closer to the true location and temperature of the heat source. When the values of optimization variables are determined, the corresponding 3D temperature field is also confirmed. The simulation results show the simulated position and temperature of the heat source are very approximate with those of the real experimental module. The method presented in this paper has enormous potential and promising prospects in clinical research and application, such as tumor hyperthermia, disease thermal diagnosis technology, etc.

Parallel Hybrid Particle Swarm-Grey Wolf Algorithms for Optimal Load-Shedding in An Isolated Network

In distribution networks integrated with distributed generation (DG), disconnection from the main grid reduces the power supply significantly. The power imbalance between DG generation and load degrades network stability. This paper proposes a hybrid parallel Particle Swarm Optimization - Grey Wolf Optimizer (PSGWO) algorithm for load shedding optimization. This optimization aims to reduce the DG power not absorbed by the remaining loads and maintain the voltage within the specified limits. The performance of PSGWO is tested on an IEEE 33 bus radial distribution system, considering loading levels of 80% to 140% of the baseload. At a 100% loading level, PSGWO showed the best performance, with a load shedding of 2.2297 MW and a voltage deviation of 0.0049. These values are the smallest compared to the results of the standard PSO and GWO algorithms. The PSGWO algorithm remains superior and converges faster than standard PSO and GWO at all loading levels.

Variational Data Assimilation Method Using Parallel Dual Populations Particle Swarm Optimization Algorithm

In recent years, numerical weather forecasting has been increasingly emphasized. Variational data assimilation furnishes precise initial values for numerical forecasting models, constituting an inherently nonlinear optimization challenge. The enormity of the dataset under consideration gives rise to substantial computational burdens, complex modeling, and high hardware requirements. This paper employs the Dual-Population Particle Swarm Optimization (DPSO) algorithm in variational data assimilation to enhance assimilation accuracy. By harnessing parallel computing principles, the paper introduces the Parallel Dual-Population Particle Swarm Optimization (PDPSO) Algorithm to reduce the algorithm processing time. Simulations were carried out using partial differential equations, and comparisons in terms of time and accuracy were made against DPSO, the Dynamic Weight Particle Swarm Algorithm (PSOCIWAC), and the Time-Varying Double Compression Factor Particle Swarm Algorithm (PSOTVCF). Experimental results indicate that the proposed PDPSO outperforms PSOCIWAC and PSOTVCF in convergence accuracy and is comparable to DPSO. Regarding processing time, PDPSO is 40% faster than PSOCIWAC and PSOTVCF and 70% faster than DPSO.

Parallel Particle Swarm Optimization Algorithm for Identifying Complex Communities in Biological Networks

Parallel Particle Swarm Optimization Algorithm for Identifying Complex Communities in Biological Networks

Minimizing Fuel Consumption for Surveillance Unmanned Aerial Vehicles Using Parallel Particle Swarm Optimization.

This paper presents a method based on particle swarm optimization (PSO) for optimizing the power settings of unmanned aerial vehicle (UAVs) along a given trajectory in order to minimize fuel consumption and maximize autonomy during surveillance missions. UAVs are widely used in surveillance missions and their autonomy is a key characteristic that contributes to their success. Providing a way to reduce fuel consumption and increase autonomy provides a significant advantage during the mission. The method proposed in this paper included path smoothing techniques in 3D for fixed-wing UAVs based on circular arcs that overfly the waypoints, an essential feature in a surveillance mission. It used the equations of motions and the decomposition of Newton's equation to compute the fuel consumption based on a given power setting. The proposed method used PSO to compute optimized power settings while respecting the absolute physical constraints, such as the load factor, the lift coefficient, the maximum speed and the maximum amount of fuel onboard. Finally, the method was parallelized on a multicore processor to accelerate the computation and provide fast optimization of the power settings in case the trajectory was changed in flight by the operator. Our results showed that the proposed PSO was able to reduce fuel consumption by up to 25% in the trajectories tested and the parallel implementation provided a speedup of 21.67× compared to a sequential implementation on the CPU.

Optimization of G1 Micromixer Structure in Two-Fluid Mixing Based on CFD and Response Surface Methodology

Optimizing the structure of micromixers to improve the mixing efficiency is of great significance for chemical engineering and biology fields. In this study, an optimization of the microchannel in two liquids mixing is carried out based on computational fluid dynamics (CFD) and response surface methodology. Firstly, CFD simulations were performed to investigate the mixing flow field and mixing efficiency in the microchannel by considering different process and structure parameters (e.g., feed pressure p, microchannel width w). The response surface methodology was adopted to construct a fitting surface by CFD discrete working conditions. Then, an optimized microchannel width w was searched using the parallel particle swarm optimization (PPSO) algorithm from the response surface. Lastly, the searched optimum was validated by CFD simulation again, and the final result showed that the predicted mixing efficiency from the response surface model is well confirmed by CFD simulation. On average, the new optimized microchannel width of 1.634 mm performs higher flow flux and mixing efficiency than the original width of 1.5 mm, increasing 13.51% and 2.45%, respectively.

A concurrent design optimization framework for IMSFRP composite structures considering material and structural parameters simultaneously

Injection molded short fiber reinforced polymer (IMSFRP) composite is a widely used lightweight material in the automobile industry. Due to the integrated molding characteristics of IMSFRP composite structures, its lightweight potential can be fully exploited through the simultaneous design of material and structural parameters. However, the efficient material-structure parallel optimization design method for IMSFRP composite structures has not been fully investigated. In this study, a framework of lightweight design containing the concurrent optimization of material and structure parameters is proposed, and it has been successfully applied to an automobile IMSFRP composite liftgate inner. Firstly, considering the skin-core-skin (SCS) structure of the material, a layered model is built up and a parameterized constitutive model is established to efficiently obtain material properties for various material parameters. Then, a parameter extraction and mapping method is proposed to map fiber distribution information to the structural analysis model to improve the accuracy of structural analysis. In addition, based on the Kriging model and a newly developed parallel boundary search particle swarm optimization algorithm, a lightweight design framework of composite liftgate inner is proposed to consider the material and structural design variables simultaneously. The results show that the proposed framework can solve the concurrent optimization design problem effectively and achieve a weight reduction of 10.5 % for the composite liftgate inner under multiple working conditions.

Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Parameter Calibration in Hydrological Simulation

ABSTRACT Parameter calibration is an important part of hydrological simulation and affects the final simulation results. In this paper, we introduce heuristic optimization algorithms, genetic algorithm (GA) to cope with the complexity of the parameter calibration problem, and use particle swarm optimization algorithm (PSO) as a comparison. For large-scale hydrological simulations, we use a multilevel parallel parameter calibration framework to make full use of processor resources, and accelerate the process of solving high-dimensional parameter calibration. Further, we test and apply the experiments on domestic supercomputers. The results of parameter calibration with GA and PSO can basically reach the ideal value of 0.65 and above, with PSO achieving a speedup of 58.52 on TianHe-2 supercomputer. The experimental results indicate that using a parallel implementation on multicore CPUs makes high-dimensional parameter calibration in large-scale hydrological simulation possible. Moreover, our comparison of the two algorithms shows that the GA obtains better calibration results, and the PSO has a more pronounced acceleration effect.

Parallel-Computing-Based Calibration for Microscopic Traffic Simulation Model

Microscopic traffic simulation is vital to assess the performances of various traffic operation and management schemes. Microscopic traffic simulation is usually not parameter-free, and it relies on independent parameters to predict traffic evolution. Thus, parameter calibration is indispensable to conveying trustworthy simulation results. Heuristic algorithms are widely used for parameter calibration. Its logic is for achieving iterative optimization through continuous trial-and-error simulations. This process is time-consuming and usually takes several hours, making the calibration unable to meet the requirements of speed and efficiency. In recent years, parallel computing technology has been gradually applied in the engineering realm, which makes rapid calibration possible. Following the three steps of parallel framework selection, algorithm bottleneck identification, and subtask load balancing, this paper designs and implements the parallelization of genetic algorithm and particle swarm optimization (PSO) calibration algorithms. Finally, the proposed parallel framework is applied to simulation parameter calibration of a section of a 5 km long highway in Australia, and the effectiveness of parallel computing is evaluated from the two dimensions of reduction in calibration computational time and scalability. The results show that the proposed parallel calibration algorithm can shorten the 5 h calibration process to less than 1 h, reducing the calibration time by 80%. The parallel PSO calibration algorithm has better scalability, and its acceleration effect is better when more processors are used.

A flexible and efficient knowledge-guided machine learning data assimilation (KGML-DA) framework for agroecosystem prediction in the US Midwest

Process-based models are widely used to predict the agroecosystem dynamics, but such modeled results often contain considerable uncertainty due to the imperfect model structure, biased model parameters, and inaccurate or inaccessible model inputs. Data assimilation (DA) techniques are widely adopted to reduce prediction uncertainty by calibrating model parameters or dynamically updating the model state variables using observations. However, high computational cost, difficulties in mitigating model structural error, and low flexibility in framework development hinder its applications in large-scale agroecosystem predictions. In this study, we addressed these challenges by proposing a novel DA framework that integrates a Knowledge-Guided Machine Learning (KGML)-based surrogate with tensorized ensemble Kalman filter (EnKF) and parallelized particle swarm optimization (PSO) to effectively assimilate historical and in-season multi-source remote sensing data. Specifically, we incorporate knowledge from a process-based model, ecosys, into a Gated Recurrent Unit (GRU)-based hierarchical neural network. The hierarchical architecture of KGML-DA mimics key processes of ecosys and builds a causal relationship between target variables. Using carbon budget quantification in the US Corn-Belt as a context, we evaluated KGML-DA's performance in predicting key processes of the carbon cycle at three agricultural sites (US-Ne1, US-Ne2, US-Ne3), along with county-level (627 counties) and 30-m pixel-level (Champaign County, IL) grain yield. The site experiments show that updating the upstream variable, e.g., gross primary production (GPP), improved the prediction of downstream variables such as ecosystem respiration, net ecosystem exchange, biomass, and leaf area index (LAI), with RMSE reductions ranging from 9.2% to 30.5% for corn and 4.8% to 24.6% for soybean. Uncertainty in downstream variables was automatically constrained after correcting the upstream variables, demonstrating the effectiveness of the causality linkages in the hierarchical surrogate. We found joint use of in-season GPP and evapotranspiration (ET) products along with historical GPP and surveyed yields achieved the best prediction for county-level yields, while assimilating in-season LAI observations benefitted the prediction in extreme years. Uncertainty and error analysis of regional yield estimation demonstrated that KGML-DA could reduce prediction error by 26.5% for corn and 36.2% for soybean. Remarkably, the GPU-based tensor operation design makes this DA framework more than 7000 times faster than the PB model with a High-Performance Computing system, indicating the high potential of the proposed framework for in-season, high-resolution agroecosystem predictions.

Parallelized Particle Swarm Optimization on FPGA for Realtime Ballistic Target Tracking.

This paper addresses the problem of tracking a high-speed ballistic target in real time. Particle swarm optimization (PSO) can be a solution to overcome the motion of the ballistic target and the nonlinearity of the measurement model. However, in general, particle swarm optimization requires a great deal of computation time, so it is difficult to apply to realtime systems. In this paper, we propose a parallelized particle swarm optimization technique using field-programmable gate array (FPGA) to be accelerated for realtime ballistic target tracking. The realtime performance of the proposed method has been tested and analyzed on a well-known heterogeneous processing system with a field-programmable gate array. The proposed parallelized particle swarm optimization was successfully conducted on the heterogeneous processing system and produced similar tracking results. Also, compared to conventional particle swarm optimization, which is based on the only central processing unit, the computation time is significantly reduced by up to 3.89×.

An Improved Parallel Particle Swarm Optimization

In the area of global optimization, a variety of techniques have been developed to find the global minimum. These techniques, in most cases, require a significant amount of computational resources and time to complete and therefore there is a need to develop parallel techniques. In addition, the wide spread of parallel architectures in recent years greatly facilitates the implementation of such techniques. Among the most widely used global optimization techniques is the particle swarm optimization technique. In this work, a series of modifications are proposed in the direction of efficient parallelization for particle swarm optimization. These modifications include an innovative velocity calculation mechanism that has also been successfully used in the serial version of the method, mechanisms for propagating the best particles between parallel computing units, but also a process termination mechanism, which has been properly configured for efficient execution in parallel computing environments. The proposed technique was applied to a multitude of computational problems from the relevant literature and the results were more than promising, since it was found that increasing the computational threads can significantly reduce the required number of function calls to find the global minimum. The proposed technique is at rate of 50–70% of the required number of function calls compared to other optimization techniques. This reduction is visible even if one to two parallel processing units are used. In addition, with the increase in parallel processing units, a drastic reduction in the number of calls is observed and therefore a reduction in the required computing time, which can reach up to 70%.

A parallel particle swarm optimization framework based on a fork-join thread pool using a work-stealing mechanism

Particle Swarm Optimization (PSO) is one of the most popular optimization algorithms that has been adopted in various fields, including design, scheduling, and biochemistry. However, the algorithm is time-consuming when facing high-dimensional or multi-objective optimizing problems. Parallel PSO is thus proposed to improve its computing efficiency, and many studies have been conducted. However, the low-level system design is seldom considered in parallel programming, which may have a nonnegligible impact on computing efficiency, creating a gap between low-level optimization and high-level algorithm design. Therefore, this paper proposes a Parallel Asynchronous PSO (PAPSO) framework based on thread pools utilizing multicore processors and adopts a cross-level approach to bridge the gap. A series of experiments are designed and conducted to examine how the aforementioned method can improve the parallel execution efficiency of PSO compared with the OpenMP framework and nonparallel PSO. Results indicate that PAPSO can significantly improve PSO computing efficiency by reducing the elapsed time, approaching approximately 20% optimization compared with OpenMP. Additionally, it achieves an approximately linear speedup of up to 4.5 threads. In addition, the particle communication experiment shows that the nonblocking communication protocol is effective for maintaining the computing elapsed time in the same level facing different neighborhood sizes. Finally, the work-stealing mechanism achieves an average of 16% improvement for general computing scenarios and maintain up to 16% improvement for imbalanced workload scenarios. Generally, the major contribution of this paper is that we innovate the thread pooling management concept with the fork-join model in parallelizing PSO to make sufficient use of multiple core CPUs for parallel computing through multithread programming.

A parallel particle swarm optimization algorithm based on GPU/CUDA

Parallel computing is the main way to improve the computational efficiency of metaheuristic algorithms for solving high-dimensional, nonlinear optimization problems. Previous studies have typically only implemented local parallelism for the particle swarm optimization (PSO) algorithm. In this study, we proposed a new parallel particle swarm optimization algorithm (GPU-PSO) based on the Graphics Processing Units (GPU) and Compute Unified Device Architecture (CUDA), which uses a combination of coarse-grained parallelism and fine-grained parallelism to achieve global parallelism. In addition, we designed a data structure based on CUDA features and utilized a merged memory access mode to further improve data-parallel processing and data access efficiency. Experimental results show that the algorithm effectively reduces the solution time of PSO for solving high-dimensional, large-scale optimization problems. The speedup ratio increases with the dimensionality of the objective function, where the speedup ratio is up to 2000 times for the high-dimensional Ackley function.

Optimization of Characteristics for a Stochastic Agent-Based Model of Goods Exchange with the Use of Parallel Hybrid Genetic Algorithm

Abstract A novel approach to modeling stochastic processes of goods exchange between multiple agents is presented, considering the possibility of optimizing the environment's characteristics and individual decision-making strategies. The proposed model makes it possible to form optimal states when choosing the moments of concluding barter and monetary transactions at the individual level of each agent maximizing the utility function. A new parallel hybrid Real-Coded Genetic Algorithm and Particle Swarm Optimization (RCGA-PSO) has been developed, combining methods of evolutionary selection based on well-known heuristic operators with methods of swarm optimization and machine learning. The algorithm is characterized by the best time efficiency and accuracy in comparison with other methods. The software implementation of the developed algorithm and model has been performed using the FLAME GPU framework. The possibility of using the RCGA-PSO Algorithm to optimize the characteristics of the environment and strategies for making individual decisions by agents involved in barter and monetary interactions is demonstrated.

Generation-based parallel particle swarm optimization for adversarial text attacks

Text adversarial attack is an effective strategy to investigate the vulnerability of Natural Language Processing (NLP) models. Most of text attack studies focus on word-level attacks with static or dynamic optimization algorithms. However, they are hard to balance (1) attack performance (i.e., attack success rate, word substitution rate) and (2) attack efficiency. Generally, static optimization is fast but suffers from low attack performance, and the dynamic adversary improves the attacking quality but is time-consuming. To address these challenges, a Generation-based Parallel Particle Swarm Optimization (GP2SO) is proposed for the adversarial text attack. Specifically, the GP2SO employs an adaptive strategy to determine the word modification priority, which produces a high attack performance owing to the aggressive objective function. To achieve time efficiency, we parallelize the PSO on multiple pipelines in a generation-overlapping manner. Extensive experiments on four public text recognition datasets are conducted by attacking four deep models to evaluate the effectiveness of the GP2SO. Experimental results manifest that the proposed GP2SO averagely improves the time efficiency by 272% with only 0.3% success rate reduction compared to the PSO. Besides, the GP2SO also shows superiorities in adversarial training and transferability compared with baselines. The code is provided to ensure reproducibility https://github.com/OutdoorManofML/GPPSO.

Optimal switching control of 1,3-propanediol fed-batch production with a cost on smooth feeding rate variation

In this paper, we propose a nonlinear switched control system for microbial 1,3-propanediol (1,3-PD) fed-batch production, where the smooth feeding rate of glycerol as well as the switching instants between batch process and feeding process are taken as control variables. To maximize 1,3-PD yield and minimize the fluctuation of feeding rate, we then present an optimal switching control problem with a cost on the smooth feeding rate variation and subject to path constraints reflecting the operational requirements. For solving this problem, we convert it to a series of parameter optimization problems by using a novel control parameterization scheme, a time-scaling transformation and a constraint transcription technique. An analytical expression of the total variation for the smooth feeding rate is also derived. On this basis, we develop a parallel Particle Swarm Optimization (PSO) algorithm together with the gradient-based optimization to solve the resulting problem. Finally, numerical simulations indicate that sacrificing a small amount of 1,3-PD can reduce the feeding rate variation significantly.

Parallel particle swarm optimization for optimal SAGD operating scenario and well placement in a high dimensional search space

Steam-assisted gravity drainage (SAGD) has been commercially tested successfully in many oilfields with different characteristics. The operational parameters and wells’ location substantially affect the performance of SAGD, and designing a scenario optimization to find the optimum values of these parameters would enhance the efficiency of the process. Although most previous studies worked on optimizing some of the mentioned parameters separately, barely any optimized them simultaneously using a full 3D model since optimizing such a high-dimensional problem is time-consuming. In this study, whole-time scenario optimization is designed to simultaneously optimize the operational (steam injection rate, temperature, and production bottom hole pressure of all intervals) and well placement parameters of an 8-year SAGD. Parallel particle swarm optimization (PSO) is developed to determine the desired number of simulations concurrently to reduce computational costs. Also, the SAGD time is divided into four time intervals, and the effect of uniform and non-uniform time intervals on the net present value (NPV) and oil production is investigated. The outcomes showed that conducting parallel PSO would reduce the optimization time by 79.8%. Also, by applying the non-uniform intervals, the highest NPV is achieved, and the SAGD time declines to 6 years.

Optimal operation of battery storage systems in standalone and grid-connected DC microgrids using parallel metaheuristic optimization algorithms

This study addresses the problem of optimal operation of batteries in standalone and grid-connected Direct Current (DC) Microgrids (MGs) that include photovoltaic (PV) generators operating at maximum power point. For that purpose, a mathematical model was formulated considering three objective functions: (1) the minimization of operating costs, (2) the reduction of energy losses associated with energy transport in DC MGs, and (3) the minimization of the total emissions of CO2 into the atmosphere produced by conventional generators. The model integrates a set of constraints that represent the operation of DC microgrids. Three parallel versions of well-known solution methodologies were used here: Parallel Particle Swarm Optimization (PPSO), the Parallel Vortex Search Algorithm (PVSA), and the Parallel Ant Lion Optimizer (PALO). These methodologies were implemented to optimize the hourly power flow method based on successive approximations and evaluate the objective functions and constraints. To validate the effectiveness of the solution methods proposed in this paper, two test systems were used: a standalone network and a grid-connected network (both in Colombia). Furthermore, to test the effectiveness of the proposed energy management system (regarding solution, repeatability, and processing times) each solution methodology was executed 100 times in each test system using MATLAB software. Based on the results of the simulations, the methodology with the best performance for standalone grids was the PVSA. In turn, the methodology that achieved the best results in the grid-connected network was the PALO. In both scenarios, the best average reductions in energy fixed costs, variable costs, energy losses, and CO2 emissions were 0.0282%, 1.3039%, 9.1655%, and 0.1254%, respectively. Although these were important benefits in technical and environmental aspects, the results of the economic indicator suggest that the operation of electrical networks should consider variable costs in order to improve the impact of energy management systems in the future.