Hybrid Parallel Algorithm Research Articles

A new hybrid parallel genetic algorithm for multi-destination path planning problem

This paper proposes a new parallel approach of multi objective genetic algorithm for path planning problem. The main contribution of this work is to reduce the population size that effect in decreasing processing times of finding the optimum path for multi destination problem. This is achieved by combining the local population of island parallel approach and global population of global parallel approach. Various experiments have been conducted to evaluate the new hybrid parallel genetic algorithm (HPGA) in solving multi-objective path planning problems. Three different test areas with 2 destinations were used to assess the performance of HPGA. Furthermore, this work compares HPGA and sequential genetic algorithm (SeqGA), as well as compared to other existing parallel genetic algorithm (GA) methods. From experimental results show that proposed HPGA outperform others, in term of processing time i.e., up to 3.6 times speedup faster, and lowest GA parameter values. This proposed HPGA can be utilized to design robots with fast and consistent path planning, especially with various obstecles.

A parallel inertial SP-iteration monotone hybrid algorithm for a finite family of G-nonexpansive mappings and its application in linear system, differential, and signal recovery problems

A parallel inertial SP-iteration monotone hybrid algorithm for a finite family of G-nonexpansive mappings and its application in linear system, differential, and signal recovery problems

Optimal allocation of enterprise marketing resources based on hybrid parallel genetic algorithm and simulated annealing algorithm

Abstract Aiming at minimizing the use of marketing resources, this article establishes the mathematical model of marketing resources allocation, designs the algorithm of marketing resources allocation, and compares the examples. An improved heuristic algorithm considering tilt angle matching is proposed and used as a local search algorithm for enterprise marketing resources. We design an innovative optimization strategy that incorporates the concept of tilt angle matching to enhance the local search efficiency of enterprise marketing resource allocation. In addition, we have introduced a novel parallel grouping genetic algorithm (PGGA), which utilizes grouping coding and exon crossover to further enhance the search and optimization efficiency of the solution. PGGA is improved by using adaptive parameters to form IPGGA, which improves the efficiency and convergence speed of enterprise marketing resource allocation. The annealing function of the simulated annealing algorithm is improved, and a model is constructed to solve the problem of enterprise marketing resource allocation. Simulated annealing algorithm is introduced to solve the problem of marketing resource allocation, and the framework of simulated annealing algorithm is analyzed. To solve the problem of fast decay rate of simulated annealing algorithm, Doppler effect function is used to optimize the algorithm. This article mainly uses qualitative and quantitative analysis methods to conduct in-depth research on enterprise marketing resource allocation. It focuses on the planning and allocation of enterprise marketing resources. Through IPGGA-ISAA research and analysis of all kinds of data of enterprise marketing resources, the present situation, efficiency, and main problems of enterprise marketing resources allocation are discussed and analyzed more deeply. Compared with other algorithms, IPGGA-ISAA can better analyze the causes of problems and provide better marketing resource allocation schemes for enterprises.

Parallel Hybrid Algorithm for Face Recognition Using Multi-Linear Methods

This paper introduces a pioneering Hybrid Parallel Multi-linear Face Recognition algorithm that capitalizes on multi-linear methodologies, such as Multi-linear Principal Component Analysis (MPCA), Linear Discriminant Analysis (LDA), and Histogram of Oriented Gradients (HOG), to attain exceptional recognition performance. The Hybrid Feature Selection (HFS) algorithm is meticulously crafted to augment the classification performance on the CK+ and FERET datasets by amalgamating the strengths of feature extraction techniques and feature selection methods. HFS seamlessly incorporates Principal Component Analysis (PCA), Local Discriminant Analysis (LDA), and HOG. The primary aim of this algorithm is to autonomously identify a subset of the most distinctive features from the extracted feature pool, thus elevating classification accuracy, precision, recall, and F1-Score. By amalgamating these methodologies, the algorithm adeptly diminishes dimensionality while conserving pivotal features. Experimental trials on facial image datasets, CK+ and FERET, underscore the algorithm's supremacy in terms of accuracy and computational efficiency when contrasted with conventional linear techniques and even certain deep learning approaches. The proposed algorithm proffers an encouraging solution for real-world face recognition applications where precision and operational efficiency are of paramount significance.

A novel approach and hybrid parallel algorithms for solving the fixed charge transportation problem

This article is dedicated to the efficient resolution of the fixed charge transport problem (FCTP) with the goal of identifying optimal solutions within reduced timeframes. FCTP is a combinatorial and NP-complete problem known for its exponential time complexity relative to problem size. Metaheuristic methods, including genetic algorithms, represent effective techniques for obtaining high-quality FCTP solutions. Consequently, the integration of parallel algorithms emerges as a strategy for expediting problem-solving. The proposed approach, referred to as the parallel genetic algorithm (PGA), entails the application of a genetic algorithm across multiple parallel architectures to tackle the FCTP problem. The primary aim is to explore fresh solutions for the fixed charge transportation problem using genetic algorithms while concurrently optimizing the time required to achieve these solutions through parallelism. The FCTP problem is fundamentally a linear programming challenge, revolving around the determination of optimal shipment quantities from numerous source locations to multiple destinations with the overarching objective of minimizing overall transportation costs. This necessitates consideration of constraints tied to product availability at the sources and demand dynamics at the destinations. In this study, a pioneering approach to addressing the Fixed Charge Transportation Problem (FCTP) using parallel genetic algorithms (PGA) is unveiled. The research introduces two distinct parallel algorithms: The Master-Slave Approach (MS-GA) and the Coarse-Grained Approach (CG-GA). Additionally, investigation into the hybridization of these approaches has led to the development of the NMS-CG-GA approach. The numerical results reveal that our parallelism-based approaches significantly improve the performance of genetic algorithms. Specifically, the Master-Slave (MS-GA) approach demonstrates its advantages in solving smaller instances of the FCTP problem, while the Coarse-Grained (CG-GA) approach exhibits greater effectiveness for larger problem instances. The conclusion reached is that the novel hybrid parallel genetic algorithm approach (NMS-CG-GA) outperforms its predecessors, yielding outstanding results, particularly across diverse FCTP problem instances.

An Efficient Hybrid Multi-Objective Optimization Method Coupling Global Evolutionary and Local Gradient Searches for Solving Aerodynamic Optimization Problems

Aerodynamic shape optimization is frequently complicated and challenging due to the involvement of multiple objectives, large-scale decision variables, and expensive cost function evaluation. This paper presents a bilayer parallel hybrid algorithm framework coupling multi-objective local search and global evolution mechanism to improve the optimization efficiency and convergence accuracy in high-dimensional design space. Specifically, an efficient multi-objective hybrid algorithm (MOHA) and a gradient-based surrogate-assisted multi-objective hybrid algorithm (GS-MOHA) are developed under this framework. In MOHA, a novel multi-objective gradient operator is proposed to accelerate the exploration of the Pareto front, and it introduces new individuals to enhance the diversity of the population. Afterward, MOHA achieves a trade-off between exploitation and exploration by selecting elite individuals in the local search space during the evolutionary process. Furthermore, a surrogate-assisted hybrid algorithm based on the gradient-enhanced Kriging with the partial least squares(GEKPLS) approach is established to improve the engineering applicability of MOHA. The optimization results of benchmark functions demonstrate that MOHA is less constrained by dimensionality and can solve multi-objective optimization problems (MOPs) with up to 1000 decision variables. Compared to existing MOEAs, MOHA demonstrates notable enhancements in optimization efficiency and convergence accuracy, specifically achieving a remarkable 5–10 times increase in efficiency. In addition, the optimization efficiency of GS-MOHA is approximately five times that of MOEA/D-EGO and twice that of K-RVEA in the 30-dimensional test functions. Finally, the multi-objective optimization results of the airfoil shape design validate the effectiveness of the proposed algorithms and their potential for engineering applications.

Development of Monte Carlo hybrid parallel algorithm and application in high resolution spatial-energy flux distribution calculation

Monte Carlo transport calculations for fusion reactors are very challenging due to factors such as large radiation decay gradients, complex geometric structures, and strong neutron anisotropy. For such deep penetration shielding calculation problems, in order to obtain accurate global flux distribution, variance reduction methods and parallel computing are necessary means. When using the global weight window variance reduction technique, the huge deviation of the particle arrival weight and the weight window can lead to an extreme number of splits, which is the long history problem. If a process encounters the long history problem in parallel computing and takes a lot of time to complete, other processes will stop computing because the histories allocated in the computing cycle have been completed, seriously degrading computing efficiency. In addition, the calculation of the shutdown dose rate, a key issue in fusion neutronics calculations, requires a high “space-energy” resolution, which brings huge challenges to computational memory, especially in parallel computing. In order to overcome the degradation of parallel efficiency caused by the long history problem and the memory limitation in large-scale shutdown dose rate calculation, a hybrid parallel algorithm based on shared memory was researched. Finally, the hybrid parallel architecture was built in the Monte Carlo code cosRMC to realize dynamic load balancing between threads. Through the memory sharing among all threads of a single node, the pressure of memory consumption can be effectively relieved. In order to verify the effectiveness of the hybrid parallel algorithm developed in this paper, it was applied to the calculation of CFETR. The results showed that the hybrid parallel algorithm can effectively alleviate the impact of the long history problem on the parallel efficiency. In terms of saving memory, the hybrid parallel algorithm also showed a significant effect, which provides a guarantee for large-scale shutdown dose rate calculation.

Tachyon: Efficient Shared Memory Parallel Computation of Extremum Graphs

AbstractThe extremum graph is a succinct representation of the Morse decomposition of a scalar field. It has increasingly become a useful data structure that supports topological feature‐directed visualization of 2D/3D scalar fields, and enables dimensionality reduction together with exploratory analysis of high‐dimensional scalar fields. Current methods that employ the extremum graph compute it either using a simple sequential algorithm for computing the Morse decomposition or by computing the more detailed Morse–Smale complex. Both approaches are typically limited to two and three‐dimensional scalar fields. We describe a GPU–CPU hybrid parallel algorithm for computing the extremum graph of scalar fields in all dimensions. The proposed shared memory algorithm utilizes both fine‐grained parallelism and task parallelism to achieve efficiency. An open source software library, tachyon, that implements the algorithm exhibits superior performance and good scaling behaviour.

Multi-depot vehicle routing programming for hazmat transportation with weight variation risk

Reasonable transportation risk models are conducive to achieving the green reform of hazardous material logistics industry. However, existing multi-depot vehicle routing programming models for hazardous material transportation may result in overemphasis on either global risk or local risk. To overcome such shortcomings, we develop two novel two-stage programming models that consider weight variety in risk measures. The ordered weighted averaging risk-based model effectively reduces both the global risk and the maximum local risk with respect to the weight distribution in the aggregation process of local risks, and the state variable weight risk-based model helps reduce the global risk and the maximum local risk based on the variable weights associated with local risk values. Furthermore, we design a constraint reduction mechanism and a variable neighbourhood search-based hybrid parallel genetic algorithm to handle the proposed models, such that they could rapidly reach the near-optimal solution using multiplication processors. Experimental investigations demonstrate that the proposed models achieve a good balance between overall risk and local risk, and proposed algorithm can obtain a satisfactory approximate solution within an acceptable time frame.

Hybrid parallel reduction algorithms for the multi-level CMFD acceleration in the neutron transport code PANDAS-MOC

The coarse mesh finite difference (CMFD) technique is considered efficiently in accelerating the convergence of the iterative solutions in the computational intensive 3D whole-core pin-resolved neutron transport simulations. However, its parallel performance in the hybrid MPI/OpenMP parallelism is inadequate, especially when running with larger number of threads. In the original Whole-code OpenMP threading hybrid model (WCP) model of the PANDAS-MOC neutron transport code, the hybrid MPI/OpenMP reduction has been determined as the principal issue that restraining the parallel speedup of the multi-level coarse mesh finite difference solver. In this paper, two advanced reduction algorithms are proposed: Count-Update-Wait reduction and Flag-Save-Update reduction, and their parallel performances are examined by the C5G7 3D core. Regarding the parallel speedup, the Flag-Save-Update reduction has attained better results than the conventional hybrid reduction and Count-Update-Wait reduction.

Numerical Simulation of Plasma Plume With Gas–Surface Interaction and Chemical Reaction Coupling Effect Based on DSMC-PIC Parallel Algorithm

The spatio-temporal evolution of plasma plumes is accompanied by a series of complex chemical reactions and gas–surface interactions that crucially affect the working characteristics and performance of plasma devices. The direct simulation Monte Carlo (DSMC) and particle-in-cell (PIC) hybrid parallel algorithm is often used to numerically simulate such phenomena. This article discusses the gas–surface interaction models of neutral molecules as elucidated by the parallel DSMC-PIC program. These models include the Maxwell model, the Cercignani–Lampis–Lord model, the complete diffuse reflection (CDR) gas–surface reflection model, and coupling with H combined reactions at the wall. Meanwhile, we consider the ion gas–surface interaction models, which include the CDR model and coupling of the four wall chemical reactions of ions. In addition, the dissociation reactions of H2 and recombination reactions of H are considered in the flow field. For various combinations of models, the simulations reveal the structural characteristics, number density, and coupling effects of gas–surface reflections, gas–surface chemical reactions, chemical reactions in the flow field and electric field, and spatio-temporal evolution of H2, H, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{H}_{2}^{+}$ </tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{H}^{+}$ </tex-math></inline-formula> , which are shown by the structural characteristics of the number-density contours for different model combinations and analyzed in detail. The gas–surface interaction critically influences the number-density distribution near the wall and in the main flow. It also strongly affects the number-density distribution of light neutral molecules but not heavy neutral molecules and ions and produces the wall stacking effect. Apart from the stacking area, it has an indirect influence on the number-density distribution. The chemical reactions of ions on the wall affect the number-density distribution of all particles in the flow field and decisively affect the ions near the wall. Acceleration by the electric field intensifies the ion diffusion. The effect of coupling of gas–surface interaction and acceleration by the electric field is obvious. Via the number density and temperature, the chemical reactions in the flow field strongly affect the distribution of neutral molecules at the inlet; further, from the inlet, this effect weakens. Neutral particles and ions interact mainly through collisions.

An accelerated variant of the projection based parallel hybrid algorithm for split null point problems

In this paper, we consider an accelerated shrinking projection based parallel hybrid algorithm to study the split null point problem (SNPP) associated with the maximal monotone operators in Hilbert spaces. The analysis of the proposed algorithm provides strong convergence results under suitable set of control conditions as well as viability with the help of a numerical experiment. The results presented in this paper improve various existing results in the current literature.

3D inversion of vertical gravity gradient with multiple graphics processing units based on matrix compression

Among single-component inversions, vertical gravity gradient data have been proven to be sufficient to obtain reasonable density distribution. However, the increasing numbers of observations and model parameters inevitably produce a large-scale sensitivity matrix, causing problems such as large memory occupation and slow computations in the inversion. Aiming at solving these long-standing problems, we have introduced a matrix compression technique to reduce memory occupation and constructed a hybrid parallel algorithm for accelerating focusing inversion. We develop the source codes and the design pattern for the proposed algorithm using C language with open multiprocessing and compute unified device architecture for further improving algorithm feasibility and reusability. A graphical user interface and visualization tools also are developed using Python. In the tests with synthetic data produced by numerical simulation and real data from Vinton Dome, it is verified that the inversion can produce an accurate result of density values and object location. The performance analysis is conducted using different scales of data. The speedup increases with the increasing number of graphics processing units (GPUs) and model parameters, proving that the proposed parallel algorithm is scalable. We compare this algorithm with others without matrix compression to demonstrate how the compressed sensitivity matrix storing and accessing method influences inversion performance. More specifically, for an inversion with 100 × 100 × 20 cells, the average speed is approximately 6 s per iteration, with a maximum speedup of 36.94. Our algorithm has a short running time and high multiple-GPU parallel efficiency. Hence, the hybrid parallel algorithm is more powerful at implementing large-scale data calculations and could be used for solving computing problems in mass data inversions.

Optimizing functional near-infrared spectroscopy (fNIRS) channels for schizophrenic identification during a verbal fluency task using metaheuristic algorithms.

ObjectiveWe aimed to reduce the complexity of the 52-channel functional near-infrared spectroscopy (fNIRS) system to facilitate its usage in discriminating schizophrenia during a verbal fluency task (VFT).MethodsOxygenated hemoglobin signals obtained using 52-channel fNIRS from 100 patients with schizophrenia and 100 healthy controls during a VFT were collected and processed. Three features frequently used in the analysis of fNIRS signals, namely time average, functional connectivity, and wavelet, were extracted and optimized using various metaheuristic operators, i.e., genetic algorithm (GA), particle swarm optimization (PSO), and their parallel and serial hybrid algorithms. Support vector machine (SVM) was used as the classifier, and the performance was evaluated by ten-fold cross-validation.ResultsGA and GA-dominant algorithms achieved higher accuracy compared to PSO and PSO-dominant algorithms. An optimal accuracy of 87.00% using 16 channels was obtained by GA and wavelet analysis. A parallel hybrid algorithm (the best 50% individuals assigned to GA) achieved an accuracy of 86.50% with 8 channels on the time-domain feature, comparable to the reported accuracy obtained using 52 channels.ConclusionThe fNIRS system can be greatly simplified while retaining accuracy comparable to that of the 52-channel system, thus promoting its applications in the diagnosis of schizophrenia in low-resource environments. Evolutionary algorithm-dominant optimization of time-domain features is promising in this regard.

SVM-SMO-SGD: A hybrid-parallel support vector machine algorithm using sequential minimal optimization with stochastic gradient descent

The Support Vector Machine (SVM) method is one of the popular machine learning algorithms as it gives high accuracy. However, like most machine learning algorithms, the resource consumption of the SVM algorithm in terms of time and memory increases linearly as the dataset grows. In this study, a parallel-hybrid algorithm that combines SVM, Sequential Minimal Optimization (SMO) with Stochastic Gradient Descent (SGD) methods have been proposed to optimize the calculation of the weight costs. The performance of the proposed SVM-SMO-SGD algorithm was compared with classical SMO and Compute Unified Device Architecture (CUDA) based approaches on the well-known datasets (i.e., Diabetes, Healthcare Stroke Prediction, Adults) with 520, 5110, and 32,560 samples, respectively. According to the results, Sequential SVM-SMO-SGD is 3.81 times faster in terms of time, and 1.04 times more efficient RAM consumption than the classical SMO algorithm. The parallel SVM-SMO-SGD algorithm, on the other hand, is 75.47 times faster than the classical SMO algorithm in terms of time. It is also 1.9 times more efficient in RAM consumption. The overall classification accuracy of all algorithms is 87% in the Diabetes dataset, 95% in the Healthcare Stroke Prediction dataset, and 82% in the Adults dataset.

Parallel Hybrid Algorithms for a Finite Family of G-Nonexpansive Mappings and Its Application in a Novel Signal Recovery

This article considers a parallel monotone hybrid algorithm for a finite family of G-nonexpansive mapping in Hilbert spaces endowed with graphs and suggests iterative schemes for finding a common fixed point by the two different hybrid projection methods. Moreover, we show the computational performance of our algorithm in comparison to some methods. Strong convergence theorems are proved under suitable conditions. Finally, we give some numerical experiments of our algorithms to show the efficiency and implementation of the LASSO problems in signal recovery with different types of blurred matrices and noise.

Constructing Dixon Matrix for Sparse Polynomial Equations Based on Hybrid and Heuristics Scheme

Solving polynomial equations inevitably faces many severe challenges, such as easily occupying storage space and demanding prohibitively expensive computation resources. There has been considerable interest in exploiting the sparsity to improve computation efficiency, since asymmetry phenomena are prevalent in scientific and engineering fields, especially as most of the systems in real applications have sparse representations. In this paper, we propose an efficient parallel hybrid algorithm for constructing a Dixon matrix. This approach takes advantage of the asymmetry (i.e., sparsity) in variables of the system and introduces a heuristics strategy. Our method supports parallel computation and has been implemented on a multi-core system. Through time-complexity analysis and extensive benchmarks, we show our new algorithm has significantly reduced computation and memory overhead. In addition, performance evaluation via the Fermat–Torricelli point problem demonstrates its effectiveness in combinatorial geometry optimizations.

A blockchain‐enabled learning model based on distributed deep learning architecture

Aiming to address the unsatisfactory performance of existing distributed deep learning architectures, such as poor accuracy, slow network communication, low arithmetic speed, and insufficient security, we propose and design a learning model based on a distributed deep learning and blockchain architecture. We use a hybrid parallel algorithm based on blockchain (HP-B) to build a distributed deep consensus learning model. The HP-B algorithm is grouped according to the performance of computing nodes participating in training, network links and training samples, and the grouped computing equipment performs optimal distributed computing. The purpose of this approach is to solve the security and scalability concerns and improve the convergence speed and accuracy of deep learning. The proposed method achieves good results on the CIFAR-100, CIFAR-10, and IMAGENET data sets. Finally, the distributed deep learning model based on blockchain is combined with the generative adversarial network to solve the segmentation problem of medical imaging data, and the experimental results are superior to those of other networks.

An Efficient Parallel Algorithm for Frequent Itemsets Mining Using BitTable on Spark

A variety of techniques have been used to improve the performance of an algorithm in finding frequent item sets, which is one of the important processes to obtain frequent pattern mining. It was found that today’s technology has resulted in an ever-increasing amount of information, which should be analyzed for various benefits. Therefore, efforts have been made to improve the aalgoruthm’s efficiency to accommodate the nature of data stored through the working process of the main internal memory. Efforts have been made to prepare algorithms for the ever-increasing information. This research provided an appropriate data structure of BitTable to help improve the functionality of the algorithms. Moreover, the principle of parallel frequent itemset mining algorithm based on Map-Reduce design was used in this research to assess the performance of algorithms, named as Adaptive Hybrid Parallel Algorithm (AHP). Additionally, to investigate the performance of the AHP Algorithm Using Apache Spark Technology with the type of data that was accumulated during the process of the main internal memory.

Image restorations using an inertial parallel hybrid algorithm with Armijo linesearch for nonmonotone equilibrium problems

In this work, we modify the inertial type parallel monotone hybrid algorithm with Armijo linesearch to solve common nonmonotone equilibrium problems. The weak convergence theorem is established under some suitable conditions in Hilbert spaces. Finally, the numerical example is shown to support our main result. The efficiencies of the proposed algorithm are presented by comparing with previously parallel algorithms in novel image restoration. It’s illustrated that the proposed algorithm can increase deblurring quality when the subproblems are increasing.