Parallel Optimization Strategy Research Articles

Method for Collaborative Layout Optimization of Ship Equipment and Pipe Based on Improved Multi-Agent Reinforcement Learning and Artificial Fish Swarm Algorithm

The engine room is the core area of a ship, critical to its operation, safety, and efficiency. Currently, many researchers merely address the ship engine room layout design (SERLD) problem using optimization algorithms and independent layout strategies. However, the engine room environment is complex, involving two significantly different challenges: equipment layout and pipe layout. Traditional methods fail to achieve optimal collaborative layout objectives. To address this research gap, this paper proposes a collaborative layout method that combines improved reinforcement learning and heuristic algorithms. For equipment layout, the engine room space is first discretized into a grid, and a Markov decision process (MDP) framework suitable for equipment layout is proposed, including state space, action space, and reward mechanisms suitable for equipment layout. An improved adaptive guided multi-agent Q-learning (AGMAQL) algorithm is employed to train the layout model in a centralized manner, with enhancements made to the agent’s exploration state, exploration action, and learning strategy. For pipe layout, this paper proposes an improved adaptive trajectory artificial fish swarm algorithm (ATAFSA). This algorithm incorporates a hybrid encoding method, adaptive strategy, scouting strategy, and parallel optimization strategy, resulting in enhanced stability, accuracy, and problem adaptability. Subsequently, by comprehensively considering layout objectives and engine room attributes, a collaborative layout method incorporating hierarchical and adaptive weight strategies is proposed. This method optimizes in phases according to the layout objectives and priorities of different stages, achieving multi-level optimal layouts and providing designers with various reference schemes with different focuses. Finally, based on a typical real-world engine room engineering case, various leading algorithms and strategies are tested and compared. The results show that the proposed AGMAQL-ATAFSA (AGMAQL-ATA) exhibits robustness, efficiency, and engineering practicality. Compared to previous research methods and algorithms, the final layout quality improved overall: equipment layout effectiveness increased by over 4.0%, pipe optimization efficiency improved by over 40.4%, and collaborative layout effectiveness enhanced by over 2.2%.

A new industrially magnetic capsule MedRobot integrated with smart motion controller

Capsule medical robots with unique application advantages have broad prospects for gastrointestinal detection, targeted drug delivery, medical assistance, and other fields. However, due to the complexity of the gastrointestinal environment and the limitations of the space magnetic field, robust motion control of capsule robots is still a challenge. Using a permanent magnet (NdFeB) as a space magnetic source, a capsule robot manipulation instrument with an integrated motion intelligent control approach is proposed in this work. First of all, a steering fixture controlling a permanent magnet is designed, and a manipulation instrument capable of five degrees of motion freedom is built with low cost and high accuracy. Furthermore, an integrated motion control approach, consisting of a primary motion subsystem and an auxiliary motion subsystem, is presented, where the motion route of the capsule robot is planned. A parallel optimization strategy is adopted to improve the traditional Gray Wolf Optimization Algorithm, the improved version of which is utilized to tune the parameters of controllers. Finally, some experiments of the designed capsule robot are carried out in different planes and slopes, as well as simulated stomach and real pig stomach, respectively. The results show that the motion characteristics are continuous and stable, and the average position error is 4.2 mm, which meets the application requirements.

A digital twin for advancing battery fast charging based on a Bayesian optimization-based method

The optimization of fast charging protocols is regarded as a key technology for promoting the use of electric vehicles because it can balance battery charging time and health. Optimizing such charging protocols through electrochemical models is a mainstream approach and can demonstrate high accuracy in simulating battery characteristics. However, the high complexity of the corresponding models makes the calculation and optimization processes difficult to perform in real time. To address this problem, this study presents an online closed-loop fast charging strategy optimization scheme that combines a battery digital twin model (BDTM) and Bayesian optimization (BO). Because the BO can quickly find a near-optimal solution in a few iterations, the optimization time is shortened, thereby reducing the computational burden incurred by highly complex models. To further improve the efficiency of the online optimization, a parallel multichannel optimization strategy is proposed, which further accelerates the process of finding the optimal protocol by simultaneously executing multiple optimization algorithms. Additionally, we analysed the effects of ageing parameters and ambient temperature on the optimization results. The results show that BO can obtain relatively stable optimization and has the highest efficiency when using four parallel channels. Specifically, the number of convergence evaluations for single-channel optimization is 2.5 times that of the four-channel optimization. Compared with the reference charging protocol, charging using the protocol optimized based on the Doyle-Fuller-Newman (DFN) model can effectively suppress the loss of lithium inventory (LLI) by up to 4.76 % within 90 cycles.

Multi-flexible integrated scheduling algorithm for multi-flexible integrated scheduling problem with setup times.

To address the multi-flexible integrated scheduling problem with setup times, a multi-flexible integrated scheduling algorithm is put forward. First, the operation optimization allocation strategy, based on the principle of the relatively long subsequent path, is proposed to assign the operations to idle machines. Second, the parallel optimization strategy is proposed to adjust the scheduling of the planned operations and machines to make the processing as parallel as possible and reduce the no-load machines. Then, the flexible operation determination strategy is combined with the above two strategies to determine the dynamic selection of the flexible operations as the planned operations. Finally, a potential operation preemptive strategy is proposed to judge whether the planned operations will be interrupted by other operations during their processing. The results show that the proposed algorithm can effectively solve the multi-flexible integrated scheduling with setup times, and it can also better solve the flexible integrated scheduling problem.

Self-optimizing multi-domain auxiliary fusion deep complex convolution recurrent network for speech enhancement

A self-optimizing multi-domain auxiliary fusion deep complex convolution recurrent network for speech enhancement (AMDCCRN) was developed to further improve the feature completeness, correlation expression, and global optimization efficiency of the deep speech enhancement model for speech signals. By constructing a multi-domain auxiliary fusion deep complex convolution recurrent network (MDCCRN), the correlation representation of speech features and features in different spatial domains was enriched. The sparrow search algorithm (PGSSA), which is based on a game theory parallel and global optimization strategy, was introduced to improve parallel search capability and to further augment the efficiency and performance of the model's hyperparameter adaptive search. At the same time, PGSSA was used for adaptive optimization of six key model parameters required for AMDCCRN construction. Finally, a speech enhancement model suitable for multi-domain auxiliary fusion with self-learning ability was constructed. Test results on two common speech corpus data sets, THCHS-30 and WSJ0, showed that the proposed method achieved a better speech enhancement effect than other existing methods and that the validity and generalization of the proposed method had been verified.

Evolutionary Statistical System Based on Novelty Search: A Parallel Metaheuristic for Uncertainty Reduction Applied to Wildfire Spread Prediction

The problem of wildfire spread prediction presents a high degree of complexity due in large part to the limitations for providing accurate input parameters in real time (e.g., wind speed, temperature, moisture of the soil, etc.). This uncertainty in the environmental values has led to the development of computational methods that search the space of possible combinations of parameters (also called scenarios) in order to obtain better predictions. State-of-the-art methods are based on parallel optimization strategies that use a fitness function to guide this search. Moreover, the resulting predictions are based on a combination of multiple solutions from the space of scenarios. These methods have improved the quality of classical predictions; however, they have some limitations, such as premature convergence. In this work, we evaluate a new proposal for the optimization of scenarios that follows the Novelty Search paradigm. Novelty-based algorithms replace the objective function by a measure of the novelty of the solutions, which allows the search to generate solutions that are novel (in their behavior space) with respect to previously evaluated solutions. This approach avoids local optima and maximizes exploration. Our method, Evolutionary Statistical System based on Novelty Search (ESS-NS), outperforms the quality obtained by its competitors in our experiments. Execution times are faster than other methods for almost all cases. Lastly, several lines of future work are provided in order to significantly improve these results.

Intelligent optimal control considering dynamic posture compensation for a cantilever roadheader

AbstractThe control of dynamic spatial posture for cantilever roadheader is one of the vital problems for intelligent mining, which directly affect the forming quality of cutting tunnel. Therefore, this paper proposed an intelligent optimal combination compensation strategy to adjust the real-time dynamic posture of cantilever roadheader. First, based on the topological structure analysis of cantilever roadheader, the structural loop compensation model for spatial posture deviation was established. Afterward, the principal component analysis (PCA) and multi-objective particle swarm optimization (MPSO) algorithm were applied to improve the analysis speed and accuracy of posture deviation. Finally, parallel dynamic cooperative optimization (PDCO) strategy was combined to achieve the accurate adjusting of posture deviation. The actual experimental and application results indicate that the intelligent optimal combination compensation strategy proposed in the paper can significantly improve the accuracy of the cutting tunnel. The intelligent optimal compensation strategy proposed in this paper transforms the transient spatial posture deviation into structural loop compensation, and implements by parallel cooperative strategy, finally to realize the fast analysis and efficient implementation of spatial dynamic posture deviation for cantilever roadheader during cutting process. The work of this paper provides an effective reference for intelligent deep and remote underground mining, and it can also be applied to effective control of dynamic spatial posture for intelligent engineering machinery products.

HCGrid: a convolution-based gridding framework for radio astronomy in hybrid computing environments

ABSTRACT Gridding operation, which is to map non-uniform data samples on to a uniformly distributed grid, is one of the key steps in radio astronomical data reduction process. One of the main bottlenecks of gridding is the poor computing performance, and a typical solution for such performance issue is the implementation of multicore CPU platforms. Although such a method could usually achieve good results, in many cases, the performance of gridding is still restricted to an extent due to the limitations of CPU, since the main workload of gridding is a combination of a large number of single instruction, multidata stream operations, which is more suitable for GPU, rather than CPU implementations. To meet the challenge of massive data gridding for the modern large single-dish radio telescopes, e.g. the Five-hundred-meter Aperture Spherical radio Telescope, inspired by existing multicore CPU gridding algorithms such as Cygrid, here we present an easy-to-install, high-performance, and open-source convolutional gridding framework, HCGrid, in CPU-GPU heterogeneous platforms. It optimizes data search by employing multithreading on CPU, and accelerates the convolution process by utilizing massive parallelization of GPU. In order to make HCGrid a more adaptive solution, we also propose the strategies of thread organization and coarsening, as well as optimal parameter settings under various GPU architectures. A thorough analysis of computing time and performance gain with several GPU parallel optimization strategies show that it can lead to excellent performance in hybrid computing environments.

Deep learning parallel computing and evaluation for embedded system clustering architecture processor

In the era of intelligence, the processing of a large amount of information and various intelligent applications need to rely on embedded devices. This trend has made machine learning algorithms play an increasingly important role. High-performance embedded computing is an effective means to solve the lack of computing power of embedded devices. Aiming at the problem that the calculation amount of new intelligent embedded applications based on machine learning technology is higher, the computing power of traditional embedded systems is difficult to meet their needs, this paper studies the parallel optimization and implementation techniques of convolutional neural networks in Parallella platform. The parallel optimization strategy of convolutional neural network on the clustering architecture processor of heterogeneous multi-core system is given. Then the high-performance implementation of convolutional neural network on Parallella platform is studied, and the function of convolutional neural network system is implemented. A set of performance evaluation methods for embedded parallel processors is proposed. From the application point of S698P, the eCos operating system is selected as the platform. The single-core mode and multi-core mode are compared on the simulator GRSIM, and the parallel performance evaluation is given. Experiments have shown that the efficiency of deep learning tasks is significantly improved compared to traditional parallel methods.

Multifractal detrended fluctuation analysis parallel optimization strategy based on openMP for image processing

In the past few years, multifractal detrended fluctuation analysis (MF-DFA) method has been widely applied in the field of agricultural image processing. However, the agricultural image feature MF-DFA analyses involves a great deal of iterative processes and complex matrix operations, which require massive computation and processing time. In order to reduce processing time and improve analysis efficiency, we first develop a MF-DFA program that involves image preprocessing, image segmentation, local area accumulation matrix calculation, local area trend fitting, local area trend elimination, a global qth-order fluctuation function, and the Hurst index. Then, we analyze and compare MF-DFA each modules’ performance characteristics and explore its parallelism according to various segmentation scales s. Lastly, we propose a parallel optimization scheme based on OpenMP for the MF-DFA. The results of our rigorous performance evaluation clearly demonstrate that our proposed parallel optimization scheme can efficiently use multicore capability to extract rape leaf image texture characteristics.

GPGPU-based parallel computing applied in the FEM using the conjugate gradient algorithm: a review

Parallelization of the finite-element method (FEM) has been contemplated by the scientific and high-performance computing community for over a decade. Most of the computations in the FEM are related to linear algebra that includes matrix and vector computations. These operations have the single-instruction multiple-data (SIMD) computation pattern, which is beneficial for shared-memory parallel architectures. General-purpose graphics processing units (GPGPUs) have been effectively utilized for the parallelization of FEM computations ever since 2007. The solver step of the FEM is often carried out using conjugate gradient (CG)-type iterative methods because of their larger convergence rates and greater opportunities for parallelization. Although the SIMD computation patterns in the FEM are intrinsic for GPU computing, there are some pitfalls, such as the underutilization of threads, uncoalesced memory access, lower arithmetic intensity, limited faster memories on GPUs and synchronizations. Nevertheless, FEM applications have been successfully deployed on GPUs over the last 10 years to achieve a significant performance improvement. This paper presents a comprehensive review of the parallel optimization strategies applied in each step of the FEM. The pitfalls and trade-offs linked to each step in the FEM are also discussed in this paper. Furthermore, some extraordinary methods that exploit the tremendous amount of computing power of a GPU are also discussed. The proposed review is not limited to a single field of engineering. Rather, it is applicable to all fields of engineering and science in which FEM-based simulations are necessary.

A multiple surrogates based PSO algorithm

Particle swarm optimization (PSO) usually requires a large number of fitness evaluations to obtain a sufficiently good solution, which poses an obstacle for applying PSO to computationally expensive problems. In this paper, a multiple surrogates based PSO (MSPSO) framework is proposed, which consists of an inner loop optimization and an outer one. In the outer loop optimization, a PSO algorithm is used in both the optimization mode and the sampling one. In the inner loop optimization, a multiple surrogate based parallel optimization strategy is designed. Furthermore, the search history and the possible solutions from the outer loop optimization are provided for the inner one, and the result of the inner loop optimization is employed to guide the search of the outer one. To verify the performance of the proposed approach, a number of numerical experiments are conducted by using ten benchmark test functions and three time series regression modeling problems. The results indicate that the proposed framework is capable of converging to a good solution for the low-dimensional, non-convex and multimodal problems.

Embedded multicore computing and applications

Embedded multicore computing and applications

A novel parallel deblocking filtering strategy for HEVC/H.265 based on GPU

SummaryThe deblocking filter in high‐efficiency video coding (HEVC) has huge computational complexity because of its high content‐adaptive coding structure as well as high‐definition. Parallelization for it based on massively parallel architectures such as graphics processing unit becomes an urgent demand. However, a large number of conditional branches and data dependencies severely hinder its efficient parallelization. In this paper, a novel parallel optimization strategy based on graphics processing unit is presented for concurrent deblocking in HEVC/H.265 standard to improve the parallel performance. First, by reducing various conditional branches, a normalization mechanism for instruction stream based on feature vector is proposed, which improves the efficiency of boundary strength computation dramatically. The idea can also be applied to edge discrimination. Second, a parallel mechanism based on an adaptive post‐correction is presented to process vertical and horizontal edges filtering concurrently, which improves the processing speed obviously, while producing negligible quality loss. Experimental results show that the strategy presented outperforms the existing state‐of‐the‐art method with accelerating factor up to 32. Copyright © 2016 John Wiley & Sons, Ltd.

Improving Parallel Performance of a Finite-Difference AGCM on Modern High-Performance Computers

Abstract The rapid development of science and technology has enabled finer and finer resolutions in atmospheric general circulation models (AGCMs). Parallelization becomes progressively more critical as the resolution of AGCMs increases. This paper presents a new parallel version of the finite-difference Gridpoint Atmospheric Model of the Institute of Atmospheric Physics (IAP)–State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG; GAMIL) with various parallel optimization strategies, including two-dimensional hybrid parallel decomposition; hybrid parallel programming; parallel communications for coupling the physical packages, land surface, and dynamical core; and a cascading solution to the tridiagonal equations used in the dynamical core. The new parallel version under two different horizontal resolutions (1° and 0.25°) is evaluated. The new parallel version enables GAMIL to achieve higher parallel efficiency and utilize a greater number of CPU cores. GAMIL1° achieves 37.8% parallel efficiency using 960 CPU cores, while GAMIL0.25° achieves 57.5% parallel efficiency.

Nature-Inspired Meta-Heuristics on Modern GPUs: State of the Art and Brief Survey of Selected Algorithms

Graphic processing units (GPUs) emerged recently as an exciting new hardware environment for a truly parallel implementation and execution of Nature and Bio-inspired Algorithms with excellent price-to-power ratio. In contrast to common multicore CPUs that contain up to tens of independent cores, the GPUs represent a massively parallel single-instruction multiple-data devices that can nowadays reach peak performance of hundreds and thousands of giga floating-point operations per second. Nature and Bio-inspired Algorithms implement parallel optimization strategies in which a single candidate solution, a group of candidate solutions (population), or multiple populations seek for optimal solution or set of solutions of given problem. Genetic algorithms (GA) constitute a family of traditional and very well-known nature-inspired populational meta-heuristic algorithms that have proved its usefulness on a plethora of tasks through the years. Differential evolution (DE) is another efficient populational meta-heuristic algorithm for real-parameter optimization. Particle swarm optimization (PSO) can be seen as nature-inspired multiagent method in which the interaction of simple independent agents yields intelligent collective behavior. Simulated annealing (SA) is global optimization algorithm which combines statistical mechanics and combinatorial optimization with inspiration in metallurgy. This survey provides a brief overview of the latest state-of-the-art research on the design, implementation, and applications of parallel GA, DE, PSO, and SA-based methods on the GPUs.

DiceKriging,DiceOptim: TwoRPackages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization

We present two recently released R packages, DiceKriging and DiceOptim, for the approximation and the optimization of expensive-to-evaluate deterministic functions. Following a self-contained mini tutorial on Kriging-based approximation and optimization, the functionalities of both packages are detailed and demonstrated in two distinct sections. In particular, the versatility of DiceKriging with respect to trend and noise specifications, covariance parameter estimation, as well as conditional and unconditional simulations are illustrated on the basis of several reproducible numerical experiments. We then put to the fore the implementation of sequential and parallel optimization strategies relying on the expected improvement criterion on the occasion of DiceOptim’s presentation. An appendix is dedicated to complementary mathematical and computational details.

CDR Walking Mutagenesis for the Affinity Maturation of a Potent Human Anti-HIV-1 Antibody into the Picomolar Range

We describe the investigation of methodologies for the creation of very high affinity human antibodies. The high affinity human antibody b4/12 was optimized for its affinity to the human envelope glycoprotein gp120 of human immunodeficiency virus type 1 (HIV-1). Five libraries of b4/12 were constructed by saturation mutagenesis of complementarity-determin ing regions (CDRs). Libraries of antibody Fab fragments were displayed on the surface of filamentous phage and selected in vitrofor binding to immobilized gp120. Sequential and parallel optimization strategies of CDRs were examined. The sequential CDR walking strategy consistently yielded b4/12 variants of improved affinity in each of the four different optimization sequences examined. This resulted in a 96-fold improvement in affinity. Additivity effects in the antibody combining site were explored by combining independently optimized CDRs in the parallel optimization strategy. Six variants containing optimized CDRs were constructed. Improvement of affinity based on additivity effects proved to be unpredictable but did lead to a modest improvement in affinity. Indeed, only one of the six combinations demonstrated additivity. The highest affinity Fab prepared using this strategy was improved 420-fold in affinity. The affinity of this Fab was 15 pM as compared to 6.3 nM for b4/12. Examination of the kinetics of Fab binding to gp120 revealed that improvements in affinity were dominated by a slowing of the off-rate of the Fab. The methodology presented here provides a route for the improvement of the affinities of antibodies typical of tertiary immune responses into the picomolar range. Such improvements may have profound effects on the utility of antibodies as therapeutic and prophylatic agents.

Concurrent function evaluations in local and global optimization

This paper discusses some basic opportunities for the use of multiprocessing in the solution of optimization problems. We consider two fundamental optimization problems, unconstrained optimization and global optimization, in the important case when function evaluation is expensive and gradients are evaluated by finite differences. First we discuss some simple parallel strategies based upon the use of concurrent function evaluations to evaluate the finite difference gradient. These include the speculative evaluation of the gradient concurrently with the evaluation of the function before it is known whether the gradient value at this point will be required. We present examples that indicate the effectiveness of these parallel strategies for unconstrained optimization. We also give experimental results that show the effect of using these strategies to parallelize each of the multiple local minimizations within a recently proposed concurrent global optimization algorithm. We briefly discuss several parallel optimization strategies that are related to these approaches but make more fundamental changes to standard sequential optimization algorithms.

One-dimensional optimization on multiprocessor systems

This paper presents a straightforward approach to determining how best to utilize an MIMD multiprocessor in the solution of one-dimensional optimization problems involving continuous unimodal functions and nongradient search techniques. A methodology is presented which allows one to consider a variety of speedup functions which may occur in parallel function and systems evaluation. It is shown how the best of two parallel optimization strategies can be determined for a given accuracy, number of processors, and speedup function.