This paper describes a method of parallelisation of the popular Nelder-Mead simplex optimizationalgorithms that can lead to enhanced performance on parallel and distributed computing resources. A reducing set of simp...
详细信息
ISBN:
(纸本)0769522106
This paper describes a method of parallelisation of the popular Nelder-Mead simplex optimizationalgorithms that can lead to enhanced performance on parallel and distributed computing resources. A reducing set of simplex vertices are used to derive search directions generally closely aligned with the local gradient. When tested on a range of problems drawn from real-world applications in science and engineering 7, this reducing set concurrent simplex (RSCS) variant of the Nelder-Mead algorithm compared favourably with the original algorithm, and also with the inherently parallel multidirectional search algorithm (MDS). All algorithms were implemented and tested in a general-purpose, grid-enabled optimization toolset.
Genetic algorithms (GAs) have proved their efficiency solving many complex optimization problems. GAs can be also applied for "black-box" problems, because they realize the "blind" search and do no...
详细信息
Genetic algorithms (GAs) have proved their efficiency solving many complex optimization problems. GAs can be also applied for "black-box" problems, because they realize the "blind" search and do not require any specific information about features of search space and objectives. It is clear that a GA uses the "Trial-and-Error" strategy to explorer search space, and collects some statistical information that is stored in the form of genes in the population. Estimation of Distribution algorithms (EDA) have very similar realization as GAs, but use an explicit representation of search experience in the form of the statistical probabilities distribution. In this study we discus some approaches for improving the standard GA performance by combining the binary GA with EDA. Finally, a novel approach for the largescale global optimization is proposed. The experimental results and comparison with some well-studied techniques are presented and discussed.
A two-level domain decomposition method is introduced for general shape optimization problems constrained by the incompressible Navier-Stokes equations. The optimization problem is first discretized with a finite elem...
详细信息
A two-level domain decomposition method is introduced for general shape optimization problems constrained by the incompressible Navier-Stokes equations. The optimization problem is first discretized with a finite element method on an unstructured moving mesh that is implicitly defined without assuming that the computational domain is known and then solved by some one-shot Lagrange-Newton-Krylov-Schwarz algorithms. In this approach, the shape of the domain, its corresponding finite element mesh, the flow fields and their corresponding Lagrange multipliers are all obtained computationally in a single solve of a nonlinear system of equations. Highly scalable parallel algorithms are absolutely necessary to solve such an expensive system. The one-level domain decomposition method works reasonably well when the number of processors is not large. Aiming for machines with a large number of processors and robust nonlinear convergence, we introduce a two-level inexact Newton method with a hybrid two-level overlapping Schwarz preconditioner. As applications, we consider the shape optimization of a cannula problem and an artery bypass problem in 2D. Numerical experiments show that our algorithm performs well on a supercomputer with over 1000 processors for problems with millions of unknowns. Copyright (c) 2014 John Wiley & Sons, Ltd.
We presenta multiple ant-colonyalgorithm (MACA)for the graph bisection problem. The aim of this paper is to compare the performance of the MACA with results on the benchmark graphs from Graph partitioning Archive at t...
详细信息
ISBN:
(纸本)9616303619
We presenta multiple ant-colonyalgorithm (MACA)for the graph bisection problem. The aim of this paper is to compare the performance of the MACA with results on the benchmark graphs from Graph partitioning Archive at the University of Greenwich. Experimental results show that the MACA is comparable with the state-of-the-art graph bisection algorithms.
In the wave of industrial modernization, a concept that comprehensively covers the product lifecycle has been proposed, namely the digital twin manufacturing system. The digital twin manufacturing system can conduct t...
详细信息
In the wave of industrial modernization, a concept that comprehensively covers the product lifecycle has been proposed, namely the digital twin manufacturing system. The digital twin manufacturing system can conduct three-dimensional simulation of the workshop, thereby achieving dynamic scheduling and energy efficiency optimization of the workshop. The optimization of digital twin manufacturing systems has become a focus of research. In order to reduce power consumption and production time in manufacturing workshops, the study adopted a non-dominated sorting genetic algorithm to improve its elitist retention strategy for the problem of easily falling into local optima. On the ground of the idea of multi-objective optimization, the optimization was carried out with the production time and power consumption of the manufacturing industry as the objectives. The experiment showcased that the improved algorithm outperforms the multi-objective optimization algorithm on the ground of decomposition and the evolutionary algorithm on the ground of Pareto dominance. Compared to the two comparison algorithms, the production time optimization effect and power consumption optimization effect of different numbers of devices were 11.12%-21.37% and 2.14%-6.89% higher, respectively. The optimization time of the improved algorithm was 713.5 seconds, that was reduced by 173.8 seconds and 179.8 seconds compared to the other two algorithms, respectively. The total power consumption of the improved optimization model was 2883.7kWs, which was reduced by 32.0kW*s and 45.5kW*s compared to the other two algorithms, respectively. This study proposed a new multi-objective optimization algorithm for the current digital twin manufacturing industry. This algorithm effectively reduces production time and power consumption, and has important guiding significance for manufacturing system optimization in actual production environments.
The proceedings contain 31 papers. The topics discussed include: swish function based LMS algorithm with variable step size;research on the comprehensive performance evaluation method of genetic algorithm based on Euc...
ISBN:
(纸本)9798331528881
The proceedings contain 31 papers. The topics discussed include: swish function based LMS algorithm with variable step size;research on the comprehensive performance evaluation method of genetic algorithm based on Euclidean distance minimization;optimization design of regional coverage remote sensing constellation based on improved particle swarm optimization algorithm;a domain-level transformation scenario classification algorithm based on multilevel feature representation;research of continuous thermoplastic coating equipment based on virtual simulation;and the mode of speed control and safety discussion of the permanent magnet synchronous elevator based on fuzzy logic control.
The conventional multilevel thresholding methods are computational expensive since they exhaustively search the optimal thresholds to optimize the objective functions. In this paper, the modified adaptive particle swa...
详细信息
The conventional multilevel thresholding methods are computational expensive since they exhaustively search the optimal thresholds to optimize the objective functions. In this paper, the modified adaptive particle swarm optimization (MAPSO) algorithm is proposed to overcome this drawback. The dynamic population (DP) strategy of the proposed algorithm enables the population size variable with the evolutionary state at run time. With the help of DP strategy, the population size can increase when the swarm converges and decrease when the swarm disperses. The MAPSO algorithm is used to find the optimal thresholds by maximizing the Otsu's objective function. The performance of the proposed algorithm has been validated on eight standard test images. The experimental results of 50 independent runs illustrate the best solution quality and stability of the MAPSO when compared with three other PSO algorithms.
The CNN literature contains several papers about how to use the genetic algorithms for template tuning. This paper shows a possible CNN-UM implementation of the control algorithm of the genetic algorithm in a special ...
详细信息
ISBN:
(纸本)9781424406395
The CNN literature contains several papers about how to use the genetic algorithms for template tuning. This paper shows a possible CNN-UM implementation of the control algorithm of the genetic algorithm in a special fine-grained parallel version. The interesting of the analogic mapping is the different set of operators, which can be evaluated fast and efficient on the CNN-UM platform. The rational behind the spatial implementation is the possibility of focal-plane optimization.
The focus of this paper is to analyze the supply chain routes by means of artificial intelligence techniques for reducing transportation costs. The simulation model, built in eM-Plant, is used to implement two differe...
详细信息
ISBN:
(纸本)9781424413478
The focus of this paper is to analyze the supply chain routes by means of artificial intelligence techniques for reducing transportation costs. The simulation model, built in eM-Plant, is used to implement two different approaches based on the ants theory and the genetic algorithms. A comparison of results is made in order to identify, the better approach to adopt for the optimization process.
We consider non-differentiable convex optimization problems that vary continuously in time and we propose algorithms that sample these problems at specific time instances and generate a sequence of converging near-opt...
详细信息
ISBN:
(纸本)9781479919635
We consider non-differentiable convex optimization problems that vary continuously in time and we propose algorithms that sample these problems at specific time instances and generate a sequence of converging near-optimal decision variables. This sequence converges up to a bounded error to the solution trajectory of the time-varying non-differentiable problems. We illustrate through analytical examples and a realistic numerical simulation the benefit of the algorithms in signal processing applications, e.g., for reconstructing time-varying sparse signals.
暂无评论