Coarse-grained Parallel Genetic Algorithm Research Articles

Application of the Logistics Management System Based on Cloud Computing Technology

In order to solve the vehicle routing optimization problem of cold chain logistics distribution, this paper proposes a vehicle routing optimization method of cold chain logistics distribution based on cloud computing technology. This paper analyzes the advantages of processing the real-time routing of distribution vehicles under the cloud computing mode and establishes the application service architecture of vehicle routing optimization in cold chain logistics; under this architecture, multisource real-time traffic information is obtained, the vehicle distribution time and comprehensive cost are analyzed, the vehicle routing optimization model of cold chain logistics distribution is constructed, and the coarse-grained parallel genetic algorithm is used to solve the model in the cloud computing environment. The experimental results show that if W 1 = 0 , W 2 = 1 , the optimal path is 0-6-7-3-1-4-2-8-5, the total cost is 1620 yuan, the total mileage is 72 km, the vehicle driving time is 2.17 hours, and the calculation time is 10.79 s; let W 1 = 1 , W 2 = 0 , the optimal path is 0-5-6-7-2-4-3-1-8, the vehicle travel time is 1.84 hours, the total cost is 1736 yuan, the total mileage is 76 km, and the calculation time is 11.56 s. The optimal path is obtained in the effective time range. Conclusion. The real-time route optimization method of refrigerated vehicles in the cloud computing environment is effective. This method has practical significance to realize fine control of cold chain logistics distribution cost and improve distribution service efficiency.

Bi-dimensional Variational Mode Decomposition for Surface Texture Analysis

In surface metrology, filtration is one of the key operations which is used to decompose the components of the surface and extract a scale-limited surface for further assessment. Although many types of filters have been proposed and some of them have been standardized in ISO16610 serials, the classic surface topography filters often inherit the mode mixing and boundary distortion problems that may lead to surface texture verification failures. This study proposes a new filter algorithm for the surface texture analysis, and it is based on the extended bi-dimensional variational mode decomposition (BVMD), named EBVMD. BVMD is widely used in the field of image processing and it is probably first-time to be used in the field of surface texture analysis. It consists of three steps. Firstly, a coarse-grained parallel genetic algorithm is applied in the devised model to select of optimal penalty factor and decomposition number of the bi-dimensional variational mode, and the best set of modes is gained. Secondly, the instantaneous wavelength value of each mode is obtained by calculating the corresponding isotropic monogenic signals. Finally, the scale-limited surface is extracted by the mean instantaneous wavelength. Experiments are conducted to verify the feasibility of the proposed filter on areal surface texture feature extraction with the mode aliasing and shape distortion phenomenon remedies. The experimental results show that the mean square error of roughness reconstructed by the proposed filter compared with the benchmarking values is 6.37×10-5, and owing to high flexibility in practice, the devised EBVMD provides a promising solution to achieve a high accuracy and efficiency filter.

Analysis of asynchronous distributed multi-master parallel genetic algorithm optimization on CAN bus

Industrial optimization problems are usually difficult to solve due to complexity and high number of constraints. Evolutionary algorithms are a conventional method to solve these problems. However, many industrial applications are real-time or we need to find a feasible optima solution in a limited time. Parallel genetic algorithm is a method to utilize properties of the genetic algorithm and parallel processing and implementation of a fast evolutionary algorithm. Controller Area Network (CAN) protocol is widely used in various industries such as automotive, medical, aerospace. In this paper, we implement a multiple-population coarse-grained parallel genetic algorithm on CAN bus to improve speed and performance of the conventional genetic algorithm which is asynchronous distributed multi-master. Evaluation criteria such as speed up, efficiency, serial fraction and reliability are calculated for the proposed parallel processing which is used for optimization problem of five benchmark functions. And finally, this structure is compared with the master–slave model. The proposed structure is created conditions for improving network reliability with very low cost of communication.

Polygonal approximation based on coarse-grained parallel genetic algorithm

This paper proposes to apply coarse-grained parallel genetic algorithm (CGPGA) to solve polygonal approximation problem. Chromosomes are used to represent digital curves and genes correspond to points of curves. This method divides the whole population into several subpopulations, each of which performs evolutionary process independently. After every migration interval number of generations, these subpopulations exchange their information with each other. Inspired by the designing theory of ensemble learning in machine learning, this paper further improves the basic CGPGA through adopting different but effective genetic algorithms, respectively, in different subpopulations. Both the diversity among different subpopulations and the accuracy in each individual subpopulation are ensured. Experimental results, based on four benchmark curves and four real image curves extracted from the lake maps, show that the basic CGPGA outperforms the used genetic algorithm, and further the improved CGPGA (ICGPGA) is more effective than the basic CGPGA, in terms of the quality of best solutions, the average solutions, and the variance of best solutions. Especially for those larger approximation problems, the ICGPGA is more remarkably superior to some representative genetic algorithms.

A multi-objective reliability optimization for reconfigurable systems considering components degradation

Reconfigurable systems have been widely used in practical engineering, especially for the reconfigurable computing systems and reconfigurable manufacturing systems. The reliability of reconfigurable systems can be improved by components replacement or components rearrangement without changing their reliability. Combining the advantages of the rearrangement method and replacement method, an integrated method is proposed to improve the reconfigurable system reliability cost-effectively in this paper. Then, a 0–1 integer programming model of multi-objective optimization is established to obtain the reconfiguration with maximum system reliability and minimum reconfiguration cost based on the integrated method. The coarse-grained parallel genetic algorithm (CPGA) is introduced to solve the multi-objective model, while the multiple objectives problem can be converted into a single objective problem through the novel fitness function. Finally, three examples based on the production monitoring system are implemented to illustrate the effectiveness of the CPGA comparing with the replacement based genetic algorithm. The changes of optimal reconfigurations with different parameters of the fitness function and different pre-determined system reliability are also discussed based on the examples.

A Model for Hepatotoxicity Prediction Based on Coarse-Grained Parallel Genetic Algorithm and Support Vector Machine

A Model for Hepatotoxicity Prediction Based on Coarse-Grained Parallel Genetic Algorithm and Support Vector Machine

A Parallel Genetic Algorithm Based Feature Selection and Parameter Optimization for Support Vector Machine

The extensive applications of support vector machines (SVMs) require efficient method of constructing a SVM classifier with high classification ability. The performance of SVM crucially depends on whether optimal feature subset and parameter of SVM can be efficiently obtained. In this paper, a coarse-grained parallel genetic algorithm (CGPGA) is used to simultaneously optimize the feature subset and parameters for SVM. The distributed topology and migration policy of CGPGA can help find optimal feature subset and parameters for SVM in significantly shorter time, so as to increase the quality of solution found. In addition, a new fitness function, which combines the classification accuracy obtained from bootstrap method, the number of chosen features, and the number of support vectors, is proposed to lead the search of CGPGA to the direction of optimal generalization error. Experiment results on 12 benchmark datasets show that our proposed approach outperforms genetic algorithm (GA) based method and grid search method in terms of classification accuracy, number of chosen features, number of support vectors, and running time.

The application of Coarse-Grained Parallel Genetic Algorithm with Hadoop in University Intelligent Course-Timetabling System

The university course-timetabling problem is a NP-C problem. The traditional method of arranging course is inefficient, causes a high conflict rate of teacher resource or classroom resource, and is poor satisfaction in students. So it does not meet the requirements of modern university educational administration management. However, parallel genetic algorithm (PGA) not only have the advantages of the traditional genetic algorithm(GA), but also take full advantage of the computing power of parallel computing. It can improve the quality and speed of solving effectively, and have a broad application prospect in solving the problem of university course-timetabling problem. In this paper, based on the cloud computing platform of Hadoop, an improved method of fusing coarse-grained parallel genetic algorithm (CGPGA) and Map/Reduce programming model is deeply researched, and which is used to solve the problem of university intelligent courses arrangement. The simulation experiment results show that, compared with the traditional genetic algorithm, the coarse-grained parallel genetic algorithm not only improves the efficiency of the course arrangement and the success rate of the course, but also reduces the conflict rate of the course. At the same time, this research makes full use of the high parallelism of Map/Reduce to improve the efficiency of the algorithm, and also solves the problem of university scheduling problem more effectively.

ISLAND MIGRATION MODEL WITH PARALLEL MUTATION STRATEGIES FOR COMPUTING THE TRAVELING SALESMAN PROBLEM ON MULTICOMPUTER PLATFORM

This paper investigates the efficiency of a model of parallel genetic computation of the traveling salesman problem with circular periodic chromosomes migration. The parallel model is verified by MPI-based program implementation on a multicomputer platform. The correlation of the application and architectural spaces has been investigated by exploring the impact of the scalability of the application and the parallel machine size over the efficiency of the parallel system. Performance profiling, evaluation and analysis have been made for different numbers of cities and different sizes of the multicomputer platform. The paper also investigates the impact of the mutation strategy on the solution quality of coarse-grained parallel genetic algorithm with circular periodic migration for the traveling salesman problem. We propose an approach to improve the quality of solution by applying parallel variable mutation rates for the local evolutions in the concurrent processes. A series of experiments has been carried out with parallel fixed and variable mutation rates in order to estimate the efficiency of the suggested approach. The best quality solutions have been obtained for the strategy with parallel fixed mutation rates.

Research on Computer Networks Reliability Optimization by Parallel Genetic Algorithms Method

This paper can be asserted that the use of parallel genetic algorithm can not only effectively improve the calculation speed and optimize the quality, but also can improve a lot of advantages. Reliability optimization for computer net works, subject s to cost constraints, is a NP-hard combinational problem. Reg ar ding a known network topology, the problem of choosing links and switchers among alternatives different in reliability and cost is settled by a Coarse-grained parallel genetic algorithm, which maximize the network availablity within a fixed budget. T he simulations on a dedicated cluster demonst rate that contracting to the sequential counterpart, o ur par allel GA improves the quality of plans greatly with an evident speed-up.

Research on Logistic Path Planning Strategy Based on Coarse-Grained Parallel Genetic Algorithm

In order to minimize the precocity and deceit happened in genetic algorithm, this thesis puts forward an improved intelligent evolutionary algorithmcoarse-grained parallel genetic algorithm which proposes schema order based cross operator, task immigration based cross operator, and improved approach for elitist strategy. Also, this paper applies the algorithm into logistic route planning system to clarify the concrete implementation steps for coding, population generation and genetic operator. According to experiment result, the improved algorithm mentioned in this assay have advanced the convergence rate and optimizing ability to some extent.

Coarse-grained parallel genetic algorithm applied to a vector based land use allocation optimization problem: the case study of Tongzhou Newtown, Beijing, China

A Coarse-Grained Parallel Genetic Algorithm (CGPGA) is utilized to search for near-optimal solutions for land use allocation optimization problems in the context of multiple objectives and constraints. Plans are obtained based on the trade-off among three spatial objectives including ecological benefit, accessibility and compatibility. The Multi-objective Optimization of Land Use model integrates these objectives with the fitness function assessed by reference point method (goal programming). The CGPGA, as the first coupling in land use allocation optimization problems, is tested through the experiments with one processor, two processors and four processors to pursue near-optimal land use allocation scenarios and the comparison to these experiments based on Generic Genetic Algorithm (GGA), which clearly shows the robustness of the model we proposed as well as its better performance. Furthermore, the successful convergent (near-convergent) case study utilizing the CGPGA in Tongzhou Newtown, Beijing, China evinces the capability and potential of CGPGA in solving land use allocation optimization problems with better efficiency and effectiveness than GGA.

Study on Coarse-Grained Parallel Genetic Algorithm

The genetic algorithm is a powerful global search and optimization technique based on the principles of natural selection and genetics, but it is not suitable in solving large-scale and complicated problems due to its the shortcomings in computational accuracy and efficiency. Against these deficiencies, a coarse-grained parallel genetic algorithm (PGA) model based on distributed cluster system is proposed in this paper. Flow chart about the model is designed and detailed analysis of migration scheme is offered. This paper investigates the parallel efficiency of the coarse-grained PGA and migration operator by experiments on a specific inverse heat conduction problem .The experimental results show that the model can achieve upper speedup rations, improve computational efficiency and the overall performance of the PGA.

Inversion on S-Wave Velocity Structure of Shallow Soil Layer Site Using Parallel Genetic Algorithm

Microtremors was developed to inverse S-wave velocity structure of sites because it costs little and easy to monitor, can be performed at any place even in a densely populated city with non-destructive measurements, the genetic algorithm is widely used in inversion and there are many disadvantages in using genetic algorithms to solve practical problems, so the authors did a lot of efforts to overcome these disadvantages. In order to solve these disadvantages, a coarse-grained parallel genetic algorithm(PGA) based on personal computer cluster was proposed to inverse S-wave velocity structure of shallow soil layer of actual engineering sites, the simulated annealing algorithm and parallel technique message passing interface(MPI) were adopted to implement the coarse-grained parallel compute. The subpopulations were collaboratively optimized through individual migration strategy and the dynamic populations were adopted to balance the computing load. The shallow S-wave velocity structures of two examples and the actual engineering sites were inversed through a 4-node PC cluster test system, the results showed that the algorithm has a good parallel efficiency and can be used in engineering site.

Research on Grid Resources Schedule Based on an Adaptive Distribute Parallel Genetic Algorithm

In this paper an improved adaptive parallel genetic algorithm is proposed to solve problems of grid resources distribution and matching, comparing with the traditional genetic algorithms, a new adaptive selection operator is introduced, which can prevent the premature convergence of genetic algorithm efficiently. Besides, in this paper, the migration strategy of the parallel genetic algorithm can prevent the population trapped in the local extreme. And a pc-cluster containing eight computers is constructed to execute the coarse-grained parallel genetic algorithm and series genetic algorithm, and different scale resources and tasks are tested on the pc-cluster. Several examples are provided to be examined and the results illustrate that the proposed algorithm has higher global optimization capability, computational efficiency and stronger stability than the traditional genetic algorithm for the max time span. From these results, the parallel genetic algorithm reduced the searching time much more than series genetic algorithm for the same solutions. Moreover, compared with series genetic algorithm, the parallel genetic algorithm can get the more optimal solutions when the iteration is same.

Sonic Boom Minimization Using Inverse Design and Probabilistic Acoustic Propagation

near-field signature is more accurately represented. The problem of F-function parameters’ estimation is reformulated as a gradient-based optimization problem and solved. Sonic boom propagation is carried out in a probabilistic fashion using parametric atmospheric models and statistical techniques. A bilevel pseudoinverse optimization is performed using coarse-grained parallel genetic algorithms to design aircraft that meet low sonic boom requirements under atmospheric uncertainty. The optimization analysis is split into two cycles with multiple conflicting objectives. Results are presented and discussed.

Parallel Genetic Algorithms for Optimizing Resource Utilization in Large-Scale Construction Projects

This paper presents the development of a parallel multiobjective genetic algorithm framework to enable an efficient and effective optimization of resource utilization in large-scale construction projects. The framework incorporates a multiobjective optimization module, a global parallel genetic algorithm module, a coarse-grained parallel genetic algorithm module, and a performance evaluation module. The framework is implemented on a cluster of 50 parallel processors and its performance was evaluated using 183 experiments that tested various combinations of construction project sizes, numbers of parallel processors and genetic algorithm setups. The results of these experiments illustrate the new and unique capabilities of the developed parallel genetic algorithm framework in: (1) Enabling an efficient and effective optimization of large-scale construction projects; (2) achieving significant computational time savings by distributing the genetic algorithm computations over a cluster of parallel processors; and (3) requiring a limited and feasible number of parallel processors/computers that can be readily available in construction engineering and management offices.

A Study of Five Parallel Approaches to a Genetic Algorithm for the Traveling Salesman Problem

Abstract This paper presents a comparative study of five different coarse-grained parallel genetic algorithms (PGAs) using the traveling salesman problem as the case application. All of these PGAs are based on the same baseline serial genetic algorithm, implemented on the same parallel machine (IBM SP2), tested on the same problem instances, and started from the same set of initial populations. Based on these experiments, a PGA that combines a new subtour technique with a known migration approach is identified to be the best for the traveling salesman problem among the five PGAs being compared.

Analysis of crossovers and selections in a coarse-grained parallel genetic algorithm

The parallel genetic algorithms (PGA) have been developed for combinatorial optimization problems, and its parallel efficiencies have been investigated on a specific problem. These investigations were concerned with how to design a topology and the determination of the optimum setting for parameters (for example, size of subpopulations, migration interval, and so on) rather than the effectiveness of genetic operators. This paper investigates a relation between the parallel efficiency of the coarse-grained PGA and genetic (crossover and selection) operators for the traveling salesman problem on an MIMD parallel computer. The following genetic operators are considered: improved edge recombination (IERX), distance preserving (DPX), and complete subtour exchange (CSEX) crossovers, and two selection operators, which have relatively high selection pressures. Computational results indicate that the parallel efficiency is significantly affected by the difference of crossovers rather than the selections, and the PGA with CSEX gives better properties.

Coarse-grained parallel genetic algorithm applied to a nuclear reactor core design optimization problem : Pereira, C. M. N. A. and Lapa, C. M. F. Annals of Nuclear Energy, 2003, 30, (5), 555–565

An important consideration with regard to ground source heat pump (GSHP) systems is ensuring high energy efficiency performance throughout the years. Multi-year analyses are thus exceedingly important to evaluate the seasonal oscillations in the ground temperature and its long-term drift.As only a limited number of results based on real cases are available in the literature, this work aims to address this paucity of information. Seasonal and long-term computer simulations were conducted in order to investigate the thermal performance of two office buildings in Italy with unbalanced load profiles. The study focuses on the evaluation of ground temperature drift which was essentially due to an imbalance of the buildings’ load profile.An analysis, which was compared to two analytical approaches available in the literature, was carried out using a detailed numerical model. The effects of improved load balancing of the ground load profile and of a higher number of boreholes on seasonal oscillations and on the thermal drift were investigated. The results showed that rebalancing the ratio between the heat extracted from and injected into the ground led to better results from an energetic viewpoint almost certainly synonymous with an energetic-economic optimization.