In this article, we present a parallel graphical processing unit (GPU)-based geneticalgorithm (GA) for solving the resource-constrained multi-project scheduling problem (RCMPSP). We assumed that activity pre-emption ...
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In this article, we present a parallel graphical processing unit (GPU)-based geneticalgorithm (GA) for solving the resource-constrained multi-project scheduling problem (RCMPSP). We assumed that activity pre-emption is not allowed. Problem is modeled in a portfolio of projects where precedence and resource constraints affect the portfolio duration. We also assume that the durations, availability of resources are deterministic and portfolio has a static nature. The objective in this article is to find a start time for each activity of the project so that the portfolio duration is minimized, while satisfying precedence relations and resource availabilities within a reasonable amount of time for small and large problem instances. In order to compare the efficiency of the proposed parallel GPU-based GA, problem is solved together with a CPU and a GPU. The results showed that GPU-based parallel GA has high potential for improving the performance of GAs for the RCMPSP particularly, for large-scale problems.
The paper considers a problem of building the hybrid algorithm for solving the optimization design tasks on the basis of integration of different methods of computation intelligence. The authors describe the definitio...
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ISBN:
(纸本)9783030392161;9783030392154
The paper considers a problem of building the hybrid algorithm for solving the optimization design tasks on the basis of integration of different methods of computation intelligence. The authors describe the definition and the main approaches to building the hybrid systems and demonstrate the possibilities of integration of the evolutionary design and multi-agent systems methods The different approaches to evolutionary design of the agents are considered. Different methods of parallelizing the computational process and the main models of parallel genetic algorithms, their benefits and shortcomings are described and analyzed in the paper. A hybrid parallel genetic algorithm for searching and optimization of the design decisions is developed in the paper. The algorithm is implemented as software subsystem and investigated in terms of its effectiveness.
Cancer classification is one of the main steps during patient healing process. This fact enforces modern clinical researchers to use advanced bioinformatics methods for cancer classification. Cancer classification is ...
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Cancer classification is one of the main steps during patient healing process. This fact enforces modern clinical researchers to use advanced bioinformatics methods for cancer classification. Cancer classification is usually performed using gene expression data gained in microarray experiment and advanced machine learning methods. Microarray experiment generates huge amount of data, and its processing via machine learning methods represents a big challenge. In this study, two-step classification paradigm which merges geneticalgorithm feature selection and machine learning classifiers is utilized. geneticalgorithm is built in MapReduce programming spirit which makes this algorithm highly scalable for Hadoop cluster. In order to improve the performance of the proposed algorithm, it is extended into a parallelalgorithm which process on microarray data in distributed manner using the Hadoop MapReduce framework. In this paper, the algorithm was tested on eleven GEMS data sets (9 tumors, 11 tumors, 14 tumors, brain tumor 1, lung cancer, brain tumor 2, leukemia 1, DLBCL, leukemia 2, SRBCT, and prostate tumor) and its accuracy reached 100% for less than 25 selected features. The proposed cloud computing-based MapReduce parallel genetic algorithm performed well on gene expression data. In addition, the scalability of the suggested algorithm is unlimited because of underlying Hadoop MapReduce platform. The presented results indicate that the proposed method can be effectively implemented for real-world microarray data in the cloud environment. In addition, the Hadoop MapReduce framework demonstrates substantial decrease in the computation time.
Synonyms, synonyms and the strong association of semantic information increase the dimension text feature vectors, and greatly affect the efficiency and accuracy of text classification. In order to reduce the dimensio...
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ISBN:
(纸本)9781538643624
Synonyms, synonyms and the strong association of semantic information increase the dimension text feature vectors, and greatly affect the efficiency and accuracy of text classification. In order to reduce the dimension of the text feature vectors, this paper presents an improved parallel genetic algorithm to sovle the text feature clustering problem. Firstly, the K-means algorithm is used to cluster the feature words. The parallel genetic algorithm is used to fine-grained the feature words. In the process of applying geneticalgorithms, crossover operator is improved so that the algorithm has a global search ability and local search capability, and reduce the dependence on the initial cluster centers. Finally, the different types of feature words are analyzed and compressed, and a set of feature words of the text category and the semantic information is formed. The experimental results verify our method of text feature extraction is better.
To maximize the reliability of a system, the traditional reliability redundancy allocation problem (RRAP) determines the component reliability and level of redundancy for each subsystem. This paper proposes an advance...
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To maximize the reliability of a system, the traditional reliability redundancy allocation problem (RRAP) determines the component reliability and level of redundancy for each subsystem. This paper proposes an advanced RRAP that also considers the optimal redundancy strategy, either active or cold standby. In addition, new examples are presented for it. Furthermore, the exact reliability function for a cold standby redundant subsystem with an imperfect detector/switch is suggested, and is expected to replace the previous approximating model that has been used in most related studies. A parallel genetic algorithm for solving the RRAP as a mixed-integer nonlinear programming model is presented, and its performance is compared with those of previous studies by using numerical examples on three benchmark problems.
The optimum design of power system components is becoming a relevant topic in power system studies. geneticalgorithms (GAs) are considered as a proper approach for optimisation problems in which non-linear elements a...
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The optimum design of power system components is becoming a relevant topic in power system studies. geneticalgorithms (GAs) are considered as a proper approach for optimisation problems in which non-linear elements are involved. Several trends are presently leading GAs to a new level;for instance, its combination with parallel computing can facilitate the solution of problems where individual evaluations of the fitness function require an important computational effort. This study presents a procedure based on a MATLAB-EMTP application and the usage of a multicore environment for the optimum selection of hybrid high-voltage DC (HVDC) circuit breaker parameters;the goal is to obtain a transient response of the hybrid design with voltages, currents and fault clearance times within specified limits.
An efficient strategy is presented for global shape optimization of wing sections with a parallel genetic algorithm. Several computational techniques are applied to increase the convergence rate and the efficiency of ...
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An efficient strategy is presented for global shape optimization of wing sections with a parallel genetic algorithm. Several computational techniques are applied to increase the convergence rate and the efficiency of the method. A variable fidelity computational evaluation method is applied in which the expensive Navier-Stokes flow solver is complemented by an inexpensive multi-layer perceptron neural network for the objective function evaluations. A population dispersion method that consists of two phases, of exploration and refinement, is developed to improve the convergence rate and the robustness of the geneticalgorithm. Owing to the nature of the optimization problem, a parallel framework based on the master/slave approach is used. The outcomes indicate that the method is able to find the global optimum with significantly lower computational time in comparison to the conventional geneticalgorithm.
Heavy lifting is a common and important task in industrial plants. It is conducted frequently during the time of plant construction, maintenance shutdown and new equipment installation. To find a safe and cost effecti...
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Heavy lifting is a common and important task in industrial plants. It is conducted frequently during the time of plant construction, maintenance shutdown and new equipment installation. To find a safe and cost effective way of lifting, a team works for weeks or even months doing site investigation, planning and evaluations. This paper considers the lifting path planning problem for terrain cranes in complex environments. The lifting path planning problem takes inputs such as the plant environment, crane mechanical data, crane position, start and end lifting configurations to generate the optimal lifting path by evaluating costs and safety risks, We formulate the crane lifting path planning as a multi-objective nonlinear integer optimization problem with implicit constraints. It aims to optimize the energy cost, time cost and human operation conformity of the lifting path under constraints of collision avoidance and operational limitations. To solve the optimization problem, we design a Master-Slave parallel genetic algorithm and implement the algorithm on Graphics Processing Units using CUDA programming. In order to handle complex plants, we propose a collision detection strategy using hybrid configuration spaces based on an image-based collision detection algorithm. The results show that the method can efficiently generate high quality lifting paths in complex environments. (C) 2015 Elsevier B.V. All rights reserved.
Web Service Composition (WSC) is the process of reusing atomic Web services and combining them together to satisfy users' requirements. The main objective of WSC is to develop composite services to satisfy the Fun...
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ISBN:
(纸本)9781538611913
Web Service Composition (WSC) is the process of reusing atomic Web services and combining them together to satisfy users' requirements. The main objective of WSC is to develop composite services to satisfy the Functional Requirements (FR), as well as optimizing the Quality of Services (QoS) requirements. This has led to the emergence of QoS-aware WSC. Due to the increase in number of Web services with the same functionality but various QoS, it became difficult to find the optimal solution in QoS-aware WSC in a given time frame. In this paper we propose a new approach that integrates the use of the parallel genetic algorithm (PGA) and Q-learning to find the optimal WSC within reasonable time. Q-learning is used to generate the initial population to enhance the effectiveness of PGA. PGA is utilized to make the algorithm as time efficient as possible. We implemented our approach *** Framework platform 4.7 using C# programming language. The experiment results show the effectiveness of our proposed approach compared to PGA or GA only.
In this paper, we compare the performance of a simple geneticalgorithm with a parallel island model GA for solving the monitoring devices placement problem. We have found that in addition to providing a speeding up t...
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ISBN:
(纸本)9781538615966
In this paper, we compare the performance of a simple geneticalgorithm with a parallel island model GA for solving the monitoring devices placement problem. We have found that in addition to providing a speeding up through the use of parallel processing the island model GA finds better quality solutions in comparison with the simple GA.
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