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

EVOLVING SYSTEMS 2020年第4期11卷 673-682页

作者： Jamshidi, Vahid Nekoukar, Vahab Refan, Mohammad Hossein Shahid Rejaee Teacher Training Univ Sch Elect Engn Tehran Iran

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.

关键词： CAN bus Multi-master architecture parallel genetic algorithm Busmaster simulator

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A parallel genetic algorithm for adaptive hardware and its application to ECG signal classification

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NEURAL COMPUTING & APPLICATIONS 2013年第7-8期22卷 1609-1626页

作者： Jewajinda, Yutana Chongstitvatana, Prabhas Natl Elect & Comp Technol Ctr Pathum Thani Thailand Chulalongkorn Univ Dept Comp Engn Bangkok Thailand

This paper presents a parallel genetic algorithm (GA) called the cellular compact genetic algorithm (c-cGA) and its implementation for adaptive hardware. An adaptive hardware based on the c-cGA is proposed to automate real-time classification of ECG signals. The c-cGA not only provides a strong search capability while maintaining genetic diversity using multiple GAs but also has a cellular-like structure and is a straight-forward algorithm suitable for hardware implementation. The c-cGA hardware and an adaptive digital filter structure also perform an adaptive feature selection in real time. The c-cGA is applied to a block-based neural network (BbNN) for online learning in the hardware. Using an adaptive hardware approach based on the c-cGA, an adaptive hardware system for classifying ECG signals is feasible. The proposed adaptive hardware can be implemented in a field programmable gate array (FPGA) for an adaptive embedded system applied to personalised ECG signal classifications for long-term patient monitoring.

关键词： parallel genetic algorithm Adaptive hardware ECG signal classification

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Dynamic Adaboost learning with feature selection based on parallel genetic algorithm for image annotation

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KNOWLEDGE-BASED SYSTEMS 2010年第3期23卷 195-201页

作者： Li, Ran Lu, Jianjiang Zhang, Yafei Zhao, Tianzhong PM Univ Sci & Technol Inst Command Automat Nanjing 210007 Peoples R China

Image annotation can be formulated as a classification problem. Recently, Adaboost learning with feature selection has been used for creating an accurate ensemble classifier. We propose dynamic Adaboost learning with feature selection based on parallel genetic algorithm for image annotation in MPEG-7 standard. In each iteration of Adaboost learning, genetic algorithm (GA) is used to dynamically generate and optimize a set of feature subsets on which the weak classifiers are constructed, so that an ensemble member is selected. We investigate two methods of GA feature selection: a binary-coded chromosome GA feature selection method used to perform optimal feature subset selection, and a bi-coded chromosome GA feature selection method used to perform optimal-weighted feature subset selection, i.e. simultaneously perform optimal feature subset selection and corresponding optimal weight subset selection. To improve the computational efficiency of our approach, master-slave GA, a parallel program of GA, is implemented. k-nearest neighbor classifier is used as the base classifier. The experiments are performed over 2000 classified Corel images to validate the performance of the approaches. (C) 2009 Elsevier K.V. All rights reserved.

关键词： Image annotation Adaboost learning Feature selection parallel genetic algorithm

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A parallel genetic algorithm to discover patterns in genetic markers that indicate predisposition to multifactorial disease

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COMPUTERS IN BIOLOGY AND MEDICINE 2008年第7期38卷 826-836页

作者： Rausch, Tobias Thomas, Alun Camp, Nicola J. Cannon-Albright, Lisa A. Facelli, Julio C. Univ Utah Sch Med Dept Biomed Informat Salt Lake City UT 84112 USA Univ Potsdam Hasso Plattner Inst D-14482 Potsdam Germany Univ Utah Ctr High Performance Comp Salt Lake City UT 84112 USA

This paper describes a novel algorithm to analyze genetic linkage data using pattern recognition techniques and genetic algorithms (GA). The method allows a search for regions of the chromosome that may contain genetic variations that jointly predispose individuals for a particular disease. The method uses correlation analysis, filtering theory and genetic algorithms to achieve this goal. Because current genome scans use from hundreds to hundreds of thousands of markers, two versions of the method have been implemented. The first is an exhaustive analysis version that can be used to visualize, explore, and analyze small genetic data sets for two marker correlations;the second is a GA version, which uses a parallel implementation allowing searches of higher-order correlations in large data sets. Results on simulated data sets indicate that the method can be informative in the identification of major disease loci and gene-gene interactions in genome-wide linkage data and that further exploration of these techniques is justified. The results presented for both variants of the method show that it can help genetic epidemiologists to identify promising combinations of genetic factors that might predispose to complex disorders. In particular, the correlation analysis of IBD expression patterns might hint to possible gene-gene interactions and the filtering might be a fruitful approach to distinguish true correlation signals from noise. (c) 2008 Elsevier Ltd. All rights reserved.

关键词： gene-gene interactions multifactorial diseases pattern recognition data mining correlation analysis parallel genetic algorithm

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Optimization of hydrogen liquefaction process based on parallel genetic algorithm

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2022年第63期47卷 27038-27048页

作者： Zhu, Jianlu Wang, Guocong Li, Yuxing Duo, Zhili Sun, Chongzheng China Univ Petr East China Qingdao 266555 Peoples R China Sinopec Engn Inc Beijing 100101 Peoples R China

The optimization process of hydrogen liquefaction process is complex and time-consuming. In order to solve the above problems and improve the solving efficiency of the optimization process, this paper proposed the method of using parallel genetic algo-rithm combined with simulation software for optimization. parallel genetic algorithm effectively overcomes the premature convergence of standard genetic algorithm and has strong global search ability. The parallel processing accelerates the optimization process by 2.01 times, and saves 50.22% of the time compared with the serial calculation. It not only improves the solving speed, but also improves the solving quality and the calculation performance. After optimization, the specific energy consumption of the system is reduced by 52.26%, the exergy loss is reduced by 49.81%, the heat exchange efficiency is improved, and the process performance of the system is improved. This work has reference signifi-cance for hydrogen liquefaction process optimization using parallel genetic algorithm. (c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： parallel genetic algorithm Mixed refrigerant Hydrogen liquefaction Optimization

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Hybrid dual-objective parallel genetic algorithm for heterogeneous multiprocessor scheduling

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2020年第2期23卷 441-450页

作者： Saroja, S. Revathi, T. Mepco Schlenk Engn Coll Dept Informat Technol Sivakasi India

Scheduling is a process of mapping resources to tasks and it's objective is either one or more. This paper focuses on scheduling in heterogeneous multiprocessor systems. Here the resources are processing elements and tasks are the jobs submitted to the processor. The main objectives of multiprocessor scheduling are reducing schedule length, reducing the overall energy consumption, reducing the temperature, reducing failure rates and so on. A Hybrid dual-objective parallel genetic algorithm is applied in the proposed work. Makespan and energy consumption are the two objectives considered. The proposed algorithm determines the global optimal solutions by generating the initial population using some heuristics and then performing parallel genetic operations on it. The main aim of employing parallelism is to find a global optimum solution by avoiding premature convergence in a local optimum and to reduce the running time of the algorithm. Hill climbing is also used in addition, to avoid local optimum solutions. The proposed algorithm balances the tradeoff between energy consumption and makespan according to the inclinations of the users by following weighted sum methodology. Our experimental results demonstrate that the proposed algorithm outperforms the other existing algorithms in terms of both makespan and energy consumption by incurring less running time.

关键词： Dual-objective parallel genetic algorithm Scheduling

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Multi robot distance based formation using parallel genetic algorithm

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APPLIED SOFT COMPUTING 2020年 86卷

作者： Lopez-Gonzalez, A. Meda Campana, J. A. Hernandez Martinez, E. G. Paniagua Contro, P. Inst Politecn Nacl SEPI ESIME Zacatenco Ave IPN S-N Cdmx 07738 DF Mexico Univ Iberoamer DICAT Prolongac Paseo Reforma 880 Cdmx 01219 Mexico

In this paper an alternative method to achieve distance based formation is presented. The method uses genetic algorithms to find a suitable solution based on angle and distance, and an appropriate constant velocity to avoid collisions. The designed algorithm is extended to a parallel scheme to improve its performance and achieve Artificial Distributed Intelligence, in which the robots share, through solution migration, the best ways to converge to desired distances while avoiding collisions, finally reaching consensus on the solution. The algorithm is tested using simulations and real robots experiments. (C) 2019 Elsevier B.V. All rights reserved.

关键词： parallel genetic algorithm Multi robot Consensus Formation Distributed Artificial Intelligence

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Optimum design of hybrid HVDC circuit breakers using a parallel genetic algorithm and a MATLAB-EMTP environment

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IET GENERATION TRANSMISSION & DISTRIBUTION 2017年第12期11卷 2974-2982页

作者： Corea-Araujo, Javier A. Martinez-Velasco, Juan A. Magnusson, Jesper Univ Politecn Cataluna Dept Engn Elect Barcelona Spain Royal Inst Technol KTH Dept Electromagnet Engn Stockholm Sweden

The optimum design of power system components is becoming a relevant topic in power system studies. genetic algorithms (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.

关键词： HVDC power convertors circuit breakers genetic algorithms mathematics computing power engineering computing optimum design hybrid HVDC circuit breakers parallel genetic algorithm MATLAB-EMTP environment power system components optimisation problems parallel computing MATLAB-EMTP application fitness function high-voltage DC circuit breaker parameters transient response fault clearance

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Accelerating global optimization of aerodynamic shapes using a new surrogate-assisted parallel genetic algorithm

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ENGINEERING OPTIMIZATION 2017年第12期49卷 2079-2094页

作者： Ebrahimi, Mehdi Jahangirian, Alireza Amirkabir Univ Technol Tehran Polytech Dept Aerosp Engn Tehran Iran Univ Windsor Turbulence & Energy Lab Ctr Engn Innovat Windsor ON Canada

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 genetic algorithm. 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 genetic algorithm.

关键词： Aerodynamic optimization shape design neural networks parallel genetic algorithm computational fluid dynamics

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Optimal cooperative path planning of unmanned aerial vehicles by a parallel genetic algorithm

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ROBOTICA 2016年第4期34卷 823-836页

作者： Shorakaei, Hamed Vahdani, Mojtaba Imani, Babak Gholami, Ali. Islamic Azad Univ Asadabad Branch Dept Mech Engn Asadabad Iran Imam Hossein Univ Tehran Iran Islamic Azad Univ Harsin Branch Dept Mech Engn Harsin Iran

The current paper presents a path planning method based on probability maps and uses a new genetic algorithm for a group of UAVs. The probability map consists of cells that display the probability which the UAV will not encounter a hostile threat. The probability map is defined by three events. The obstacles are modeled in the probability map, as well. The cost function is defined such that all cells are surveyed in the path track. The simple formula based on the unique vector is presented to find this cell position. Generally, the cost function is formed by two parts;one part for optimizing the path of each UAV and the other for preventing UAVs from collision. The first part is a combination of safety and length of path and the second part is formed by an exponential function. Then, the optimal paths of each UAV are obtained by the genetic algorithm in a parallel form. According to the dimensions of path planning, genetic encoding has two or three indices. A new genetic operator is introduced to select an appropriate pair of chromosome for crossover operation. The effectiveness of the method is shown by several simulations.

关键词： Optimal cooperative path planning parallel genetic algorithm Probability map Unmanned aerial vehicle / UAV Cost function

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