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2012 IEEE International Conference on Engineering Technology and Economic Management（ICETEM2012）

作者： Shengwei peng Scientific Research Dept.Henan vocational college of chemical technology Zhengzhou,China

The study presents a multi-objective scheduling model on nonidentical parallel machines (MOSP),and proposes a new genetic algorithm based on a vector group encoding technique and immune *** MOSP is different from other scheduling problems on parallel machines for the following characteristic:Firstly,parallel machines are non-identical;Secondly,the sort of jobs processed on every machine can be restricted;Finally,take minimizing the total earliness penalty,and minimizing the total tardiness penalty,and minimizing the total completion time into account as a multi-objective *** the algorithm,its encoding method is simple and can effectively reflect the virtual scheduling *** initial method of population and genetic operator,such as selection,crossover,mutation,are also ***,an immune operator is adopted in order to guarantee variety of colony and improve quality of colony every ***,under the mode of master-slave control networks,parallel genetic algorithm is applied in order to adapt to lager scale and real-time scheduling *** experiments show that it is efficient,and is better than the common genetic algorithm,and has the better parallel efficiency.

关键词： Scheduling Special process constraint parallel machines parallel genetic algorithm Immune operator

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Probabilistic Chain-Enhanced parallel genetic algorithm for UAV Reconnaissance Task Assignment

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DRONES 2024年第6期8卷 213-213页

作者： Tang, Jiaze Liu, Dan Wang, Qisong Li, Junbao Sun, Jinwei Harbin Inst Technol Elect Measurement Technol & Intelligent Control In Sch Instrumentat Sci & Engn Harbin 150080 Peoples R China Harbin Inst Technol Informat Countermeasure Tech Inst Sch Comp Sci & Technol Harbin 150080 Peoples R China

With the increasing diversity and complexity of tasks assigned to unmanned aerial vehicles (UAVs), the demands on task assignment and sequencing technologies have grown significantly, particularly for large UAV tasks such as multi-target reconnaissance area surveillance. While the current exhaustive methods offer thorough solutions, they encounter substantial challenges in addressing large-scale task assignments due to their extensive computational demands. Conversely, while heuristic algorithms are capable of delivering satisfactory solutions with limited computational resources, they frequently struggle with converging on locally optimal solutions and are characterized by low iteration rates. In response to these limitations, this paper presents a novel approach: the probabilistic chain-enhanced parallel genetic algorithm (PC-EPGA). The PC-EPGA combines probabilistic chains with genetic algorithms to significantly enhance the quality of solutions. In our approach, each UAV flight is considered a Dubins vehicle, incorporating kinematic constraints. In addition, it integrates parallel genetic algorithms to improve hardware performance and processing speed. In our study, we represent task points as chromosome nodes and construct probabilistic connection chains between these nodes. This structure is specifically designed to influence the genetic algorithm's crossover and mutation processes by taking into account both the quantity of tasks assigned to UAVs and the associated costs of inter-task flights. In addition, we propose a fitness-based adaptive crossover operator to circumvent local optima more effectively. To optimize the parameters of the PC-EPGA, Bayesian networks are utilized, which improves the efficiency of the whole parameter search process. The experimental results show that compared to the traditional heuristic algorithms, the probabilistic chain algorithm significantly improves the quality of solutions and computational efficiency.

关键词： task planning cooperating robots parallel genetic algorithm probabilistic chain Bayesian network UAV reconnaissance task

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The Automated Fault-recovery for Four-legged Robots using parallel genetic algorithm

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Procedia Computer Science 2013年 24卷 158-166页

作者： Hyunsoo Park Kyung Joong Kim Department of Computer Engineering Sejong University Seoul Republic of Korea

It is expected that robots could operate autonomously in extreme environments without human intervention. However, it is not a trivial task to design robots robust to all kinds of faults. On the other hand, Biological entities have capability to create new behaviors overcoming unexpected damage on their body. This is also one of desirable properties for industrial robots operating remotely in extreme conditions. In this paper, we propose to use a bio- inspired learning algorithm to generate new behaviors on a four-legged robot against unexpected body damages. Since the learning algorithm can be accelerated using parallelism on multiple machines, it is possible to adapt to changes (damages) quickly using remote computational resources. Experimental results show that the robot could adapt to damages on different part of robot's body successfully.

关键词： fault-tolerant robot parallel genetic algorithm neural network physical-based simulation

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An MPI-based parallel genetic algorithm for multiple geographical feature label placement based on the hybrid of fixed-sliding models

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GEO-SPATIAL INFORMATION SCIENCE 2024年

作者： Lessani, M. Naser Li, Zhenlong Deng, Jiqiu Guo, Zhiyong Penn State Univ Geoinformat & Big Data Res Lab State Coll PA 16801 USA Cent South Univ Sch Geosci & Infophys Changsha Peoples R China

Multiple Geographical Feature Label Placement (MGFLP) has been a fundamental problem in geographic information visualization for decades. Moreover, the nature of label positioning has proven to be an Nondeterministic polynomial-time hard (NP-hard) problem. Although advances in computer technology and robust approaches have addressed the problem of label positioning, the lengthy running time of MGFLP has not been a major focus of recent studies. Based on a hybrid of the fixed-position and sliding models, a Message Passing Interface (MPI) parallel genetic algorithm is proposed in the present study for MGFLP to label mixed types of geographical features. To evaluate the quality of label placement, a quality function is defined based on four quality metrics: label-feature conflict;label-label conflict;label association with the corresponding feature;label position priority for all three types of features. The experimental results show that the proposed algorithm outperforms the DDEGA, DDEGA-NM, and parallel-MS in both label placement quality and computation time efficiency. Across three datasets, compared to parallel-MS, running times decreased from 118.45 to 8.34, 45.98 to 3.51, and 20.01 to 0.43 min, with further reductions in label-label and label-feature conflicts.

关键词： Label placement fixed position geographical features parallel genetic algorithm message passing interface

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Modeling of glubam roof truss, parameter identification and updating based on parallel genetic algorithm

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ENGINEERING STRUCTURES 2024年 316卷

作者： Shi, Da Marano, Giuseppe Carlo Demartino, Cristoforo Zhejiang Univ Coll Civil Engn & Architecture 866 Yuhangtang Rd Hangzhou 310058 Zhejiang Peoples R China Politecn Torino DISEG Dipartimento Ingn Strutturale Edile & Geotecn Corso Duca Degli Abruz 24 I-10128 Turin Italy Roma Tre Univ Dept Architecture Largo G Marzi 10 I-00153 Rome Italy

This research introduces an innovative approach to the design and simulation of bio-based laminated structures, specifically focusing on glue-laminated bamboo (glubam) used in roof trusses. Our study fills a critical gap by investigating the mechanical behaviors of bolted connections in bamboo-based structures, which have not been comprehensively studied before. We employ a dual-phase methodology: initially, cyclic tests on bolted steel to glubam joints assess their hysteretic behavior, followed by tests on glubam planar roof trusses to evaluate structural responses under practical conditions. Our novel contribution is the development of a simplified mechanical-based hysteretic model, incorporating connector and spring elements in series or parallel within the ABAQUS software. This model significantly improves on existing models by allowing for initial calibration through a parallel genetic algorithm (PGA), enhancing both accuracy and efficiency. Subsequent incorporation of this model into the simulation of truss joints enabled the creation of an advanced hybrid roof truss model within ABAQUS. The final stage of our research demonstrates the application of a PGA-based model-updating framework, which substantially increases the model's predictive accuracy. This work not only advances the understanding of structural behavior in bio-based construction materials but also introduces a robust framework for model updating that can be applied to other engineering simulations, contributing to more sustainable and resource-efficient construction practices.

关键词： Bolted connections Glubam Roof truss Model updating parallel genetic algorithm

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Synthesized optimal design via parallel genetic algorithm of multispectral metasurfaces with ultra-wideband microwave absorption, low infrared emissivity and visible transparency

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INFRARED PHYSICS & TECHNOLOGY 2021年 117卷 103826-103826页

作者： Zhu, Ruichao Jia, Yuxiang Wang, Jiafu Xu, Cuilian Meng, Zhen Sui, Sai Zhang, Zhongtao Zhu, Ying Zhang, Lei Wang, Jun Qu, Shaobo Air Force Engn Univ Dept Basic Sci Xian 710051 Peoples R China

With the rapid development of multi-sensor detection technology, the demand for multispectral camouflage materials or devices becomes more and more necessary. In this work, a multispectral metasurface (MSM) is designed using parallel genetic algorithm (PGA) to achieve simultaneously ultra-wideband microwave absorption and low infrared emissivity, together with visible transparency. In order to achieve visible transparency, all the raw materials are transparent at visible spectrum. To achieve low emissivity at infrared, a layer of periodic conducting patches with high occupation ratio (OR) is put on the top surface of the MSM. To realize microwave absorption, an electromagnetic absorbing layer is designed using PGA and is put just under beneath the lowinfrared layer. In order to verify this method, a prototype was fabricated and measured. The simulated and measured results are well consistent, which convincingly verifies our design. The microwave absorption keeps above 90% in 5.8-27.8 GHz and the mean infrared emissivity is reduced down to 0.32 in 3.0-14.0 mu m. Meanwhile, optical transparency is maintained. This work provides a parallel-optimization method of designing multispectral metasurfaces, which can be readily extended to other application scenarios.

关键词： Metasurface Multispectral parallel genetic algorithm Low Infrared emissivity Ultra-wideband Microwave Absorption Visible Transparency

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A parallel genetic algorithm for Optimization of Trading Strategies

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NUMERICAL ANALYSIS AND APPLICATIONS 2008年第4期1卷 347-354页

作者： Monakhov, O. G. Russian Acad Sci Inst Computat Math & Math Geophys Siberian Branch Pr Aka Lavrenteva 6 Novosibirsk 630090 Russia

An approach to optimization of trading strategies (algorithms) is described based on indicators of financial markets and on evolutionary computations. A parallel genetic algorithm is presented for searching optimal pa... 详细信息

关键词： trading strategy parallel genetic algorithm technical analysis financial indicator template evolutionary computations

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Solving job shop scheduling problem using a hybrid parallel micro genetic algorithm

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APPLIED SOFT COMPUTING 2011年第8期11卷 5782-5792页

作者： Yusof, Rubiyah Khalid, Marzuki Hui, Gan Teck Yusof, Syafawati Md Othman, Mohd Fauzi Univ Teknol Malaysia Int Campus Ctr Artificial Intelligence & Robot CAIRO Kuala Lumpur 54100 Malaysia

The effort of searching an optimal solution for scheduling problems is important for real-world industrial applications especially for mission-time critical systems. In this paper, a new hybrid parallel GA (PGA) based on a combination of asynchronous colony GA (ACGA) and autonomous immigration GA (AIGA) is employed to solve benchmark job shop scheduling problem. An autonomous function of sharing the best solution across the system is enabled through the implementation of a migration operator and a "global mailbox". The solution is able to minimize the makespan of the scheduling problem, as well as reduce the computation time. To further improve the computation time, micro GA which works on small population is used in this approach. The result shows that the algorithm is able to decrease the makespan considerably as compared to the conventional GA. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Job shop scheduling problem parallel genetic algorithm Micro GA Asynchronous colony GA Autonomous immigration GA

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An Improved parallel genetic algorithm Based on Injection Island Approach and K1 Triangulation for the Optimal Design of the Flexible Multi-body Model Vehicle Suspensions

An Improved Parallel Genetic Algorithm Based on Injection Is...

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2009 ISECS International Colloquium on Computing, Communication, Control, and Management

作者： Guangyuan Liu College of Info & Electric Engineering Hebei University of Engineering Handan,China Jingjun Zhang Ruizhen Gao Yang Sun Scientific Research Office Hebei University of Engineering Handan,China

In this paper,we introduced IPGA(Improved parallel genetic algorithm),a new parallel genetic algorithm which is based on k1 triangulation of fixed point theory and an injection island *** approach guarantees that the processors only generate solutions within their assigned *** what's more,the k1 triangulation theory is introduced to the genetic algorithm to design convergence criteria objectively and to solve the convergence problem in the later *** running environment is constituted in this paper and the optimization of multi-body model vehicle suspension is implemented through establishing the interface between ADAMS software and genetic *** results show that the parallel genetic algorithm developed in this paper is efficient.

关键词： parallel genetic algorithm island model K1 triangulation Cluster system Vehicle suspension

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A Improved parallel genetic algorithm Based on Fixed Point Theory for the Optimal Design of Multi-body Model Vehicle Suspensions

A Improved Parallel Genetic Algorithm Based on Fixed Point T...

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2009 2nd IEEE International Conference on Computer Science and Information Technology（ICCSIT 2009）

作者： Guangyuan Liu Scientific Research Office Hebei University of Engineering Handan,China Jingjun Zhang Ruizhen Gao Yang Sun College of Info & Electric Engineering Hebei University of Engineering Handan,China

Based on an improved genetic algorithm,a parallel genetic algorithm is presented and the skeleton implementing is constituted in this *** van der Laan-Talman algorithm is introduced to the genetic algorithm to design convergence criteria objectively and to solve the convergence problem in the later *** parallel genetic algorithm of multi-body model vehicle suspension optimization is implemented through establishing the interface between ADAMS software and the genetic *** results show that the parallel genetic algorithm developed in this paper is efficient.

关键词： Vehicle suspension genetic algorithm parallel genetic algorithm Fixed Point Theory Cluster system

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