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检索条件"主题词=Ant Colony Optimization algorithm"

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A Novel Fault-Tolerant Scheduling of Real-Time Tasks on Multiprocessor using Discrete-Elitist Multi-ACO

A Novel Fault-Tolerant Scheduling of Real-Time Tasks on Mult...

引用

2015 International Conference on Communications and Signal Processing (ICCSP)

作者： Samal, Abhaya Kumar Parida, Ajaya Kumar Pani, Subhendu Kumar Dash, Akshaya Kumar Trident Acad Technol Dept CSE Bhubaneswar Orissa India Orissa Engn Coll Dept IT Bhubaneswar Orissa India Orissa Engn Coll Dept CSE Bhubaneswar Orissa India

ISBN: (纸本)9781479980819

Fault-tolerant scheduling of real-time (RT) tasks in multiprocessor based systems is essentially a NP-hard problem. Basically this is a permutation based combinatorial optimization problem which involves allocating a set of tasks on a set of processors so as to minimize the makespan and ensures tasks to meet their timing constraints. Conventional approach to meet fault-tolerance (FT) in scheduling RT tasks based on traditional heuristic approach yields poor performance, like inefficient processor utilization and higher task rejection. The primary-backup (PB) scheme is a popular approach used as a fault-tolerant technique to schedule RT tasks with guarantee to meet their deadlines despite presence of fault. In this paper, we propose a novel scheduling algorithm using discrete elitist multiobjective ant colony optimization (DEMACO) technique for providing FT in multiprocessor environment. Exhaustive simulation study reveals that the proposed primary-backup based fault-tolerant scheduling (PBFTS) scheme using DEMACO outperforms other traditional fault-tolerant scheduling schemes in terms of performance, system utilization and efficiency.

关键词： Real-time system Primary-backup approach Fault-tolerance ant colony optimization algorithm Heuristic based scheduling approach Elitism Combinatorial optimization

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Multi-objective path selection model and algorithm for emergency evacuation

Multi-objective path selection model and algorithm for emerg...

引用

IEEE International Conference on Automation and Logistics

作者： Yuan, Yuan Wang, Dingwei Northeastern Univ Sch Informat Sci & Engn Shenyang Liaoning Peoples R China

ISBN: (纸本)9781424415304

Evacuation planning is one of the most important aspects of emergency management. Path selection is one of the fundamental problems in evacuation planning. A multi-objective mathematical model is presented for path selection in emergency evacuation. The two objectives of the model are to minimize the total travel time along a path and to minimize the path complexity respectively. Taking into account the real-time effect of disaster extension, the travel speed on each arc is modeled as a continuous decrease function with respect to time. The complexity of the path is modeled as the total number of arcs included in the path. An ant colony optimization algorithm is proposed to solve the model. Simulation results show the effectiveness and feasibility of the model and algorithm.

关键词： emergency evacuation path selection disaster extension multi-objective model ant colony optimization algorithm

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Comparative Analysis of Different Prediction Models of Inorganic Fertilizers N, P, and K Under Different Soil Fertility Conditions 36

Comparative Analysis of Different Prediction Models of Inorg...

引用

36th Chinese Control and Decision Conference (CCDC)

作者： Luo, Yuzhu Yu, Xiaoyu Zhou, Yue Bai, Bing Dong, Huan Peng, Xiuyuan Liaoning Acad Agr Sci Inst Informat Shenyang Peoples R China Liaoning Acad Agr Sci Inst Plant Nutr & Environm Resources Shenyang Peoples R China

ISBN: (纸本)9798350387780;9798350387797

Nutrient management is a key measure to achieve the target crop yield. Choosing appropriate predictive models and adjusting nutrient supply according to actual conditions can effectively reduce nutrient loss, improve crop yield and product quality. Therefore, we conducted research on the prediction models of inorganic fertilizers N, P, and K in greenhouse tomatoes based on the target yield. In this paper, we analyzed four different neural network prediction models: the neural network prediction model based on ant, colony optimization algorithm, the neural network prediction model based on sparrow search algorithm, neural network prediction model based on genetic algorithm and neural network prediction model based on particle swarm optimization algorithm, and compared and analyzed the prediction results of inorganic fertilizer N, P and K under different soil fertility conditions to understand which neural network prediction model will produce the best prediction effect. The simulation results showed that under medium soil fertility, the neural network prediction model based on genetic algorithm had the best prediction effect of inorganic fertilizer N, P, and K. The verification results showed that its mean square error (MSE) and coefficient of determination (R-2) were the best, which were 0.0031 and 0.8200 respectively. However, under low soil fertility and high soil fertility, the neural network prediction model based on the sparrow search algorithm had the best prediction performance, and its MSE and R-2 were the best.

关键词： ant colony optimization algorithm sparrow search algorithm genetic algorithm particle swarm optimization algorithm inorganic fertilizer prediction

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A Novel method for Ship Detection Based on NSCT and ACO

A Novel method for Ship Detection Based on NSCT and ACO

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2nd International Congress on Image and Signal Processing

作者： Xie, Hong Li, Lin-lin Bo, Hua Zhang, Yun-nong Shanghai Maritime Univ Informat Engn Coll Shanghai 200135 Peoples R China

ISBN: (纸本)9781424441303

Ship detection is an important section of Synthetic Aperture Radar's marine applications. Through analyzing the distribution characteristics of ship targets and speckle, a novel ship detection method based on NSCT and ACO is proposed for SAR image processing. First, apply NSCT adaptive threshold method to de-noise the SAR images, then detect the edges by ACO, last ship targets can be precisely detected. Simulating results indicate that this method can not only pick-up contour information of ship objects in complex speckle efficiently, but also keep ships structure well, show ideal anti-jamming competence, guarantee the detection accuracy.

关键词： NSCT ant colony optimization algorithm edge detection ship detection

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ACO-Based Projection Pursuit: A Novel Clustering algorithm

ACO-Based Projection Pursuit: A Novel Clustering Algorithm

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International Conference on E-business Technology and Strategy

作者： Li, Yancang Zhao, Lina Zhou, Shujing Hebei Univ Engn Coll Civil Engn Handan 056038 Peoples R China

ISBN: (纸本)9783642163968

In order to find an effective method of solving the problem of subjectivity and difficulty in the high-dimension data clustering, a new method-an improved Projection Pursuit based on ant colony optimization algorithm was introduced. The ant colony optimization algorithm was employed to optimize the function of the projected indexes in the PP. The ant colony optimization algorithm has the strong global optimization ability and the PP method is a powerful technique for extracting statistically significant features from high-dimension data for automatic target detection and classification. Application results show that the method can complete the selection more objectivity and rationality with objective weight, high resolving power, and stable result. The study provides a novel algorithm for the high-dimension data clustering.

关键词： Clustering algorithm Projection Pursuit algorithm ant colony optimization algorithm High-dimension Data Improved

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Multi-objective ACO algorithm for slab selecting and charging scheduling in hot rolling production 5

Multi-objective ACO algorithm for slab selecting and chargin...

引用

IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)

作者： Sun, Xuesheng Lu, Chao Liu, Shixin Zhang, Ruiyou Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

ISBN: (纸本)9781479987306

Reheating furnace charging and hot rolling play an important role in reducing energy loss and production cost. According to the production process and constraints in the process of reheating furnace charging and hot rolling, a multi-objective mathematical model is developed for selecting slabs and scheduling reheating furnaces. A multi-objective ant colony optimization algorithm is therefore designed to solve the model, which is embedded with several optimization strategies. Simulation experiment results of a practical instance show that the proposed model and algorithm are effective in solving the problem of this paper.

关键词： selecting slabs scheduling reheating furnaces hot rolling plan multi-objective ant colony optimization algorithm

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Diagnosis of complex systems using ant colony decision Petri nets

Diagnosis of complex systems using ant colony decision Petri...

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1st International Conference on Availability, Reliability and Security

作者： Ciufudean, Calin Graur, Adrian Filote, Constantin Turcu, Cornel Popa, Valentin Ştefan cel Mare University of Suceava RO-720225 9 University str. Romania

ISBN: (纸本)0769525679

Failure diagnosis in large and complex systems is a critical task. A discrete event system (DES) approach to the problem of failure diagnosis is presented in this paper. A classic solution to solve DES's diagnosis is a stochastic Petri nets. Unfortunately, the solution of a stochastic Petri net is severely restricted by the size of its underlying Markov chain. On the other hand, it has been shown that foraging behavior of ant colonies can give rise to the shortest path, which will reduce the state explosion of stochastic Petri net. Therefore, a new model of stochastic Petri net, based on foraging behavior of real ant colonies is introduced in this paper. This model can contribute to the diagnosis, the performance analysis and design of supervisory control systems.

关键词： stochastic Petri nets discrete-event systems ant colony optimization algorithm

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An Efficient Scheduling and Navigation Approach for Warehouse Multi-Mobile Robots 13th

An Efficient Scheduling and Navigation Approach for Warehous...

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13th International Conference on Swarm Intelligence (ICSI)

作者： Zhao, Kang Xu, Benlian Lu, Mingli Shi, Jian Li, Zhen Soochow Univ Sch Mech & Elect Engn Suzhou Peoples R China Changshu Inst Technol Sch Mech Engn Changshu Jiangsu Peoples R China Changshu Inst Technol Sch Elect & Automat Engn Changshu Jiangsu Peoples R China

ISBN: (纸本)9783031097263;9783031097263

Multi-robot scheduling and navigation methods are critical for efficient warehouse handling. In this paper, we propose a Robot Operating System (ROS) based scheduling and navigation method for multi-mobile robots. In order to solve the problem of multi-robot multi-task point assignment in the warehouse environment, we establish a target model that minimizes the total transportation time and propose a hierarchical Genetic algorithm-ant colony optimization algorithm. By repeating the upper and lower operations, the shortest total transport time allocation scheme for multi-robot multi-tasking can be obtained. In order to realize the multi-robot path planning after task assignment, a multi-robot communication system is designed on the basis of ROS, and the autonomous navigation of mobile robots is employed with the help of SLAM map. The experimental results show that the proposed multi-robot scheduling method can effectively reduce the overall transportation time, realize the reasonable allocation of multi-robots and multi-tasks, and successfully complete the cargo transportation task.

关键词： Multi-robot scheduling Genetic algorithm ant colony optimization algorithm Navigation SLAM

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Comparison of ABC and ACO algorithms Applied for Solving the Inverse Heat Conduction Problem

Comparison of ABC and ACO Algorithms Applied for Solving the...

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International Symposium on Swarm and Evolutionary Computation/ Symposium on Swarm Intelligence and Differential Evolution

作者： Hetmaniok, Edyta Slota, Damian Zielonka, Adam Witula, Roman Silesian Tech Univ Inst Math PL-44100 Gliwice Poland

ISBN: (纸本)9783642293528;9783642293535

In this paper we present the comparison of numerical methods applied for solving the inverse heat conduction problem in which two algorithms of swarm intelligence are used: Artificial Bee colony algorithm (ABC) and ant colony optimization algorithm (ACO). Both algorithms belong to the group of algorithms inspired by the behavior of swarms of insects and they are applied for minimizing the proper functional representing the crucial part of the method used for solving the inverse heat conduction problems. Methods applying the respective algorithms are compared with regard to their velocity and precision of the received results.

关键词： Swarm Intelligence Artificial Bee colony algorithm ant colony optimization algorithm Inverse Heat Conduction Problem

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Fault Diagnosis of Chemical Process Based on ACO-BP Neural Network

Fault Diagnosis of Chemical Process Based on ACO-BP Neural N...

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2nd International Conference on Advanced Design and Manufacturing Engineering (ADME 2012)

作者： Chen Jian-xue Shanghai Univ Engn Sci Coll Elect & Elect Engn Shanghai 201620 Peoples R China

ISBN: (纸本)9783037855027

Fault diagnosis is an important problem in the process of chemical industry and the artificial neural network is widely applied in fault diagnosis of chemical process. A hybrid algorithm combining ant colony optimization (ACO) algorithm with back-propagation (BP) algorithm, also referred to as ACO-BP algorithm, is proposed to train the neural network weights and thresholds. The basic theory and steps of ACO-BP algorithm are given, and applied in fault diagnosis of the continuous stirred-tank reactor (CSTR). Experimental results prove that ACO-BP algorithm has good fault diagnosis precision, and it can detect the fault in CSTR promptly and effectively.

关键词： Fault diagnosis ant colony optimization algorithm Back-propagation algorithm Neural network CSTR

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