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3rd UKSim European Symposium on Computer Modeling and Simulation

作者： Julai, S. Tokhi, M. O. Salleh, S. Mohd Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England

ISBN: (纸本)9781424453450

This paper presents investigations into modeling and active vibration control (AVC) of a flexible plate structure using continuous ant system algorithm (CASA) such structures. The optimization technique is utilized to obtain a dynamic model of a flexible plate structure based on auto-regressive with exogenous (ARX) input model structure. The flexible plate structure is subjected to two different disturbance signal types, namely random, and finite duration step. The fitness function for the CASA is the mean-squared error (MSE) between the measured and estimated outputs of the plate. The validation of the algorithm is presented in both time and frequency domains. The developed CASA modeling approach is used for AVC system design to suppress the vibration of the flexible plate. The performance of the controller is assessed in terms of level of attenuation achieved in the power spectral density of the observed signal.

关键词： ant system algorithm system identification active vibration control flexible plate structures

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Heuristic Factors in ant system algorithm for Course Timetabling Problem

Heuristic Factors in Ant System Algorithm for Course Timetab...

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9th International Conference on Intelligent systems Design and Applications

作者： Djamarus, Djasli Ku-Mahamud, Ku Ruhana Trisakti Univ Dept Informat Jakarta 11440 Indonesia Univ Utara Malaysia Coll Arts & Sci Kedah 06010 Malaysia

ISBN: (纸本)9781424447350

This paper presents an algorithm that is based on ant system to solve the course timetabling problem. The problem is modeled using the bipartite graph. Four heuristic factors are derived from the graph characteristic, are used to direct ants as the agent in finding course timetable elements. The concept of negative pheromone was also applied to ensure that paths leading to dead ends are not chosen. The performance of this proposed algorithm is promising when comparison of performance was made with the original ant system algorithm.

关键词： ant system algorithm Course Timetabling Problem Heuristic Pheromone

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Satellite Constellation Design with Adaptively Continuous ant system algorithm

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Chinese Journal of Aeronautics 2007年第4期20卷 297-303页

作者： He Quan Han Chao School of Astronautics Beijing University of Aeronautics and Astronautics Beijing 100083 China

The ant system algorithm （ASA） has proved to be a novel meta-heuristic algorithm to solve many multivariable problems. In this paper, the earth coverage of satellite constellation is analyzed and a n ＋ 1^ -fold coverage rate is put forward to evaluate the coverage performance of a satellite constellation. An optimization model of constellation parameters is established on the basis of the coverage performance. As a newly developed method, ASA can be applied to optimize the constellation parameters. In order to improve the ASA, a rule for adaptive number of ants is proposed, by which the search range is obviously enlarged and the convergence speed increased. Simulation results have shown that the ASA is more quick and efficient than other methodV211.71s.

关键词： ant system algorithm satellite constellation optimization design coverage performance adaptive adjusting

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Design of PID controller with incomplete derivation based on ant system algorithm

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控制理论与应用（英文版） 2004年第3期2卷 246-252页

作者： Guanzheng TAN Qingdong ZENG Wenbin LI School of Information Science and Engineering Central South UniversityChangsha Hunan 410083China

A new and intelligent design method for PID controller with incomplete derivation is proposed based on the ant system algorithm ( ASA) . For a given control system with this kind of PID controller, a group of optimal PID controller parameters K p * , T i * , and T d * can be obtained by taking the overshoot, settling time, and steady-state error of the system's unit step response as the performance indexes and by use of our improved ant system algorithm. K p * , T i * , and T d * can be used in real-time control. This kind of controller is called the ASA-PID controller with incomplete derivation. To verify the performance of the ASA-PID controller, three different typical transfer functions were tested, and three existing typical tuning methods of PID controller parameters, including the Ziegler-Nichols method (ZN),the genetic algorithm (GA),and the simulated annealing (SA), were adopted for comparison. The simulation results showed that the ASA-PID controller can be used to control different objects and has better performance compared with the ZN-PID and GA-PID controllers, and comparable performance compared with the SA-PID controller.

关键词： PID controller Incomplete derivation Parameter tuning ant system algorithm Genetic algorithm Simulated annealing

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Hybrid Adaptive ant system algorithm and Its Application Research

Hybrid Adaptive Ant System Algorithm and Its Application Res...

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Asian Simulation Conference/The 6th International conference on system Simulation and Scientific Computing

作者： ZHANG Haiying, YANG Rui (Departent of information and Control, Xi'an University of Technology, Xi'an P.R.China 710048 Waterborne Transportation Institute , The Ministry of Communications P.R.China, Beijing 100088)

In order to solve the problem of pheromone shortage and slow convergent speed of existing ant system algorithm (AS) in its application to complex optimal searching, this paper presents a new hybrid adaptive ant system algorithm with pheromone weight multiplier and pheromone balance operator, which can adaptively adjust select probabilities and pheromone strength. The simulation results - the global optimum value searching of multi-extremum continuous functions and nonlinear continuous function , solving complex TSP problem and neural network parameter learning with ant system algorithm -- present a good characteristic with lesser probability at local optimization, more strong global optimization capability and some good characteristics on reducing CPU time, and demonstrate the effectiveness of algorithm in improving optimization speed and adding result diversity.

关键词： ant system algorithm hybrid adaptive weight multiplier balance operator neural network

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Running-time Analysis of ant system algorithms with Upper-bound Comparison

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2017年第4期8卷 1-17页

作者： Huang, Han Wu, Hongyue Zhang, Yushan Lin, Zhiyong Hao, Zhifeng South China Univ Technol Sch Software Engn Guangzhou Guangdong Peoples R China Guangdong Univ Finance & Econ Sch Stat & Math Guangzhou Guangdong Peoples R China Guangdong Polytech Normal Univ Dept Comp Sci Guangzhou Guangdong Peoples R China Guangdong Univ Technol Sch Appl Math Guangzhou Guangdong Peoples R China

Running- time analysis of ant colony optimization (ACO) is crucial for understanding the power of the algorithm in computation. This paper conducts a running-time analysis of ant system algorithms (AS) as a kind of ACO for traveling salesman problems (TSP). The authors model the AS algorithm as an absorbing Markov chain through jointly representing the best-so-far solutions and pheromone matrix as a discrete stochastic status per iteration. The running-time of AS can be evaluated by the expected first-hitting time (FHT), the least number of iterations needed to attain the global optimal solution on average. The authors derive upper bounds of the expected FHT of two classical AS algorithms (i.e., ant quantity system and ant-cycle system) for TSP. They further take regular-polygon TSP (RTSP) as a case study and obtain numerical results by calculating six RTSP instances. The RTSP is a special but real-world TSP where the constraint of triangle inequality is stringently imposed. The numerical results derived from the comparison of the running time of the two AS algorithms verify our theoretical findings.

关键词： ant system algorithm First-Hitting Time Pheromone Rate Running-Time Analysis Swarm Intelligence

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Performance evaluation of ant system optimization processes

Performance evaluation of ant system optimization processes

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4th World Congress on Intelligent Control and Automation

作者： Wang, L Wu, QD Tongji Univ Informat & Control Dept Shanghai 200092 Peoples R China

ISBN: (纸本)0780372689

In this paper, ant system (AS) optimization algorithm in continuous space[23,24] is used for examples of optimum value searching of continuous function. The algorithm used is defined in detail in paper [24]. In this paper, the general evaluation parameters are defined. It considers the general optimization dynamics of the whole ant system. Two evaluation functions are defined. Using the same example and same parameters setting in [24], this paper discussed the performance evaluation of the defined AS algorithm in continuous space.

关键词： ant system algorithm continuous function optimization performance evaluation

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Task Allocation of Multiple Autonomous Underwater Vehicle system Based on Multi-objective Optimization

Task Allocation of Multiple Autonomous Underwater Vehicle Sy...

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IEEE International Conference on Mechatronics and Automation

作者： Lv Hongli Wang Hongjian Li Qing Yao Hongfei Harbin Engn Univ Automat Coll Harbin Heilongjiang Pr Peoples R China

ISBN: (纸本)9781509023967

When executing a complex mission, the multi-AUV system need to confirm a better task allocation scheme to make sure that the system is efficient. However, there are many standards which are utilized to determine the system's efficient, and there maybe confliction between them. In order to solve the task allocation of multi-AUV system based on multi-objective optimization, a multi-objective optimization based on ant system algorithm was proposed. By the single-objective optimization in single ant system and the interaction between different ants systems, this algorithm achieve the Pareto solution's set at last. On the other hand, an algorithm evaluation method was given out to evaluate the performance of the algorithm above. Based on this algorithm, the task allocation of multi-AUV system based on multi-objective optimization was studied in details and the stimulation of task allocation was provided.

关键词： task allocation multi-AUV system cooperative work multi-object optimization ant system algorithm

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Hardware/software partitioning for platform-based design method 05

Hardware/software partitioning for platform-based design met...

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10th Asia and South Pacific Design Automation Conference

作者： Xiong, Zhihui Chen, Jihua Li, Sikun Univ Def Technol Sch Comp Sci Changsha 410073 Peoples R China

ISBN: (纸本)0780387368

ant system algorithm has the advantages of positive feedback and efficient convergence in optimal searching, but it lacks initial pheromone, which greatly limits this algorithm's searching speed. Oriented to Platform-Based Design of system-on-a-Chip, we present a hardware/software bi-partitioning algorithm based on ant system algorithm with Initial Pheromone. The main ideas are: a). Reuse the partitioning result. of reference design provided by Platform-Based Design method as current design's initial partitioning, which is then converted into the initial pheromone needed by ant system algorithm. b). Search for the optimal partitioning scheme with the ant system algorithm based on the initial pheromone. Our algorithm adopts system level reusing feature of Platform-Based Design method to prevent the disadvantages of ant system algorithm. Experiments show our algorithm-improves the efficiency of ant system algorithm by an average of forty percent.

关键词： system-on-chip logic partitioning software reusability hardware-software codesign hardware/software partitioning platform-based design method ant system algorithm positive feedback efficient convergence optimal searching initial pheromone system-on-a-chip reference design optimal partitioning system level reusing

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Adaptive Artificial ant Colonies for Edge Detection in Digital Images

Adaptive Artificial Ant Colonies for Edge Detection in Digit...

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36th Annual Conference of the IEEE Industrial-Electronics-Society (IECON)/4th IEEE International Conference on E-Learning in Industrial Electronics/IES Industry Forum

作者： Jevtic, Aleksandar Andina, Diego Tech Univ Madrid Grp Automat Signal & Commun Madrid Spain

ISBN: (纸本)9781424452262

ant Colony Optimization (ACO) is a group of algorithms inspired by the foraging behavior of ant colonies in nature. Like their biological counterparts, a colony of artificial ants is able to adapt to the changes in their environment, such as exhaustion of a food source and discovery of a new one. In this paper, one of the basic ACO algorithms, the ant system algorithm, was applied for edge detection where the edge pixels represent food for the ants. A set of grayscale images obtained by a nonlinear contrast enhancement technique called Multiscale Adaptive Gain is used to create a variable environment. As the images change, the ant colony adapts to those changes leaving pheromone trails where the new edges appear while the pheromone trails that are not reinforced evaporate over time. Although the images were used to create an environmental setup in which the ants move, the colony's adaptive behavior could be demonstrated on any type of digital habitat.

关键词： adaptive artificial ant colonies adaptive systems ant system algorithm digital images edge detection edge detection edge pixels grayscale images image colour analysis image enhancement multiscale adaptive gain nonlinear contrast enhancement optimisation

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