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8th International Conference on Intelligent systems Design and Applications (ISDA 2008)

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

ISBN: (纸本)9780769533827

This paper presents a proposed ant system algorithm that incorporates negative pheromone to solve the course scheduling problem. The performance of the proposed algorithm is tested with a case study data on course scheduling. Comparisons of results were performed with ant system algorithm that does not incorporate the negative pheromone.

关键词： ant system algorithm Course Scheduling Problem Meta-heuristic Negative Pheromone

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Combining the ant system algorithm and simulated annealing for 3D/2D fixed-outline floorplanning

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APPLIED SOFT COMPUTING 2016年 40卷 150-160页

作者： Xu, Qi Chen, Song Li, Bin Univ Sci & Technol China Dept Elect Sci & Technol Hefei 230027 Peoples R China

Three dimensional integrated circuits (3D ICs) can alleviate the problem of interconnection, a critical problem in the nanoscale era, and are also promising for heterogeneous integration. This paper proposes a two-phase method combining the ant system algorithm (AS) and simulated annealing (SA) to handle 3D IC floorplanning with fixed-outline constraints. In the first AS phase, the floorplans are constructed by sequentially packing the block one by one, and the AS is used to explore the appropriate packing order and device layer assignment for the blocks. When packing a block, a proper position including the coordinates and the appropriate layer in the partially constructed floorplan should be chosen from all possible positions. While packing the blocks, a probability layer assignment strategy is proposed to determine the device layer assignment of unpacked blocks. After the AS phase, the SA phase is used to perform further optimization. The proposed method can also be easily applied to 2D floorplanning problems. Compared with the state of the art 3D/2D fixed-outline floorplanner, the experimental results demonstrate the effectiveness of the proposed method. (C) 2015 Elsevier B.V. All rights reserved.

关键词： ant system algorithm 3D IC Floorplanning Fixed-outline

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Global optimal path planning for mobile robot based onimproved Dijkstra algorithm and ant system algorithm

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Journal of Central South University of Technology 2006年第1期13卷 80-86页

作者：谭冠政贺欢 Aaron Sloman School of Information Science and Engineering Central South University Changsha 410083 China School of Computer Science The University of Birmingham Birmingham B15 2TT UK School of Information Science and Engineering Central South University Changsha 410083 China School of Computer Science The University of Birmingham Birmingham B15 2TT UK

A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm. This method includes three steps: the first step is adopting the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is adopting the improved Dijkstra algorithm to find out a sub-optimal collision-free path, and the third step is using the ant system algorithm to adjust and optimize the location of the sub-optimal path so as to generate the global optimal path for the mobile robot. The computer simulation experiment was carried out and the results show that this method is correct and effective. The comparison of the results confirms that the proposed method is better than the hybrid genetic algorithm in the global optimal path planning.

关键词： mobile robot global optimal path planning improved Dijkstra algorithm ant system algorithm MAKLINK graph free MAKLINK line

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Intelligent PID controller based on ant system algorithm and fuzzy inference and its application to bionic artificial leg

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Journal of Central South University of Technology 2004年第3期11卷 316-322页

作者：谭冠政曾庆冬李文斌 School of Information Science and Engineering Central South University Changsha 410083 China

A designing method of intelligent proportional-integral-derivative(PID) controllers was proposed based on the ant system algorithm and fuzzy inference. This kind of controller is called Fuzzy-ant system PID controller. It consists of an off-line part and an on-line part. In the off-line part, for a given control system with a PID controller,by taking the overshoot, setting time and steady-state error of the system unit step response as the performance indexes and by using the ant system algorithm, a group of optimal PID parameters K*p , Ti* and T*d can be obtained, which are used as the initial values for the on-line tuning of PID parameters. In the on-line part, based on Kp* , Ti*and Td* and according to the current system error e and its time derivative, a specific program is written, which is used to optimize and adjust the PID parameters on-line through a fuzzy inference mechanism to ensure that the system response has optimal transient and steady-state performance. This kind of intelligent PID controller can be used to control the motor of the intelligent bionic artificial leg designed by the authors. The result of computer simulation experiment shows that the controller has less overshoot and shorter setting time.

关键词： ant system algorithm fuzzy inference PID controller Fuzzy-ant system PID controller intelligent bionic artificial leg

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Convergence time analysis of ant system algorithm

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2012年第4期15卷 304-309页

作者： Zhang, Yu-shan Hao, Zhi-feng Huang, Han South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China Guangdong Univ Business Studies Sch Math & Computat Sci Guangzhou 510320 Guangdong Peoples R China Guangdong Univ Technol Fac Comp Guangzhou 510006 Guangdong Peoples R China South China Univ Technol Sch Software Engn Guangzhou 510006 Guangdong Peoples R China City Univ Hong Kong Coll Business Dept Management Sci Hong Kong Hong Kong Peoples R China

ant colony optimisation (ACO) which is one of the most popular algorithms in machine learning has been used widely to solve combinatorial optimisation problems. However, there are few studies for its runtime analysis which can reflect the computational complexity of ACO algorithm. The presented paper proposes a method for analysing the convergence time of ant system algorithm with pheromone rate. The analysis is a process of estimating the iteration time that pheromone rate attains the objective value and the mean convergence time based on the objective pheromone rate in expectation. The proposed method can be used to analyse the computational complexity of other ACO algorithms. Finally, a brief ant system algorithm is analysed as an example of using the method.

关键词： ant system algorithm ACO computational complexity running time pheromone rate

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Shortest route finding by ant system algorithm in web geographical information system-based advanced traveller information system

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JOURNAL OF ENGINEERING-JOE 2014年第10期2014卷 563-573页

作者： Kumar, Praveen Suman, Sanjeev Mishra, Shambhavi IIT Roorkee Dept Civil Engn Roorkee Uttar Pradesh India GB Pant Univ Agr & Technol Dept Civil Engn Pantnagar Uttarkhand India

This paper discusses the methodology used in the development of advanced traveller information system (ATIS). This system is designed as a part of web geographical information system (GIS) based advanced public transport systems. Web GIS-based ATIS system includes spatial data for the designed functionalities and provides GIS capabilities to the users through the internet. In addition to these functionalities, a route planning algorithm to plan the shortest route between the selected bus transit points is also designed using ant system algorithm and is integrated with web GIS. This study presents the ant system algorithm adopted for the shortest route finding with the methodology developed for the web GIS-based ATIS system for the study area of the city Chandigarh in India using open source software MapServer as web map server. This study also discusses the three-tier logical architecture used in the methodology for providing GIS capabilities to the user over the internet.

关键词： Internet geographic information systems public transport traffic information systems shortest route finding ant system algorithm Web geographical information system advanced traveller information system ATIS advanced public transport systems bus transit points Chandigarh city India open source software MapServer Web map server three-tier logical architecture Internet

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An ant system algorithm for Automated Trajectory Planning

An Ant System Algorithm for Automated Trajectory Planning

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2010 IEEE World Congress on Computational Intelligence

作者： Ceriotti, Matteo Vasile, Massimiliano Univ Strathclyde Dept Mech Engn Adv Space Concepts Lab Glasgow G1 1XJ Lanark Scotland Univ Glasgow Dept Aerosp Engn Space Adv Res Team Glasgow G12 8QQ Lanark Scotland

ISBN: (纸本)9781424481262

The paper presents an ant system based algorithm to optimally plan multi-gravity assist trajectories. The algorithm is designed to solve planning problems in which there is a strong dependency of one decision on all the previously made decisions. In the case of multi-gravity assist trajectory planning, the number of possible paths grows exponentially with the number of planetary encounters. The proposed algorithm avoids scanning all the possible paths and provides good results at a low computational cost. The algorithm builds the solution incrementally according to ant system paradigms. Unlike standard ACO, at every planetary encounter, each ant makes a decision based on the information stored in a tabu and feasible list. The approach demonstrated to be competitive, on a number of instances of a real trajectory design problem, against known GA and PSO algorithms

关键词： aerospace control ant system algorithm automated trajectory planning multigravity assist trajectory planning optimisation path planning planetary encounter position control trajectory design problem

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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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Asia Simulation Conference/6th International Conference on system Simulation and Scientific Computing

作者： Zhang, HY Yang, R Xian Univ Technol Dept Informat & Control Xian 710048 Peoples R China

ISBN: (纸本)7506278413

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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Deterministic Model for Analyzing the Dynamics of ant system algorithm and Performance Amelioration through a New Pheromone Deposition Approach

Deterministic Model for Analyzing the Dynamics of Ant System...

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4th International Conference on Information and Automation for Sustainability

作者： Acharya, Ayan Maiti, Deepyaman Konar, Amit Ramadoss, Janarthanan Jadavpur Univ Dept Elect & Telecommun Engn Kolkata 700032 India Jaya Engn Coll Dept Informat Technol Madras 600017 Tamil Nadu India

ISBN: (纸本)9781424428991

ant Colony Optimization (ACO) is a metaheuristic for solving difficult discrete optimization problems. This paper presents a deterministic model based on differential equation to analyze the dynamics of basic ant system algorithm. Traditionally, in all ant system algorithms developed so far, the deposition of pheromone on different parts of the tour of a particular ant is always kept unvarying. This implies that the pheromone concentration remains uniform throughout the entire path of an ant. This article introduces an exponentially increasing pheromone deposition approach by artificial ants to improve the performance of basic ant system algorithm. The idea here is to introduce an additional attracting force to guide the ants towards destination more easily by constructing an artificial potential field identified by increasing pheromone concentration towards the goal. Apart from carrying out analysis of ant system dynamics with both traditional and the newly proposed deposition rules, the paper presents an exhaustive set of experiments performed to find out suitable parameter ranges for best performance of ant system with the proposed deposition approach. Simulations with this empirically obtained parameter set reveal that the proposed deposition rule outperforms the traditional one by a large extent both in terms of solution quality and algorithm convergence. Thus, the contributions of the article can be presented as follows: i) it introduces differential equation and explores a novel method of analyzing the dynamics of ant system algorithms, ii) it initiates an exponentially increasing pheromone deposition approach by artificial ants to improve the performance of algorithm in terms of solution quality and convergence time, iii) exhaustive experimentation performed facilitates the discovery of an algebraic relationship between the parameter set of the algorithm and feature of the problem environment.

关键词： ant system algorithm Stability Analysis of ant system dynamics Uniform deposition rule Non-uniform pheromone deposition approach Solution Quality Convergence Speed

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Further example study on ant system algorithm based continuous space optimization

Further example study on ant system algorithm based continuo...

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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[14,15] is under further study and used for other examples of optimum value searching of multi-minimum continuous function and another linear continuous function. The multi-minimum function of Rosenbrock function is chosen. The algorithm used is defined in detail in paper [14] and. [15]. In this paper, the general optimization error function for algorithm evaluation is modified, and the detail is given in another paper of the authors in WCICA'02 conference [16]. The applicability characters of AS application in continuous space optimization problems sire summarized at the end of this paper. The authors of this paper firmly believe that this paper will have reference meaning to intelligent theory and control application research area.

关键词： ant system algorithm continuous function optimization multi-minimum function

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