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An ant colony optimization method for generalized TSP problem

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Progress in Natural Science:Materials International 2008年第11期18卷 1417-1422页

作者： Maurizio Marchese Department of Information Engineering and Computer Science University of TrentoVia Sommarive 1438050 Povo(TN)Italy

Focused on a variation of the euclidean traveling salesman problem(TSP),namely,the generalized traveling salesman problem (GTSP),this paper extends the ant colony optimization method from TSP to this *** considering the group influence,an improved method is further *** avoid locking into local minima,a mutation process and a local searching technique are also introduced into this *** results show that the proposed method can deal with the GTSP problems fairly well,and the developed mutation process and local search technique are effective.

关键词： Generalized traveling salesman problem ant colony optimization Mutation 2-OPT

ant colony optimization as a descriptor selection in QSPR modeling for prediction of λ_(max) of azo dyes

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Chinese Chemical Letters 2012年第10期23卷 1209-1212页

作者： Morteza Atabati Farzaneh Khandani School of Chemistry Damghan UniversityDamghanIran

A quantitative structure-property relationship （QSPR） study was suggested for the prediction of λ-max of azo dyes. After optimization of 3D geometry of structures, different descriptors were calculated by the HyperChem and Dragon softwares. A major problem of QSPR is the high dimensionality of the descriptor space; therefore, descriptor selection is the most important step for these studies. In this paper, an ant colony optimization （ACO） algorithm was proposed to select the best descriptors.

关键词： QSPR ant colony optimization Azo dyes λmax

An ant colony optimization Based Dimension Reduction Method for High-Dimensional Datasets

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Journal of Bionic Engineering 2013年第2期10卷 231-241页

作者： Ying Li Gang Wang Huiling Chen Lian Shi Lei Qin College of Computer Science and Technology Jilin University Changchun 130012 P. R. China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 P. R. China College of Geo Exploration Science and Technology Jilin University Changchun 130026 P. R. China College of physics and electronic information Wenzhou University Wenzhou 325035 P. R. China

In this paper, a bionic optimization algorithm based dimension reduction method named ant colony optimization -Selection （ACO-S） is proposed for high-dimensional datasets. Because microarray datasets comprise tens of thousands of features （genes）, they are usually used to test the dimension reduction techniques. ACO-S consists of two stages in which two well-known ACO algorithms, namely ant system and ant colony system, are utilized to seek for genes, respectively. In the first stage, a modified ant system is used to filter the nonsignificant genes from high-dimensional space, and a number of promising genes are reserved in the next step. In the second stage, an improved ant colony system is applied to gene selection. In order to enhance the search ability of ACOs, we propose a method for calculating priori available heuristic information and design a fuzzy logic controller to dynamically adjust the number of ants in ant colony system. Furthermore, we devise another fuzzy logic controller to tune the parameter （q0） in ant colony system. We evaluate the performance of ACO-S on five microarray datasets, which have dimensions varying from 7129 to 12000. We also compare the performance of ACO-S with the results obtained from four existing well-known bionic optimization algorithms. The comparison results show that ACO-S has a notable ability to＂ generate a gene subset with the smallest size and salient features while yielding high classification accuracy. The comparative results generated by ACO-S adopting different classifiers are also given. The proposed method is shown to be a promising and effective tool for mining high-dimension data and mobile robot navigation.

关键词： gene selection feature selection ant colony optimization high-dimensional data

Binary-Coding-Based ant colony optimization and Its Convergence

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Journal of Computer Science & Technology 2004年第4期19卷 472-478页

作者： Tian-MingBu Song-NianYu Hui-WeiGuan SchoolofComputerEngineeringandScience ShanghaiUniversityShanhai200072P.R.China DepartmentofComputerScience NorthShoreCommunityCollegeMA01923USA

ant colony optimization (ACO for short) is a meta-heuristics for hard combinatorial optimization problems. It is a population-based approach that uses exploitation of positive feedback as well as greedy search. In this paper, genetic algorithm's (GA for short) ideas are introduced into ACO to present a new binary-coding based ant colony optimization. Compared with the typical ACO, the algorithm is intended to replace the problem's parameter-space with coding-space, which links ACO with GA so that the fruits of GA can be applied to ACO directly. Furthermore, it can not only solve general combinatorial optimization problems, but also other problems such as function optimization. Based on the algorithm, it is proved that if the pheromone remainder factor rho is under the condition of rho greater than or equal to 1, the algorithm can promise to converge at the optimal, whereas if 0 < rho < 1, it does not.

关键词： ant colony optimization genetic algorithm binary-coding convergence heuristic function optimization

Improved ant colony optimization algorithm for the traveling salesman problems

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Journal of Systems Engineering and Electronics 2010年第2期21卷 329-333页

作者： Rongwei Gan Qingshun Guo Huiyou Chang Yang Yi School of Information Science and Technology Sun Yat-sen University Guangzhou 510275 E R. China Information and Network Center Sun Yat-sen University Guangzhou 510275 R R. China

ant colony optimization （ACO） is a new heuristic algo- rithm which has been proven a successful technique and applied to a number of combinatorial optimization problems. The traveling salesman problem （TSP） is among the most important combinato- rial problems. An ACO algorithm based on scout characteristic is proposed for solving the stagnation behavior and premature con- vergence problem of the basic ACO algorithm on TSP. The main idea is to partition artificial ants into two groups： scout ants and common ants. The common ants work according to the search manner of basic ant colony algorithm, but scout ants have some differences from common ants, they calculate each route＇s muta- tion probability of the current optimal solution using path evaluation model and search around the optimal solution according to the mutation probability. Simulation on TSP shows that the improved algorithm has high efficiency and robustness.

关键词： ant colony optimization heuristic algorithm scout ants path evaluation model traveling salesman problem.

Hybrid ant colony optimization for the resource-constrained project scheduling problem

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Journal of Systems Engineering and Electronics 2010年第1期21卷 67-71页

作者： Linyi Deng Yan Lin Ming Chen Department of Computer Dalian University of Technology Dalian 116024 E R. China State Key Laboratory of Structural Analysis for Industrial Equipment Dalian University of Technology Dalian 116024 E R. China

To solve the resource-constrained project scheduling problem （RCPSP）, a hybrid ant colony optimization （HACO） approach is presented. To improve the quality of the schedules, the HACO is incorporated with an extended double justification in which the activity splitting is applied to predict whether the schedule could be improved. The HACO is tested on the set of large benchmark problems from the project scheduling problem library （PSPLIB）. The computational result shows that the proposed algo- rithm can improve the quality of the schedules efficiently.

关键词： project scheduling double justification ant colony optimization activity splitting.

Tropical cyclone spiral band extraction and center locating by binary ant colony optimization

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Science China Earth Sciences 2012年第2期55卷 332-346页

作者： BAI QiuChan WEI Kun JING ZhongLiang LI YuanXiang TUO HongYa LIU ChengGang Faculty of Electronic and Electric Engineering Huaiyin Institute of TechnologyHuai'an 223003China School of Aeronautics and Astronautics Shanghai Jiaotong UniversityShanghai 200240China

Tropical cyclone (TC) center locating is crucial because it lays the foundation for TC forecasting. Locating TC centers, usually by manual means, continues to present many difficulties. Not least is the problem of inconsistency between TC center locations forecast by different agencies. In this paper, an objective TC center locating scheme is developed, using infrared satellite images. We introduce a pattern-matching concept, which we illustrate using a spiral curve model. A spiral band model, based on a spiral band region, is designed to extract the spiral cloud-rain bands (SCRBs) of TCs. We propose corresponding criteria on which to score the fitting value of a candidate template defined by our models. In the proposed scheme, TC location is an optimization problem, solved by an ant colony optimization algorithm. In numerical experiments, a minimal mean distance error of 17.9 km is obtained when the scheme is tested against best-track data. The scheme is suitable for TCs with distinct SCRBs or symmetrical central dense overcast, and for TCs both with and without clear eyes.

关键词： weather forecast tropical cyclones center locating spiral band model ant colony optimization

Unordered rule discovery using ant colony optimization

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Science China(Information Sciences) 2014年第9期57卷 189-203页

作者： KHAN Salabat BAIG Abdul Rauf ALI Armughan HAIDER Bilal KHAN Farman Ali DURRANI Mehr Yahya ISHTIAQ Muhammad Department of Computer Science COMSATS Institute of Information TechnologyAttock CampusPakistan Department of Computer Science National University of Computer and Emerging SciencesIslamabadPakistan Al Imam Mohammad Ibn Saud Islamic University (IMSIU) College of Computer and Information SciencesRiyadhSaudi Arabia

In this article,a novel unordered classification rule list discovery algorithm is presented based on ant colony optimization(ACO).The proposed classifier is compared empirically with two other ACO-based classification techniques on 26 data sets,selected from miscellaneous domains,based on several performance *** opposed to its ancestors,our technique has the flexibility of generating a list of IF-THEN rules with unrestricted *** makes the generated classification model more comprehensible and easily *** results indicate that the performance of the proposed method is statistically significantly better as compared with previous versions of antMiner based on predictive accuracy and comprehensibility of the classification model.

关键词： classification ant colony optimization data mining unordered rule set comprehensibility pattern recognition

Co-evolutionary design of discrete structures based on the ant colony optimization

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Progress in Natural Science:Materials International 2007年第9期17卷 1082-1094页

作者： Tao Zhenwu and Xiao Renbin(CAD Center,Huazhong University of Science and Technology,Wuhan 430074,China) CAD Center Huazhong University of Science and Technology Wuhan 430074 China

In order to optimize the sizing and topology of discrete structures together and resist the combinatorial explosion of the solution space,a co-evolutionary design(CED)method based on ant colony optimization(ACO)for discrete structures is *** tailored ant colony optimization for the sizing of structures(TACO-SS)and the tailored ant colony optimization for the topology of structures(TACO-TS)are implemented *** analysis shows that the computation complexity of each sub-process in CED based on ACO above is polynomial and it guarantees the efficiency of this *** the parameter settings in TACO-SS and TACO-TS are discussed,the convergence history of a sub-process is studied through an instance in ***,according to the design examples of the 10-bar and 15-bar trusses under different cases,the effectiveness of the CED based on ACO is validated and the effects of the loads and the deflection constraints on the co-evolutionary design are discussed.

关键词： evolutionary design ant colony optimization sizing optimization topology optimization