GRID SCHEDULING USING ENHANCED ANT COLONY ALGORITHM
Mr. P.Mathiyalagan; U.R. Dhepthie; S.N. Sivanandam
2010-01-01
Grid computing is a high performance computing used to solve larger scale computational demands. Task scheduling is a major issue in grid computing systems. Scheduling of tasks is the NP hard problem. The heuristic approach provides optimal solution for NP hard problems .The ant colony algorithm provides optimal solution. The existing ant colony algorithm takes more time to schedule the tasks. In this paper ant colony algorithm improved by enhancing pheromone updating rule such that it schedu...
GRID SCHEDULING USING ENHANCED ANT COLONY ALGORITHM
Directory of Open Access Journals (Sweden)
P. Mathiyalagan
2010-10-01
Full Text Available Grid computing is a high performance computing used to solve larger scale computational demands. Task scheduling is a major issue in grid computing systems. Scheduling of tasks is the NP hard problem. The heuristic approach provides optimal solution for NP hard problems .The ant colony algorithm provides optimal solution. The existing ant colony algorithm takes more time to schedule the tasks. In this paper ant colony algorithm improved by enhancing pheromone updating rule such that it schedules the tasks efficiently and better resource utilization. The simulation results prove that proposed method reduces the execution time of tasks compared to existing ant colony algorithm.
Liqiang Liu; Yuntao Dai; Jinyu Gao
2014-01-01
Ant colony optimization algorithm for continuous domains is a major research direction for ant colony optimization algorithm. In this paper, we propose a distribution model of ant colony foraging, through analysis of the relationship between the position distribution and food source in the process of ant colony foraging. We design a continuous domain optimization algorithm based on the model and give the form of solution for the algorithm, the distribution model of pheromone, the update rules...
Optimized Ant Colony Algorithm by Local Pheromone Update
Hui Yu
2013-01-01
Ant colony algorithm, a heuristic simulated algorithm, provides better solutions for non-convex, non-linear and discontinuous optimization problems. For ant colony algorithm, it is frequently to be trapped into local optimum, which might lead to stagnation. This article presents the city-select strategy, local pheromone update strategy, optimum solution prediction strategy and local optimization strategy to optimize ant colony algorithm, provides ant colony algorithm based on local pheromone...
An Improved Ant Colony Routing Algorithm for WSNs
Tan Zhi; Zhang Hui
2015-01-01
Ant colony algorithm is a classical routing algorithm. And it are used in a variety of application because it is economic and self-organized. However, the routing algorithm will expend huge amounts of energy at the beginning. In the paper, based on the idea of Dijkstra algorithm, the improved ant colony algorithm was proposed to balance the energy consumption of networks. Through simulation and comparison with basic ant colony algorithms, it is obvious that improved algorithm can effectively...
Liu, Liqiang; Dai, Yuntao; Gao, Jinyu
2014-01-01
Ant colony optimization algorithm for continuous domains is a major research direction for ant colony optimization algorithm. In this paper, we propose a distribution model of ant colony foraging, through analysis of the relationship between the position distribution and food source in the process of ant colony foraging. We design a continuous domain optimization algorithm based on the model and give the form of solution for the algorithm, the distribution model of pheromone, the update rules of ant colony position, and the processing method of constraint condition. Algorithm performance against a set of test trials was unconstrained optimization test functions and a set of optimization test functions, and test results of other algorithms are compared and analyzed to verify the correctness and effectiveness of the proposed algorithm. PMID:24955402
Loading pattern optimization using ant colony algorithm
Energy Technology Data Exchange (ETDEWEB)
Hoareau, Fabrice [EDF R and D, Clamart (France)
2008-07-01
Electricite de France (EDF) operates 58 nuclear power plants (NPP), of the Pressurized Water Reactor type. The loading pattern optimization of these NPP is currently done by EDF expert engineers. Within this framework, EDF R and D has developed automatic optimization tools that assist the experts. LOOP is an industrial tool, developed by EDF R and D and based on a simulated annealing algorithm. In order to improve the results of such automatic tools, new optimization methods have to be tested. Ant Colony Optimization (ACO) algorithms are recent methods that have given very good results on combinatorial optimization problems. In order to evaluate the performance of such methods on loading pattern optimization, direct comparisons between LOOP and a mock-up based on the Max-Min Ant System algorithm (a particular variant of ACO algorithms) were made on realistic test-cases. It is shown that the results obtained by the ACO mock-up are very similar to those of LOOP. Future research will consist in improving these encouraging results by using parallelization and by hybridizing the ACO algorithm with local search procedures. (author)
Loading pattern optimization using ant colony algorithm
International Nuclear Information System (INIS)
Electricite de France (EDF) operates 58 nuclear power plants (NPP), of the Pressurized Water Reactor type. The loading pattern optimization of these NPP is currently done by EDF expert engineers. Within this framework, EDF R and D has developed automatic optimization tools that assist the experts. LOOP is an industrial tool, developed by EDF R and D and based on a simulated annealing algorithm. In order to improve the results of such automatic tools, new optimization methods have to be tested. Ant Colony Optimization (ACO) algorithms are recent methods that have given very good results on combinatorial optimization problems. In order to evaluate the performance of such methods on loading pattern optimization, direct comparisons between LOOP and a mock-up based on the Max-Min Ant System algorithm (a particular variant of ACO algorithms) were made on realistic test-cases. It is shown that the results obtained by the ACO mock-up are very similar to those of LOOP. Future research will consist in improving these encouraging results by using parallelization and by hybridizing the ACO algorithm with local search procedures. (author)
Optimization Planning based on Improved Ant Colony Algorithm for Robot
Xin Zhang; Zhanwen Wu
2014-01-01
As the ant colony algorithm has the defects in robot optimization path planning such as that low convergence cause local optimum, an improved ant colony algorithm is proposed to apply to the planning of path finding for robot. This algorithm uses the search way of exhumation ant to realize the complementation of advantages and accelerate the convergence of algorithm. The experimental result shows that the algorithm of this paper make the optimization planning of robot more reasonable
Improved Ant Colony Clustering Algorithm and Its Performance Study.
Gao, Wei
2016-01-01
Clustering analysis is used in many disciplines and applications; it is an important tool that descriptively identifies homogeneous groups of objects based on attribute values. The ant colony clustering algorithm is a swarm-intelligent method used for clustering problems that is inspired by the behavior of ant colonies that cluster their corpses and sort their larvae. A new abstraction ant colony clustering algorithm using a data combination mechanism is proposed to improve the computational efficiency and accuracy of the ant colony clustering algorithm. The abstraction ant colony clustering algorithm is used to cluster benchmark problems, and its performance is compared with the ant colony clustering algorithm and other methods used in existing literature. Based on similar computational difficulties and complexities, the results show that the abstraction ant colony clustering algorithm produces results that are not only more accurate but also more efficiently determined than the ant colony clustering algorithm and the other methods. Thus, the abstraction ant colony clustering algorithm can be used for efficient multivariate data clustering. PMID:26839533
Model Specification Searches Using Ant Colony Optimization Algorithms
Marcoulides, George A.; Drezner, Zvi
2003-01-01
Ant colony optimization is a recently proposed heuristic procedure inspired by the behavior of real ants. This article applies the procedure to model specification searches in structural equation modeling and reports the results. The results demonstrate the capabilities of ant colony optimization algorithms for conducting automated searches.
Improved Ant Colony Clustering Algorithm and Its Performance Study
Wei Gao
2015-01-01
Clustering analysis is used in many disciplines and applications; it is an important tool that descriptively identifies homogeneous groups of objects based on attribute values. The ant colony clustering algorithm is a swarm-intelligent method used for clustering problems that is inspired by the behavior of ant colonies that cluster their corpses and sort their larvae. A new abstraction ant colony clustering algorithm using a data combination mechanism is proposed to improve the computational ...
An Improved Ant Colony Routing Algorithm for WSNs
Directory of Open Access Journals (Sweden)
Tan Zhi
2015-01-01
Full Text Available Ant colony algorithm is a classical routing algorithm. And it are used in a variety of application because it is economic and self-organized. However, the routing algorithm will expend huge amounts of energy at the beginning. In the paper, based on the idea of Dijkstra algorithm, the improved ant colony algorithm was proposed to balance the energy consumption of networks. Through simulation and comparison with basic ant colony algorithms, it is obvious that improved algorithm can effectively balance energy consumption and extend the lifetime of WSNs.
Protein structure optimization with a "Lamarckian" ant colony algorithm.
Oakley, Mark T; Richardson, E Grace; Carr, Harriet; Johnston, Roy L
2013-01-01
We describe the LamarckiAnt algorithm: a search algorithm that combines the features of a "Lamarckian" genetic algorithm and ant colony optimization. We have implemented this algorithm for the optimization of BLN model proteins, which have frustrated energy landscapes and represent a challenge for global optimization algorithms. We demonstrate that LamarckiAnt performs competitively with other state-of-the-art optimization algorithms. PMID:24407312
An ant colony algorithm on continuous searching space
Xie, Jing; Cai, Chao
2015-12-01
Ant colony algorithm is heuristic, bionic and parallel. Because of it is property of positive feedback, parallelism and simplicity to cooperate with other method, it is widely adopted in planning on discrete space. But it is still not good at planning on continuous space. After a basic introduction to the basic ant colony algorithm, we will propose an ant colony algorithm on continuous space. Our method makes use of the following three tricks. We search for the next nodes of the route according to fixed-step to guarantee the continuity of solution. When storing pheromone, it discretizes field of pheromone, clusters states and sums up the values of pheromone of these states. When updating pheromone, it makes good resolutions measured in relative score functions leave more pheromone, so that ant colony algorithm can find a sub-optimal solution in shorter time. The simulated experiment shows that our ant colony algorithm can find sub-optimal solution in relatively shorter time.
Dynamic Task Scheduling Algorithm based on Ant Colony Scheme
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Kamolov Nizomiddin Baxodirjonovich
2015-08-01
Full Text Available Many scientific applications running in Cloud Computing system are workflow applications that contains large number of tasks and in which tasks are connected by precedence relations. Efficient scheduling the workflow tasks become a challenging issue in Cloud Computing environments because the scheduling decides performance of the applications. Unfortunately, finding the optimal scheduling is known as NP-hard. Ant Colony Optimization algorithm can be applied to design efficient scheduling algorithms. Previous scheduling algorithms that use Ant Colony mechanism lack rapid adaptivity. This paper proposes a task scheduling algorithm that uses a modified Ant Colony Optimization. The modified version uses probability in order for ants to decide target machine. The proposed task scheduling algorithm is implemented in WorkflowSim in order to measure performance. The experimental results show that the proposed scheduling algorithm reduce average makespan to about 6.4% compared to a scheduling algorithm that uses basic Ant Colony Optimization scheme.
Improvement and Implementation of Best-worst Ant Colony Algorithm
Xianmin Wei
2013-01-01
In this study, we introduced the ant colony algorithm of best-worst ant system based on the pheromone update. By update improvements of local pheromone and global pheromone, as well as the optimal solution enhancement to a greater extent and the weakening of the worst solution, the algorithm further increased the difference of pheromone amount between the edge of the optimal path and the edge of the worst path and allowed the ant colony search behavior more focused near the optimal solution. ...
Ant Colony Algorithm for Solving QoS Routing Problem
Institute of Scientific and Technical Information of China (English)
SUN Li-juan; WANG Liang-jun; WANG Ru-chuan
2004-01-01
Based on the state transition rule, the local updating rule and the global updating rule of ant colony algorithm, we propose an improved ant colony algorithm of the least-cost quality of service (QoS) unicast routing. The algorithm is used for solving the routing problem with delay, delay jitter, bandwidth, and packet loss-constrained. In the simulation, about 52.33% ants find the successful QoS routing , and converge to the best. It is proved that the algorithm is efficient and effective.
Data transmission optimal routing in WSN using ant colony algorithm
Jun, Su; Yatskiv, Vasyl; Sachenko, Anatoly; Yatskiv, Nataliya
2012-01-01
Ant colony algorithm to search an optimal route of data transmission in Wireless Sensor Network was explored. Correspondent software was designed and the dynamics and the decision search time was investigated for the given network topology.
Optimization of PID Controllers Using Ant Colony and Genetic Algorithms
Ünal, Muhammet; Topuz, Vedat; Erdal, Hasan
2013-01-01
Artificial neural networks, genetic algorithms and the ant colony optimization algorithm have become a highly effective tool for solving hard optimization problems. As their popularity has increased, applications of these algorithms have grown in more than equal measure. While many of the books available on these subjects only provide a cursory discussion of theory, the present book gives special emphasis to the theoretical background that is behind these algorithms and their applications. Moreover, this book introduces a novel real time control algorithm, that uses genetic algorithm and ant colony optimization algorithms for optimizing PID controller parameters. In general, the present book represents a solid survey on artificial neural networks, genetic algorithms and the ant colony optimization algorithm and introduces novel practical elements related to the application of these methods to process system control.
Introduction to Ant Colony Algorithm and Its Application in CIMS
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
Ant colony algorithm is a novel simulated ecosystem e volutionary algorithm, which is proposed firstly by Italian scholars M.Dorigo, A . Colormi and V. Maniezzo. Enlightened by the process of ants searching for food , scholars bring forward this new evolutionary algorithm. This algorithm has sev eral characteristics such as positive feedback, distributed computing and stro nger robustness. Positive feedback and distributed computing make it easier to find better solutions. Based on these characteristics...
Feng, Yinda
2010-01-01
The aim of this work is to investigate Ant Colony Algorithm for the traveling salesman problem (TSP). Ants of the artificial colony are able to generate successively shorter feasible tours by using information accumulated in the form of a pheromone trail deposited on the edges of the TSP graph. This paper is based on the ideas of ant colony algorithm and analysis the main parameters of the ant colony algorithm. Experimental results for solving TSP problems with ant colony algorithm show great...
Ant Colony versus Genetic Algorithm based on Travelling Salesman Problem
Directory of Open Access Journals (Sweden)
Mohammed Alhanjouri
2011-05-01
Full Text Available The travelling salesman problem (TSP is a nondeterministic Polynomial hard problem in combinatorial optimization studied in operations research and theoretical computer science. And to solve this problem we used two popular meta-heuristics techniques that used for optimization tasks; the first one is Ant Colony Optimization (ACO, and the second is Genetic Algorithm (GA. In this work, we try to apply both techniques to solve TSP by using the same dataset and compare between them to determine the best one for travelling salesman problem. for Ant Colony Optimization, we studied the effect of some parameters on the produced results, these parameters as: number of used Ants, evaporation, and number of iterations. On the other hand, we studied the chromosome population, crossover probability, and mutation probability parameters that effect on the Genetic Algorithm results.The comparison between Genetic Algorithm and Ant Colony Optimization is accomplished to state the better one for travelling salesman problem.
Hybrid ant colony algorithm for traveling salesman problem
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
A hybrid approach based on ant colony algorithm for the traveling salesman problem is proposed, which is an improved algorithm characterized by adding a local search mechanism, a cross-removing strategy and candidate lists. Experimental results show that it is competitive in terms of solution quality and computation time.
PRACTICAL APPLICATION OF POPULATION BASED ANT COLONY OPTIMIZATION ALGORITHM
Valeeva, A.; Goncharova, Yu
2013-01-01
In this paper we consider the Split Delivery Vehicle Routing Problem, which has a wide practical application. The SDVRP is NP-hard problem. We propose a population based ant colony optimization algorithm for solving the SDVRP. Computational experiments for developed algorithm are reported.
Core Business Selection Based on Ant Colony Clustering Algorithm
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Yu Lan
2014-01-01
Full Text Available Core business is the most important business to the enterprise in diversified business. In this paper, we first introduce the definition and characteristics of the core business and then descript the ant colony clustering algorithm. In order to test the effectiveness of the proposed method, Tianjin Port Logistics Development Co., Ltd. is selected as the research object. Based on the current situation of the development of the company, the core business of the company can be acquired by ant colony clustering algorithm. Thus, the results indicate that the proposed method is an effective way to determine the core business for company.
AN IMPROVED ANT COLONY ALGORITHM IN CONTINUOUS OPTIMIZATION
Institute of Scientific and Technical Information of China (English)
Ling CHEN; Jie SHEN; Ling QIN; Hongjian CHEN
2003-01-01
A modified ant colony algorithm for solving optimization problem with continuous parameters is presented. In the method, groups of candidate values of the components are constructed, and each value in the group has its trail information. In each iteration of the ant colony algorithm, the method first chooses initial values of the components using the trail information. Then GA operations of crossover and mutation can determine the values of the components in the solution. Our experimental results on the problem of nonlinear programming show that our method has a much higher convergence speed and stability than those of simulated annealing (SA) and GA.
Ant Colony Search Algorithm for Solving Unit Commitment Problem
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M.Surya Kalavathi
2013-07-01
Full Text Available In this paper Ant Colony Search Algorithm is proposed to solve thermal unit commitment problem. Ant colony search (ACS studies are inspired from the behavior of real ant colonies that are used to solve function or combinatorial optimization problems. In the ACSA a set of cooperating agents called ants cooperates to find good solution of unit commitment problem of thermal units. The UC problem is to determine a minimal cost turn-on and turn-off schedule of a set of electrical power generating units to meet a load demand while satisfying a set of operational constraints. This proposed approach is a tested on 10 unit power system and compared to conventional methods.
An ant colony optimization algorithm for job shop scheduling problem
Edson Flórez; Wilfredo Gómez; MSc. Lola Bautista
2013-01-01
The nature has inspired several metaheuristics, outstanding among these is Ant Colony Optimization (ACO), which have proved to be very effective and efficient in problems of high complexity (NP-hard) in combinatorial optimization. This paper describes the implementation of an ACO model algorithm known as Elitist Ant System (EAS), applied to a combinatorial optimization problem called Job Shop Scheduling Problem (JSSP). We propose a method that seeks to reduce delays designating th...
Advances on image interpolation based on ant colony algorithm.
Rukundo, Olivier; Cao, Hanqiang
2016-01-01
This paper presents an advance on image interpolation based on ant colony algorithm (AACA) for high resolution image scaling. The difference between the proposed algorithm and the previously proposed optimization of bilinear interpolation based on ant colony algorithm (OBACA) is that AACA uses global weighting, whereas OBACA uses local weighting scheme. The strength of the proposed global weighting of AACA algorithm depends on employing solely the pheromone matrix information present on any group of four adjacent pixels to decide which case deserves a maximum global weight value or not. Experimental results are further provided to show the higher performance of the proposed AACA algorithm with reference to the algorithms mentioned in this paper. PMID:27047729
Global path planning approach based on ant colony optimization algorithm
Institute of Scientific and Technical Information of China (English)
WEN Zhi-qiang; CAI Zi-xing
2006-01-01
Ant colony optimization (ACO) algorithm was modified to optimize the global path. In order to simulate the real ant colonies, according to the foraging behavior of ant colonies and the characteristic of food, conceptions of neighboring area and smell area were presented. The former can ensure the diversity of paths and the latter ensures that each ant can reach the goal. Then the whole path was divided into three parts and ACO was used to search the second part path. When the three parts pathes were adjusted,the final path was found. The valid path and invalid path were defined to ensure the path valid. Finally, the strategies of the pheromone search were applied to search the optimum path. However, when only the pheromone was used to search the optimum path, ACO converges easily. In order to avoid this premature convergence, combining pheromone search and random search, a hybrid ant colony algorithm(HACO) was used to find the optimum path. The comparison between ACO and HACO shows that HACO can be used to find the shortest path.
A Hybrid Optimization Algorithm based on Genetic Algorithm and Ant Colony Optimization
Zainudin Zukhri; Irving Vitra Paputungan
2013-01-01
In optimization problem, Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO) have been known as good alternative techniques. GA is designed by adopting the natural evolution process, while ACO is inspired by the foraging behaviour of ant species. This paper presents a hybrid GA-ACO for Travelling Salesman Problem (TSP), called Genetic Ant Colony Optimization (GACO). In this method, GA will observe and preserve the fittest ant in each cycle in every generation and on...
Cooperation-based Ant Colony Algorithm in WSN
Directory of Open Access Journals (Sweden)
Jianbin Xue
2013-04-01
Full Text Available This paper proposed a routing algorithm based on ant colony algorithm. The traditional ant colony algorithm updates pheromone according to the path length, to get the shortest path from the initial node to destination node. But MIMO system is different from the SISO system. The distance is farther but the energy is not bigger. Similarly, the closer the distance, the smaller the energy is not necessarily. So need to select the path according to the energy consumption of the path. This paper is based on the energy consumption to update the pheromone which from the cluster head node to the next hop node. Then, can find a path which the communication energy consumption is least. This algorithm can save more energy consumption of the network. The simulation results of MATLAB show that the path chosen by the algorithm is better than the simple ant colony algorithm, and the algorithm can save the network energy consumption better and can prolong the life cycle of the network.
Antenna synthesis based on the ant colony optimization algorithm
Slyusar, V. I.; Ermolaev, S. Y.
2009-01-01
This report are described the versions and the synthesis results of new designs of electrically small antenna based on ant colony optimization algorithms. To study the parameters of the frame and non-loopback vibrators MMANA package was used. Geometric forms that were obtained might be used as contour lines of printed, slot antenna or as forming surface of the crystal dielectric resonator antenna. A constructive meta-heuristic search algorithm for optimization of the antennas form...
Modal parameters estimation using ant colony optimisation algorithm
Sitarz, Piotr; Powałka, Bartosz
2016-08-01
The paper puts forward a new estimation method of modal parameters for dynamical systems. The problem of parameter estimation has been simplified to optimisation which is carried out using the ant colony system algorithm. The proposed method significantly constrains the solution space, determined on the basis of frequency plots of the receptance FRFs (frequency response functions) for objects presented in the frequency domain. The constantly growing computing power of readily accessible PCs makes this novel approach a viable solution. The combination of deterministic constraints of the solution space with modified ant colony system algorithms produced excellent results for systems in which mode shapes are defined by distinctly different natural frequencies and for those in which natural frequencies are similar. The proposed method is fully autonomous and the user does not need to select a model order. The last section of the paper gives estimation results for two sample frequency plots, conducted with the proposed method and the PolyMAX algorithm.
A Hybrid Ant Colony Algorithm for Loading Pattern Optimization
Hoareau, F.
2014-06-01
Electricité de France (EDF) operates 58 nuclear power plant (NPP), of the Pressurized Water Reactor (PWR) type. The loading pattern (LP) optimization of these NPP is currently done by EDF expert engineers. Within this framework, EDF R&D has developed automatic optimization tools that assist the experts. The latter can resort, for instance, to a loading pattern optimization software based on ant colony algorithm. This paper presents an analysis of the search space of a few realistic loading pattern optimization problems. This analysis leads us to introduce a hybrid algorithm based on ant colony and a local search method. We then show that this new algorithm is able to generate loading patterns of good quality.
AN ANT COLONY ALGORITHM FOR MINIMUM UNSATISFIABLE CORE EXTRACTION
Institute of Scientific and Technical Information of China (English)
Zhang Jianmin; Shen Shengyu; Li Sikun
2008-01-01
Explaining the causes of infeasibility of Boolean formulas has many practical applications in electronic design automation and formal verification of hardware. Furthermore,a minimum explanation of infeasibility that excludes all irrelevant information is generally of interest. A smallest-cardinality unsatisfiable subset called a minimum unsatisfiable core can provide a succinct explanation of infea-sibility and is valuable for applications. However,little attention has been concentrated on extraction of minimum unsatisfiable core. In this paper,the relationship between maximal satisfiability and mini-mum unsatisfiability is presented and proved,then an efficient ant colony algorithm is proposed to derive an exact or ncarly exact minimum unsatisfiable core based on the relationship. Finally,ex-perimental results on practical benchmarks compared with the best known approach are reported,and the results show that the ant colony algorithm strongly outperforms the best previous algorithm.
A Novel Algorithm for Manets using Ant Colony
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Javad Pashaei Barbin
2012-01-01
Full Text Available Mobile Ad-hoc Networks have recently attracted a lot of attention in the research community as well as the industry. Quality of Service support for MANETs is an exigent task due to dynamic topology and limited resource. Routing, the act of moving information across network from a source to a destination. Conventional routing algorithms are difficult to be applied to a dynamic network topology, therefore modeling and design an efficient routing protocol in such dynamic networks is an important issue. It is important that MANETs should provide QoS support routing, such as acceptable delay, jitter and energy in the case of multimedia and real time applications. One of the meta-heuristic algorithms which are inspired by the behavior of real ants is called Ant Colony Optimization algorithm. In this paper we propose a new on demand QoS routing algorithm "Ant Routing for Mobile Ad Hoc Networks" based on ant colony. The proposed algorithm will be highly adaptive, efficient and scalable and mainly reduces end-to-end delay in high mobility cases.
All-Optical Implementation of the Ant Colony Optimization Algorithm
Hu, Wenchao; Wu, Kan; Shum, Perry Ping; Zheludev, Nikolay I.; Soci, Cesare
2016-05-01
We report all-optical implementation of the optimization algorithm for the famous “ant colony” problem. Ant colonies progressively optimize pathway to food discovered by one of the ants through identifying the discovered route with volatile chemicals (pheromones) secreted on the way back from the food deposit. Mathematically this is an important example of graph optimization problem with dynamically changing parameters. Using an optical network with nonlinear waveguides to represent the graph and a feedback loop, we experimentally show that photons traveling through the network behave like ants that dynamically modify the environment to find the shortest pathway to any chosen point in the graph. This proof-of-principle demonstration illustrates how transient nonlinearity in the optical system can be exploited to tackle complex optimization problems directly, on the hardware level, which may be used for self-routing of optical signals in transparent communication networks and energy flow in photonic systems.
An ant colony algorithm for solving Max-cut problem
Institute of Scientific and Technical Information of China (English)
Lin Gao; Yan Zeng; Anguo Dong
2008-01-01
Max-cut problem is an NP-complete and classical combinatorial optimization problem that has a wide range of appfications in dif-ferent domains,such as bioinformatics,network optimization,statistical physics,and very large scale integration design.In this paper we investigate the capabilities of the ant colony optimization(ACO)heuristic for solving the Max-cut problem and present an AntCut algo-rithm.A large number of simulation experiments show that the algorithm can solve the Max-cut problem more efficiently and effectively.
Multiple-Agent Task Allocation Algorithm Utilizing Ant Colony Optimization
Kai Zhao
2013-01-01
Task allocation in multiple agent system has been widely applied many application fields, such as unmanned aerial vehicle, multi-robot system and manufacturing system et al. Therefore, it becomes one of the hot topics in distributed artificial intelligence research field for several years. Therefore, in this paper, we propose a novel task allocation algorithm in multiple agent systems utilizing ant colony optimization. Firstly, the basic structure of agent organization is described, which inc...
An improved ant colony algorithm with diversified solutions based on the immune strategy
Qin, Ling; Pan, Yi; Chen, Ling; Chen, Yixin
2006-01-01
Background Ant colony algorithm has emerged recently as a new meta-heuristic method, which is inspired from the behaviours of real ants for solving NP-hard problems. However, the classical ant colony algorithm also has its defects of stagnation and premature. This paper aims at remedying these problems. Results In this paper, we propose an adaptive ant colony algorithm that simulates the behaviour of biological immune system. The solutions of the problem are much more diversified than traditi...
A New Technique to Increase the Working Performance of the Ant Colony Optimization Algorithm
Reena Jindal; Dr.Samidha D.Sharma,; Prof.Manoj Sharma,
2013-01-01
The DBSCALE [1] algorithm is a popular algorithm in Data Mining field as it has the ability to mine the noiseless arbitrary shape Clusters in an elegant way. Such meta-heuristic algorithms include Ant Colony Optimization Algorithms, Particle Swarm Optimizations and Genetic Algorithm has received increasing attention in recent years. Ant Colony Optimization (ACO) is a technique that was introduced in the early 1990’s and it is inspired by the foraging behavior of ant colonies. .This paper pres...
The analysis of the convergence of ant colony optimization algorithm
Institute of Scientific and Technical Information of China (English)
ZHU Qingbao; WANG Lingling
2007-01-01
The ant colony optimization algorithm has been widely studied and many important results have been obtained.Though this algorithm has been applied to many fields.the analysis about its convergence is much less,which will influence the improvement of this algorithm.Therefore,the convergence of this algorithm applied to the traveling salesman problem(TSP)was analyzed in detail.The conclusion that this algorithm will definitely converge to the optimal solution under the condition of 0＜q0＜1 was proved true.In addition,the influence on its convergence caused by the properties of the closed path,heuristic functions,the pheromone and q0 was analyzed.Based on the above-mentioned,some conclusions about how to improve the speed of its convergence are obtained.
Solution to the problem of ant being stuck by ant colony routing algorithm
Institute of Scientific and Technical Information of China (English)
ZHAO Jing; TONG Wei-ming
2009-01-01
Many ant colony routing (ACR) algorithms have been presented in recent years, but few have studied the problem that ants will get stuck with probability in any terminal host when they are searching paths to route packets around a network. The problem has to be faced when designing and implementing the ACR algorithm. This article analyzes in detail the differences between the ACR and the ant colony optimization (ACO). Besides, particular restrictions on the ACR are pointed out and the three causes of ant being-stuck problem are obtained. Furthermore, this article proposes a new ant searching mechanism through dual path-checking and online routing loop removing by every intermediate node an ant visited and the destination host respectively, to solve the problem of ant being stuck and routing loop simultaneously. The result of numerical simulation is abstracted from one real network. Compared with existing two typical ACR algorithms, it shows that the proposed algorithm can settle the problem of ant being stuck and achieve more effective searching outcome for optimization path.
A HYBRID OPTIMIZATION ALGORITHM BASED ON GENETIC ALGORITHM AND ANT COLONY OPTIMIZATION
Directory of Open Access Journals (Sweden)
Zainudin Zukhri
2013-09-01
Full Text Available In optimization problem, Genetic Algorithm (GA and Ant Colony Optimization Algorithm (ACO have been known as good alternative techniques. GA is designed by adopting the natural evolution process, while ACO is inspired by the foraging behaviour of ant species. This paper presents a hybrid GA-ACO for Travelling Salesman Problem (TSP, called Genetic Ant Colony Optimization (GACO. In this method, GA will observe and preserve the fittest ant in each cycle in every generation and only unvisited cities will be assessed by ACO. From experimental result, GACO performance is significantly improved and its time complexity is fairly equal compared to the GA and ACO.
Design of broadband omnidirectional antireflection coatings using ant colony algorithm.
Guo, X; Zhou, H Y; Guo, S; Luan, X X; Cui, W K; Ma, Y F; Shi, L
2014-06-30
Optimization method which is based on the ant colony algorithm (ACA) is described to optimize antireflection (AR) coating system with broadband omnidirectional characteristics for silicon solar cells incorporated with the solar spectrum (AM1.5 radiation). It's the first time to use ACA method for optimizing the AR coating system. In this paper, for the wavelength range from 400 nm to 1100 nm, the optimized three-layer AR coating system could provide an average reflectance of 2.98% for incident angles from Raveθ+ to 80° and 6.56% for incident angles from 0° to 90°. PMID:24978076
Multiple-Agent Task Allocation Algorithm Utilizing Ant Colony Optimization
Directory of Open Access Journals (Sweden)
Kai Zhao
2013-11-01
Full Text Available Task allocation in multiple agent system has been widely applied many application fields, such as unmanned aerial vehicle, multi-robot system and manufacturing system et al. Therefore, it becomes one of the hot topics in distributed artificial intelligence research field for several years. Therefore, in this paper, we propose a novel task allocation algorithm in multiple agent systems utilizing ant colony optimization. Firstly, the basic structure of agent organization is described, which include context-aware module, information processing module, the executing module, decision-making and intelligent control module, knowledge base and task table. Based the above agent structure, these module utilize the knowledge in the external environment to process the information in agent communicating. Secondly, we point out that task allocation process in multiple agent systems can be implement by creating the space to the mapping of the multi-agent organization. Thirdly, a modified multiple agent system oriented ant colony optimization algorithm is given, which contain pre-processing steps and the task allocation results are obtained by executing the trust region sqp algorithm in local solver. Finally, performance evaluation is conducted by experiments comparing with Random strategy and Instant optimal strategy, and very positive results are obtained
Li Hui; Zhang Jingxiao; Ren Lieyan; Shi Zhen
2013-01-01
In this paper, the basic theory and procedure for working out solutions of ant colony genetic algorithm were first introduced; the optimization, constraints and objectives of construction project scheduling were described; then a basic model for optimization of construction project scheduling was established; and an improved ant colony genetic algorithm for solving the basic model was put forward. Performance of ant colony genetic algorithm was analyzed and evaluated from the aspect of schedu...
Ant Colony Based Path Planning Algorithm for Autonomous Robotic Vehicles
Directory of Open Access Journals (Sweden)
Yogita Gigras
2012-11-01
Full Text Available The requirement of an autonomous robotic vehicles demand highly efficient algorithm as well as software. Today’s advanced computer hardware technology does not provide these types of extensive processing capabilities, so there is still a major space and time limitation for the technologies that are available for autonomous robotic applications. Now days, small to miniature mobile robots are required for investigation, surveillance and hazardous material detection for military and industrial applications. But these small sized robots have limited power capacity as well as memory and processing resources. A number of algorithms exist for producing optimal path for dynamically cost. This paper presents a new ant colony based approach which is helpful in solving path planning problem for autonomous robotic application. The experiment of simulation verified its validity of algorithm in terms of time.
Zahálka, Jaroslav
2007-01-01
This diploma thesis deals with Ant Colony algorithms and their usage for solving Travelling Salesman Problems and Vehicle Routing Problems. These algorithms are metaheuristics offering new approach to solving NP-hard problems. Work begins with a description of the forementioned tasks including ways to tackle them. Next chapter analyses Ant Colony metaheuristic and its possible usage and variations. The most important part of the thesis is practical and is represented by application Ant Colony...
Wavelet phase estimation using ant colony optimization algorithm
Wang, Shangxu; Yuan, Sanyi; Ma, Ming; Zhang, Rui; Luo, Chunmei
2015-11-01
Eliminating seismic wavelet is important in seismic high-resolution processing. However, artifacts may arise in seismic interpretation when the wavelet phase is inaccurately estimated. Therefore, we propose a frequency-dependent wavelet phase estimation method based on the ant colony optimization (ACO) algorithm with global optimization capacity. The wavelet phase can be optimized with the ACO algorithm by fitting nearby-well seismic traces with well-log data. Our proposed method can rapidly produce a frequency-dependent wavelet phase and optimize the seismic-to-well tie, particularly for weak signals. Synthetic examples demonstrate the effectiveness of the proposed ACO-based wavelet phase estimation method, even in the presence of a colored noise. Real data example illustrates that seismic deconvolution using an optimum mixed-phase wavelet can provide more information than that using an optimum constant-phase wavelet.
Ant Colony Algorithm for the Weighted Item Layout Optimization Problem
Xu, Yi-Chun; Liu, Yong; Xiao, Ren-Bin; Amos, Martyn
2010-01-01
This paper discusses the problem of placing weighted items in a circular container in two-dimensional space. This problem is of great practical significance in various mechanical engineering domains, such as the design of communication satellites. Two constructive heuristics are proposed, one for packing circular items and the other for packing rectangular items. These work by first optimizing object placement order, and then optimizing object positioning. Based on these heuristics, an ant colony optimization (ACO) algorithm is described to search first for optimal positioning order, and then for the optimal layout. We describe the results of numerical experiments, in which we test two versions of our ACO algorithm alongside local search methods previously described in the literature. Our results show that the constructive heuristic-based ACO performs better than existing methods on larger problem instances.
Electromagnetic Wave Propagation Modeling Using the Ant Colony Optimization Algorithm
Directory of Open Access Journals (Sweden)
P. Pechac
2002-09-01
Full Text Available The Ant Colony Optimization algorithm - a multi-agent approach tocombinatorial optimization problems - is introduced for a simple raytracing performed on only an ordinary bitmap describing atwo-dimensional scenario. This bitmap can be obtained as a simple scanwhere different colors represent different mediums or obstacles. It isshown that using the presented algorithm a path minimizing the wavetraveling time can be found according to the Fermat's principle. Anexample of practical application is a simple ray tracing performed ononly an ordinary scanned bitmap of the city map. Together with theBerg's recursive model a non-line-of-sight path loss could becalculated without any need of building database. In this way thecoverage predictions for urban microcells could become extremely easyand fast to apply.
A hybrid ant colony algorithm for loading pattern optimization
International Nuclear Information System (INIS)
EDF (Electricity of France) operates 58 nuclear power plant (NPP), all of the Pressurized Water Reactor (PWR) type. The loading pattern (LP) optimization of these NPP is currently done by EDF expert engineers. Within this framework, EDF has developed automatic optimization tools that assist the experts. This paper presents firstly a description of the LP optimization problem listing its constraints. Secondly, a study of the search space is performed using the 'landscape fitness analysis' paradigm. Lastly, a hybrid algorithm based on ant colony and a local search method, is introduced to take advantage of the features of the problem. Tests have been performed on realistic cases. This hybrid algorithm has turned out to give very encouraging results when compared to a randomized local search method
Road Network Vulnerability Analysis Based on Improved Ant Colony Algorithm
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Yunpeng Wang
2014-01-01
Full Text Available We present an improved ant colony algorithm-based approach to assess the vulnerability of a road network and identify the critical infrastructures. This approach improves computational efficiency and allows for its applications in large-scale road networks. This research involves defining the vulnerability conception, modeling the traffic utility index and the vulnerability of the road network, and identifying the critical infrastructures of the road network. We apply the approach to a simple test road network and a real road network to verify the methodology. The results show that vulnerability is directly related to traffic demand and increases significantly when the demand approaches capacity. The proposed approach reduces the computational burden and may be applied in large-scale road network analysis. It can be used as a decision-supporting tool for identifying critical infrastructures in transportation planning and management.
An Ant Colony Optimization Algorithm for Microwave Corrugated Filters Design
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Ivan A. Mantilla-Gaviria
2013-01-01
Full Text Available A practical and useful application of the Ant Colony Optimization (ACO method for microwave corrugated filter design is shown. The classical, general purpose ACO method is adapted to deal with the microwave filter design problem. The design strategy used in this paper is an iterative procedure based on the use of an optimization method along with an electromagnetic simulator. The designs of high-pass and band-pass microwave rectangular waveguide filters working in the C-band and X-band, respectively, for communication applications, are shown. The average convergence performance of the ACO method is characterized by means of Monte Carlo simulations and compared with that obtained with the well-known Genetic Algorithm (GA. The overall performance, for the simulations presented herein, of the ACO is found to be better than that of the GA.
Automatic fault extraction using a modified ant-colony algorithm
International Nuclear Information System (INIS)
The basis of automatic fault extraction is seismic attributes, such as the coherence cube which is always used to identify a fault by the minimum value. The biggest challenge in automatic fault extraction is noise, including that of seismic data. However, a fault has a better spatial continuity in certain direction, which makes it quite different from noise. Considering this characteristic, a modified ant-colony algorithm is introduced into automatic fault identification and tracking, where the gradient direction and direction consistency are used as constraints. Numerical model test results show that this method is feasible and effective in automatic fault extraction and noise suppression. The application of field data further illustrates its validity and superiority. (paper)
The optimal time-frequency atom search based on a modified ant colony algorithm
Institute of Scientific and Technical Information of China (English)
GUO Jun-feng; LI Yan-jun; YU Rui-xing; ZHANG Ke
2008-01-01
In this paper,a new optimal time-frequency atom search method based on a modified ant colony algorithm is proposed to improve the precision of the traditional methods.First,the discretization formula of finite length time-frequency atom is inferred at length.Second; a modified ant colony algorithm in continuous space is proposed.Finally,the optimal timefrequency atom search algorithm based on the modified ant colony algorithm is described in detail and the simulation experiment is carried on.The result indicates that the developed algorithm is valid and stable,and the precision of the method is higher than that of the traditional method.
CACONET: Ant Colony Optimization (ACO) Based Clustering Algorithm for VANET
Bajwa, Khalid Bashir; Khan, Salabat; Chaudary, Nadeem Majeed; Akram, Adeel
2016-01-01
A vehicular ad hoc network (VANET) is a wirelessly connected network of vehicular nodes. A number of techniques, such as message ferrying, data aggregation, and vehicular node clustering aim to improve communication efficiency in VANETs. Cluster heads (CHs), selected in the process of clustering, manage inter-cluster and intra-cluster communication. The lifetime of clusters and number of CHs determines the efficiency of network. In this paper a Clustering algorithm based on Ant Colony Optimization (ACO) for VANETs (CACONET) is proposed. CACONET forms optimized clusters for robust communication. CACONET is compared empirically with state-of-the-art baseline techniques like Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO). Experiments varying the grid size of the network, the transmission range of nodes, and number of nodes in the network were performed to evaluate the comparative effectiveness of these algorithms. For optimized clustering, the parameters considered are the transmission range, direction and speed of the nodes. The results indicate that CACONET significantly outperforms MOPSO and CLPSO. PMID:27149517
Improved Ant Colony Optimization Algorithm based Expert System on Nephrology
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Sri.N.V.Ramana Murty
2010-07-01
Full Text Available Expert system Nephrology is a computer program that exhibits, within a specific domain, a degree of expertise in problem solving that is comparable to that of a human expert. The knowledge base consistsof information about a particular problem area. This information is collected from domain experts (doctors. This system mainly contains two modules one is Information System and the other is Expert Advisory system. The Information System contains the static information about different diseases and drugs in the field of Nephrology. This information system helps the patients /users to know about the problems related to kidneys. The Nephrology Advisory system helps the Patients /users to get the required and suitable advice depending on their queries. This medical expert system is developedusing Java Server Pages (JSP as front-end and MYSQL database as Backend in such a way that all the activities are carried out in a user-friendly manner. Improved Ant Colony Optimization Algorithm (ACO along with RETE algorithm is also used for better results.
CACONET: Ant Colony Optimization (ACO) Based Clustering Algorithm for VANET.
Aadil, Farhan; Bajwa, Khalid Bashir; Khan, Salabat; Chaudary, Nadeem Majeed; Akram, Adeel
2016-01-01
A vehicular ad hoc network (VANET) is a wirelessly connected network of vehicular nodes. A number of techniques, such as message ferrying, data aggregation, and vehicular node clustering aim to improve communication efficiency in VANETs. Cluster heads (CHs), selected in the process of clustering, manage inter-cluster and intra-cluster communication. The lifetime of clusters and number of CHs determines the efficiency of network. In this paper a Clustering algorithm based on Ant Colony Optimization (ACO) for VANETs (CACONET) is proposed. CACONET forms optimized clusters for robust communication. CACONET is compared empirically with state-of-the-art baseline techniques like Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO). Experiments varying the grid size of the network, the transmission range of nodes, and number of nodes in the network were performed to evaluate the comparative effectiveness of these algorithms. For optimized clustering, the parameters considered are the transmission range, direction and speed of the nodes. The results indicate that CACONET significantly outperforms MOPSO and CLPSO. PMID:27149517
Ant Colony Optimization: A Review and Comparison
Sundus Shaukat; Riaz Ahmed Bhatti; Khalid Ibrahim Qureshi; Shafqat Ali Shad
2014-01-01
Many optmization algorithms are developed over period of time, among these most famous and widely used is Ant Colony systems (ACA). Ant Colony Systems (ACS) are the collection of different ant colony optimization algorithms. Different algorithms are used for solve the Travelling salesmen Problem (TCP) but ant colony algorithm is more preferred to solve the travelling salesmen problem. In ant colony best solution is found with the help of cooperating agents called ants. Ants cooperate with eac...
Text clustering based on fusion of ant colony and genetic algorithms
Institute of Scientific and Technical Information of China (English)
Yun ZHANG; Boqin FENG; Shouqiang MA; Lianmeng LIU
2009-01-01
Focusing on the problem that the ant colony algorithm gets into stagnation easily and cannot fully search in solution space,a text clustering approach based on the fusion of the ant colony and genetic algorithms is proposed.The four parameters that influence the performance of the ant colony algorithm are encoded as chromosomes,thereby the fitness function,selection,crossover and mutation operator are designed to find the combination of optimal parameters through a number of iteration,and then it is applied to text clustering.The simulation.results show that compared with the classical k-means clustering and the basic ant colony clustering algorithm,the proposed algorithm has better performance and the value of F-Measure is enhanced by 5.69%,48.60% and 69.60%,respectively,in 3 test datasets.Therefore,it is more suitable for processing a larger dataset.
Zhou, Dongsheng; Wang, Lan; Zhang, Qiang
2016-01-01
With the development of aerospace engineering, the space on-orbit servicing has been brought more attention to many scholars. Obstacle avoidance planning of space manipulator end-effector also attracts increasing attention. This problem is complex due to the existence of obstacles. Therefore, it is essential to avoid obstacles in order to improve planning of space manipulator end-effector. In this paper, we proposed an improved ant colony algorithm to solve this problem, which is effective and simple. Firstly, the models were established respectively, including the kinematic model of space manipulator and expression of valid path in space environment. Secondly, we described an improved ant colony algorithm in detail, which can avoid trapping into local optimum. The search strategy, transfer rules, and pheromone update methods were all adjusted. Finally, the improved ant colony algorithm was compared with the classic ant colony algorithm through the experiments. The simulation results verify the correctness and effectiveness of the proposed algorithm. PMID:27186473
QoS Multicast Routing Algorithm Based on Crowding Ant Colony Algorithm
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Yongsheng Li
2013-10-01
Full Text Available The basic ant colony algorithm is easy to fall into local optimum and its convergent speed is slow for solving multiple QoS multicast routing problems. Therefore, a crowding ant colony algorithm is proposed in this paper to solve the problems. Crowded degree in artificial fish swarm algorithm is used to adjust nodes transition strategy dynamically according to the congestion of nodes. Stagnation behavior is judged by the similarity of multicast tree and chaos perturbation is utilized to update the pheromone trail on the multicast tree that may fall into local optimum in order that solutions can range out of local optimum. According to simulations, the global search is enhanced at the initial and convergence rate has improved greatly at the later. The improved algorithm is feasible and effective.
International Nuclear Information System (INIS)
As a heuristic intelligent optimization algorithm, the Ant Colony Optimization (ACO) algorithm was applied to the inverse problem of a one-dimensional (1-D) transient radiative transfer in present study. To illustrate the performance of this algorithm, the optical thickness and scattering albedo of the 1-D participating slab medium were retrieved simultaneously. The radiative reflectance simulated by Monte-Carlo Method (MCM) and Finite Volume Method (FVM) were used as measured and estimated value for the inverse analysis, respectively. To improve the accuracy and efficiency of the Basic Ant Colony Optimization (BACO) algorithm, three improved ACO algorithms, i.e., the Region Ant Colony Optimization algorithm (RACO), Stochastic Ant Colony Optimization algorithm (SACO) and Homogeneous Ant Colony Optimization algorithm (HACO), were developed. By the HACO algorithm presented, the radiative parameters could be estimated accurately, even with noisy data. In conclusion, the HACO algorithm is demonstrated to be effective and robust, which had the potential to be implemented in various fields of inverse radiation problems. -- Highlights: • The ACO-based algorithms were firstly applied to the inverse transient radiation problem. • Three ACO-based algorithms were developed based on the BACO algorithm for continuous domain problem. • HACO shows a robust performance for simultaneous estimation of the radiative properties
Identification of Dynamic Parameters Based on Pseudo-Parallel Ant Colony Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
ZHAO Feng-yao; MA Zhen-yue; ZHANG Yun-liang
2007-01-01
For the parameter identification of dynamic problems, a pseudo-parallel ant colony optimization (PPACO) algorithm based on graph-based ant system (AS) was introduced. On the platform of ANSYS dynamic analysis, the PPACO algorithm was applied to the identification of dynamic parameters successfully. Using simulated data of forces and displacements, elastic modulus E and damping ratio ξ was identified for a designed 3D finite element model, and the detailed identification step was given. Mathematical example and simulation example show that the proposed method has higher precision, faster convergence speed and stronger antinoise ability compared with the standard genetic algorithm and the ant colony optimization (ACO) algorithms.
Using nonlinear optical networks for optimization: primer of the ant colony algorithm
Hu, W; Wu, K; Shum, P. P.; Zheludev, N. I.; Soci, C.; Adamo, G.
2014-01-01
Using nonlinear Erbium doped optical fiber network we have implemented an optimization algorithm for the famous problem of finding the shortest path on the map for the ant colony to travel to the foraging area.
Zhang, Gexiang; Cheng, Jixiang; Gheorghe, Marian; Research Group on Natural Computing (Universidad de Sevilla) (Coordinador)
2010-01-01
This paper proposes an approximate optimization algorithm combining P systems with ant colony optimization, called ACOPS, to solve traveling salesman prob- lems, which are well-known and extensively studied NP-complete combinatorial optimization problems. ACOPS uses the pheromone model and pheromone update rules defined by ant colony optimization algorithms, and the hierarchical membrane structure and transformation/communication rules of P systems. First, the parameter setting of...
A Hybrid Routing Algorithm Based on Ant Colony and ZHLS Routing Protocol for MANET
Rafsanjani, Marjan Kuchaki; Asadinia, Sanaz; Pakzad, Farzaneh
Mobile Ad hoc networks (MANETs) require dynamic routing schemes for adequate performance. This paper, presents a new routing algorithm for MANETs, which combines the idea of ant colony optimization with Zone-based Hierarchical Link State (ZHLS) protocol. Ant colony optimization (ACO) is a class of Swarm Intelligence (SI) algorithms. SI is the local interaction of many simple agents to achieve a global goal. SI is based on social insect for solving different types of problems. ACO algorithm uses mobile agents called ants to explore network. Ants help to find paths between two nodes in the network. Our algorithm is based on ants jump from one zone to the next zones which contains of the proactive routing within a zone and reactive routing between the zones. Our proposed algorithm improves the performance of the network such as delay, packet delivery ratio and overhead than traditional routing algorithms.
An adaptive ant colony system algorithm for continuous-space optimization problems
Institute of Scientific and Technical Information of China (English)
李艳君; 吴铁军
2003-01-01
Ant colony algorithms comprise a novel category of evolutionary computation methods for optimization problems, especially for sequencing-type combinatorial optimization problems. An adaptive ant colony algorithm is proposed in this paper to tackle continuous-space optimization problems, using a new objective-function-based heuristic pheromone assignment approach for pheromone update to filtrate solution candidates. Global optimal solutions can be reached more rapidly by self-adjusting the path searching behaviors of the ants according to objective values. The performance of the proposed algorithm is compared with a basic ant colony algorithm and a Square Quadratic Programming approach in solving two benchmark problems with multiple extremes. The results indicated that the efficiency and reliability of the proposed algorithm were greatly improved.
An adaptive ant colony system algorithm for continuous-space optimization problems
Institute of Scientific and Technical Information of China (English)
李艳君; 吴铁军
2003-01-01
Ant colony algorithms comprise a novel category of evolutionary computation methods for optimization problems, especially for sequencing-type combinatorial optimization problems. An adaptive ant colony algorithm is proposed in this paper to tackle continuous-space optimization problems, using a new objective-function-based heuristic pheromone assignment approach for pheromone update to filtrate solution candidates.Global optimal solutions can be reached more rapidly by self-adjusting the path searching behaviors of the ants according to objective values. The performance of the proposed algorithm is compared with a basic ant colony algorithm and a Square Quadratic Programming approach in solving two benchmark problems with multiple extremes. The results indicated that the efficiency and reliability of the proposed algorithm were greatly improved.
Optimization design of drilling string by screw coal miner based on ant colony algorithm
Institute of Scientific and Technical Information of China (English)
ZHANG Qiang; MAO Jun; DING Fei
2008-01-01
It took that the weight minimum and drive efficiency maximal were as double optimizing target, the optimization model had built the drilling string, and the optimization solution was used of the ant colony algorithm to find in progress. Adopted a two-layer search of the continuous space ant colony algorithm with overlapping or variation global ant search operation strategy and conjugated gradient partial ant search operation strat-egy. The experiment indicates that the spiral drill weight reduces 16.77% and transports the efficiency enhance 7.05% through the optimization design, the ant colony algorithm application on the spiral drill optimized design has provided the basis for the system re-search screw coal mine machine.
Optimization design of drilling string by screw coal miner based on ant colony algorithm
Institute of Scientific and Technical Information of China (English)
ZHANG Qiang; MAO Jun; DING Fei
2008-01-01
It took that the weight minimum and drive efficiency maximal were as double optimizing target,the optimization model had built the drilling string,and the optimization solution was used of the ant colony algorithm to find in progress.Adopted a two-layer search of the continuous space ant colony algorithm with overlapping or variation global ant search operation strategy and conjugated gradient partial ant search operation strategy.The experiment indicates that the spiral drill weight reduces 16.77% and transports the efficiency enhance 7.05% through the optimization design,the ant colony algorithm application on the spiral drill optimized design has provided the basis for the system research screw coal mine machine.
Zhang, B.; Qi, H.; Ren, Y. T.; Sun, S. C.; Ruan, L. M.
2014-01-01
As a heuristic intelligent optimization algorithm, the Ant Colony Optimization (ACO) algorithm was applied to the inverse problem of a one-dimensional (1-D) transient radiative transfer in present study. To illustrate the performance of this algorithm, the optical thickness and scattering albedo of the 1-D participating slab medium were retrieved simultaneously. The radiative reflectance simulated by Monte-Carlo Method (MCM) and Finite Volume Method (FVM) were used as measured and estimated value for the inverse analysis, respectively. To improve the accuracy and efficiency of the Basic Ant Colony Optimization (BACO) algorithm, three improved ACO algorithms, i.e., the Region Ant Colony Optimization algorithm (RACO), Stochastic Ant Colony Optimization algorithm (SACO) and Homogeneous Ant Colony Optimization algorithm (HACO), were developed. By the HACO algorithm presented, the radiative parameters could be estimated accurately, even with noisy data. In conclusion, the HACO algorithm is demonstrated to be effective and robust, which had the potential to be implemented in various fields of inverse radiation problems.
Karla Vittori; Alexandre C B Delbem; Pereira, Sérgio L
2008-01-01
We propose a new distance algorithm for phylogenetic estimation based on Ant Colony Optimization (ACO), named Ant-Based Phylogenetic Reconstruction (ABPR). ABPR joins two taxa iteratively based on evolutionary distance among sequences, while also accounting for the quality of the phylogenetic tree built according to the total length of the tree. Similar to optimization algorithms for phylogenetic estimation, the algorithm allows exploration of a larger set of nearly optimal solutions. We appl...
A clustering routing algorithm based on improved ant colony clustering for wireless sensor networks
Xiao, Xiaoli; Li, Yang
Because of real wireless sensor network node distribution uniformity, this paper presents a clustering strategy based on the ant colony clustering algorithm (ACC-C). To reduce the energy consumption of the head near the base station and the whole network, The algorithm uses ant colony clustering on non-uniform clustering. The improve route optimal degree is presented to evaluate the performance of the chosen route. Simulation results show that, compared with other algorithms, like the LEACH algorithm and the improve particle cluster kind of clustering algorithm (PSC - C), the proposed approach is able to keep away from the node with less residual energy, which can improve the life of networks.
An Ant Colony Optimization Algorithm for Microwave Corrugated Filters Design
Mantilla-Gaviria, Ivan A.; Alejandro Díaz-Morcillo; Balbastre-Tejedor, Juan V.
2013-01-01
A practical and useful application of the Ant Colony Optimization (ACO) method for microwave corrugated filter design is shown. The classical, general purpose ACO method is adapted to deal with the microwave filter design problem. The design strategy used in this paper is an iterative procedure based on the use of an optimization method along with an electromagnetic simulator. The designs of high-pass and band-pass microwave rectangular waveguide filters working in the C-band and X-band, res...
Chaudhuri, Arindam
2013-01-01
We present a dynamic algorithm for solving the Longest Common Subsequence Problem using Ant Colony Optimization Technique. The Ant Colony Optimization Technique has been applied to solve many problems in Optimization Theory, Machine Learning and Telecommunication Networks etc. In particular, application of this theory in NP-Hard Problems has a remarkable significance. Given two strings, the traditional technique for finding Longest Common Subsequence is based on Dynamic Programming which cons...
Application of ant colony algorithm in plant leaves classification based on infrared spectroscopy
Guo, Tiantai; Hong, Bo; Kong, Ming; Zhao, Jun
2014-04-01
This paper proposes to use ant colony algorithm in the analysis of spectral data of plant leaves to achieve the best classification of different plants within a short time. Intelligent classification is realized according to different components of featured information included in near infrared spectrum data of plants. The near infrared diffusive emission spectrum curves of the leaves of Cinnamomum camphora and Acer saccharum Marsh are acquired, which have 75 leaves respectively, and are divided into two groups. Then, the acquired data are processed using ant colony algorithm and the same kind of leaves can be classified as a class by ant colony clustering algorithm. Finally, the two groups of data are classified into two classes. Experiment results show that the algorithm can distinguish different species up to the percentage of 100%. The classification of plant leaves has important application value in agricultural development, research of species invasion, floriculture etc.
Institute of Scientific and Technical Information of China (English)
Wang Yanxia; Qian Longjun; Guo Zhi; Ma Lifeng
2008-01-01
A weapon target assignment (WTA) model satisfying expected damage probabilities with an ant colony algorithm is proposed.In order to save armament resource and attack the targets effectively,the strategy of the weapon assignment is that the target with greater threat degree has higher priority to be intercepted.The effect of this WTA model is not maximizing the damage probability but satisfying the whole assignment result.Ant colony algorithm has been successfully used in many fields,especially in combination optimization.The ant colony algorithm for this WTA problem is described by analyzing path selection,pheromone update,and tabu table update.The effectiveness of the model and the algorithm is demonstrated with an example.
Novel Approach to Nonlinear PID Parameter Optimization Using Ant Colony Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
Duan Hai-bin; Wang Dao-bo; Yu Xiu-fen
2006-01-01
This paper presents an application of an Ant Colony Optimization (ACO) algorithm to optimize the parameters in the design of a type of nonlinear PID controller. The ACO algorithm is a novel heuristic bionic algorithm, which is based on the behaviour of real ants in nature searching for food. In order to optimize the parameters of the nonlinear PID controller using ACO algorithm,an objective function based on position tracing error was constructed, and elitist strategy was adopted in the improved ACO algorithm. Detailed simulation steps are presented. This nonlinear PID controller using the ACO algorithm has high precision of control and quick response.
Directory of Open Access Journals (Sweden)
Jiang Ting
2010-01-01
Full Text Available We optimize the cluster structure to solve problems such as the uneven energy consumption of the radar sensor nodes and random cluster head selection in the traditional clustering routing algorithm. According to the defined cost function for clusters, we present the clustering algorithm which is based on radio-free space path loss. In addition, we propose the energy and distance pheromones based on the residual energy and aggregation of the radar sensor nodes. According to bionic heuristic algorithm, a new ant colony-based clustering algorithm for radar sensor networks is also proposed. Simulation results show that this algorithm can get a better balance of the energy consumption and then remarkably prolong the lifetime of the radar sensor network.
Leite, Walter L.; Huang, I-Chan; Marcoulides, George A.
2008-01-01
This article presents the use of an ant colony optimization (ACO) algorithm for the development of short forms of scales. An example 22-item short form is developed for the Diabetes-39 scale, a quality-of-life scale for diabetes patients, using a sample of 265 diabetes patients. A simulation study comparing the performance of the ACO algorithm and…
Ant Colony Algorithm and Simulation for Robust Airport Gate Assignment
Directory of Open Access Journals (Sweden)
Hui Zhao
2014-01-01
Full Text Available Airport gate assignment is core task for airport ground operations. Due to the fact that the departure and arrival time of flights may be influenced by many random factors, the airport gate assignment scheme may encounter gate conflict and many other problems. This paper aims at finding a robust solution for airport gate assignment problem. A mixed integer model is proposed to formulate the problem, and colony algorithm is designed to solve this model. Simulation result shows that, in consideration of robustness, the ability of antidisturbance for airport gate assignment scheme has much improved.
Directory of Open Access Journals (Sweden)
Puneet Rai
2014-02-01
Full Text Available Ant Colony Optimization (ACO is nature inspired algorithm based on foraging behavior of ants. The algorithm is based on the fact how ants deposit pheromone while searching for food. ACO generates a pheromone matrix which gives the edge information present at each pixel position of image, formed by ants dispatched on image. The movement of ants depends on local variance of image's intensity value. This paper proposes an improved method based on heuristic which assigns weight to the neighborhood. Thus by assigning the weights or priority to the neighboring pixels, the ant decides in which direction it can move. The method is applied on Medical images and experimental results are provided to support the superior performance of the proposed approach and the existing method.
A Multi Ant Colony Optimization algorithm for a Mixed Car Assembly Line
Pulido, Raúl; García Sánchez, Álvaro; Diego, Francisco Javier; Andrés-Romano, Carlos
2013-01-01
This paper presents an ant colony optimization algorithm to sequence the mixed assembly lines considering the inventory and the replenishment of components. This is a NP-problem that cannot be solved to optimality by exact methods when the size of the problem growth. Groups of specialized ants are implemented to solve the different parts of the problem. This is intended to differentiate each part of the problem. Different types of pheromone structures are created to identify good car sequence...
Pixel-based ant colony algorithm for source mask optimization
Kuo, Hung-Fei; Wu, Wei-Chen; Li, Frederick
2015-03-01
Source mask optimization (SMO) was considered to be one of the key resolution enhancement techniques for node technology below 20 nm prior to the availability of extreme-ultraviolet tools. SMO has been shown to enlarge the process margins for the critical layer in SRAM and memory cells. In this study, a new illumination shape optimization approach was developed on the basis of the ant colony optimization (ACO) principle. The use of this heuristic pixel-based ACO method in the SMO process provides an advantage over the extant SMO method because of the gradient of the cost function associated with the rapid and stable searching capability of the proposed method. This study was conducted to provide lithographic engineers with references for the quick determination of the optimal illumination shape for complex mask patterns. The test pattern used in this study was a contact layer for SRAM design, with a critical dimension and a minimum pitch of 55 and 110 nm, respectively. The optimized freeform source shape obtained using the ACO method was numerically verified by performing an aerial image investigation, and the result showed that the optimized freeform source shape generated an aerial image profile different from the nominal image profile and with an overall error rate of 9.64%. Furthermore, the overall average critical shape difference was determined to be 1.41, which was lower than that for the other off-axis illumination exposure. The process window results showed an improvement in exposure latitude (EL) and depth of focus (DOF) for the ACO-based freeform source shape compared with those of the Quasar source shape. The maximum EL of the ACO-based freeform source shape reached 7.4% and the DOF was 56 nm at an EL of 5%.
An ant colony based algorithm for overlapping community detection in complex networks
Zhou, Xu; Liu, Yanheng; Zhang, Jindong; Liu, Tuming; Zhang, Di
2015-06-01
Community detection is of great importance to understand the structures and functions of networks. Overlap is a significant feature of networks and overlapping community detection has attracted an increasing attention. Many algorithms have been presented to detect overlapping communities. In this paper, we present an ant colony based overlapping community detection algorithm which mainly includes ants' location initialization, ants' movement and post processing phases. An ants' location initialization strategy is designed to identify initial location of ants and initialize label list stored in each node. During the ants' movement phase, the entire ants move according to the transition probability matrix, and a new heuristic information computation approach is redefined to measure similarity between two nodes. Every node keeps a label list through the cooperation made by ants until a termination criterion is reached. A post processing phase is executed on the label list to get final overlapping community structure naturally. We illustrate the capability of our algorithm by making experiments on both synthetic networks and real world networks. The results demonstrate that our algorithm will have better performance in finding overlapping communities and overlapping nodes in synthetic datasets and real world datasets comparing with state-of-the-art algorithms.
An ant colony algorithm for the sequential testing problem under precedence constraints
Çatay, Bülent; Catay, Bulent; Özlük, Özgür; Ozluk, Ozgur; Ünlüyurt, Tonguç; Unluyurt, Tonguc
2008-01-01
We consider the problem of minimum cost sequential testing of a series (parallel) system under precedence constraints that can be modeled as a nonlinear integer program. We develop and implement an ant colony algorithm for the problem. We demonstrate the performance of this algorithm for special type of instances for which the optimal solutions can be found in polynomial time. In addition, we compare the performance of the algorithm with a special branch and bound algo...
MOEA/D-ACO: a multiobjective evolutionary algorithm using decomposition and AntColony.
Ke, Liangjun; Zhang, Qingfu; Battiti, Roberto
2013-12-01
Combining ant colony optimization (ACO) and the multiobjective evolutionary algorithm (EA) based on decomposition (MOEA/D), this paper proposes a multiobjective EA, i.e., MOEA/D-ACO. Following other MOEA/D-like algorithms, MOEA/D-ACO decomposes a multiobjective optimization problem into a number of single-objective optimization problems. Each ant (i.e., agent) is responsible for solving one subproblem. All the ants are divided into a few groups, and each ant has several neighboring ants. An ant group maintains a pheromone matrix, and an individual ant has a heuristic information matrix. During the search, each ant also records the best solution found so far for its subproblem. To construct a new solution, an ant combines information from its group's pheromone matrix, its own heuristic information matrix, and its current solution. An ant checks the new solutions constructed by itself and its neighbors, and updates its current solution if it has found a better one in terms of its own objective. Extensive experiments have been conducted in this paper to study and compare MOEA/D-ACO with other algorithms on two sets of test problems. On the multiobjective 0-1 knapsack problem,MOEA/D-ACO outperforms the MOEA/D with conventional genetic operators and local search on all the nine test instances. We also demonstrate that the heuristic information matrices in MOEA/D-ACO are crucial to the good performance of MOEA/D-ACO for the knapsack problem. On the biobjective traveling salesman problem, MOEA/D-ACO performs much better than the BicriterionAnt on all the 12 test instances. We also evaluate the effects of grouping, neighborhood, and the location information of current solutions on the performance of MOEA/D-ACO. The work in this paper shows that reactive search optimization scheme, i.e., the "learning while optimizing" principle, is effective in improving multiobjective optimization algorithms. PMID:23757576
An ant colony optimization based algorithm for identifying gene regulatory elements.
Liu, Wei; Chen, Hanwu; Chen, Ling
2013-08-01
It is one of the most important tasks in bioinformatics to identify the regulatory elements in gene sequences. Most of the existing algorithms for identifying regulatory elements are inclined to converge into a local optimum, and have high time complexity. Ant Colony Optimization (ACO) is a meta-heuristic method based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of real ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper designs and implements an ACO based algorithm named ACRI (ant-colony-regulatory-identification) for identifying all possible binding sites of transcription factor from the upstream of co-expressed genes. To accelerate the ants' searching process, a strategy of local optimization is presented to adjust the ants' start positions on the searched sequences. By exploiting the powerful optimization ability of ACO, the algorithm ACRI can not only improve precision of the results, but also achieve a very high speed. Experimental results on real world datasets show that ACRI can outperform other traditional algorithms in the respects of speed and quality of solutions. PMID:23746735
Liu Xinyu; Wang Yupeng; Robbins Kelly; Rekaya Romdhane
2010-01-01
Abstract Background Epistatic interactions of multiple single nucleotide polymorphisms (SNPs) are now believed to affect individual susceptibility to common diseases. The detection of such interactions, however, is a challenging task in large scale association studies. Ant colony optimization (ACO) algorithms have been shown to be useful in detecting epistatic interactions. Findings AntEpiSeeker, a new two-stage ant colony optimization algorithm, has been developed for detecting epistasis in ...
Novel Voltage Scaling Algorithm Through Ant Colony Optimization for Embedded Distributed Systems
Institute of Scientific and Technical Information of China (English)
ZHANG Li-sheng; DING Dan
2007-01-01
Dynamic voltage scaling (DVS), supported by many DVS-enabled processors, is an efficient technique for energy-efficient embedded systems. Many researchers work on DVS and have presented various DVS algorithms, some with quite good results . However, the previous algorithms either have a large time complexity or obtain results sensitive to the count of the voltage modes. Fine-grained voltage modes lead to optimal results, but coarse-grained voltage modes cause less optimal one. A new algorithm is presented, which is based on ant colony optimization, called ant colony optimization voltage and task scheduling (ACO-VTS) with a low time complexity implemented by parallelizing and its linear time approximation algo rithm. Both of them generate quite good results, saving up to 30% more energy than that of the previous ones under coarse-grained modes, and their results don't depend on the number of modes available.
Ant- and Ant-Colony-Inspired ALife Visual Art.
Greenfield, Gary; Machado, Penousal
2015-01-01
Ant- and ant-colony-inspired ALife art is characterized by the artistic exploration of the emerging collective behavior of computational agents, developed using ants as a metaphor. We present a chronology that documents the emergence and history of such visual art, contextualize ant- and ant-colony-inspired art within generative art practices, and consider how it relates to other ALife art. We survey many of the algorithms that artists have used in this genre, address some of their aims, and explore the relationships between ant- and ant-colony-inspired art and research on ant and ant colony behavior. PMID:26280070
Nourelfath, M.; Nahas, N.; Montreuil, B.
2007-12-01
This article uses a hybrid optimization approach to solve the discrete facility layout problem (FLP), modelled as a quadratic assignment problem (QAP). The idea of this approach design is inspired by the ant colony meta-heuristic optimization method, combined with the extended great deluge (EGD) local search technique. Comparative computational experiments are carried out on benchmarks taken from the QAP-library and from real life problems. The performance of the proposed algorithm is compared to construction and improvement heuristics such as H63, HC63-66, CRAFT and Bubble Search, as well as other existing meta-heuristics developed in the literature based on simulated annealing (SA), tabu search and genetic algorithms (GAs). This algorithm is compared also to other ant colony implementations for QAP. The experimental results show that the proposed ant colony optimization/extended great deluge (ACO/EGD) performs significantly better than the existing construction and improvement algorithms. The experimental results indicate also that the ACO/EGD heuristic methodology offers advantages over other algorithms based on meta-heuristics in terms of solution quality.
Adaptive Search Protocol Based on Optimized Ant Colony Algorithm in Peer-to-Peer Network
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Chun-Ying Liu
2013-04-01
Full Text Available In order to solve the low searching efficiency in the peer-to-peer (P2P network, introduce the ant colony algorithm with the particle swarm optimization in searching procedure. Present a new adaptive search protocol (SACASP based on the ant colony algorithm with the particle swarm optimization in the Peer-to-Peer Network. The approach simulates the process of the ants’ searching food, and can direct the query routing efficiently according to the adaptive strategy and the positive feedback principle of the pheromone. Decrease the blindness of the messages transmitting in early searching stage by adding the particle swarm optimization to the ant colony algorithm. Give the adaptive P2P search model based on the fusion algorithm, and design the data structure and steps of the model. The simulation experiment shows, PSACASP can effectively shorten the time and reduce the search query packets comparing with the other search algorithms, and it can achieve better search performance and decrease the network loads.
Selective Marketing for Retailers to promote Stock using improved Ant Colony Algorithm
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S.SURIYA
2013-10-01
Full Text Available Data mining is a knowledge discovery process which deals with analysing large storage of data in order to identify the relevant data. It is a powerful tool to uncover relationships within the data.Association rule mining is an important data mining model to mine frequent items in huge repository of data. It frames out association rules with the help of minimum support and confidence value which inturns paves way to identify the occurrence of frequent item sets. Frequent pattern mining starts from analysis of customers buying habits. From which various associations between the different items that the customers purchase are identified. With the help of such associations retailers perform selective marketing to promote their business. Biologically inspired algorithms have their process observed in nature as their origin. The best feature of Ant colony algorithm, which is a bio inspired algorithm based on the behaviour of natural ant colonies, is its parallel search over the problem data and previously obtained results from it. Dynamic memory management is done by pheromone updating operation. During each cycle, solutions are constructed by evaluation of the transition probability throughpheromone level modification. An improved pheromone updating rule is used to find out all the frequent items. The proposed approach was tested using MATLAB along with WEKA toolkit. The experimental results prove that the stigmeric communication of improved ant colony algorithm helps in mining the frequent items faster and effectively than the existing algorithms.
Institute of Scientific and Technical Information of China (English)
DUAN Hai-bin; WANG Dao-bo; YU Xiu-fen
2006-01-01
Although ant colony algorithm for the heuristic solution of hard combinational optimization problems enjoy a rapidly growing popularity, but little is known about its convergence properties. Based on the introduction of the basic principle and mathematical model, a novel approach to the convergence proof that applies directly to the ant colony algorithm is proposed in this paper. Then, a MATLAB GUI- based ant colony algorithm simulation platform is developed, and the interface of this simulation platform is very friendly, easy to use and to modify.
Institute of Scientific and Technical Information of China (English)
KE Xi-zheng; HE Hua; WU Chang-li
2011-01-01
Aiming at the unidirectional links coming from nodes with different transmitting power and the obstacle blocking in UV mesh wireless communication network and the traditional ant colony algorithm only supporting bidirectional links, a new ant colony based routing algorithm with unidirectional link in UV mesh communication wireless network is proposed. The simulation results show that the proposed algorithm can improve the overall network connectivity and the survivability by supporting the combination of unidirectional link and bidirectional link.
A Schedule Optimization Model on Multirunway Based on Ant Colony Algorithm
Yu Jiang; Zhaolong Xu; Xinxing Xu; Zhihua Liao; Yuxiao Luo
2014-01-01
In order to make full use of the slot of runway, reduce flight delay, and ensure fairness among airlines, a schedule optimization model for arrival-departure flights is established in the paper. The total delay cost and fairness among airlines are two objective functions. The ant colony algorithm is adopted to solve this problem and the result is more efficient and reasonable when compared with FCFS (first come first served) strategy. Optimization results show that the flight delay and fair d...
Improvement to the cooperative rules methodology by using the ant colony system algorithm
Alcalá Fernández, Rafael; Casillas Barranquero, Jorge; Cordón García, Oscar; Herrera Triguero, Francisco
2001-01-01
The cooperative rules (COR) methodology [2] is based on a combinatorial search of cooperative rules performed over a set of previously generated candidate rule consequents. It obtains accurate models preserving the highest interpretability of the linguistic fuzzy rule-based systems. Once the good behavior of the COR methodology has been proven in previous works, this contribution focuses on developing the process with a novel kind of metaheuristic algorithm: the ant colony system one. ...
A hierarchical classification ant colony algorithm for predicting gene ontology terms
Otero, Fernando E. B.; Freitas, Alex. A.; Johnson, Colin G.
2009-01-01
This paper proposes a novel Ant Colony Optimisation algorithm for the hierarchical problem of predicting protein functions using the Gene Ontology (GO). The GO structure represents a challenging case of hierarchical classification, since its terms are organised in a direct acyclic graph fashion where a term can have more than one parent in contrast to only one parent in tree structures. The proposed method discovers an ordered list of classification rules which is able to predict all GO terms...
Žumer, Viljem; Brest, Janez; Pešl, Ivan
2015-01-01
Ant colony optimization is a relatively new approach to solving NP-Hard problems. It is based on the behavior of real ants, which always find the shortest path between their nest and a food source. Such behavior can be transferred into the discrcte world, were real ants are replaced by simple agents. Such simple agents are placed into the environment where different combinatorial problems can be solved In this paper we describe an artificial ant colony capable of solving the travelling salesm...
Search tree-based approach for the p-median problem using the ant colony optimization algorithm
Gabriel Bodnariuc; Sergiu Cataranciuc
2014-01-01
In this paper we present an approximation algorithm for the $p$-median problem that uses the principles of ant colony optimization technique. We introduce a search tree that keeps the partial solutions during the solution process of the $p$-median problem. An adaptation is proposed that allows ant colony optimization algorithm to perform on this tree and obtain good results in short time.
Polygon star identification based on ant colony algorithm
Ma, Baolin; Wu, Jie; Zhang, Hongbo
2014-11-01
In order to enhance the rate of star identification under different view fields and reduce memory storage, this paper presents a polygon star identification based on ACO algorithm .First, fast cluster analysis. Second, calculate argument for each guide star, using the advantages of ACO in fast path optimization to complete building feature polygon. Third, comparing optimization results and optimization data of guide database to realize match and identifying. Through the simulation shows that the above method can simplify searching process and structure of storage. It can promise the completeness of characteristic patterns of star image. The robustness and reliability are better than traditional triangle identification.
Ant colony optimization in continuous problem
Institute of Scientific and Technical Information of China (English)
YU Ling; LIU Kang; LI Kaishi
2007-01-01
Based on the analysis of the basic ant colony optimization and optimum problem in a continuous space,an ant colony optimization (ACO) for continuous problem is constructed and discussed. The algorithm is efficient and beneficial to the study of the ant colony optimization in a continuous space.
Optimization of China Crude Oil Transportation Network with Genetic Ant Colony Algorithm
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Yao Wang
2015-08-01
Full Text Available Taking into consideration both shipping and pipeline transport, this paper first analysed the risk factors for different modes of crude oil import transportation. Then, based on the minimum of both transportation cost and overall risk, a multi-objective programming model was established to optimize the transportation network of crude oil import, and the genetic algorithm and ant colony algorithm were employed to solve the problem. The optimized result shows that VLCC (Very Large Crude Carrier is superior in long distance sea transportation, whereas pipeline transport is more secure than sea transport. Finally, this paper provides related safeguard suggestions on crude oil import transportation.
An Energy Aware Ant Colony Algorithm for the Routing of Wireless Sensor Networks
Cheng, Deqiang; Xun, Yangyang; Zhou, Ting; Li, Wenjie
Based on the characteristics of routing protocol for wireless sensor networks, an energy aware ant colony algorithm (EAACA) for the routing of wireless sensor networks is proposed in this paper. When EAACA routing protocol chooses the next neighbor node, not only the distance of sink node, but also the residual energy of the next node and the path of the average energy are taken into account. Theoretical analysis and simulation results show that compared with the traditional ACA algorithm for the routing of wireless sensor network, EAACA routing protocol balances the energy consumption of nodes in the network and extends the network lifetime.
Event Space-Correlation Analysis Algorithm Based on Ant Colony Optimization
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Mingsheng Hu
2013-03-01
Full Text Available Historical disaster events are taken as a case for space-correlation analysis, three-dimensional disasters space-time network are modeled and chain relationship of disaster nodes are mined by looking for similar space vector in network. Then transformed the vector discover problem into a path optimization problem and solved by using ant colony algorithm, where the pheromone parameter in the process of optimal-path finding is concerned as the algorithm result, in order to solve the problem of path competition which existed when only to solve the optimal path. Experimental results of MATLAB show that this method has high accuracy and practicality.
Ant Colony Algorithm and Optimization of Test Conditions in Analytical Chemistry
Institute of Scientific and Technical Information of China (English)
丁亚平; 吴庆生; 苏庆德
2003-01-01
The research for the new algorithm is in the forward position and an issue of general interest in chemometrics all along.A novel chemometrics method,Chemical Ant Colony Algorithm,has first been developed.In this paper,the basic principle,theevaluation function,and the parameter choice were discussed.This method has been successfully applied to the fitting of nonlinear multivariate function and the optimization of test conditions in chrome-azure-S-Al spctrophotometric system.The sum of residual square of the results is 0.0009,which has reached a good convergence result.
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Karla Vittori
2008-12-01
Full Text Available We propose a new distance algorithm for phylogenetic estimation based on Ant Colony Optimization (ACO, named Ant-Based Phylogenetic Reconstruction (ABPR. ABPR joins two taxa iteratively based on evolutionary distance among sequences, while also accounting for the quality of the phylogenetic tree built according to the total length of the tree. Similar to optimization algorithms for phylogenetic estimation, the algorithm allows exploration of a larger set of nearly optimal solutions. We applied the algorithm to four empirical data sets of mitochondrial DNA ranging from 12 to 186 sequences, and from 898 to 16,608 base pairs, and covering taxonomic levels from populations to orders. We show that ABPR performs better than the commonly used Neighbor-Joining algorithm, except when sequences are too closely related (e.g., population-level sequences. The phylogenetic relationships recovered at and above species level by ABPR agree with conventional views. However, like other algorithms of phylogenetic estimation, the proposed algorithm failed to recover expected relationships when distances are too similar or when rates of evolution are very variable, leading to the problem of long-branch attraction. ABPR, as well as other ACO-based algorithms, is emerging as a fast and accurate alternative method of phylogenetic estimation for large data sets.
Automatic pressurized water reactor loading pattern design using ant colony algorithms
International Nuclear Information System (INIS)
Highlights: ► An automatic core reload design tool was developed for a pressurized water reactor. ► Three different algorithms, i.e., the rank-based ant system, max–min ant system, and Ant-Q are adopted. ► Safety requirements are formulated as penalty terms of the quality function. ► Firstly, fuel assemblies are permutated to some degree and then fuel assemblies are rotated. - Abstract: An automatic core reload design tool was developed for a pressurized water reactor (PWR). A loading pattern (LP) was searched for using three different algorithms: the rank-based ant system (RAS), max–min ant system (MMAS), and Ant-Q which are variants of the ant colony algorithm. The fuel assemblies (FAs) were permuted in a one eighth core position and then the LP was copied to the other one eighth core with mirror symmetry, to form a quarter core LP, which was extended to a full core LP with rotational symmetry. Heuristic information was implemented to reduce search space and thus computation time. Safety requirements, such as the hot channel factor FΔH and moderator temperature coefficient (MTC), which must be satisfied, were formulated as penalty terms of the quality function. The search procedure contained two steps. The first step was to place the FA so that FΔH and MTC might be slightly violated, and the second step was to rotate the FA, which would improve the FΔH and MTC and the fuel cycle length. When the LP was designed, the SIMULATE-3 code calculated the FΔH, MTC, and cycle length, which were used to update the pheromone. The results demonstrated that the developed tool can obtain a LP which possesses the desired cycle length and also satisfies safety requirements.
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Lamiaa F. Ibrahim
2011-01-01
Full Text Available Problem statement: The process of network planning is divided into two sub steps. The first step is determining the location of the Multi Service Access Node (MSAN. The second step is the construction of subscriber network lines from MSAN to subscribers to satisfy optimization criteria and design constraints. Due to the complexity of this process artificial intelligence and clustering techniques have been successfully deployed to solve many problems. The problems of the locations of MSAN, the cabling layout and the computation of optimum cable network layouts have been addressed in this study. The proposed algorithm, Clustering density-Based Spatial of Applications with Noise original, minimal Spanning tree and modified Ant-Colony-Based algorithm (CBSCAN-SPANT, used two clustering algorithms which are density-based and agglomerative clustering algorithm using distances which are shortest paths distance and satisfying the network constraints. This algorithm used wire and wireless technology to serve the subscribers demand and place the switches in a real optimal place. Approach: The density-based Spatial Clustering of Applications with Noise original (DBSCAN algorithm has been modified and a new algorithm (NetPlan algorithm has been proposed by the author in a recent work to solve the first step in the problem of network planning. In the present study, the NetPlan algorithm is modified by introduce the modified Ant-Colony-Based algorithm to find the optimal path between any node and the corresponding MSAN node in the first step of network planning process to determine nodes belonging to each cluster. The second step, in the process of network planning, is also introduced in the present study. For each cluster, the optimal cabling layout from each MSAN to the subscriber premises is determining by introduce the Prime algorithm which construct minimal spanning tree. Results: Experimental results and analysis indicate that the
基于异类蚁群的双种群蚁群算法%Dual population ant colony algorithm based on heterogeneous ant colonies
Institute of Scientific and Technical Information of China (English)
何雪莉; 张鹏; 马苗; 林杰; 黄鑫
2009-01-01
提出一种基于异类蚁群的双种群蚁群(Dual Population Ant Colony Algorithm Based on Heterogeneous Ant Colonies,DPACBH)算法,算法将两种信息素更新机制不同的蚁群分别独立进行进化求解,并定期交换优良解和信息来改善解的多样性,增强跳出局部最优的能力,使算法更容易收敛到全局最优解.以TSP(Travel Salesman Problem)问题为例所进行的计算表明,该算法比基本双种群蚁群算法具有更好的收敛速度和准确性.
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Milinkovitch Michel C
2007-11-01
Full Text Available Abstract Background Distance matrix methods constitute a major family of phylogenetic estimation methods, and the minimum evolution (ME principle (aiming at recovering the phylogeny with shortest length is one of the most commonly used optimality criteria for estimating phylogenetic trees. The major difficulty for its application is that the number of possible phylogenies grows exponentially with the number of taxa analyzed and the minimum evolution principle is known to belong to the NP MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGacaGaaiaabeqaaeqabiWaaaGcbaWenfgDOvwBHrxAJfwnHbqeg0uy0HwzTfgDPnwy1aaceaGae8xdX7Kaeeiuaafaaa@3888@-hard class of problems. Results In this paper, we introduce an Ant Colony Optimization (ACO algorithm to estimate phylogenies under the minimum evolution principle. ACO is an optimization technique inspired from the foraging behavior of real ant colonies. This behavior is exploited in artificial ant colonies for the search of approximate solutions to discrete optimization problems. Conclusion We show that the ACO algorithm is potentially competitive in comparison with state-of-the-art algorithms for the minimum evolution principle. This is the first application of an ACO algorithm to the phylogenetic estimation problem.
cAnt-Miner: an ant colony classification algorithm to cope with continuous attributes
Otero, Fernando E.B.; Freitas, Alex. A.; Johnson, Colin G.
2008-01-01
This paper presents an extension to Ant-Miner, named cAnt-Miner (Ant-Miner coping with continuous attributes), which incorporates an entropy-based discretization method in order to cope with continuous attributes during the rule construction process. By having the ability to create discrete intervals for continuous attributes "on-the-fly", cAnt-Miner does not requires a discretization method in a preprocessing step, as Ant-Miner requires. cAnt-Miner has been compared against Ant-Miner in eigh...
Ant colony optimization algorithm and its application to Neuro-Fuzzy controller design
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
An adaptive ant colony algorithm is proposed based on dynamically adjusting the strategy of updating trail information.The algorithm can keep good balance between accelerating convergence and averting precocity and stagnation.The results of function optimization show that the algorithm has good searching ability and high convergence speed.The algorithm is employed to design a neuro-fuzzy controller for real-time control of an inverted pendulum.In order to avoid the combinatorial explosion of fuzzy.rules due to multivariable inputs,a state variable synthesis scheme is emploved to reduce the number of fuzzy rules greatly.The simulation results show that the designed controller can control the inverted pendulum successfully.
Zhang, Hong; Sun, Yanfeng; Zhai, Bing; Wang, Yiding
2013-07-01
This paper studies on the image registration of the medical images. Wavelet transform is adopted to decompose the medical images because the resolution of the medical image is high and the computational amount of the registration is large. Firstly, the low frequency sub-images are matched. Then source images are matched. The image registration was fulfilled by the ant colony optimization algorithm to search the extremum of the mutual information. The experiment result demonstrates the proposed approach can not only reduce calculation amount, but also skip from the local extremum during optimization process, and search the optimization value.
CACER:A Novel E-commerce Recommendation Model Based on Crazy Ant Colony Algorithms
Institute of Scientific and Technical Information of China (English)
王征; 刘庆强
2013-01-01
In order to deal with the problems of E-commerce online marketing, a novel E-commerce recommendation system model was given to lead consumers to efficient retrieval and consumption. And the system model was built with a crazy ant colony algorithm. Then its model, message structures and working flows were presented as following. At last, an application example and compared results were given to be analyzed. Simulation results show the model can perform better in real-time and customer satisfaction than the olds do.
Ant colony system algorithm for the optimization of beer fermentation control
Institute of Scientific and Technical Information of China (English)
肖杰; 周泽魁; 张光新
2004-01-01
Beer fermentation is a dynamic process that must be guided along a temperature profile to obtain the desired results. Ant colony system algorithm was applied to optimize the kinetic model of this process. During a fixed period of fermentation time, a series of different temperature profiles of the mixture were constructed. An optimal one was chosen at last. Optimal temperature profile maximized the final ethanol production and minimized the byproducts concentration and spoilage risk. The satisfactory results obtained did not require much computation effort.
DANTE - The combination between an ant colony optimization algorithm and a depth search method
Cardoso, Pedro J. S.; Jesus, Mário Carlos Machado; Marquez, A.
2008-01-01
The ε-DANTE method is an hybrid meta-heuristic. In combines the evolutionary Ant Colony Optimization (ACO) algorithms with a limited Depth Search. This Depth Search is based in the pheromone trails used by the ACO, which allows it to be oriented to the more promising areas of the search space. Some results are presented for the multiple objective k-Degree Spanning Trees problem, proving the effectiveness of the method when compared with other already tested evolutionary methods. © 2008 IEEE.
An Improved Ant Colony Algorithm for a Single-machine Scheduling Problem with Setup Times
Institute of Scientific and Technical Information of China (English)
YE Qiang; LIU Xinbao; LIU Lin; YANG Shanglin
2006-01-01
Motivated by industrial applications we study a single-machine scheduling problem in which all the jobs are mutually independent and available at time zero. The machine processes the jobs sequentially and it is not idle if there is any job to be processed. The operation of each job cannot be interrupted. The machine cannot process more than one job at a time. A setup time is needed if the machine switches from one type of job to another. The objective is to find an optimal schedule with the minimal total jobs' completion time. While the sum of jobs' processing time is always a constant, the objective is to minimize the sum of setup times. Ant colony optimization (ACO) is a meta-heuristic that has recently been applied to scheduling problem. In this paper we propose an improved ACO-Branching Ant Colony with Dynamic Perturbation (DPBAC) algorithm for the single-machine scheduling problem. DPBAC improves traditional ACO in following aspects: introducing Branching Method to choose starting points; improving state transition rules; introducing Mutation Method to shorten tours; improving pheromone updating rules and introducing Conditional Dynamic Perturbation Strategy. Computational results show that DPBAC algorithm is superior to the traditional ACO algorithm.
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Kanchan Singla
2014-06-01
Full Text Available MC CDMA is a rising candidate for future generation broadband wireless communication and gained great attention from researchers. It provides benefits of both OFDM and CDMA. Main challenging problem of MC CDMA is high PAPR. It occurs in HPA and reduces system efficiency. There are many PAPR reduction techniques for MC CDMA. In this paper we proposed Ant colony optimization algorithm to reduce PAPR with different number of user using BPSK and QPSK modulation. ACO is a metaheuristic technique and based on the foraging behavior of real ants. It provides solution to many complex problems. Simulation result proves that ACO using BPSK modulation is effective for reducing PAPR in MC CDMA.
Vishal Arora; Vadlamani Ravi
2013-01-01
Ant Colony Optimization (ACO) is gaining popularity as data mining technique in the domain of Swarm Intelligence for its simple, accurate and comprehensive nature of classification. In this paper the authors propose a novel advanced version of the original ant colony based miner (Ant-Miner) in order to extract classification rules from data. They call this Advanced ACO-Miner (ADACOM). The main goal of ADACOM is to explore the flexibility of using a different knowledge extraction heuristic app...
Solving optimum operation of single pump unit problem with ant colony optimization (ACO) algorithm
International Nuclear Information System (INIS)
For pumping stations, the effective scheduling of daily pump operations from solutions to the optimum design operation problem is one of the greatest potential areas for energy cost-savings, there are some difficulties in solving this problem with traditional optimization methods due to the multimodality of the solution region. In this case, an ACO model for optimum operation of pumping unit is proposed and the solution method by ants searching is presented by rationally setting the object function and constrained conditions. A weighted directed graph was constructed and feasible solutions may be found by iteratively searching of artificial ants, and then the optimal solution can be obtained by applying the rule of state transition and the pheromone updating. An example calculation was conducted and the minimum cost was found as 4.9979. The result of ant colony algorithm was compared with the result from dynamic programming or evolutionary solving method in commercial software under the same discrete condition. The result of ACO is better and the computing time is shorter which indicates that ACO algorithm can provide a high application value to the field of optimal operation of pumping stations and related fields.
Remote Sensing Classification based on Improved Ant Colony Rules Mining Algorithm
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Shuying Liu
2014-09-01
Full Text Available Data mining can uncover previously undetected relationships among data items using automated data analysis techniques. In data mining, association rule mining is a prevalent and well researched method for discovering useful relations between variables in large databases. This paper investigates the principle of traditional rule mining, which will produce more non-essential candidate sets when it reads data into candidate items. Particularly when it deals with massive data, if the minimum support and minimum confidence are relatively small, combinatorial explosion of frequent item sets will occur and computing power and storage space required are likely to exceed the limits of machine. A new ant colony algorithm based on conventional Ant-Miner algorithm is proposed and is used in rules mining. Measurement formula of effectiveness of the rules is improved and pheromone concentration update strategy is also carried out. The experiment results show that execution time of proposed algorithm is lower than traditional algorithm and has better execution time and accuracy
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Fatemeh Rismanian
2013-07-01
Full Text Available Considering the features of non-uniformly distributed traffic load and possibly existing of the traffics requiring different performance in wireless sensor networks, this study proposes , a novel routing protocol based on an improved Ant colony optimization routing algorithm. The algorithm concentrates on the provision of Quality of Service in multi-criteria routing algorithm such as hop count, energy consumption, resident power, bandwidth and end to end delay. These metrics are used by means of colored pheromones of the ant colony system. There are different ants with colored pheromones, which each color is for a level of service. Simulation experiments show that the proposed algorithm has many advantages comparing with existing algorithm: proposing different service classes such as Real time and Best effort traffic; achieve slower delay and longer lifetime; besides, the proposed method behaves more scalable and robust.
Energy Efficient Ant Colony Algorithms for Data Aggregation in Wireless Sensor Networks
Lin, Chi; Xia, Feng; Li, Mingchu; Yao, Lin; Pei, Zhongyi
2012-01-01
In this paper, a family of ant colony algorithms called DAACA for data aggregation has been presented which contains three phases: the initialization, packet transmission and operations on pheromones. After initialization, each node estimates the remaining energy and the amount of pheromones to compute the probabilities used for dynamically selecting the next hop. After certain rounds of transmissions, the pheromones adjustment is performed periodically, which combines the advantages of both global and local pheromones adjustment for evaporating or depositing pheromones. Four different pheromones adjustment strategies are designed to achieve the global optimal network lifetime, namely Basic-DAACA, ES-DAACA, MM-DAACA and ACS-DAACA. Compared with some other data aggregation algorithms, DAACA shows higher superiority on average degree of nodes, energy efficiency, prolonging the network lifetime, computation complexity and success ratio of one hop transmission. At last we analyze the characteristic of DAACA in th...
T-QoS-aware based parallel ant colony algorithm for services composition
Institute of Scientific and Technical Information of China (English)
Lin Zhang; Kaili Rao; Ruchuan Wang
2015-01-01
In order to make cloud users get credible, high-quality composition of services, the trust quality of service aware (T-QoS-aware) based paral el ant colony algorithm is proposed. Our approach takes the service credibility as the weight of the quality of service, then calculates the trust service quality T-QoS for each service, making the service composition situated in a credible environment. Through the establishment on a per-service T-QoS initialization pheromone matrix, we can reduce the colony’s initial search time. By modifying the pheromone updating rules and intro-ducing two ant colonies to search from different angles in paral el, we can avoid fal ing into the local optimal solution, and quickly find the optimal combination of global solutions. Experiments show that our approach can combine high-quality services and the improve-ment of the operational success rate. Also, the convergence rate and the accuracy of optimal combination are improved.
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G.Keerthi Lakshmi
2012-03-01
Full Text Available Performing regression testing on a pre production environment is often viewed by software practitioners as a daunting task since often the test execution shall by-pass the stipulated downtime or the test coverage would be non linear. Choosing the exact test cases to match this type of complexity not only needs prior knowledge of the system, but also a right use of calculations to set the goals right. On systems that are just entering the production environment after getting promoted from the staging phase, trade-offs are often needed to between time and the test coverage to ensure the maximum test cases are covered within the stipulated time. There arises a need to refine the test cases to accommodate the maximum test coverage it makes within the stipulated period of time since at most of the times, the most important test cases are often not deemed to qualify under the sanity test suite and any bugs that creped in them would go undetected until it is found out by the actual user at firsthand. Hence An attempt has been made in the paper to layout a testing framework to address the process of improving the regression suite by adopting a modified version of the Ant Colony Algorithm over and thus dynamically injecting dependency over the best route encompassed by the ant colony.
Apply Ant Colony Algorithm to Search All Extreme Points of Function
Pang, Chao-Yang; Liu, Hui; Li, Xia; Wang, Yun-fei; Hu, Ben-Qiong
2009-01-01
To find all extreme points of multimodal functions is called extremum problem, which is a well known difficult issue in optimization fields. Applying ant colony optimization (ACO) to solve this problem is rarely reported. The method of applying ACO to solve extremum problem is explored in this paper. Experiment shows that the solution error of the method presented in this paper is less than 10^-8. keywords: Extremum Problem; Ant Colony Optimization (ACO)
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Minakshi
2015-06-01
Full Text Available Aco is a well –known metahuristic in which a colony of artificial ants cooperates in explain Good solution to a combinational optimization problem. Wireless sensor consisting of nodes with limited power is deployed to gather useful information From the field. In wireless sensor network it is critical to collect the information in an energy efficient Manner.ant colony optimization, a swarm intelligence based optimization technique, is widely used In network routing. A novel routing approach using an ant colony optimization algorithm is proposed for wireless sensor Network consisting of stable nodes illustrative example details description and cooperative performance test result the proposed approach are included. The approach is also implementing to a small sized hardware component as a router chip simulation result show that proposed algorithm Provides promising solution allowing node designers to efficiency operate routing tasks.
Improved multi-objective ant colony optimization algorithm and its application in complex reasoning
Wang, Xinqing; Zhao, Yang; Wang, Dong; Zhu, Huijie; Zhang, Qing
2013-09-01
The problem of fault reasoning has aroused great concern in scientific and engineering fields. However, fault investigation and reasoning of complex system is not a simple reasoning decision-making problem. It has become a typical multi-constraint and multi-objective reticulate optimization decision-making problem under many influencing factors and constraints. So far, little research has been carried out in this field. This paper transforms the fault reasoning problem of complex system into a paths-searching problem starting from known symptoms to fault causes. Three optimization objectives are considered simultaneously: maximum probability of average fault, maximum average importance, and minimum average complexity of test. Under the constraints of both known symptoms and the causal relationship among different components, a multi-objective optimization mathematical model is set up, taking minimizing cost of fault reasoning as the target function. Since the problem is non-deterministic polynomial-hard(NP-hard), a modified multi-objective ant colony algorithm is proposed, in which a reachability matrix is set up to constrain the feasible search nodes of the ants and a new pseudo-random-proportional rule and a pheromone adjustment mechinism are constructed to balance conflicts between the optimization objectives. At last, a Pareto optimal set is acquired. Evaluation functions based on validity and tendency of reasoning paths are defined to optimize noninferior set, through which the final fault causes can be identified according to decision-making demands, thus realize fault reasoning of the multi-constraint and multi-objective complex system. Reasoning results demonstrate that the improved multi-objective ant colony optimization(IMACO) can realize reasoning and locating fault positions precisely by solving the multi-objective fault diagnosis model, which provides a new method to solve the problem of multi-constraint and multi-objective fault diagnosis and
Towards a multilevel ant colony optimization
Lian, Thomas Andreé; Llave, Marilex Rea
2014-01-01
Ant colony optimization is a metaheuristic approach for solving combinatorial optimization problems which belongs to swarm intelligence techniques. Ant colony optimization algorithms are one of the most successful strands of swarm intelligence which has already shown very good performance in many combinatorial problems and for some real applications. This thesis introduces a new multilevel approach for ant colony optimization to solve the NP-hard problems shortest path and traveling salesman....
Automatic boiling water reactor loading pattern design using ant colony optimization algorithm
International Nuclear Information System (INIS)
An automatic boiling water reactor (BWR) loading pattern (LP) design methodology was developed using the rank-based ant system (RAS), which is a variant of the ant colony optimization (ACO) algorithm. To reduce design complexity, only the fuel assemblies (FAs) of one eight-core positions were determined using the RAS algorithm, and then the corresponding FAs were loaded into the other parts of the core. Heuristic information was adopted to exclude the selection of the inappropriate FAs which will reduce search space, and thus, the computation time. When the LP was determined, Haling cycle length, beginning of cycle (BOC) shutdown margin (SDM), and Haling end of cycle (EOC) maximum fraction of limit for critical power ratio (MFLCPR) were calculated using SIMULATE-3 code, which were used to evaluate the LP for updating pheromone of RAS. The developed design methodology was demonstrated using FAs of a reference cycle of the BWR6 nuclear power plant. The results show that, the designed LP can be obtained within reasonable computation time, and has a longer cycle length than that of the original design.
Automatic boiling water reactor loading pattern design using ant colony optimization algorithm
Energy Technology Data Exchange (ETDEWEB)
Wang, C.-D. [Department of Engineering and System Science, National Tsing Hua University, 101, Section 2 Kuang Fu Road, Hsinchu 30013, Taiwan (China); Nuclear Engineering Division, Institute of Nuclear Energy Research, No. 1000, Wenhua Rd., Jiaan Village, Longtan Township, Taoyuan County 32546, Taiwan (China)], E-mail: jdwang@iner.gov.tw; Lin Chaung [Department of Engineering and System Science, National Tsing Hua University, 101, Section 2 Kuang Fu Road, Hsinchu 30013, Taiwan (China)
2009-08-15
An automatic boiling water reactor (BWR) loading pattern (LP) design methodology was developed using the rank-based ant system (RAS), which is a variant of the ant colony optimization (ACO) algorithm. To reduce design complexity, only the fuel assemblies (FAs) of one eight-core positions were determined using the RAS algorithm, and then the corresponding FAs were loaded into the other parts of the core. Heuristic information was adopted to exclude the selection of the inappropriate FAs which will reduce search space, and thus, the computation time. When the LP was determined, Haling cycle length, beginning of cycle (BOC) shutdown margin (SDM), and Haling end of cycle (EOC) maximum fraction of limit for critical power ratio (MFLCPR) were calculated using SIMULATE-3 code, which were used to evaluate the LP for updating pheromone of RAS. The developed design methodology was demonstrated using FAs of a reference cycle of the BWR6 nuclear power plant. The results show that, the designed LP can be obtained within reasonable computation time, and has a longer cycle length than that of the original design.
International Nuclear Information System (INIS)
Reactive power management is essential to transfer real energy and support power system security. Developing an accurate and feasible method for reactive power pricing is important in the electricity market. In conventional optimal power flow models the production cost of reactive power was ignored. In this paper, the production cost of reactive power and investment cost of capacitor banks were included into the objective function of the OPF problem. Then, using ant colony search algorithm, the optimal problem was solved. Marginal price theory was used for calculation of the cost of active and reactive power at each bus in competitive electric markets. Application of the proposed method on IEEE 14-bus system confirms its validity and effectiveness. Results from several case studies show clearly the effects of various factors on reactive power price. (author)
DESIGNING DAILY PATROL ROUTES FOR POLICING BASED ON ANT COLONY ALGORITHM
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H. Chen
2015-07-01
Full Text Available In this paper, we address the problem of planning police patrol routes to regularly cover street segments of high crime density (hotspots with limited police forces. A good patrolling strategy is required to minimise the average time lag between two consecutive visits to hotspots, as well as coordinating multiple patrollers and imparting unpredictability in patrol routes. Previous studies have designed different police patrol strategies for routing police patrol, but these strategies have difficulty in generalising to real patrolling and meeting various requirements. In this research we develop a new police patrolling strategy based on Bayesian method and ant colony algorithm. In this strategy, virtual marker (pheromone is laid to mark the visiting history of each crime hotspot, and patrollers continuously decide which hotspot to patrol next based on pheromone level and other variables. Simulation results using real data testifies the effective, scalable, unpredictable and extensible nature of this strategy.
Energy Technology Data Exchange (ETDEWEB)
Ketabi, Abbas; Alibabaee, Ahmad [Department of Electrical Engineering, University of Kashan, Kashan (Iran); Feuillet, R. [Laboratoire d' Electrotechnique de Grenoble, INPG/ENSIEG, 38402 Saint Martin d' Heres, Cedex (France)
2010-07-15
Reactive power management is essential to transfer real energy and support power system security. Developing an accurate and feasible method for reactive power pricing is important in the electricity market. In conventional optimal power flow models the production cost of reactive power was ignored. In this paper, the production cost of reactive power and investment cost of capacitor banks were included into the objective function of the OPF problem. Then, using ant colony search algorithm, the optimal problem was solved. Marginal price theory was used for calculation of the cost of active and reactive power at each bus in competitive electric markets. Application of the proposed method on IEEE 14-bus system confirms its validity and effectiveness. Results from several case studies show clearly the effects of various factors on reactive power price. (author)
Rescheduling of observing spacecraft using fuzzy neural network and ant colony algorithm
Institute of Scientific and Technical Information of China (English)
Li Yuqing; Wang Rixin; Xu Minqiang
2014-01-01
This paper aims at rescheduling of observing spacecraft imaging plans under uncertain-ties. Firstly, uncertainties in spacecraft observation scheduling are analyzed. Then, considering the uncertainties with fuzzy features, this paper proposes a fuzzy neural network and a hybrid resched-uling policy to deal with them. It then establishes a mathematical model and manages to solve the rescheduling problem by proposing an ant colony algorithm, which introduces an adaptive control mechanism and takes advantage of the information in an existing schedule. Finally, the above method is applied to solve the rescheduling problem of a certain type of earth-observing satellite. The computation of the example shows that the approach is feasible and effective in dealing with uncertainties in spacecraft observation scheduling. The approach designed here can be useful in solving the problem that the original schedule is contaminated by disturbances.
Designing Daily Patrol Routes for Policing Based on ANT Colony Algorithm
Chen, H.; Cheng, T.; Wise, S.
2015-07-01
In this paper, we address the problem of planning police patrol routes to regularly cover street segments of high crime density (hotspots) with limited police forces. A good patrolling strategy is required to minimise the average time lag between two consecutive visits to hotspots, as well as coordinating multiple patrollers and imparting unpredictability in patrol routes. Previous studies have designed different police patrol strategies for routing police patrol, but these strategies have difficulty in generalising to real patrolling and meeting various requirements. In this research we develop a new police patrolling strategy based on Bayesian method and ant colony algorithm. In this strategy, virtual marker (pheromone) is laid to mark the visiting history of each crime hotspot, and patrollers continuously decide which hotspot to patrol next based on pheromone level and other variables. Simulation results using real data testifies the effective, scalable, unpredictable and extensible nature of this strategy.
Institute of Scientific and Technical Information of China (English)
YAN Shiliang; WANG Yinling
2007-01-01
Travelling Salesman Problem (TSP) is a classical optimization problem and it is one of a class of NP-Problem. The purposes of this work is to apply data mining methodologies to explore the patterns in data generated by an Ant Colony Algorithm (ACA) performing a searching operation and to develop a rule set searcher which approximates the ACA'S searcher. An attribute-oriented induction methodology was used to explore the relationship between an operations' sequence and its attributes and a set of rules has been developed. At the end of this paper, the experimental results have shown that the proposed approach has good performance with respect to the quality of solution and the speed of computation.
A New Tool Wear Monitoring Method Based on Ant Colony Algorithm
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Qianjian Guo
2013-06-01
Full Text Available Tool wear prediction is a major contributor to the dimensional errors of a work piece in precision machining, which plays an important role in industry for higher productivity and product quality. Tool wear monitoring is an effective way to predict the tool wear loss in milling process. In this paper, a new bionic prediction model is presented based on the generation mechanism of tool wear loss. Different milling conditions are estimated as the input variables, tool wear loss is estimated as the output variable, neural network method is proposed to establish the mapping relation and ant algorithm is used to train the weights of BP neural networks during tool wear modeling. Finally, a real-time tool wear loss estimator is developed based on ant colony alogrithm and experiments have been conducted for measuring tool wear based on the estimator in a milling machine. The experimental and estimated results are found to be in satisfactory agreement with average error lower than 6%.
International Nuclear Information System (INIS)
This paper presents some results of the implementation of several optimization algorithms based on ant colonies, applied to the fuel reload design in a Boiling Water Reactor. The system called Azcaxalli is constructed with the following algorithms: Ant Colony System, Ant System, Best-Worst Ant System and MAX-MIN Ant System. Azcaxalli starts with a random fuel reload. Ants move into reactor core channels according to the State Transition Rule in order to select two fuel assemblies into a 1/8 part of the reactor core and change positions between them. This rule takes into account pheromone trails and acquired knowledge. Acquired knowledge is obtained from load cycle values of fuel assemblies. Azcaxalli claim is to work in order to maximize the cycle length taking into account several safety parameters. Azcaxalli's objective function involves thermal limits at the end of the cycle, cold shutdown margin at the beginning of the cycle and the neutron effective multiplication factor for a given cycle exposure. Those parameters are calculated by CM-PRESTO code. Through the Haling Principle is possible to calculate the end of the cycle. This system was applied to an equilibrium cycle of 18 months of Laguna Verde Nuclear Power Plant in Mexico. The results show that the system obtains fuel reloads with higher cycle lengths than the original fuel reload. Azcaxalli results are compared with genetic algorithms, tabu search and neural networks results.
Energy Technology Data Exchange (ETDEWEB)
Esquivel-Estrada, Jaime, E-mail: jaime.esquivel@fi.uaemex.m [Facultad de Ingenieria, Universidad Autonoma del Estado de Mexico, Cerro de Coatepec S/N, Toluca de Lerdo, Estado de Mexico 50000 (Mexico); Instituto Nacional de Investigaciones Nucleares, Carr. Mexico Toluca S/N, Ocoyoacac, Estado de Mexico 52750 (Mexico); Ortiz-Servin, Juan Jose, E-mail: juanjose.ortiz@inin.gob.m [Instituto Nacional de Investigaciones Nucleares, Carr. Mexico Toluca S/N, Ocoyoacac, Estado de Mexico 52750 (Mexico); Castillo, Jose Alejandro; Perusquia, Raul [Instituto Nacional de Investigaciones Nucleares, Carr. Mexico Toluca S/N, Ocoyoacac, Estado de Mexico 52750 (Mexico)
2011-01-15
This paper presents some results of the implementation of several optimization algorithms based on ant colonies, applied to the fuel reload design in a Boiling Water Reactor. The system called Azcaxalli is constructed with the following algorithms: Ant Colony System, Ant System, Best-Worst Ant System and MAX-MIN Ant System. Azcaxalli starts with a random fuel reload. Ants move into reactor core channels according to the State Transition Rule in order to select two fuel assemblies into a 1/8 part of the reactor core and change positions between them. This rule takes into account pheromone trails and acquired knowledge. Acquired knowledge is obtained from load cycle values of fuel assemblies. Azcaxalli claim is to work in order to maximize the cycle length taking into account several safety parameters. Azcaxalli's objective function involves thermal limits at the end of the cycle, cold shutdown margin at the beginning of the cycle and the neutron effective multiplication factor for a given cycle exposure. Those parameters are calculated by CM-PRESTO code. Through the Haling Principle is possible to calculate the end of the cycle. This system was applied to an equilibrium cycle of 18 months of Laguna Verde Nuclear Power Plant in Mexico. The results show that the system obtains fuel reloads with higher cycle lengths than the original fuel reload. Azcaxalli results are compared with genetic algorithms, tabu search and neural networks results.
Directory of Open Access Journals (Sweden)
GUO Yue
2014-01-01
Full Text Available With the development of robotics and artificial intelligence field unceasingly thorough, path planning as an important field of robot calculation has been widespread concern. This paper analyzes the current development of robot and path planning algorithm and focuses on the advantages and disadvantages of the traditional intelligent path planning as well as the path planning. The problem of mobile robot path planning is studied by using ant colony algorithm, and it also provides some solving methods.
Guo, Yue; SHEN Xuelian; ZHU Zhanfeng
2014-01-01
With the development of robotics and artificial intelligence field unceasingly thorough, path planning as an important field of robot calculation has been widespread concern. This paper analyzes the current development of robot and path planning algorithm and focuses on the advantages and disadvantages of the traditional intelligent path planning as well as the path planning. The problem of mobile robot path planning is studied by using ant colony algorithm, and it also provide...
Automatic Programming with Ant Colony Optimization
Green, Jennifer; Jacqueline L. Whalley; Johnson, Colin G.
2004-01-01
Automatic programming is the use of search techniques to find programs that solve a problem. The most commonly explored automatic programming technique is genetic programming, which uses genetic algorithms to carry out the search. In this paper we introduce a new technique called Ant Colony Programming (ACP) which uses an ant colony based search in place of genetic algorithms. This algorithm is described and compared with other approaches in the literature.
Institute of Scientific and Technical Information of China (English)
李树刚; 吴智铭; 庞小红
2004-01-01
In order to study the capacitated lot sizing problem for a supply chain of corporate multi-location fac-tories to minimize the total costs of production, inventory and transportation under the system capacity restriction and product due date, while at the same time considering the menu distributed balance, the mathematical pro-gramming models are decomposed and reduced from the 3 levels into 2 levels according to the idea of just-in-time production. In order to overcome the premature convergence of ACA (ant colony algorithms) , the idea of mute operation is adopted in genetic algorithms and a PACA (parallel ant colony algorithms) is proposed forsupply chain optimization. Finally, an illustrative example is given, and a comparison is made with standard BAR ( Branch and Bound) and PACA approach. The result shows that the latter is more effective and promis-ing.
新型的双种群蚁群算法%Novel dual population ant colony algorithm
Institute of Scientific and Technical Information of China (English)
张晓伟; 李笑雪
2011-01-01
A novel ant colony algorithm is proposed based on the bionics of cooperation relation between soldier ant and worker ant in the foraging process. Soldier ant population and worker ant population are designed to search problem solution by parallel way in proposed algorithm.The dynamic equilibrium between solution diversity and convergence speed is achieved by using the effect of the soldier ant's distribution to worker ants' movement choice. Experimental results on traveling salesman problem show that proposed algorithm has a good global searching ability and high convergence speed.%基于对蚂蚁种群中兵蚁和工蚁在觅食过程中合作关系的仿生,提出了一种改进型蚁群算法.在该算法中同时存在着兵蚁子种群与工蚁子种群两个种群,两个子种群并行搜索,通过兵蚁的分布来影响到工蚁的移动选择,以取得各蚂蚁子群体中解的多样性和收敛性之间的动态平衡.基于旅行商问题的实验证明,算法具有较好的全局搜索能力和收敛速度.
Simulated Annealing-Based Ant Colony Algorithm for Tugboat Scheduling Optimization
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Qi Xu
2012-01-01
Full Text Available As the “first service station” for ships in the whole port logistics system, the tugboat operation system is one of the most important systems in port logistics. This paper formulated the tugboat scheduling problem as a multiprocessor task scheduling problem (MTSP after analyzing the characteristics of tugboat operation. The model considers factors of multianchorage bases, different operation modes, and three stages of operations (berthing/shifting-berth/unberthing. The objective is to minimize the total operation times for all tugboats in a port. A hybrid simulated annealing-based ant colony algorithm is proposed to solve the addressed problem. By the numerical experiments without the shifting-berth operation, the effectiveness was verified, and the fact that more effective sailing may be possible if tugboats return to the anchorage base timely was pointed out; by the experiments with the shifting-berth operation, one can see that the objective is most sensitive to the proportion of the shifting-berth operation, influenced slightly by the tugboat deployment scheme, and not sensitive to the handling operation times.
Optimal management of substrates in anaerobic co-digestion: An ant colony algorithm approach.
Verdaguer, Marta; Molinos-Senante, María; Poch, Manel
2016-04-01
Sewage sludge (SWS) is inevitably produced in urban wastewater treatment plants (WWTPs). The treatment of SWS on site at small WWTPs is not economical; therefore, the SWS is typically transported to an alternative SWS treatment center. There is increased interest in the use of anaerobic digestion (AnD) with co-digestion as an SWS treatment alternative. Although the availability of different co-substrates has been ignored in most of the previous studies, it is an essential issue for the optimization of AnD co-digestion. In a pioneering approach, this paper applies an Ant-Colony-Optimization (ACO) algorithm that maximizes the generation of biogas through AnD co-digestion in order to optimize the discharge of organic waste from different waste sources in real-time. An empirical application is developed based on a virtual case study that involves organic waste from urban WWTPs and agrifood activities. The results illustrate the dominate role of toxicity levels in selecting contributions to the AnD input. The methodology and case study proposed in this paper demonstrate the usefulness of the ACO approach in supporting a decision process that contributes to improving the sustainability of organic waste and SWS management. PMID:26868846
International Nuclear Information System (INIS)
In this work we have developed a simulation tool, based on the PENELOPE code, to study the response of MOSFET devices to irradiation with high-energy photons. The energy deposited in the extremely thin silicon dioxide layer has been calculated. To reduce the statistical uncertainties, an ant colony algorithm has been implemented to drive the application of splitting and Russian roulette as variance reduction techniques. In this way, the uncertainty has been reduced by a factor of ∼5, while the efficiency is increased by a factor of above 20. As an application, we have studied the dependence of the response of the pMOS transistor 3N163, used as a dosimeter, with the incidence angle of the radiation for three common photons sources used in radiotherapy: a 60Co Theratron-780 and the 6 and 18 MV beams produced by a Mevatron KDS LINAC. Experimental and simulated results have been obtained for gantry angles of 0 deg., 15 deg., 30 deg., 45 deg., 60 deg. and 75 deg. The agreement obtained has permitted validation of the simulation tool. We have studied how to reduce the angular dependence of the MOSFET response by using an additional encapsulation made of brass in the case of the two LINAC qualities considered.
Energy Technology Data Exchange (ETDEWEB)
Carvajal, M A; Palma, A J [Departamento de Electronica y Tecnologia de Computadores, Universidad de Granada, E-18071 Granada (Spain); Garcia-Pareja, S [Servicio de Radiofisica Hospitalaria, Hospital Regional Universitario ' Carlos Haya' , Avda Carlos Haya, s/n, E-29010 Malaga (Spain); Guirado, D [Servicio de RadiofIsica, Hospital Universitario ' San Cecilio' , Avda Dr Oloriz, 16, E-18012 Granada (Spain); Vilches, M [Servicio de Fisica y Proteccion Radiologica, Hospital Regional Universitario ' Virgen de las Nieves' , Avda Fuerzas Armadas, 2, E-18014 Granada (Spain); Anguiano, M; Lallena, A M [Departamento de Fisica Atomica, Molecular y Nuclear, Universidad de Granada, E-18071 Granada (Spain)], E-mail: carvajal@ugr.es, E-mail: garciapareja@gmail.com, E-mail: dguirado@ugr.es, E-mail: mvilches@ugr.es, E-mail: mangui@ugr.es, E-mail: ajpalma@ugr.es, E-mail: lallena@ugr.es
2009-10-21
In this work we have developed a simulation tool, based on the PENELOPE code, to study the response of MOSFET devices to irradiation with high-energy photons. The energy deposited in the extremely thin silicon dioxide layer has been calculated. To reduce the statistical uncertainties, an ant colony algorithm has been implemented to drive the application of splitting and Russian roulette as variance reduction techniques. In this way, the uncertainty has been reduced by a factor of {approx}5, while the efficiency is increased by a factor of above 20. As an application, we have studied the dependence of the response of the pMOS transistor 3N163, used as a dosimeter, with the incidence angle of the radiation for three common photons sources used in radiotherapy: a {sup 60}Co Theratron-780 and the 6 and 18 MV beams produced by a Mevatron KDS LINAC. Experimental and simulated results have been obtained for gantry angles of 0 deg., 15 deg., 30 deg., 45 deg., 60 deg. and 75 deg. The agreement obtained has permitted validation of the simulation tool. We have studied how to reduce the angular dependence of the MOSFET response by using an additional encapsulation made of brass in the case of the two LINAC qualities considered.
Carvajal, M A; García-Pareja, S; Guirado, D; Vilches, M; Anguiano, M; Palma, A J; Lallena, A M
2009-10-21
In this work we have developed a simulation tool, based on the PENELOPE code, to study the response of MOSFET devices to irradiation with high-energy photons. The energy deposited in the extremely thin silicon dioxide layer has been calculated. To reduce the statistical uncertainties, an ant colony algorithm has been implemented to drive the application of splitting and Russian roulette as variance reduction techniques. In this way, the uncertainty has been reduced by a factor of approximately 5, while the efficiency is increased by a factor of above 20. As an application, we have studied the dependence of the response of the pMOS transistor 3N163, used as a dosimeter, with the incidence angle of the radiation for three common photons sources used in radiotherapy: a (60)Co Theratron-780 and the 6 and 18 MV beams produced by a Mevatron KDS LINAC. Experimental and simulated results have been obtained for gantry angles of 0 degrees, 15 degrees, 30 degrees, 45 degrees, 60 degrees and 75 degrees. The agreement obtained has permitted validation of the simulation tool. We have studied how to reduce the angular dependence of the MOSFET response by using an additional encapsulation made of brass in the case of the two LINAC qualities considered. PMID:19794247
Energy Technology Data Exchange (ETDEWEB)
Hemmateenejad, Bahram, E-mail: hemmatb@sums.ac.ir [Department of Chemistry, Shiraz University, Shiraz (Iran, Islamic Republic of); Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz (Iran, Islamic Republic of); Shamsipur, Mojtaba [Department of Chemistry, Razi University, Kermanshah (Iran, Islamic Republic of); Zare-Shahabadi, Vali [Young Researchers Club, Mahshahr Branch, Islamic Azad University, Mahshahr (Iran, Islamic Republic of); Akhond, Morteza [Department of Chemistry, Shiraz University, Shiraz (Iran, Islamic Republic of)
2011-10-17
Highlights: {yields} Ant colony systems help to build optimum classification and regression trees. {yields} Using of genetic algorithm operators in ant colony systems resulted in more appropriate models. {yields} Variable selection in each terminal node of the tree gives promising results. {yields} CART-ACS-GA could model the melting point of organic materials with prediction errors lower than previous models. - Abstract: The classification and regression trees (CART) possess the advantage of being able to handle large data sets and yield readily interpretable models. A conventional method of building a regression tree is recursive partitioning, which results in a good but not optimal tree. Ant colony system (ACS), which is a meta-heuristic algorithm and derived from the observation of real ants, can be used to overcome this problem. The purpose of this study was to explore the use of CART and its combination with ACS for modeling of melting points of a large variety of chemical compounds. Genetic algorithm (GA) operators (e.g., cross averring and mutation operators) were combined with ACS algorithm to select the best solution model. In addition, at each terminal node of the resulted tree, variable selection was done by ACS-GA algorithm to build an appropriate partial least squares (PLS) model. To test the ability of the resulted tree, a set of approximately 4173 structures and their melting points were used (3000 compounds as training set and 1173 as validation set). Further, an external test set containing of 277 drugs was used to validate the prediction ability of the tree. Comparison of the results obtained from both trees showed that the tree constructed by ACS-GA algorithm performs better than that produced by recursive partitioning procedure.
International Nuclear Information System (INIS)
Highlights: → Ant colony systems help to build optimum classification and regression trees. → Using of genetic algorithm operators in ant colony systems resulted in more appropriate models. → Variable selection in each terminal node of the tree gives promising results. → CART-ACS-GA could model the melting point of organic materials with prediction errors lower than previous models. - Abstract: The classification and regression trees (CART) possess the advantage of being able to handle large data sets and yield readily interpretable models. A conventional method of building a regression tree is recursive partitioning, which results in a good but not optimal tree. Ant colony system (ACS), which is a meta-heuristic algorithm and derived from the observation of real ants, can be used to overcome this problem. The purpose of this study was to explore the use of CART and its combination with ACS for modeling of melting points of a large variety of chemical compounds. Genetic algorithm (GA) operators (e.g., cross averring and mutation operators) were combined with ACS algorithm to select the best solution model. In addition, at each terminal node of the resulted tree, variable selection was done by ACS-GA algorithm to build an appropriate partial least squares (PLS) model. To test the ability of the resulted tree, a set of approximately 4173 structures and their melting points were used (3000 compounds as training set and 1173 as validation set). Further, an external test set containing of 277 drugs was used to validate the prediction ability of the tree. Comparison of the results obtained from both trees showed that the tree constructed by ACS-GA algorithm performs better than that produced by recursive partitioning procedure.
Directory of Open Access Journals (Sweden)
Khaled Loukhaoukha
2013-01-01
Full Text Available We present a new optimal watermarking scheme based on discrete wavelet transform (DWT and singular value decomposition (SVD using multiobjective ant colony optimization (MOACO. A binary watermark is decomposed using a singular value decomposition. Then, the singular values are embedded in a detailed subband of host image. The trade-off between watermark transparency and robustness is controlled by multiple scaling factors (MSFs instead of a single scaling factor (SSF. Determining the optimal values of the multiple scaling factors (MSFs is a difficult problem. However, a multiobjective ant colony optimization is used to determine these values. Experimental results show much improved performances of the proposed scheme in terms of transparency and robustness compared to other watermarking schemes. Furthermore, it does not suffer from the problem of high probability of false positive detection of the watermarks.
Intercluster Ant Colony Optimization Algorithm for Wireless Sensor Network in Dense Environment
Jung-Yoon Kim; Tripti Sharma; Brijesh Kumar; Tomar, G. S.; Karan Berry; Won-Hyung Lee
2014-01-01
Wireless sensor networks have grown rapidly with the innovation in Information Technology. Sensor nodes are distributed and deployed over the area for gathering requisite information. Sensor nodes possess a negative characteristic of limited energy which pulls back the network from exploiting its peak capabilities. Hence, it is necessary to gather and transfer the information in an optimized way which reduces the energy dissipation. Ant Colony Optimization (ACO) is being widely used in optimi...
Fonooni, Benjamin; Jevtić, Aleksandar; Hellström, Thomas; Janlert, Lars-Erik
2015-01-01
International audience In domains where robots carry out human’s tasks, the ability to learn new behaviors easily and quickly plays an important role. Two major challenges with Learning from Demonstration (LfD) are to identify what information in a demonstrated behavior requires attention by the robot, and to generalize the learned behavior such that the robot is able to perform the same behavior in novel situations.The main goal of this paper is to incorporate Ant Colony Optimization (ACO...
Implementasi Algoritma Ant Colony System Dalam Menentukan Optimisasi Network Routing .
Lubis, Dini Anggraini
2011-01-01
Ant Colony System is an algorithm that adapt from ants biologic behavior which the ant colony can hold to find shortest path. Ant Colony System can implement for several optimization problems and one of them is in network routing. Ant colony system that talked in this paper is about optimization cases in network routing called AntNet. The purpose of AntNet is to search shortest path between source node to destination node based the table routing read by AntNet. In this research, it implemente...
Blind noisy image quality evaluation using a deformable ant colony algorithm
Chen, Li; Huang, Xiaotong; Tian, Jing; Fu, Xiaowei
2014-04-01
The objective of blind noisy image quality assessment is to evaluate the quality of the degraded noisy image without the knowledge of the ground truth image. Its performance relies on the accuracy of the noise statistics estimated from homogenous blocks. The major challenge of block-based approaches lies in the block size selection, as it affects the local noise derivation. To tackle this challenge, a deformable ant colony optimization (DACO) approach is proposed in this paper to adaptively adjust the ant size for image block selection. The proposed DACO approach considers that the size of the ant is adjustable during foraging. For the smooth image blocks, more pheromone is deposited, and then the size of ant is increased. Therefore, this strategy enables the ants to have dynamic food-search capability, leading to more accurate selection of homogeneous blocks. Furthermore, the regression analysis is used to obtain image quality score by exploiting the above-estimated noise statistics. Experimental results are provided to justify that the proposed approach outperforms conventional approaches to provide more accurate noise statistics estimation and achieve a consistent image quality evaluation performance for both the artificially generated and real-world noisy images.
Energy Technology Data Exchange (ETDEWEB)
Guan, L.; Zhang, X.; Wang, T. [South China Univ. of Technology, Guangzhou (China). College of Electrical Power
2009-03-11
This study presented an optimized ant colony optimization algorithm combined with a K-nearest neighbour (K-NN) classifier. Ant colony optimization is used to simulate the information exchange and cooperation schemes among individual ants in the process of searching for food. The processes are used to simulate positive feedback, distributed computation, and the use of constructive heuristic searches. In this study, each feature was regarded as a node that the ant may visit. Feature selection processes were described as a path-forming process. The weighted sum of the K-NN classification error and a selected feature dimension was used to construct a fitness function for assessing transient stability. A local search loop wa used to remove redundant or strongly-correlated features. The algorithm was verified using a set of artificial test data. The scheme was then used to obtain a security-related kernel feature for an Institute of Electrical and Electronics Engineers (IEEE) 10-unit 39-bus system. The study demonstrated that the proposed scheme accurately assessed transient stability. 14 refs., 6 figs.
Mengjun Tong; Yangli Chen; Fangxiang Chen; Xiaoping Wu; Guozhong Shou
2015-01-01
An energy-efficient ACO-based multipath routing algorithm (EAMR) is proposed for energy-constrained wireless sensor networks. EAMR is a hybrid multipath algorithm, which is reactive in path discovery and proactive in route maintenance. EAMR has improvement and innovation in the ant packet structure, pheromone update formula, pheromone update mode, and the mechanism of multipath. Average energy consumption and congestion of path make pheromone update formula more reasonable. Incremental pherom...
Kwarciak, Kamil; Radom, Marcin; Formanowicz, Piotr
2016-04-01
The classical sequencing by hybridization takes into account a binary information about sequence composition. A given element from an oligonucleotide library is or is not a part of the target sequence. However, the DNA chip technology has been developed and it enables to receive a partial information about multiplicity of each oligonucleotide the analyzed sequence consist of. Currently, it is not possible to assess the exact data of such type but even partial information should be very useful. Two realistic multiplicity information models are taken into consideration in this paper. The first one, called "one and many" assumes that it is possible to obtain information if a given oligonucleotide occurs in a reconstructed sequence once or more than once. According to the second model, called "one, two and many", one is able to receive from biochemical experiment information if a given oligonucleotide is present in an analyzed sequence once, twice or at least three times. An ant colony optimization algorithm has been implemented to verify the above models and to compare with existing algorithms for sequencing by hybridization which utilize the additional information. The proposed algorithm solves the problem with any kind of hybridization errors. Computational experiment results confirm that using even the partial information about multiplicity leads to increased quality of reconstructed sequences. Moreover, they also show that the more precise model enables to obtain better solutions and the ant colony optimization algorithm outperforms the existing ones. Test data sets and the proposed ant colony optimization algorithm are available on: http://bioserver.cs.put.poznan.pl/download/ACO4mSBH.zip. PMID:26878124
Optimum Distribution Generator Placement in Power Distribution System Using Ant Colony Algorithm
Directory of Open Access Journals (Sweden)
Mehdi Mahdavi
2009-03-01
Full Text Available The recent development in renewable energy systems and the high demand for having clean and low cost energy sources encourage people to use distributed generator (DG systems. Proper addition and placement of DG units can increase reliability and reduce the loss and production cost. In this paper using Ant Colony method, we developed an optimum placing scheme for DGs. The proposed method is tested on an IEEE 34-shinhe system. Results show that if DGs are able to generate active power, their effectiveness will increase.
Ting Jiang; Wei Zang; Chenglin Zhao; Jiong Shi
2010-01-01
We optimize the cluster structure to solve problems such as the uneven energy consumption of the radar sensor nodes and random cluster head selection in the traditional clustering routing algorithm. According to the defined cost function for clusters, we present the clustering algorithm which is based on radio-free space path loss. In addition, we propose the energy and distance pheromones based on the residual energy and aggregation of the radar sensor nodes. According to bionic heuristic a...
Ant Colony Optimization for Control
Van Ast, J.M.
2010-01-01
The very basis of this thesis is the collective behavior of ants in colonies. Ants are an excellent example of how rather simple behavior on a local level can lead to complex behavior on a global level that is beneficial for the individuals. The key in the self-organization of ants is communication
双种群改进蚁群算法%Dual population ant colony optimization algorithm
Institute of Scientific and Technical Information of China (English)
郏宣耀; 滕少华
2006-01-01
基本蚁群优化(Basic Ant Colony Optimization,BACO)算法在进化中容易出现停滞,其根源是蚁群算法中信息的正反馈. 在大量蚂蚁选择相同路径后,该路径上的信息素浓度远高于其他路径,算法很难再搜索到邻域空间中的其他优良解. 对此,提出一种双种群改进蚁群(Dual Population Ant Colony Optimization,DPACO)算法. 借鉴遗传算法中个体多样性特点,将蚁群算法中的蚂蚁分成两个群体分别独立进行进化,并定期进行信息交换. 这一方法缓解了因信息素浓度失衡而造成的局部收敛,有效改进算法的搜索性能,实验结果表明该算法有效可行.
Web service selection based on ant colony algorithm%基于蚁群算法的Web服务选择
Institute of Scientific and Technical Information of China (English)
王秀亭; 马力
2013-01-01
The Web Service composition is one of the most important ways to satisfy the users’personalized requirements and supply the high quality service for users. And the foundation of service composition is the selection of Web service. The prin⁃ciple of ant colony algorithm is expounded. The model of Web service selection is analyzed. The algorithm is brought into the field of Web service selection to translate the question of QoS⁃based Web service selection into the question of finding the optimi⁃zation path. The steps for solving the question of Web service selection by the ant colony algorithm are offered in this paper. The influence of the ant colony algorithm with the different parameters on correctness of service selection is analyzed. The validity of ant colony algorithm in solving the problems of Web Service selection was tested in a certain scene.% Web服务组合是为Web用户提供高质量、个性化服务的主要手段，而Web服务选择是进行组合的前提和基础。阐述了蚁群算法的原理，分析了Web服务选择的模型，把蚁群算法引入Web服务选择领域，将基于QoS的Web服务选择问题转化为最优路径选择问题。给出了使用蚁群算法解决Web服务选择问题的实施步骤，对比分析了蚁群算法在不同参数下对服务选择正确率的影响，通过具体的场景测试了蚁群算法对于解决Web服务选择问题的有效性。
Energy Technology Data Exchange (ETDEWEB)
Moeini, R.; Afshar, M.H.
2011-07-15
Hydropower is currently the number one source of electricity production in the world. For the design and construction of such systems, mathematical modelling is often use for reservoir operations. As conventional methods present some shortcomings in solving reservoir operation problems, a new method is presented here. It consists in an arc-based formulation of hydropower reservoir operation problems which can be applied to ant colony optimization algorithms. This paper first described this formulation and then applied it to solve two hydropower reservoir operation problems. The results showed that this formulation can optimally solve large-scale hydropower reservoir operation problems while offering a clear definition of heuristic information.
The Optimization of Running Queries in Relational Databases Using ANT-Colony Algorithm
Directory of Open Access Journals (Sweden)
Adel Alinezhad Kolaei
2013-10-01
Full Text Available The issue of optimizing queries is a cost-sensitive process and with respect to the number of associatedtables in a query, its number of permutations grows exponentially. On one hand, in comparison with otheroperators in relational database, join operator is the most difficult and complicated one in terms ofoptimization for reducing its runtime. Accordingly, various algorithms have so far been proposed to solvethis problem. On the other hand, the success of any database management system (DBMS meansexploiting the query model. In the current paper, the heuristic ant algorithm has been proposed to solve thisproblem and improve the runtime of join operation. Experiments and observed results reveal the efficiencyof this algorithm compared to its similar algorithms.
Verdaguer, M; Clara, N; Gutiérrez, O; Poch, M
2014-07-01
The first flush effect in combined sewer systems during storm events often causes overflows and overloads of the sewage treatment, which reduces the efficiency of the sewage treatment and decreases the quality of the receiving waters due to the pollutants that are contributed. The use of retention tanks constitutes a widely used way to mitigate this effect. However, the management of the pollutant loads encounters difficulties when the retention tanks are emptied. A new approach is proposed to solve this problem by fulfilling the treatment requirements in real time, focussing on the characteristics of the wastewater. The method is based on the execution of an Ant Colony Optimisation algorithm to obtain a satisfactory sequence for the discharge of the retention tanks. The discharge sequence considers the volume of stormwater and its concentration of pollutants including Suspended Solids, Biological Oxygen Demand and Chemical Oxygen Demand, Total Nitrogen and Total Phosphorus. The Ant Colony Optimisation algorithm was applied successfully to a case study with overall reduction of pollutant loads stored in retention tanks. The algorithm can be adapted in a simple way to the different scenarios, infrastructures and controllers of sewer systems. PMID:24704965
Hemmateenejad, Bahram; Shamsipur, Mojtaba; Zare-Shahabadi, Vali; Akhond, Morteza
2011-10-17
The classification and regression trees (CART) possess the advantage of being able to handle large data sets and yield readily interpretable models. A conventional method of building a regression tree is recursive partitioning, which results in a good but not optimal tree. Ant colony system (ACS), which is a meta-heuristic algorithm and derived from the observation of real ants, can be used to overcome this problem. The purpose of this study was to explore the use of CART and its combination with ACS for modeling of melting points of a large variety of chemical compounds. Genetic algorithm (GA) operators (e.g., cross averring and mutation operators) were combined with ACS algorithm to select the best solution model. In addition, at each terminal node of the resulted tree, variable selection was done by ACS-GA algorithm to build an appropriate partial least squares (PLS) model. To test the ability of the resulted tree, a set of approximately 4173 structures and their melting points were used (3000 compounds as training set and 1173 as validation set). Further, an external test set containing of 277 drugs was used to validate the prediction ability of the tree. Comparison of the results obtained from both trees showed that the tree constructed by ACS-GA algorithm performs better than that produced by recursive partitioning procedure. PMID:21907021
An Improved Multi--Objective Ant Colony Optimization Algorithm of Quantum%一种改进的量子多目标蚁群优化算法
Institute of Scientific and Technical Information of China (English)
杨剑; 张敏辉
2011-01-01
提出一种新的量子多目标蚁群算法．在蚁群算法的基础上中引入量子理论，将量子计算与蚁群进行融合，并用于求解多目标问题．该算法的核心是在蚁群中引入量子算法中的量子态矢量和量子旋转门来分别表示和更新信息素．该算法在全局寻优能力和种群多样性方面比蚁群算法有所改进，测试表明：该算法是求解多目标问题的一种有效的算法．%Proposed a new quantum multi--objective anf colony algorithm. In the ant colony algorithm based on the introduction of quantum theory, quantum computation and ant colony fusion, and for solving multi--objective problem. The core of the algorithm is introduced in the colony quantum quantum algorithm and quantum state vector, respectively, and the revolving door to update the pheromone. The ability of global optimization algorithm and population diversity than improved ant colony algorithm, the algorithm was tested, the results shows that the algorithm for solving Multi--objective problem is an effective algorithm.
He, Zhenzong; Qi, Hong; Wang, Yuqing; Ruan, Liming
2014-10-01
Four improved Ant Colony Optimization (ACO) algorithms, i.e. the probability density function based ACO (PDF-ACO) algorithm, the Region ACO (RACO) algorithm, Stochastic ACO (SACO) algorithm and Homogeneous ACO (HACO) algorithm, are employed to estimate the particle size distribution (PSD) of the spheroidal particles. The direct problems are solved by the extended Anomalous Diffraction Approximation (ADA) and the Lambert-Beer law. Three commonly used monomodal distribution functions i.e. the Rosin-Rammer (R-R) distribution function, the normal (N-N) distribution function, and the logarithmic normal (L-N) distribution function are estimated under dependent model. The influence of random measurement errors on the inverse results is also investigated. All the results reveal that the PDF-ACO algorithm is more accurate than the other three ACO algorithms and can be used as an effective technique to investigate the PSD of the spheroidal particles. Furthermore, the Johnson's SB (J-SB) function and the modified beta (M-β) function are employed as the general distribution functions to retrieve the PSD of spheroidal particles using PDF-ACO algorithm. The investigation shows a reasonable agreement between the original distribution function and the general distribution function when only considering the variety of the length of the rotational semi-axis.
基于蚁群算法的过程神经网络研究%Research on process neural networks based on ant colony algorithm
Institute of Scientific and Technical Information of China (English)
葛利; 李新东
2013-01-01
For improving global convergence ability and training speed,an ant colony process neural network model was proposed.Making use of distributed computing and strong robustness of ant colony algorithm,ant colony algorithm was applied in feedforward process neural network training.Topology structure of ant colony process neural network was given,and discussed on training mechanism of ant colony process neural network,analyzed the calculation features.And ant colony process neural network had been used in the annual GDP forecast of Heilongjiang province,verified the effectiveness of ant colony process neural network.%为提高前馈过程神经网络的全局收敛能力和训练速度,提出一种蚁群过程神经网络模型,利用蚁群算法分布式计算、鲁棒性强的特点,将蚁群算法应用于前馈过程神经网络的训练,给出了蚁群过程神经网络的拓扑结构,讨论了蚁群过程神经网络的训练机制,分析了其计算特点.并将蚁群过程神经网络应用于黑龙江省年度GDP(Gross Domestic Product)预测,验证了蚁群过程神经网络的有效性.
Image feature extraction based multiple ant colonies cooperation
Zhang, Zhilong; Yang, Weiping; Li, Jicheng
2015-05-01
This paper presents a novel image feature extraction algorithm based on multiple ant colonies cooperation. Firstly, a low resolution version of the input image is created using Gaussian pyramid algorithm, and two ant colonies are spread on the source image and low resolution image respectively. The ant colony on the low resolution image uses phase congruency as its inspiration information, while the ant colony on the source image uses gradient magnitude as its inspiration information. These two ant colonies cooperate to extract salient image features through sharing a same pheromone matrix. After the optimization process, image features are detected based on thresholding the pheromone matrix. Since gradient magnitude and phase congruency of the input image are used as inspiration information of the ant colonies, our algorithm shows higher intelligence and is capable of acquiring more complete and meaningful image features than other simpler edge detectors.
Directory of Open Access Journals (Sweden)
Safaa Khudair Leabi
2016-03-01
Full Text Available Energy limitations have become fundamental challenge for designing wireless sensor networks. Network lifetime represent the most important and interested metric. Several attempts have been made for efficient utilization of energy in routing techniques. This paper proposes an energy efficient routing technique for maximizing the networks lifetime called swarm intelligence routing. This is achieved by using ant colony algorithm (ACO and artificial immune system (AIS. AIS is used for solving packet LOOP problem and to control route direction. While ACO algorithm is used for determining optimum route for sending data packets. The proposed routing technique seeks for determining the optimum route from nodes towards base station so that energy exhaustion is balanced and lifetime is maximized. Proposed routing technique is compared with Dijkstra routing method. Results show significant increase in network lifetime of about 1.2567.
Path Optimization for WSN Based on Improved Ant Colony Algorithm%基于改进蚁群算法的WSN路径优化
Institute of Scientific and Technical Information of China (English)
杨新锋; 刘克成
2012-01-01
Against path optimization problem for wireless sensor network (WSN), this paper proposes a path optimization for WSN based on improved ant colony algorithm by combining with the advantages of genetic algorithm and ant colony algorithm and introducing the genetic algorithm selection, crossover and mutation operators into ant colony algorithm to improve the algorithm's capability of convergence and global search. Simulation experimental results show that the improved ant colony algorithm improves WSN routing efficiency and success rate, prolongs the survival time of network and improves the overall network performance.%针对无线传感器网络(WSN)路径优化问题,提出一种改进蚁群算法的WSN路径优化方法,结合遗传算法和蚁群算法的优点,在蚁群算法中引入遗传算法选择、交叉和变异算子,提高算法收敛和全局寻优能力.仿真对比实验结果表明,改进蚁群算法提高了WSN路径优化效率和成功率,有效延长了WSN的生命周期,改善了网络整体性能.
Optic disc detection using ant colony optimization
Dias, Marcy; Monteiro, Fernando C.
2012-01-01
The retinal fundus images are used in the treatment and diagnosis of several eye diseases, such as diabetic retinopathy and glaucoma. This paper proposes a new method to detect the optic disc (OD) automatically, due to the fact that the knowledge of the OD location is essential to the automatic analysis of retinal images. Ant Colony Optimization (ACO) is an optimization algorithm inspired by the foraging behaviour of some ant species that has been applied in image processing for edge detectio...
Zhang, Zili; Gao, Chao; Liu, Yuxin; Qian, Tao
2014-09-01
Ant colony optimization (ACO) algorithms often fall into the local optimal solution and have lower search efficiency for solving the travelling salesman problem (TSP). According to these shortcomings, this paper proposes a universal optimization strategy for updating the pheromone matrix in the ACO algorithms. The new optimization strategy takes advantages of the unique feature of critical paths reserved in the process of evolving adaptive networks of the Physarum-inspired mathematical model (PMM). The optimized algorithms, denoted as PMACO algorithms, can enhance the amount of pheromone in the critical paths and promote the exploitation of the optimal solution. Experimental results in synthetic and real networks show that the PMACO algorithms are more efficient and robust than the traditional ACO algorithms, which are adaptable to solve the TSP with single or multiple objectives. Meanwhile, we further analyse the influence of parameters on the performance of the PMACO algorithms. Based on these analyses, the best values of these parameters are worked out for the TSP. PMID:24613939
International Nuclear Information System (INIS)
Ant colony optimization (ACO) algorithms often fall into the local optimal solution and have lower search efficiency for solving the travelling salesman problem (TSP). According to these shortcomings, this paper proposes a universal optimization strategy for updating the pheromone matrix in the ACO algorithms. The new optimization strategy takes advantages of the unique feature of critical paths reserved in the process of evolving adaptive networks of the Physarum-inspired mathematical model (PMM). The optimized algorithms, denoted as PMACO algorithms, can enhance the amount of pheromone in the critical paths and promote the exploitation of the optimal solution. Experimental results in synthetic and real networks show that the PMACO algorithms are more efficient and robust than the traditional ACO algorithms, which are adaptable to solve the TSP with single or multiple objectives. Meanwhile, we further analyse the influence of parameters on the performance of the PMACO algorithms. Based on these analyses, the best values of these parameters are worked out for the TSP. (paper)
Janich, Karl W.
2005-01-01
The At-Least version of the Generalized Minimum Spanning Tree Problem (L-GMST) is a problem in which the optimal solution connects all defined clusters of nodes in a given network at a minimum cost. The L-GMST is NPHard; therefore, metaheuristic algorithms have been used to find reasonable solutions to the problem as opposed to computationally feasible exact algorithms, which many believe do not exist for such a problem. One such metaheuristic uses a swarm-intelligent Ant Colony System (ACS) algorithm, in which agents converge on a solution through the weighing of local heuristics, such as the shortest available path and the number of agents that recently used a given path. However, in a network using a solution derived from the ACS algorithm, some nodes may move around to different clusters and cause small changes in the network makeup. Rerunning the algorithm from the start would be somewhat inefficient due to the significance of the changes, so a genetic algorithm based on the top few solutions found in the ACS algorithm is proposed to quickly and efficiently adapt the network to these small changes.
International Nuclear Information System (INIS)
The redundancy allocation problem (RAP) is a well known NP-hard problem which involves the selection of elements and redundancy levels to maximize system reliability given various system-level constraints. As telecommunications and internet protocol networks, manufacturing and power systems are becoming more and more complex, while requiring short developments schedules and very high reliability, it is becoming increasingly important to develop efficient solutions to the RAP. This paper presents an efficient algorithm to solve this reliability optimization problem. The idea of a heuristic approach design is inspired from the ant colony meta-heuristic optimization method and the degraded ceiling local search technique. Our hybridization of the ant colony meta-heuristic with the degraded ceiling performs well and is competitive with the best-known heuristics for redundancy allocation. Numerical results for the 33 test problems from previous research are reported and compared. The solutions found by our approach are all better than or are in par with the well-known best solutions
Minakshi,; Monika Bansal
2015-01-01
Aco is a well –known metahuristic in which a colony of artificial ants cooperates in explain Good solution to a combinational optimization problem. Wireless sensor consisting of nodes with limited power is deployed to gather useful information From the field. In wireless sensor network it is critical to collect the information in an energy efficient Manner.ant colony optimization, a swarm intelligence based optimization technique, is widely used In network routing. A novel rout...
International Nuclear Information System (INIS)
Highlights: • A probabilistic optimization framework incorporated with uncertainty is proposed. • A hybrid optimization approach combining ACO and ABC algorithms is proposed. • The problem is to deal with technical, environmental and economical aspects. • A fuzzy interactive approach is incorporated to solve the multi-objective problem. • Several strategies are implemented to compare with literature methods. - Abstract: In this paper, a hybrid configuration of ant colony optimization (ACO) with artificial bee colony (ABC) algorithm called hybrid ACO–ABC algorithm is presented for optimal location and sizing of distributed energy resources (DERs) (i.e., gas turbine, fuel cell, and wind energy) on distribution systems. The proposed algorithm is a combined strategy based on the discrete (location optimization) and continuous (size optimization) structures to achieve advantages of the global and local search ability of ABC and ACO algorithms, respectively. Also, in the proposed algorithm, a multi-objective ABC is used to produce a set of non-dominated solutions which store in the external archive. The objectives consist of minimizing power losses, total emissions produced by substation and resources, total electrical energy cost, and improving the voltage stability. In order to investigate the impact of the uncertainty in the output of the wind energy and load demands, a probabilistic load flow is necessary. In this study, an efficient point estimate method (PEM) is employed to solve the optimization problem in a stochastic environment. The proposed algorithm is tested on the IEEE 33- and 69-bus distribution systems. The results demonstrate the potential and effectiveness of the proposed algorithm in comparison with those of other evolutionary optimization methods
Directory of Open Access Journals (Sweden)
J. Anitha
2010-10-01
Full Text Available This paper deals with the development of Web based online expert systems using Evolutionary Algorithms. An expert system is a computer application that performs a task that would otherwise be performed by a human expert. Here one of the evolutionary algorithms (ACO Algorithm is considered to find a good match of symptoms in the database. In the present paper, Ant Colony Optimization1 (ACO algorithm has been taken as the base and the concept of optimization is included, so that the new algorithm mainly focuses on the determination of the quality of eggs in the poultry farms. At first, the symptoms provided by the user are processed by a rule based expert system for identifying the quality of the eggs. If the rules required for processing the data by the above are not present in the database, then the system automatically calls the machine learning algorithm technique. As a whole, the system results good optimized solution for recognizing the quality and viruses if any affected to eggs in poultry farms. And corresponding treatments to the viruses may also be suggested to the users. This expert system is designed with JSP as front end and MySQL as backend.
Gao, Ming-ke; Chen, Yi-min; Liu, Quan; Huang, Chen; Li, Ze-yu; Zhang, Dian-hua
2015-11-01
Preoperative path planning plays a critical role in vascular access surgery. Vascular access surgery has superior difficulties and requires long training periods as well as precise operation. Yet doctors are on different leves, thus bulky size of blood vessels is usually chosen to undergo surgery and other possible optimal path is not considered. Moreover, patients and surgeons will suffer from X-ray radiation during the surgical procedure. The study proposed an improved ant colony algorithm to plan a vascular optimal three-dimensional path with overall consideration of factors such as catheter diameter, vascular length, diameter as well as the curvature and torsion. To protect the doctor and patient from exposing to X-ray long-term, the paper adopted augmented reality technology to register the reconstructed vascular model and physical model meanwhile, locate catheter by the electromagnetic tracking system and used Head Mounted Display to show the planning path in real time and monitor catheter push procedure. The experiment manifests reasonableness of preoperative path planning and proves the reliability of the algorithm. The augmented reality experiment real time and accurately displays the vascular phantom model, planning path and the catheter trajectory and proves the feasibility of this method. The paper presented a useful and feasible surgical scheme which was based on the improved ant colony algorithm to plan vascular three-dimensional path in augmented reality. The study possessed practical guiding significance in preoperative path planning, intraoperative catheter guiding and surgical training, which provided a theoretical method of path planning for vascular access surgery. It was a safe and reliable path planning approach and possessed practical reference value. PMID:26319273
Analysis of Ant Colony Optimization and Population-Based Evolutionary Algorithms on Dynamic Problems
DEFF Research Database (Denmark)
Lissovoi, Andrei
exist more complex oscillations that cannot be tracked with a polynomial-size colony. MMAS and (μ+1) EA on Maze We analyse the behaviour of a (μ + 1) EA with genotype diversity on a dynamic fitness function Maze, extended to a finite-alphabet search space. We prove that the (μ + 1) EA is able to track...... the dynamic optimum for finite alphabets up to size μ, while MMAS is able to do so for any finite alphabet size. Parallel Evolutionary Algorithms on Maze. We prove that while a (1 + λ) EA is unable to track the optimum of the dynamic fitness function Maze for offspring population size up to λ = O(n1-ε...... analysis showing how closely the EA can track the dynamically moving optimum over time. These results are also extended to a finite-alphabet search space....
International Nuclear Information System (INIS)
Highlights: ► PSO and ACO algorithms are hybridized for forecasting energy demands of Turkey. ► Linear and quadratic forms are developed to meet the fluctuations of indicators. ► GDP, population, export and import have significant impacts on energy demand. ► Quadratic form provides better fit solution than linear form. ► Proposed approach gives lower estimation error than ACO and PSO, separately. - Abstract: This paper proposes a new hybrid method (HAP) for estimating energy demand of Turkey using Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). Proposed energy demand model (HAPE) is the first model which integrates two mentioned meta-heuristic techniques. While, PSO, developed for solving continuous optimization problems, is a population based stochastic technique; ACO, simulating behaviors between nest and food source of real ants, is generally used for discrete optimizations. Hybrid method based PSO and ACO is developed to estimate energy demand using gross domestic product (GDP), population, import and export. HAPE is developed in two forms which are linear (HAPEL) and quadratic (HAPEQ). The future energy demand is estimated under different scenarios. In order to show the accuracy of the algorithm, a comparison is made with ACO and PSO which are developed for the same problem. According to obtained results, relative estimation errors of the HAPE model are the lowest of them and quadratic form (HAPEQ) provides better-fit solutions due to fluctuations of the socio-economic indicators.
The ant colony metaphor in continuous spaces using boundary search
Leguizamón, Guillermo
2003-01-01
This paper presents an application of the ant colony metaphor for continuous space optimization problems. The ant algortihm proposed works following the principle of the ant colony approach, i.e., a population of agents iteratively, cooperatively, and independently search for a solution. Each ant in the distributed algorithm applies a local search operator which explores the neighborhood region of a particular point in the search space (individual search level). The local search operator i...
求解TSP的新量子蚁群算法%Novel quantum ant colony algorithm for TSP
Institute of Scientific and Technical Information of China (English)
李絮; 刘争艳; 谭拂晓
2011-01-01
鉴于蚁群算法(ACA)在求解TSP时表现出的优越性,以及量子进化算法(QEA)在求解组合优化问题时表现出的高效性,将ACA与QEA的算法思想进行融合,提出一种新的求解TSP的量子蚁群算法.该算法对各路径上的信息素进行量子比特编码,设计了一种新的信息素表示方式,即量子信息素；采用量子旋转门及最优路径对信息素进行更新,加快算法收敛速度；为了避免搜索陷入局部最优,设计了一种量子交叉策略,以改善种群信息结构.仿真实验结果表明了该算法具有较快的收敛速度和全局寻优能力,性能明显优于ACS.%Ant Colony Algorithm (ACA) demonstrates the superiority in solving TSP, and Quantum Evolution Algorithm (QEA) has the performance of high efficiency on combinational optimization problems, so combining the thought of AC A with QEA, a novel quantum ant colony algorithm for TSP is proposed.In this algorithm,the pheromone on each path is encoded by a group of quantum bits, and a new pheromone representation is designed,called quantum pheromone.The quantum rotation gate and the best tour are applied to update the pheromone so as to accelerate its convergence speed.To avoid the search falling into local optimum,the strategy of quantum crossover is designed to improve the information structure of population. Simulation results show that the algorithm has fast convergence speed and global optimal ability, and the algorithm is more effective than ACS.
Tuning PID Controller Using Multiobjective Ant Colony Optimization
Pierre Borne; Noureddine Liouane; Ibtissem Chiha
2012-01-01
This paper treats a tuning of PID controllers method using multiobjective ant colony optimization. The design objective was to apply the ant colony algorithm in the aim of tuning the optimum solution of the PID controllers (Kp, Ki, and Kd) by minimizing the multiobjective function. The potential of using multiobjective ant algorithms is to identify the Pareto optimal solution. The other methods are applied to make comparisons between a classic approach based on the “Ziegler-Nichols” method an...
Runtime analysis of the 1-ANT ant colony optimizer
DEFF Research Database (Denmark)
Doerr, Benjamin; Neumann, Frank; Sudholt, Dirk;
2011-01-01
The runtime analysis of randomized search heuristics is a growing field where, in the last two decades, many rigorous results have been obtained. First runtime analyses of ant colony optimization (ACO) have been conducted only recently. In these studies simple ACO algorithms such as the 1-ANT are...... investigated. The influence of the evaporation factor in the pheromone update mechanism and the robustness of this parameter w.r.t. the runtime behavior have been determined for the example function OneMax.This work puts forward the rigorous runtime analysis of the 1-ANT on the example functions Leading......Ones and BinVal. With respect to Evolutionary Algorithms (EAs), such analyses were essential to develop methods for the analysis on more complicated problems. The proof techniques required for the 1-ANT, unfortunately, differ significantly from those for EAs, which means that a new reservoir of methods has...
Directory of Open Access Journals (Sweden)
Jun Chen
2014-01-01
Full Text Available In vibration-based structural health monitoring of existing large civil structures, it is difficult, sometimes even impossible, to measure the actual excitation applied to structures. Therefore, an identification method using output-only measurements is crucial for the practical application of structural health monitoring. This paper integrates the ant colony optimization (ACO algorithm into the framework of the complete inverse method to simultaneously identify unknown structural parameters and input time history using output-only measurements. The complete inverse method, which was previously suggested by the authors, converts physical or spatial information of the unknown input into the objective function of an optimization problem that can be solved by the ACO algorithm. ACO is a newly developed swarm computation method that has a very good performance in solving complex global continuous optimization problems. The principles and implementation procedure of the ACO algorithm are first introduced followed by an introduction of the framework of the complete inverse method. Construction of the objective function is then described in detail with an emphasis on the common situation wherein a limited number of actuators are installed on some key locations of the structure. Applicability and feasibility of the proposed method were validated by numerical examples and experimental results from a three-story building model.
网格环境中一种改进的蚁群任务调度算法%Improved ant colony algorithm for task scheduling in grid
Institute of Scientific and Technical Information of China (English)
黄漾; 李肯立; 曾文
2012-01-01
针对在蚁群算法中初始参数设置对算法收敛性能的影响较大,提出了一种新的改进蚁群算法NACA(new ant colony algorithm),针对蚁群算法中的四个关键参数随机编码,得到初始的染色体,从而获得一组较优解；再利用遗传算法的优点对上一步的结果单点顺序交叉、对换变异、选择操作以产生更好的解；然后以这组数据为蚁群算法下一次的工作备选值,并进行最大次数的循环迭代直至停止,即求得参数组合的近似最优解.将它应用于网格系统任务调度中,系统的性能得到了明显的改善.仿真模拟结果表明,所提出的算法具有更短的调度长度和更宽的适应性,当任务已知时,执行时间约缩短了21.7％,且负载变化时对网格中各处理器资源的影响大大减小.%It has greater impact on the algorithm convergence that setting the initial parameters in ant colony algorithm. This paper presented an improved ant colony algorithm NACA. Firstly, it made the four parameters of the ant colony algorithm coding randomly and got the chromosomes, a set of optimum solutions could be gained by using the ant colony algorithm. Then they crossover, mutate and select by using the advantages of genetic algorithms. Finally, took the value of this group to explore the next round as the ant colony' s original value, ran the maximum number of loop iterations until it stopping. The performance of the system had been significantly improved when it was applied to the grid task scheduling systems. The result of algorithm analysis shows the proposed scheduling algorithm has a shorter length and wider adaptability. When the task is known, execution time can be reduced about 21. 7% . The execution time of the task is shorten greatly.
Lifecycle-Based Binary Ant Colony Optimization Algorithm%基于生命周期的二元蚁群优化算法
Institute of Scientific and Technical Information of China (English)
程美英; 倪志伟; 朱旭辉
2014-01-01
将自然生态系统中生物生命周期的思想引入二元蚁群优化算法中，通过对蚂蚁设置相应的营养阈值而执行繁殖、迁徙、死亡操作，从而保持种群的动态多样性，进而克服二元蚁群优化算法易陷入局部最优的缺陷，然后结合分形维数将该算法应用于属性约简问题中，通过UCI中的6个数据集进行测试，结果表明该算法具有较好的可行性和有效性。%The biological life cycle in natural ecosystem is introduced into binary ant colony optimization algorithm, and the main idea is to execute breeding, migrating and dying operations by setting relevant nutritious threshold value to the ants. Thus, the dynamic diversity of the population is maintained and the drawback that binary ant colony optimization algorithm easily traps in local optimum is overcome. The proposed algorithm, lifecycle-based binary ant colony optimization algorithm ( LCBBACO) , is combined with fractal dimension to attribute reduction problem. The experimental results on 6 UCI datasets show that the method has preferable feasibility and effectiveness.
Shuai Deng; Yanhui Li; Hao Guo; Bailing Liu
2016-01-01
This paper presents a closed-loop location-inventory-routing problem model considering both quality defect returns and nondefect returns in e-commerce supply chain system. The objective is to minimize the total cost produced in both forward and reverse logistics networks. We propose a combined optimization algorithm named hybrid ant colony optimization algorithm (HACO) to address this model that is an NP-hard problem. Our experimental results show that the proposed HACO is considerably effici...
Incremental Web Usage Mining Based on Active Ant Colony Clustering
Institute of Scientific and Technical Information of China (English)
SHEN Jie; LIN Ying; CHEN Zhimin
2006-01-01
To alleviate the scalability problem caused by the increasing Web using and changing users' interests, this paper presents a novel Web Usage Mining algorithm-Incremental Web Usage Mining algorithm based on Active Ant Colony Clustering. Firstly, an active movement strategy about direction selection and speed, different with the positive strategy employed by other Ant Colony Clustering algorithms, is proposed to construct an Active Ant Colony Clustering algorithm, which avoid the idle and "flying over the plane" moving phenomenon, effectively improve the quality and speed of clustering on large dataset. Then a mechanism of decomposing clusters based on above methods is introduced to form new clusters when users' interests change. Empirical studies on a real Web dataset show the active ant colony clustering algorithm has better performance than the previous algorithms, and the incremental approach based on the proposed mechanism can efficiently implement incremental Web usage mining.
An ant colony algorithm and simulation for solving minimum MPR sets%求解最小MPR集的蚁群算法与仿真
Institute of Scientific and Technical Information of China (English)
钟珞; 赵先明; 夏红霞
2011-01-01
Based on analyzing the defects of a heuristic algorithm of greedy strategy, an ant colony algorithm was imported to solve the minimum MPR set.First of all, a node and its out and in-degrees were defined, and in accordance with the out and in-degree constraints of the node, ant colony algorithms were given based on the graphics to find the minimum MPR set.Then, three kinds of ant colony algorithm models, the Ant-Cycle, Ant-Quantity,and Ant-Density models, were improved, and the convergence curves of the three kinds of models were analyzed and tested.An ideal uniform topology and a circular distribution topology were both used in experiments.Former experimental results showed that the Ant-Cycle model was faster in convergence speed; the latter results showed that the Ant-Cycle and Ant-Density models both have advantages.Therefore, ant colony algorithm model selection of the minimum MPR set might be subject to topology.Finally, OPNET was used based on the above algorithm for simulation.It adopted the data link ' s point-to-multipoint calling mode.The selected statistics show connectivity and data consistency among the nodes, which means that the algorithm is reasonable.%在分析利用贪心策略启发式算法求解最小MPR集的缺陷基础上,引入蚁群算法对最小MPR集进行求解.首 先定义了节点及其出度和入度,并根据节点的出度和入度限制,给出了求解最小MPR集的蚁群算法.然后,对蚁群算法的3种模型Ant-Cycle、Ant-Quantity和Ant-Density加以改进,并对这3种改进模型的收敛性进行分析与实验.实验采用了圆形分布和理想均匀分布2种拓扑结构,前者实验结果表明Ant-Cycle模型的收敛速度较快,后者结果表明Ant-Cycle模型和Ant-Density模型各有优势.因此,最小MPR集的蚁群算法的模型选择需依据拓扑结构确定.最后,使用OPNET基于该算法对数据链的点对多点的点名呼叫工作方式进行模拟仿真,选择的统计量显示了节点的连
Energy Technology Data Exchange (ETDEWEB)
Garcia-Pareja, S.; Galan, P.; Manzano, F.; Brualla, L.; Lallena, A. M. [Servicio de Radiofisica Hospitalaria, Hospital Regional Universitario ' ' Carlos Haya' ' , Avda. Carlos Haya s/n, E-29010 Malaga (Spain); Unidad de Radiofisica Hospitalaria, Hospital Xanit Internacional, Avda. de los Argonautas s/n, E-29630 Benalmadena (Malaga) (Spain); NCTeam, Strahlenklinik, Universitaetsklinikum Essen, Hufelandstr. 55, D-45122 Essen (Germany); Departamento de Fisica Atomica, Molecular y Nuclear, Universidad de Granada, E-18071 Granada (Spain)
2010-07-15
Purpose: In this work, the authors describe an approach which has been developed to drive the application of different variance-reduction techniques to the Monte Carlo simulation of photon and electron transport in clinical accelerators. Methods: The new approach considers the following techniques: Russian roulette, splitting, a modified version of the directional bremsstrahlung splitting, and the azimuthal particle redistribution. Their application is controlled by an ant colony algorithm based on an importance map. Results: The procedure has been applied to radiosurgery beams. Specifically, the authors have calculated depth-dose profiles, off-axis ratios, and output factors, quantities usually considered in the commissioning of these beams. The agreement between Monte Carlo results and the corresponding measurements is within {approx}3%/0.3 mm for the central axis percentage depth dose and the dose profiles. The importance map generated in the calculation can be used to discuss simulation details in the different parts of the geometry in a simple way. The simulation CPU times are comparable to those needed within other approaches common in this field. Conclusions: The new approach is competitive with those previously used in this kind of problems (PSF generation or source models) and has some practical advantages that make it to be a good tool to simulate the radiation transport in problems where the quantities of interest are difficult to obtain because of low statistics.
International Nuclear Information System (INIS)
Purpose: In this work, the authors describe an approach which has been developed to drive the application of different variance-reduction techniques to the Monte Carlo simulation of photon and electron transport in clinical accelerators. Methods: The new approach considers the following techniques: Russian roulette, splitting, a modified version of the directional bremsstrahlung splitting, and the azimuthal particle redistribution. Their application is controlled by an ant colony algorithm based on an importance map. Results: The procedure has been applied to radiosurgery beams. Specifically, the authors have calculated depth-dose profiles, off-axis ratios, and output factors, quantities usually considered in the commissioning of these beams. The agreement between Monte Carlo results and the corresponding measurements is within ∼3%/0.3 mm for the central axis percentage depth dose and the dose profiles. The importance map generated in the calculation can be used to discuss simulation details in the different parts of the geometry in a simple way. The simulation CPU times are comparable to those needed within other approaches common in this field. Conclusions: The new approach is competitive with those previously used in this kind of problems (PSF generation or source models) and has some practical advantages that make it to be a good tool to simulate the radiation transport in problems where the quantities of interest are difficult to obtain because of low statistics.
Ant Colony Optimization for Train Scheduling: An Analysis
Sudip Kumar Sahana; Aruna Jain; Prabhat Kumar Mahanti
2014-01-01
This paper deals on cargo train scheduling between source station and destination station in Indian railways scenario. It uses Ant Colony Optimization (ACO) technique which is based on ant’s food finding behavior. Iteration wise convergence process and the convergence time for the algorithm are studied and analyzed. Finally, the run time analysis of Ant Colony Optimization Train Scheduling (ACOTS) and Standard Train Scheduling (STS) algorithm has been performed.
Ant Colony Optimization for Inferring Key Gene Interactions
Raza, Khalid; Kohli, Mahish
2014-01-01
Inferring gene interaction network from gene expression data is an important task in systems biology research. The gene interaction network, especially key interactions, plays an important role in identifying biomarkers for disease that further helps in drug design. Ant colony optimization is an optimization algorithm based on natural evolution and has been used in many optimization problems. In this paper, we applied ant colony optimization algorithm for inferring the key gene interactions f...
A critical analysis of parameter adaptation in ant colony optimization
PELLEGRINI, Paola; Stützle, Thomas; Birattari, Mauro
2012-01-01
Applying parameter adaptation means operating on parameters of an algorithm while it is tackling an instance. For ant colony optimization, several parameter adaptation methods have been proposed. In the literature, these methods have been shown to improve the quality of the results achieved in some particular contexts. In particular, they proved to be successful when applied to novel ant colony optimization algorithms for tackling problems that are not a classical testbed for optimization alg...
改进蚁群算法在二次分配问题中的应用%Application of Improved Ant Colony Algorithm for Quadratic Assignment Problems
Institute of Scientific and Technical Information of China (English)
袁东锋; 吕聪颖
2013-01-01
为了解决基本蚁群算法在求解大规模二次分配问题时暴露出的缺陷,本文提出一种改进的蚁群算法.在基本蚂蚁算法中,采用全局信息素更新策略,使用距离及流量作为启发式信息并引入局部优化策略,对每代的最优解进行改进,进一步加快算法的收敛速度.通过对于二次分配问题的3种不同类型的问题进行实验,将改进的蚁群算法与基本蚂蚁算法及混合遗传算法进行比较,结果表明该改进算法具有更优的性能.%In order to solve the problems that the basic ant colony algorithm for solving large scale quadratic assignment has revealed defects, this paper proposes an improved ant colony algorithm. This algorithm adopts the global pheromone update strategy, the use of distance and traffic as heuristic information and the introduction of local optimization strategy. The optimal solution for each generation is to improve and further accelerate the convergence speed. For the quadratic assignment problem through three different types of problems, and improved ant colony algorithm with the basic ant algorithm and the hybrid genetic algorithm are compared, the experiments show that the improved method has better performance.
Optic disc detection using ant colony optimization
Dias, Marcy A.; Monteiro, Fernando C.
2012-09-01
The retinal fundus images are used in the treatment and diagnosis of several eye diseases, such as diabetic retinopathy and glaucoma. This paper proposes a new method to detect the optic disc (OD) automatically, due to the fact that the knowledge of the OD location is essential to the automatic analysis of retinal images. Ant Colony Optimization (ACO) is an optimization algorithm inspired by the foraging behaviour of some ant species that has been applied in image processing for edge detection. Recently, the ACO was used in fundus images to detect edges, and therefore, to segment the OD and other anatomical retinal structures. We present an algorithm for the detection of OD in the retina which takes advantage of the Gabor wavelet transform, entropy and ACO algorithm. Forty images of the retina from DRIVE database were used to evaluate the performance of our method.
Directory of Open Access Journals (Sweden)
P. Mathiyalagan
2013-10-01
Full Text Available As grid is a heterogeneous environment, finding an optimal schedule for the job is always a complex task. In this paper, a hybridization technique using intelligent water drops and Ant colony optimization which are nature-inspired swarm intelligence approaches are used to find the best resource for the job. Intelligent water drops involves in finding out all matching resources for the job requirements and the routing information (optimal path to reach those resources. Ant Colony optimization chooses the best resource among all matching resources for the job. The objective of this approach is to converge to the optimal schedule faster, minimize the make span of the job, improve load balancing of resources and efficient utilization of available resources.
TestAnt: an ant colony system approach to sequential testing under precedence constraints
Çatay, Bülent; Catay, Bulent; Özlük, Özgür; Ozluk, Ozgur; Ünlüyurt, Tonguç; Unluyurt, Tonguc
2011-01-01
We consider the problem of minimum cost sequential testing (diagnosis) of a series (or parallel) system under precedence constraints. We model the problem as a nonlinear integer program. We develop and implement an ant colony algorithm for the problem. We demonstrate the performance of this algorithm for special type of instances for which the optimal solutions can be found in polynomial time. In addition, we compare the performance of the ant colony algorithm with a branch and bound algorith...
An effective method for segmentation of MR brain images using the ant colony optimization algorithm.
Taherdangkoo, Mohammad; Bagheri, Mohammad Hadi; Yazdi, Mehran; Andriole, Katherine P
2013-12-01
Since segmentation of magnetic resonance images is one of the most important initial steps in brain magnetic resonance image processing, success in this part has a great influence on the quality of outcomes of subsequent steps. In the past few decades, numerous methods have been introduced for classification of such images, but typically they perform well only on a specific subset of images, do not generalize well to other image sets, and have poor computational performance. In this study, we provided a method for segmentation of magnetic resonance images of the brain that despite its simplicity has a high accuracy. We compare the performance of our proposed algorithm with similar evolutionary algorithms on a pixel-by-pixel basis. Our algorithm is tested across varying sets of magnetic resonance images and demonstrates high speed and accuracy. It should be noted that in initial steps, the algorithm is computationally intensive requiring a large number of calculations; however, in subsequent steps of the search process, the number is reduced with the segmentation focused only in the target area. PMID:23563793
Directory of Open Access Journals (Sweden)
Sharvani G S
2012-10-01
Full Text Available Designing an effective load balancing algorithm is difficult due to Dynamic topology of MANET. Toaddress the problem, a load balancing routing algorithm namely Modified Termite Algorithm (MTA hasbeen developed based on ant’s food foraging behavior. Stability of the link is determined based on nodestability factor ‘’. The stability factor “ “of the node is the ratio defined between the “hello sent” and“hello replied” by a node to its neighbors. This also indicates the link stability in relation to other pathstowards the destination. A higher ratio of “” indicates that the neighbor node is more stable. Using thisconcept pheromone evaporation for the stable node is fine tuned such that if the ratio “” is more, theevaporation is slow and if “” is less the evaporation is faster. This leads to decreasing of the pheromonecontent in an optimal path which may result in congestion. These paths can be avoided using efficientevaporation technique. The MTA developed by adopting efficient pheromone evaporation technique willaddress the load balancing problems and expected to enhance the performance of the network in terms ofthroughput, and reduces End-to-end delay and Routing overheads
Modeling of Vector Quantization Image Coding in an Ant Colony System
Institute of Scientific and Technical Information of China (English)
LIXia; LUOXuehui; ZHANGJihong
2004-01-01
Ant colony algorithm is a newly emerged stochastic searching optimization algorithm in recent years. In this paper, vector quantization image coding is modeled as a stochastic optimization problem in an Ant colony system (ACS). An appropriately adapted ant colony algorithm is proposed for vector quantization codebook design. Experimental results show that the ACS-based algorithm can produce a better codebook and the improvement of Pixel signal-to-noise ratio (PSNR) exceeds 1dB compared with the conventional LBG algorithm.
Ant Colony Optimization and Hypergraph Covering Problems
Pat, Ankit
2011-01-01
Ant Colony Optimization (ACO) is a very popular metaheuristic for solving computationally hard combinatorial optimization problems. Runtime analysis of ACO with respect to various pseudo-boolean functions and different graph based combinatorial optimization problems has been taken up in recent years. In this paper, we investigate the runtime behavior of an MMAS*(Max-Min Ant System) ACO algorithm on some well known hypergraph covering problems that are NP-Hard. In particular, we have addressed the Minimum Edge Cover problem, the Minimum Vertex Cover problem and the Maximum Weak- Independent Set problem. The influence of pheromone values and heuristic information on the running time is analysed. The results indicate that the heuristic information has greater impact towards improving the expected optimization time as compared to pheromone values. For certain instances of hypergraphs, we show that the MMAS* algorithm gives a constant order expected optimization time when the dominance of heuristic information is ...
Using Improved Ant Colony Algorithm to Investigate EMU Circulation Scheduling Problem
Directory of Open Access Journals (Sweden)
Yu Zhou
2014-01-01
Full Text Available High-speed railway is one of the most important ways to solve the long-standing travel difficulty problem in China. However, due to the high acquisition and maintenance cost, it is impossible for decision-making departments to purchase enough EMUs to satisfy the explosive travel demand. Therefore, there is an urgent need to study how to utilize EMU more efficiently and reduce costs in the case of completing a given task in train diagram. In this paper, an EMU circulation scheduling model is built based on train diagram constraints, maintenance constraints, and so forth; in the model solving process, an improved ACA algorithm has been designed. A case study is conducted to verify the feasibility of the model. Moreover, contrast tests have been carried out to compare the efficiency between the improved ACA and the traditional approaches. The results reveal that improved ACA method can solve the model with less time and the quality of each representative index is much better, which means that efficiency of the improved ACA method is higher and better scheduling scheme can be obtained.
Exploration adjustment by ant colonies.
Doran, Carolina; Stumpe, Martin C; Sendova-Franks, Ana; Franks, Nigel R
2016-01-01
How do animals in groups organize their work? Division of labour, i.e. the process by which individuals within a group choose which tasks to perform, has been extensively studied in social insects. Variability among individuals within a colony seems to underpin both the decision over which tasks to perform and the amount of effort to invest in a task. Studies have focused mainly on discrete tasks, i.e. tasks with a recognizable end. Here, we study the distribution of effort in nest seeking, in the absence of new nest sites. Hence, this task is open-ended and individuals have to decide when to stop searching, even though the task has not been completed. We show that collective search effort declines when colonies inhabit better homes, as a consequence of a reduction in the number of bouts (exploratory events). Furthermore, we show an increase in bout exploration time and a decrease in bout instantaneous speed for colonies inhabiting better homes. The effect of treatment on bout effort is very small; however, we suggest that the organization of work performed within nest searching is achieved both by a process of self-selection of the most hard-working ants and individual effort adjustment. PMID:26909180
An ant colony approach for image texture classification
Ye, Zhiwei; Zheng, Zhaobao; Ning, Xiaogang; Yu, Xin
2005-10-01
Ant colonies, and more generally social insect societies, are distributed systems that show a highly structured social organization in spite of the simplicity of their individuals. As a result of this swarm intelligence, ant colonies can accomplish complex tasks that far exceed the individual capacities of a single ant. As is well known that aerial image texture classification is a long-term difficult problem, which hasn't been fully solved. This paper presents an ant colony optimization methodology for image texture classification, which assigns N images into K type of clusters as clustering is viewed as a combinatorial optimization problem in the article. The algorithm has been tested on some real images and performance of this algorithm is superior to k-means algorithm. Computational simulations reveal very encouraging results in terms of the quality of solution found.
Enhanced ant colony optimization for multiscale problems
Hu, Nan; Fish, Jacob
2016-03-01
The present manuscript addresses the issue of computational complexity of optimizing nonlinear composite materials and structures at multiple scales. Several solutions are detailed to meet the enormous computational challenge of optimizing nonlinear structures at multiple scales including: (i) enhanced sampling procedure that provides superior performance of the well-known ant colony optimization algorithm, (ii) a mapping-based meshing of a representative volume element that unlike unstructured meshing permits sensitivity analysis on coarse meshes, and (iii) a multilevel optimization procedure that takes advantage of possible weak coupling of certain scales. We demonstrate the proposed optimization procedure on elastic and inelastic laminated plates involving three scales.
Optimization design of missile structure based on improved ant colony algorithm%基于改进型蚁群算法的导弹结构优化设计
Institute of Scientific and Technical Information of China (English)
邓建军; 韩晓明; 韩小斌
2012-01-01
简要探讨基本蚁群算法及改进型蚁群算法的原理，分别运用蚁群算法及改进型蚁群算法对导弹结构设计实例进行了优化计算，结果表明改进型蚁群算法比基本蚁群算法具有更优的计算结果，验证了改进型蚁群算法应用于导弹结构优化设计的合理性与有效性，该算法对复杂的结构优化设计亦有一定的参考价值。%The principle of basic ant colony algorithm and improved ant colony algorithm are briefly introduced. Both of the two algorithms are applied to an example of missile structure optimization. The results show that improved ant colony algo-rithm is much better than basic ant colony algorithm, it also verified improved ant colony algorithm is effective and feasible in missile structure optimization. The algorithm has a certain reference value for the design of complex structure optimization design.
蚁群聚类LF算法在MATLAB中的实现%Implementation of LF ant colony clustering algorithm in MATLAB
Institute of Scientific and Technical Information of China (English)
闫保权
2013-01-01
聚类是数据挖掘的主要任务之一,基于蚂蚁堆形成原理的LF算法是蚁群聚类的经典算法.给出了LF算法在Matlab环境下的详细实现过程,包含算法的基本思想、使用的数据结构、算法的流程图,最后使用UCI数据集Iris进行了验证,给出了算法运行的参数设置数据和算法运行结果的图形表示.%Clustering is one of the major tasks of data mining; the LF algorithm based on ant heap forming principle is ant colony clustering classic algorithms. This paper presents the detailed implementation process of the LF algorithm in MATLAB the environment contains the basic idea of the algorithm, and use of the data structure, algorithm flow chart, the last UCI data sets Iris verified given set of parameters of the algorithm running data and algorithm running results of the graphical.
Ant Colony Optimization for Capacity Problems
Directory of Open Access Journals (Sweden)
Tad Gonsalves
2015-01-01
Full Text Available This paper deals with the optimization of the capac ity of a terminal railway station using the Ant Colony Optimization algorithm. The capacity of the terminal station is defined as the number of trains that depart from the station in un it interval of time. The railway capacity optimization problem is framed as a typical symmetr ical Travelling Salesman Problem (TSP, with the TSP nodes representing the train arrival / departure events and the TSP total cost representing the total time-interval of the schedul e. The application problem is then optimized using the ACO algorithm. The simulation experiments validate the formulation of the railway capacity problem as a TSP and the ACO algorithm pro duces optimal solutions superior to those produced by the domain experts.
Institute of Scientific and Technical Information of China (English)
李彦苍; 恒北北; 彭双红; 程秋月; 伴晨光
2012-01-01
针对基本蚁群算法的过早收敛问题,引入信息熵,通过优化参数,对基本蚁群算法进行改进,进而寻找结构的最短失效路径.从可靠指标的几何意义出发,利用罚函数法,将结构可靠指标的求解问题转化成相应的无约束优化问题,采用粒子群算法对结构可靠指标进行求解计算.以十杆桁架为例,采用响应面法、遗传算法与本算法对结构可靠指标进行对比计算,结果表明改进蚁群与粒子群算法的收敛速度快,计算精度高.%In view of the premature convergence problem of the basic Ant Colony Optimization Algorithm , the basic ant colony algorithm was improved through the introduction of information entropy and the improvement of parameter T , and the improved ant colony algorithm was used to find the structure of the weakest failure path. On the basis of the geometrical meaning of structural reliability index, the problem of structural reliability index was converted to the corresponding unconstrained optimization problem by use of the penalty function method, and then the Particle Swarm Optimization algorithm was used to calculate the structural reliability index. Taking 10 -bar truss for example, its reliability index calculation was compared respectively by means of response surface method, the improved genetic algorithm and this algorithm, it is concluded that particle swarm algorithm and ant colony algorithm has high convergent rapid and high precision for solving reliability index.
Institute of Scientific and Technical Information of China (English)
杨惠; 李峰
2009-01-01
为了克服粒子群算法和蚁群算法的缺陷,将改进的粒子群算法和蚁群算法进行融合,形成了PAAA算法,并将此算法应用于自主清洁机器人行为路径的仿真实验.结果表明:PAAA在求解性能上优于粒子群算法,在时间效率上优于蚁群算法.%In order to overcome the deficiencies of particle swarm optimization and ant colony algorithm,this paper integrates the improved particle swarm optimization and ant colony algorithm,formats the PAAA,this algorithm is applied to auto-cleaning robot simulation path.The results show that:PAAA superior performance in solving particle swarm optimization,in terms of time better than the ant colony algorithm efficiency.
Ant Colony Optimization and the Minimum Cut Problem
DEFF Research Database (Denmark)
Kötzing, Timo; Lehre, Per Kristian; Neumann, Frank;
2010-01-01
Ant Colony Optimization (ACO) is a powerful metaheuristic for solving combinatorial optimization problems. With this paper we contribute to the theoretical understanding of this kind of algorithm by investigating the classical minimum cut problem. An ACO algorithm similar to the one that was proved...
Institute of Scientific and Technical Information of China (English)
叶文; 马登武; 范洪达
2005-01-01
蚁群算法是一种新型的基于群体的仿生算法.采用蚁群算法实现了飞机低空突防的航路规划,为航路规划问题提供了新的解决思路.并对原始蚁群算法进行了改进,提出了保留最优解、自适应选择策略和自适应信息素调整准则,有效地提高了算法的收敛速度和解的性能.最后用计算机进行了仿真,取得了较好的结果.%The ant colony algorithm is a new class of population basic algorithm. The path planning is realized by the use of ant colony algorithm when the plane executes the low altitude penetration, which provides a new method for the path planning. In the paper the traditional ant colony algorithm is improved, and measures of keeping optimization, adaptively selecting and adaptively adjusting are applied, by which better path at higher convergence speed can be found. Finally the algorithm is implemented with computer simulation and preferable results are obtained.
Recruitment Strategies and Colony Size in Ants
Planqué, Robert; van den Berg, Jan Bouwe; Franks, Nigel R
2010-01-01
Ants use a great variety of recruitment methods to forage for food or find new nests, including tandem running, group recruitment and scent trails. It has been known for some time that there is a loose correlation across many taxa between species-specific mature colony size and recruitment method. Very small colonies tend to use solitary foraging; small to medium sized colonies use tandem running or group recruitment whereas larger colonies use pheromone recruitment trails. Until now, explana...
Aircraft Route Planning Based on Improved Ant Colony Algorithm%基于改进蚁群算法的飞行器航迹规划
Institute of Scientific and Technical Information of China (English)
张臻; 王光磊
2011-01-01
针对蚁群算法在航迹规划中易于过早陷入局部最优解这一问题,提出了一种双向自适应改进蚁群算法。使用栅格节点对飞行空间进行建模,在搜索过程中以移动方向一定范围内最大信息素和目标引导函数作为启发因子。根据蚁群算法处理该问题时的信息素散播特点,重构了信息素的更新策略和散播方式。通过信息素的震荡变化和挥发系数的自适应调整,扩大了搜索空间,提高了搜索全局性,获得了一种有效的航迹规划算法,并取得了较好的仿真结果。%The prominent problem of the ant colony algorithm in aircraft route planning is its tendency to be trapped into local optimal solution too early.An adaptive dual population ant colony algorithm is proposed to solve the problem.Using modeling information g
Recruitment strategies and colony size in ants.
Directory of Open Access Journals (Sweden)
Robert Planqué
Full Text Available Ants use a great variety of recruitment methods to forage for food or find new nests, including tandem running, group recruitment and scent trails. It has been known for some time that there is a loose correlation across many taxa between species-specific mature colony size and recruitment method. Very small colonies tend to use solitary foraging; small to medium sized colonies use tandem running or group recruitment whereas larger colonies use pheromone recruitment trails. Until now, explanations for this correlation have focused on the ants' ecology, such as food resource distribution. However, many species have colonies with a single queen and workforces that grow over several orders of magnitude, and little is known about how a colony's organization, including recruitment methods, may change during its growth. After all, recruitment involves interactions between ants, and hence the size of the colony itself may influence which recruitment method is used--even if the ants' behavioural repertoire remains unchanged. Here we show using mathematical models that the observed correlation can also be explained by recognizing that failure rates in recruitment depend differently on colony size in various recruitment strategies. Our models focus on the build up of recruiter numbers inside colonies and are not based on optimality arguments, such as maximizing food yield. We predict that ant colonies of a certain size should use only one recruitment method (and always the same one rather than a mix of two or more. These results highlight the importance of the organization of recruitment and how it is affected by colony size. Hence these results should also expand our understanding of ant ecology.
Heuristic Ant Colony Optimization with Applications in Communication Systems
Directory of Open Access Journals (Sweden)
Mateus de P. Marques
2014-05-01
Full Text Available This work explores the heuristic optimization algorithm based on ant colonies (ACO, deployed on complex optimization problems, aiming to achieve an iterative and feasible method which is able to solve NP and NP-Hard problems related to wireless networks. Furthermore, the convergence and performance of the Ant Colony Optimization algorithm for continuous domains are addressed through dozens of benchmark functions, which in turn, differ on each other regarding the number of dimensions and the difficulty w.r.t. the optimization (number of local optima. Finally, the applicability of the ACO is depicted in an minimum power control problem for CDMA networks.
基于元胞蚂蚁算法的防空靶机航路规划研究%Route Planning of Anti-Air Target Drone Based on Cellular-Ant Colony Algorithm
Institute of Scientific and Technical Information of China (English)
刘志强; 雷宇曜; 阳再清
2014-01-01
防空靶机飞行航路设计是实现靶机有效控制，确保高效完成供靶任务的保障。通过对靶机三维航路规划模型进行分析，给出了元胞蚂蚁算法的航路规划模型的求解方法及算法实现的具体流程，并分别应用蚁群算法和元胞蚂蚁算法进行仿真实验。结果表明：元胞蚂蚁算法克服了蚁群算法收敛速度慢、陷于局部最小值的缺陷，可得到较优的航路。%The design of the flight airway of anti-air target is essential to the effective target control and the high effective completion of target supply task. Through the analysis of the three-dimensional airway design model, the solution method and corresponding algorithm flow of the cellular-ant colony algorithm is provided in this paper. The simulation experiment of the ant colony and cellular-ant colony algorithms is carried out, which shows that the cellular ant algorithm over comes the ant colony algorithm disadvantages of the slow convergence and local optima, and it is able to obtain optimal airway.
Solving Large-scale Traveling Salesman Problem by Quantum Ant Colony Algorithm%用量子蚁群算法求解大规模旅行商问题
Institute of Scientific and Technical Information of China (English)
李煜; 马良
2012-01-01
针对旅行商问题(TSP),提出了一种新的混合量子优化算法——量子蚁群算法.量子蚁群算法采用量子比特的概率幅表示蚂蚁的当前位置,采用量子旋转门更新蚂蚁的位置,选取国际通用的TSP实例库中多个实例进行测试.仿真实验表明,该算法具有很好的精确度和鲁棒性,可使搜索空间加倍,比传统的蚁群算法具有更好的种群多样性.%Based on the combination of the quantum theory and ant colony optimization, a novel algorithm, the quantum ant colony algorithm, was proposed. Ants's positions were represented by a group of quantum bits and the quantum rotation gates were designed to update the ants' positions for enabling the ants' movements. The classical TSP was successfully solved by using the quantum ant colony algorithm, taking series of typical instances as the examples. The computational results show the effectiveness and robustness of the algorithm in numerical simulations. The algorithm can find the satisfactory solutions with a small size of populations and minimal relative error.
Solution to complex container loading problem based on ant colony algorithm%蚁群算法求解复杂集装箱装载问题
Institute of Scientific and Technical Information of China (English)
杜立宁; 张德珍; 陈世峰
2011-01-01
针对复杂集装箱装载问题(CLP),应用启发式信息与蚁群算法求解了最优装栽方案.首先,建立了复杂集装箱装载问题的数学模型,利用蚁群算法对解空间的强搜索能力、潜在并行性及可扩充性,结合三空间分解策略将布局空间依次分割;然后,装入满足约束条件的最优货物块,完成不同大小三维矩形货物的装载布局.在此基础上,设计了基于空间划分策略的蚁群算法.最后以700件货物装入40尺(12.025 m)高柜箱进行计算,结果表明该方法能提高集装箱的空间利用率,同时兼顾了多个装载约束条件,可应用性好.%In view of the complex Container Loading Problem ( CLP), the optimal loading plan with heuristic information and the ant colony algorithm was proposed. Firstly, a mathematical model was generated. Considering the strong search ability, potential parallelism and scalability of ant colony algorithm, the proposed algorithm was combined with the triple-tree structure to split the layout of space in turn. Then, the three-dimensional rectangular objects of different sizes were placed to the layout space under the constraints. An ant colony algorithm based on spatial partition was designed to solve the optimal procedure. Finally, a design example that 700 pieces of goods were loaded into a 40-foot ( 12. 025 m) high cubic was calculated. The experimental results show that the proposed method can enhance the utilization of the container and it has a strong practicality.
Application of Job- Shop Scheduling Problem Based on Ant Colony Optimization Algorithm%车间作业调度问题的仿真研究
Institute of Scientific and Technical Information of China (English)
赵辉; 李杰; 王振夺
2011-01-01
研究车间作业调度优化问题,使资源、车辆调试、交通分配等达到优化配置,因此车间作业调度问题是一个多约束条件的目标优化问题,采用多项式求解方法不能获得最优解,导致车间作业调度效率低.为了提高车间作业调度效率,提出了一种蚁群算法的车间作业调度优化算法.首先以最小加工时间作为优化目标,蚂蚁爬行路径为作业调度方案,通过蚁群中个体间互相协作和信息交流获得最优车间作业调度方案.通过车间作业调度测试案例对算法进行验证性实验,实验结果表明,蚁群算法提高了车间作业调度效率,能在最短时间找到最优调度方案,为车间作业调度优化提供了依据.%Research job shop scheduling problem and make resources optimized configuration. The job-Shop scheduling problem is a NP-hard problem, and some polynomial solutions to the problem are not the best one, leading to low efficiency workshop scheduling problem. In order to improve the efficiency of job-Shop scheduling problem , a job-Shop scheduling method was put forward based on ant colony algorithm. Taking minimize processingtime as optimal objective, ants crawling path as a job scheduling schemes, individuals collaboration and information exchanging in ant colony were carried out to obltain the optimal workshop scheduling solutions. The method was tested by job-shop scheduling problem, and the results show that the ant colony algorithm improves the efficiency, shortens the optimization time, and is effective for solving Job-Shop scheduling problem.
A Survey Paper on Solving TSP using Ant Colony Optimization on GPU
Khushbu khatri; Vinit Kumar Gupta
2014-01-01
Ant Colony Optimization (ACO) is meta-heuristic algorithm inspired from nature to solve many combinatorial optimization problem such as Travelling Salesman Problem (TSP). There are many versions of ACO used to solve TSP like, Ant System, Elitist Ant System, Max-Min Ant System, Rank based Ant System algorithm. For improved performance, these methods can be implemented in parallel architecture like GPU, CUDA architecture. Graphics Processing Unit (GPU) provides highly parallel and f...
Determining the Optimum Section of Tunnels Using Ant Colony Optimization
Directory of Open Access Journals (Sweden)
S. Talatahari
2013-01-01
Full Text Available Ant colony optimization is developed to determine optimum cross sections of tunnel structures. Tunnel structures are expensive infrastructures in terms of material, construction, and maintenance and the application of optimization methods has a great role in minimizing their costs. This paper presents the formulation of objective function and constraints of the problem for the first time, and the ant colony optimization, as a developed metaheuristic approach, has been used to solve the problem. The results and comparisons based on numerical examples show the efficiency of the algorithm.
Application of ant colony optimization in NPP classification fault location
International Nuclear Information System (INIS)
Nuclear Power Plant is a highly complex structural system with high safety requirements. Fault location appears to be particularly important to enhance its safety. Ant Colony Optimization is a new type of optimization algorithm, which is used in the fault location and classification of nuclear power plants in this paper. Taking the main coolant system of the first loop as the study object, using VB6.0 programming technology, the NPP fault location system is designed, and is tested against the related data in the literature. Test results show that the ant colony optimization can be used in the accurate classification fault location in the nuclear power plants. (authors)
Lu, Shi Jing; Salleh, Abdul Hakim Mohamed; Mohamad, Mohd Saberi; Deris, Safaai; Omatu, Sigeru; Yoshioka, Michifumi
2014-09-28
Reconstructions of genome-scale metabolic networks from different organisms have become popular in recent years. Metabolic engineering can simulate the reconstruction process to obtain desirable phenotypes. In previous studies, optimization algorithms have been implemented to identify the near-optimal sets of knockout genes for improving metabolite production. However, previous works contained premature convergence and the stop criteria were not clear for each case. Therefore, this study proposes an algorithm that is a hybrid of the ant colony optimization algorithm and flux balance analysis (ACOFBA) to predict near optimal sets of gene knockouts in an effort to maximize growth rates and the production of certain metabolites. Here, we present a case study that uses Baker's yeast, also known as Saccharomyces cerevisiae, as the model organism and target the rate of vanillin production for optimization. The results of this study are the growth rate of the model organism after gene deletion and a list of knockout genes. The ACOFBA algorithm was found to improve the yield of vanillin in terms of growth rate and production compared with the previous algorithms. PMID:25462325
PARAMETER ESTIMATION OF VALVE STICTION USING ANT COLONY OPTIMIZATION
Directory of Open Access Journals (Sweden)
S. Kalaivani
2012-07-01
Full Text Available In this paper, a procedure for quantifying valve stiction in control loops based on ant colony optimization has been proposed. Pneumatic control valves are widely used in the process industry. The control valve contains non-linearities such as stiction, backlash, and deadband that in turn cause oscillations in the process output. Stiction is one of the long-standing problems and it is the most severe problem in the control valves. Thus the measurement data from an oscillating control loop can be used as a possible diagnostic signal to provide an estimate of the stiction magnitude. Quantification of control valve stiction is still a challenging issue. Prior to doing stiction detection and quantification, it is necessary to choose a suitable model structure to describe control-valve stiction. To understand the stiction phenomenon, the Stenman model is used. Ant Colony Optimization (ACO, an intelligent swarm algorithm, proves effective in various fields. The ACO algorithm is inspired from the natural trail following behaviour of ants. The parameters of the Stenman model are estimated using ant colony optimization, from the input-output data by minimizing the error between the actual stiction model output and the simulated stiction model output. Using ant colony optimization, Stenman model with known nonlinear structure and unknown parameters can be estimated.
Hierarchical interactive ant colony optimization algorithm and its application%分层交互式蚁群优化算法及其应用
Institute of Scientific and Technical Information of China (English)
黄永青; 郝国生; 张俊岭; 王剑
2012-01-01
Conventional ant colony optimization algorithm cannot effectively solve the systems whose optimization performance indices are difficult to be quantifiable. In order to overcome this weakness, a novel Hierarchical Interactive Ant Colony Optimization (HIACO) that the objective function values of the potential solutions are determined by subjective human evaluation is proposed. The structure of a primal Interactive Ant Colony Optimization (IACO) model is designed. Appropriate pheromone update rule and the characters of pheromone in IACO are presented. The ideal of hierarchy, the chance to hierarchy and the method of hierarchy are given. The evaluation way of user is so simple that he or she only needs selecting a mostly interesting individual of current generation and not evaluating quantization of every solution. So user fatigue is reduced efficiently. IACO and HIACO are applied to car styling design. The experimental results demonstrate that the proposed algorithm has good performance.%传统蚁群优化算法在求解优化性能指标难以数量化的定性系统问题时无能为力,为此提出一种利用人对问题解进行评价的分层交互式蚁群优化算法.设计了一个基本交互式蚁群优化模型结构,讨论了信息素的更新策略和性质.给出分层的思想、分层的时机和分层的具体实现方法.算法用户参与评价时,只需指出每一代中最感兴趣的解,而不必给出每个解的具体数量值,可以极大降低用户评价疲劳.将算法应用于汽车造型设计,实验结果表明所提出算法具有较高运行性能.
Directory of Open Access Journals (Sweden)
Bo Ye
2014-04-01
Full Text Available Detection and quantitative estimation of deep defects in multi-layered structures is an essential task in a range of technological applications, such as maintaining the integrity of structures, enhancing the safety of aging aircraft, and assuring the quality of products. A novel approach to accurately quantify the two-dimensional axisymmetric deep defect size from eddy current nondestructive testing (NDT signals is presented here. The method uses a finite element forward model to simulate the underlying physical process and an improved ant colony algorithm (IACA to solve the inverse problem. Experiments are carried out. The performance comparison between the IACA method and the least square method is shown. The comparison results demonstrate the feasibility and validity of the IACA method. Between them, the IACA method gives a better estimation performance than the least square method at present.
Ant Colony Optimization With Combining Gaussian Eliminations for Matrix Multiplication.
Zhou, Yuren; Lai, Xinsheng; Li, Yuanxiang; Dong, Wenyong
2013-02-01
One of the main unsolved problems in computer algebra is to determine the minimal number of multiplications which is necessary to compute the product of two matrices. For practical value, the small format is of special interest. This leads to a combinatorial optimization problem which is unlikely solved in polynomial time. In this paper, we present a method called combining Gaussian eliminations to reduce the number of variables in this optimization problem and use heuristic ant colony algorithm to solve the problem. The results of experiments on 2 × 2 case show that our algorithm achieves significant performance gains. Extending this algorithm from 2 × 2 case to 3 × 3 case is also discussed. Index Terms—Ant colony optimization (ACO), evolutionary algorithms, Gaussian eliminations, matrix multiplication, multiplicative complexity, Strassen's algorithm. PMID:22835561
Viewpoint optimization based on ant colony algorithm for volume rendering%基于蚁群算法的体绘制视点优化
Institute of Scientific and Technical Information of China (English)
张尤赛; 辛莉
2013-01-01
针对体绘制的最佳视点问题，提出了一种基于蚁群算法的体绘制视点优化方法。该方法利用信息熵的形式，构造了一种基于体数据2维投影图像的不透明度及其结构信息的视点评价函数作为视点优化的依据；在体绘制的进程中，应用蚁群算法进行视点优化，自动、智能地实现全局最佳视点的选择。实验结果表明：应用该方法进行体绘制的视点优化，具有收敛速度快、精度高和性能稳定的特点，可以显著提高体绘制的效率。%In this paper , we presented a method of viewpoint optimization using ant colony algorithm for the opti-mal viewpoint of volume rendering .Utilizing the opacity and structure features of the two-dimensional projected image of volume data , a viewpoint evaluation function was constructed in the form of information-theoretic entropy and regarded as the criterion for optimizing viewpoint .During the process of volume rendering , ant colony algo-rithm was introduced to select the optimal viewpoint automatically and intelligently .Experimental results have shown this method can increase the convergence rate , accuracy and stability in viewpoint optimization , and sig-nificantly improve the efficiency of volume rendering .
基于蚁群算法优化软件测试策略%AN OPTIMIZED SOFTWARE TESTING STRATEGY BASED ON ANT COLONY ALGORITHM
Institute of Scientific and Technical Information of China (English)
查日军; 张德平
2011-01-01
It is an essential issue to improve the fault detecting ability and reduce the testing cost of software testings in the study of software testing optimization. Based on Markov decision model for software testing, targeting at reducing the software testing cost and improving the fault detection capability of testing, the paper makes use of the ant colony algorithm to offer a learning strategy for optimizing the testing profile, and applies the acquired optimal testing profile to optimizing software tests. Experiment results show that the learning strategy that uses the ant colony algorithm is far better than the random testing strategy with respect to significantly reducing the testing cost and improving the fault detecting capability. It is an important supplementary for heuristic methods of software testing optimization.%提高软件测试的缺陷检测能力,有效降低测试成本是软件测试优化研究中的关键问题.基于软件测试的Markov决策模型,以降低软件测试成本,提高测试的缺陷检测能力为目标,运用蚁群算法给出一种优化测试剖面的学习策略,将所得到的最优测试剖面用于优化软件测试.实验结果表明运用蚁群算法的学习策略要远优于随机测试策略,能显著降低测试成本和提高缺陷检测能力,是软件测试优化启发式方法的一个重要补充.
SWARM INTELLIGENCE FROM NATURAL TO ARTIFICIAL SYSTEMS: ANT COLONY OPTIMIZATION
Directory of Open Access Journals (Sweden)
O. Deepa
2016-03-01
Full Text Available Successful applications coming from biologically inspired algorithm like Ant Colony Optimization (ACO based on artificial swarm intelligence which is inspired by the collective behavior of social insects. ACO has been inspired from natural ants system, their behavior, team coordination, synchronization for the searching of optimal solution and also maintains information of each ant. At present, ACO has emerged as a leading metaheuristic technique for the solution of combinatorial optimization problems which can be used to find shortest path through construction graph. This paper describe about various behavior of ants, successfully used ACO algorithms, applications and current trends. In recent years, some researchers have also focused on the application of ACO algorithms to design of wireless communication network, bioinformatics problem, dynamic problem and multi-objective problem.
Institute of Scientific and Technical Information of China (English)
魏林; 付华; 尹玉萍
2013-01-01
针对于求解一般的整数规划问题，提出了和声蚁群耦合算法。采用和声搜索更新种群策略和个体扰动策略改善了蚁群算法过早收敛的问题，同时采用蚁群算法对寻优路径信息素的正反馈来加快和声搜索收敛于最优路径。实验结果表明，相比于蚁群算法和和声搜索算法，新算法大大提高了一般整数规划问题的搜索效率。%An improved hybrid optimization algorithm based on harmony search algorithm and ant colony algorithm is proposed to solve the general integer programming problem. The new algorithm utilizes harmony algorithm with updating population and individual disturbance strategy to improve ant colony algorithm premature convergence, and utilizes the pheromone positive feedback effect to speed up harmony search algorithm searching optimum paths. Experimental results show that compared to the ant colony algorithm and harmony search algorithm, the new algorithm greatly improves the general integer programming problem search efficiency.
Institute of Scientific and Technical Information of China (English)
冀俊忠; 张鸿勋; 胡仁兵; 刘椿年
2009-01-01
To solve the drawbacks of the ant colony optimization for learning Bayesian networks (ACO-B), this paper proposes an improved algorithm based on the conditional independence test and ant colony optimization (I-ACO-B). First, the I-ACO-B uses order-0 independence tests to effectively restrict the space of candidate solutions, so that many unnecessary searches of ants can be avoided. And then, by combining the global score increase of a solution and local mutual information between nodes, a new heuristic function with better heuristic ability is given to induct the process of stochastic searches. The experimental results on the benchmark data sets show that the new algorithm is effective and efficient in large scale databases, and greatly enhances convergence speed compared to the original algorithm.
Robustness of Ant Colony Optimization to Noise.
Friedrich, Tobias; Kötzing, Timo; Krejca, Martin S; Sutton, Andrew M
2016-01-01
Recently, ant colony optimization (ACO) algorithms have proven to be efficient in uncertain environments, such as noisy or dynamically changing fitness functions. Most of these analyses have focused on combinatorial problems such as path finding. We rigorously analyze an ACO algorithm optimizing linear pseudo-Boolean functions under additive posterior noise. We study noise distributions whose tails decay exponentially fast, including the classical case of additive Gaussian noise. Without noise, the classical [Formula: see text] EA outperforms any ACO algorithm, with smaller [Formula: see text] being better; however, in the case of large noise, the [Formula: see text] EA fails, even for high values of [Formula: see text] (which are known to help against small noise). In this article, we show that ACO is able to deal with arbitrarily large noise in a graceful manner; that is, as long as the evaporation factor [Formula: see text] is small enough, dependent on the variance [Formula: see text] of the noise and the dimension n of the search space, optimization will be successful. We also briefly consider the case of prior noise and prove that ACO can also efficiently optimize linear functions under this noise model. PMID:26928850
Binary ant colony algorithm with controllable search bias%可控搜索偏向的二元蚁群算法
Institute of Scientific and Technical Information of China (English)
胡钢; 熊伟清; 张翔; 袁军良
2011-01-01
蚁群算法按照信息素轨迹产生的偏向对解空间进行搜索．当前改进蚁群算法性能的主要方法是提高种群的多样性，少有对搜索偏向进行控制．本文以可控搜索偏向作为研究的出发点，通过对至今最优信息素更新方式的分析，得出了从任意代到算法收敛没有发现较优解的概率下限．并以此为基础，把访问量与蚂蚁数量的关系作为控制偏向的依据，在兼顾提高种群多样性的前提下，设计了可控搜索偏向的二元蚁群算法．通过多个函数的测试以及0—1多背包问题的应用，其实验结果表明该算法有较好的搜索能力以及较快的收敛速度．%Ant colony algorithm explores the solution space according to the bias produced by pheromone trail. However, most of the existing improvements concentrate in raising the population diversity, instead of controlling the search bias. On the basis of the controllable search bias and by the update pattern of the current pheromone, we determine for any given iteration the lower bound of the probability of no further improvement in solution up to the convergence. Using the relation between the number of visitors and the ant population, and considering the population diversity, we develop a binary ant colony algorithm with controllable search bias. In the test of function optimization and the application to the 0-1 multiple knapsack problem, the algorithm exhibits a good search ability and a high convergence speed.
基于蚁群算法多层次动态信息提取仿真研究%Interest Management Based on Ant Colony Algorithm
Institute of Scientific and Technical Information of China (English)
王娅森; 刘厚泉
2012-01-01
The research dynamic interest preference captures accuracy problems. For large data in the network structure caused by defects such as slow searching information, in order to be able to retrieve the user interest of more information, and proposes an improved ant colony algorithm user interests mode acquisition algorithm was presented for hierarchical structure, this paper firstly information website, according to the website and user interests have gra-dation , and then modified characteristics nf ant colony algorithm higher searching mechanism, using the ants foraging cycle from all levels for activities, the corresponding path pheromone strength, and timely implement pheromone up-date mechanism, thus obtains the user to the node preferences function values, again according to the user's interests mode. For value Simulation experiments show that the proposed method can effectively capture a user interests infor-mation , capture more accurate, is a kind of effective method.%研究动态信息偏好捕捉精确度问题.网络数据存在重复性信息和随机性强.针对互联网中的大量数据,而造成了有效的信息的查找速度慢等缺陷,为了能够快速的获取更多的用户比较感兴趣信息,提出了一种改进的蚁群算法用户兴趣模式获取技术.面向层次结构的信息网站,算法首先根据网站和用户兴趣所具有的层次性特征,然后采用改进的蚁群算法较高的寻优机制,利用蚂蚁的觅食周期活动,从各个层次求出相应路径的信息素浓度,并适时的实行信息素更新机制,从而得到用户对该结点的偏好函数值,再依据此值求得用户兴趣模式.仿真结果表明,提出的方法能够有效地捕捉出用户兴趣信息,捕捉精确度较高,是一种有效的方法,具有一定的推广价值.
Institute of Scientific and Technical Information of China (English)
张明光; 赵金亮; 王维洲; 张彦凯; 路染妮; 李正元
2011-01-01
The economics, power quality and reliability of distribution system operation can be improved through the combinative optimum of the switches. The article is based on the combination of adaptive genetic algorithm and ant colony algorithm to reduce the line loss, improve system voltage and optimize speed. The end adaptive genetic algorithm and is based on the concept of Chromosome similarity, Population similarity and the adaptive adjustment to the crossover rate and mutation rate, not based on the set maximum iteration, the combination of adaptive genetic algorithm and ant colony algorithm is based on the rapid global search capability of genetic algorithm and convergence of positive feedback mechanism of ant colony algorithm, beginning use adaptive genetic algorithm to produce pheromone distribution, then use positive feedback mechanism of ant colony algorithm to seek exact Solutions. Thus time efficiency of the combination of both algorithm is superior to the ant colony algorithm,Finding exact solutions in the efficiency of genetic algorithms better than that . As far as a new intelligent algorithm be concerned, time and efficiency have it both ways. At last, the simulation result of the IEEE69 note system verified the efficient and feasible of the algorithm.%配电网重构可以降低线损,均衡负荷,提高电压质量和增加配电网可靠性.主要在降低线损、提高电压质量和提高寻优效率方面,采用了自适应遗传算法和蚁群算法融合的方法.对遗传算法的交叉因子和变异因子进行了自适应控制,也不再人为规定迭代的最大代数,而是引入了染色体相似度和种群相似度的概念,使遗传算法的终止条件更加合理.自适应遗传算法和蚁群算法融合算法初期采用遗传算法利用快速全局搜索能力强求得初始解,利用这些解生成蚁群算法的信息素分布,后期利用蚁群算法的正反馈机制求得精确解.进而形成时间效率和精确解效率
Apriori and Ant Colony Optimization of Association Rules
Anshuman Singh Sadh; Nitin Shukla
2013-01-01
Association Rule mining is one of the important and most popular data mining technique. Association rule mining can be efficiently used in any decision making processor decision based rule generation. In this paper we present an efficient mining based optimization techniques for rule generation. By using apriori algorithm we find the positive and negative association rules. Then we apply ant colony optimization algorithm (ACO) for optimizing the association rules. Our results show the effecti...
基于蚁群优化的ESP控制算法仿真%Simulation of ant colony optimization-based ESP algorithm
Institute of Scientific and Technical Information of China (English)
信瑛南; 张新; 曾志伟
2012-01-01
In order to accelerate the ESP system response time,improve the stability of the vehicle,this paper adopts fuzzy control theory to make feedback control of the ESP system.Four wheels vehicle model with eight degrees of freedom and Gim tire model are established,and in the good flexibility,robustness of fuzzy PID controller is introduced based on the ant colony algorithm,the design based on ant colony optimization of fuzzy PID control system structure and its calculation method,so as to achieve parameters optimization design,then a simulation model generated by Simulink software is used.Simulation experiment proves that this method can accelerate the speed of convergence system and shorten the system response time.%为了加快ESP系统的响应时间,提高车辆的稳定性,采用模糊控制理论对车辆ESP系统进行反馈控制.建立了八自由度四轮整车模型与Gim轮胎模型,并在具有较好的灵活性、鲁棒性的模糊PID控制器的基础上引入了蚁群算法,设计了基于蚁群优化的模糊PID控制系统的结构与计算方法,从而达到对参数kp,ki,kd的优化设计,同时利用Simulink软件进行仿真.仿真结果证明,引入蚁群算法的模糊PID控制系统可加快系统的收敛速度,缩短系统响应时间.
基于蚁群算法在机器人足球比赛中的应用%The Application of Ant Colony Algorithm in Robot Soccer Competition
Institute of Scientific and Technical Information of China (English)
贾翠玲; 李卫国; 郭连考; 陈杰
2012-01-01
To solve the problems of the efficiency of football robot searching path and multi-agents collaboration,this paper studies the algorithm and strategy issues for AS-MF09 robot soccer.Ant colony algorithm is applied to the competition. The actual match proved that the algorithm in the robot soccer can improve offensive and defensive performance and can get good results.%针对机器人足球中寻路效率和多智能体协作问题,提出相应的解决方案,文章主要研究在机器人足球比赛中算法和策略问题.将蚁群算法应用到该比赛中,经实际比赛证明该算法在机器人足球比赛中对于提高机器人寻找足球和攻防性能方面都能得到很好的结果.
Research on Improved Ant Colony Algorithm in EMU Routing Schemes%求解动车组交路计划的改进型蚁群算法
Institute of Scientific and Technical Information of China (English)
李华; 韩宝明; 李得伟; 李晓娟
2013-01-01
In this paper for the given schedule of paired runs, we established the multi-objective integer programming model for the routing plan of EMUs. Then considering that the EMU schedule consisted of multiple closed-ended loops, we improved elements of the algorithm, such as structure of the solution, path selection, loop generation, and pheromone updating, etc., and designed the improved ant colony algorithm to solve the model. At the end, in the case of the Wu-guang passenger dedicated line, we demonstrated that the algorithm constructed could effectively solve the scheduling problem of the EMUs.%基于给定成对列车运行图,建立了求解动车组交路计划的多目标整数规划模型.针对动车组交路计划由若干条闭环回路构成的特点,通过对解的构造、路径选择与回路生成、信息素更新等算法要素进行改进,设计了求解模型的改进型蚁群算法.以武广客运专线实际列车运行图数据为例进行计算的结果表明,该算法可有效求解动车组交路计划.
Tasks Scheduling using Ant Colony Optimization
Directory of Open Access Journals (Sweden)
A. P. Shanthi
2012-01-01
Full Text Available Problem statement: Efficient scheduling of the tasks to heterogeneous processors for any application is critical in order to achieve high performance. Finding a feasible schedule for a given task set to a set of heterogeneous processors without exceeding the capacity of the processors, in general, is NP-Hard. Even if there are many conventional approaches available, people have been looking at unconventional approaches for solving this problem. This study uses a paradigm using Ant Colony Optimisation (ACO for arriving at a schedule. Approach: An attempt is made to arrive at a feasible schedule of a task set on heterogeneous processors ensuring load balancing across the processors. The heterogeneity of the processors is modelled by assuming different utilisation times for the same task on different processors. ACO, a bio-inspired computing paradigm, is used for generating the schedule. Results: For a given instance of the problem, ten runs are conducted based on an ACO algorithm and the average wait time of all tasks is computed. Also the average utilisation of each processor is calculated. For the same instance, the two parameters: average wait time of tasks and utilisation of processors are computed using the First Come First Served (FCFS. The results are tabulated and compared and it is found that ACO performs better than the FCFS with respect to the wait time. Although the processor utilisation is more for some processors using FCFS algorithm, it is found that the load is better balanced among the processors in ACO. There is a marginal increase in the time for arriving at a schedule in ACO compared to FCFS algorithm. Conclusion: This approach to the tasks assignment problem using ACO performs better with respect to the two parameters used compared to the FCFS algorithm but the time taken to come up with the schedule using ACO is slightly more than that of FCFS.
New results of ant algorithms for the Linear Ordering Problem
Pintea, Camelia-M.; Chira, Camelia; Dumitrescu, D.
2012-01-01
Ant-based algorithms are successful tools for solving complex problems. One of these problems is the Linear Ordering Problem (LOP). The paper shows new results on some LOP instances, using Ant Colony System (ACS) and the Step-Back Sensitive Ant Model (SB-SAM).
Research of grid task schedule based on quantum ant colony algorithm.%基于量子蚁群算法的网格任务调度研究
Institute of Scientific and Technical Information of China (English)
苏日娜; 王宇
2011-01-01
任务调度策略是网格计算的核心问题.在系统任务调度和资源分配中,提出一种基于量子蚁群算法的任务调度策略.算法将量子计算与蚁群算法相融合,通过对蚁群进行量子化编码并采用量子旋转门及非门操作,实现对任务自适应启发式的分配和优化.算法有效增强了种群的多样性、克服了遗传算法和蚁群算法的早熟收敛和退化现象.仿真实验中,分别与基于遗传算法和基于蚁群算法的任务调度策略相对比,结果表明算法有效缩短了任务调度的时间跨度,增强了网格系统的性能.%Task schedule strategy is the key issue of grid computing,During the schedule and allocation of the system tasks,task schedule strategy based on quantum ant colony algorithm is proposed. This algorithm combines quantum computing with the ant colony algorithm and achieves optimal task schedule by quantum coding and quantum evolution operator. It ensures the diversity of population and overcomes premature convergence and degradation of the genetic algorithm and ant colony algorithm. Compared with the genetic algorithm and ant colony algorithm task schedule strategy, simulations show that the search ability of this algorithm is better,and it can reduce the timo span of tho task schedule and enhance the performance of grid system effectively.
Improved ant algorithms for software testing cases generation.
Yang, Shunkun; Man, Tianlong; Xu, Jiaqi
2014-01-01
Existing ant colony optimization (ACO) for software testing cases generation is a very popular domain in software testing engineering. However, the traditional ACO has flaws, as early search pheromone is relatively scarce, search efficiency is low, search model is too simple, positive feedback mechanism is easy to produce the phenomenon of stagnation and precocity. This paper introduces improved ACO for software testing cases generation: improved local pheromone update strategy for ant colony optimization, improved pheromone volatilization coefficient for ant colony optimization (IPVACO), and improved the global path pheromone update strategy for ant colony optimization (IGPACO). At last, we put forward a comprehensive improved ant colony optimization (ACIACO), which is based on all the above three methods. The proposed technique will be compared with random algorithm (RND) and genetic algorithm (GA) in terms of both efficiency and coverage. The results indicate that the improved method can effectively improve the search efficiency, restrain precocity, promote case coverage, and reduce the number of iterations. PMID:24883391
蚁群聚类算法的T-S模糊模型辨识%Identification of T-S fuzzy models based on ant colony clustering algorithm
Institute of Scientific and Technical Information of China (English)
赵宝江
2011-01-01
基于T-S模型,提出一种非线性系统的模型辨识方法.利用蚁群聚类算法来进行结构辨识,确定系统的模糊空间和模糊规则数.在聚类的基础上,利用遗传算法辨识模糊模型的后件加权参数,得到一个精确的模糊模型,从而实现参数辨识.仿真结果验证了该方法的有效性,表明该方法能够实现非线性系统的辨识,辨识精度高,可当作复杂系统建模的一种有效手段.%A model identification approach of nonlinear systems is presented based on T-S model.To automatically acquire the fuzzy space structure of system and the number of fuzzy rules,the ant colony clustering algorithm is used in structure identification.Based on the cluster result, the parameters of conclusion of fuzzy model are identified by means of the genetic algorithm to obtain a precise fuzzy model and realize parameters identification.This proposed method realizes the identification of nonlinear system and improves greatly the precision of identification.The simulation results show the effectiveness of the proposed method.
Institute of Scientific and Technical Information of China (English)
张颖; 周韧; 钟凯
2011-01-01
Although good application effect of locating faulty section in complex distribution network can be attained by applying ant colony algorithm, however there are also disadvantages such as too long search time and computation time. For this reason, traditional ant colony algorithm should be improved. Firstly, a switching function that can dynamically adapt to topological structure of distribution network is constructed; then a method to set the initial values of ant colony's pheromone and the disturbance rule of the solution are led in; finally the method to apply the improved ant colony in fault section location of distribution network with multi power supplies is determined. The advantages of the improved ant colony algorithm in calculation speed and error tolerance are verified by results of calculation example.%蚁群算法在配电网故障区段定位中应用效果良好,但具有搜索时间长、计算速度慢等缺点,为此对蚁群算法进行了改进.首先构造了动态适应配电网拓扑结构的开关函数,其次提出了蚁群信息素初值设置方法,引入解的扰动规则,最后确定了蚁群算法应用于多电源条件下配电网故障区段定位的方法.算例结果验证了该算法在计算速度和容错性方面的优势.
基于蚁群算法的贝叶斯网结构学习%Learning Bayesian Network Structure Based on Ant Colony Optimization Algorithms
Institute of Scientific and Technical Information of China (English)
冀俊忠; 张鸿勋; 胡仁兵; 刘椿年
2011-01-01
To learn Bayesian Network (BN) structure from incomplete data, this paper proposed an approach combined with both processes of data completing and Ant Colony Optimization (ACO). First, unobserved data are randomly initialized, thus a complete data is got. Based on such a data set, an initialization BN is learned by Ant Colony Algorithm. Second, in light of the current best structure of evolutionary process, Expectation Maximization (EM)estimating and randomly sampling are performed to complete the data. Third, on the basis of the new complete data set, the BN structure is evolved by an improved ACO process. Finally, the second and third steps are iterated until the global best structure is obtained. Experimental results show the approach can effectively learn BN structure form incomplete data, and is more accurate than MS-EM 、EGA 、BN-GS algorithms.%针对具有丢失数据的贝叶斯网结构学习问题,提出了一种将数据的完备化与结构的蚁群优化相结合的学习方法.随机初始化未观察到的数据,得到完整的数据集,并利用蚁群算法学习得到初始网络结构;然后进行迭代学习,在每次迭代中根据当前最好的贝叶斯网结构,利用EM估计和随机的采样插入对数据进行完备化,在完备数据下,利用改进的蚁群优化过程使结构不断进化,直到获得全局最优解.实验结果表明,该方法能有效地从不完备数据中学习贝叶斯网结构且与新近的MS-EM、EGA、BN-GS方法相比,具有更高的学习精度.
Institute of Scientific and Technical Information of China (English)
陈昌敏; 谢维成; 范颂颂
2011-01-01
Aiming at the drawbacks of slow convergence speed and being easy to fall into local optimal point for basic ant colony algorithm in logistics vehicle routing optimization issue, this paper adopted an adaptive ant colony algorithm and the max-min ant colony algorithm to overcome the basic ant colony' s shortcomings.The analysis and comparison for the two algorithms were conducted,and the simulation of vehicle routing optimization in Matlab environment using adaptive and max-min ant colony algorithm was performed as well.Experimental results show that the max-min ant colony algorithm is better than adaptive ant colony algorithm in convergence speed and shortest path search, so max-min ant colony algorithm is superior to adaptive ant colony algorithm for logistics vehicle routing optimization.%针对物流车辆路径优化问题,考虑到基本蚁群算法有收敛速度慢、易陷入局部最优的缺点,采用了自适应蚁群算法和最大最小蚁群算法进行车辆路径优化,分析、比较了这两种算法的不同并在Matlab上做了仿真.仿真实验结果显示自适应蚁群算法在收敛速度和寻找最短路径上都略逊于最大最小蚁群算法,最大最小蚁群算法在物流车辆路径优化上优于适应蚁群算法.
Path Planning Based on Cloud Model of Ant Colony Algorithm%云模型蚁群算法在无人机航迹规划中的应用
Institute of Scientific and Technical Information of China (English)
房建卿; 王和平
2012-01-01
A new route planning algorithm is proposed for the high-flying unmanned aerial vehicles. This method called ant colony algorithm based on cloud model. The basic ant colony algorithm has some drawbacks, such as it' s easily to fall into local optimal solution and need long computing time. This improved ant colony algorithm proposed the cloud model to control the size of Q and p, which lead to better convergence and avoid falling into local optimal solution. Then this algorithm in the TSP is simulated. The UAV mission map by grid, then use of the cloud model of ant colony algorithm for route planning are dispersed.%为中高空飞行的无人机提出了一种新型航路规划算法.该方法基于云模型蚁群算法.基本蚁群算法有着突出的缺陷:易陷入局部最优解而且需要计算时间长.提出的改进型蚁群算法,通过云模型来控制信息素强度Q和挥发系数ρ的大小,从而得到更好的收敛性与避免陷入局部最优解,并进行了TSP问题的仿真计算.通过将无人机任务地图网格离散化,运用云模型蚁群算法进行航迹规划.
Institute of Scientific and Technical Information of China (English)
何凡; 祁世民; 谢贵武; 吴桐
2014-01-01
Ant colony algorithm is a population intelligent optimization algorithm,and its theory is based on the nature of the ant search for make. For surface-to-air multi-target radar jamming system in actual combat target more cases of interference resource allocation problem, and a model of distribution optimization based on ant colony algorithm is put forward,to solve the traditional ant colony algorithm optimization speed slow,easy to fall into local optimal solutions,such as faults,in the ant routing strategy,pheromone volatilization,pheromone updating and elite reservation etc. The corresponding improvement strategy is put forward, the concrete realization steps of the improved algorithm is given,finally cite the actual case,through the simulation experiment,the superiority of the algorithm is proved.%蚁群算法是一种群智能优化算法，其理论来源于自然界中蚂蚁群的寻径行为。针对地对空多目标雷达干扰系统在实战中目标较多情况下的干扰资源分配问题，提出了一种基于蚁群算法的分配优化模型，为解决传统蚁群算法寻优速度慢，容易陷入局部最优解等缺点，在蚂蚁路径选择策略、信息素挥发、信息素更新和精英保留等方面提出相应改进策略，给出改进后算法的具体实现步骤，最后举出实际算例，通过仿真实验，证明了算法的优越性。
Ant colony algorithm based on genetic method for continuous optimization problem%基于遗传机制的蚁群算法求解连续优化问题
Institute of Scientific and Technical Information of China (English)
朱经纬; 蒙陪生; 王乘
2007-01-01
A new algorithm is presented by using the ant colony algorithm based on genetic method (ACG) to solve the continuous optimization problem. Each component has a seed set. The seed in the set has the value of component, trail information and fitness. The ant chooses a seed from the seed set with the possibility determined by trail information and fitness of the seed. The genetic method is used to form new solutions from the solutions got by the ants. Best solutions are selected to update the seeds in the sets and trail information of the seeds. In updating the trail information, a diffusion function is used to achieve the diffuseness of trail information. The new algorithm is tested with 8 different benchmark functions.
Enhanced Bee Colony Algorithm for Complex Optimization Problems
S.Suriya; R. Deepalakshmi; S.Suresh kannan; Dr.S.P.SHANTHARAJAH
2012-01-01
Optimization problems are considered to be one kind of NP hard problems. Usually heuristic approaches are found to provide solutions for NP hard problems. There are a plenty of heuristic algorithmsavailable to solve optimization problems namely: Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, etc. The basic Bee Colony algorithm, a population based search algorithm, is analyzed to be a novel tool for complex optimization problems. The algorithm mimics the food fo...
Using ant colonies for solve the multiprocessor task graph scheduling
Bremang, Appah
2006-01-01
The problem of scheduling a parallel program presented by a weighted directed acyclic graph (DAG) to the set of homogeneous processors for minimizing the completion time of the program has been extensively studied as academic optimization problem which occurs in optimizing the execution time of parallel algorithm with parallel computer.In this paper, we propose an application of the Ant Colony Optimization (ACO) to a multiprocessor scheduling problem (MPSP). In the MPSP, no preemption is allo...
Polyethism in a colony of artificial ants
Marriott, Chris
2011-01-01
We explore self-organizing strategies for role assignment in a foraging task carried out by a colony of artificial agents. Our strategies are inspired by various mechanisms of division of labor (polyethism) observed in eusocial insects like ants, termites, or bees. Specifically we instantiate models of caste polyethism and age or temporal polyethism to evaluated the benefits to foraging in a dynamic environment. Our experiment is directly related to the exploration/exploitation trade of in machine learning.
Ant colony optimization and constraint programming
Solnon, Christine
2013-01-01
Ant colony optimization is a metaheuristic which has been successfully applied to a wide range of combinatorial optimization problems. The author describes this metaheuristic and studies its efficiency for solving some hard combinatorial problems, with a specific focus on constraint programming. The text is organized into three parts. The first part introduces constraint programming, which provides high level features to declaratively model problems by means of constraints. It describes the main existing approaches for solving constraint satisfaction problems, including complete tree search
Institute of Scientific and Technical Information of China (English)
祁金佺
2013-01-01
According to the traditional software testing methods ’ shortcoming of bigger workload and higher testing process repeatability , this paper proposes a new software testing method .The method is based on ant colony algorithm and genetic algorithm , using genetic algorithm and ant colony algorithm to extract each software test sequence features , after selection , crossover and mutation operations to combi-nate to get the software testing sequence .Experimental results show that the algorithm has the implicit parallelism and global searching ability , which can be in improving software testing precision under the condition of no reduction velocity .%针对传统软件测试方法工作量大，测试过程重复性高等缺点，提出了一种新的软件测试用例生成方法。该方法基于蚁群算法和遗传算法，利用遗传算法与蚁群算法提取每组软件测试程序特征值，再经过选择、交叉和变异操作，获得软件测试用例的组合。实验结果证明：该算法具有隐性并行性和全局寻优能力，可在不降低速度的情况下提高软件测试用例生成的精度。
Optimum Selection Strategy on Selling Channels Based on Ant Colony Algorithm%基于蚁群算法的产品销售渠道优选策略
Institute of Scientific and Technical Information of China (English)
王越超; 李毅
2011-01-01
Ant colony algorithm has strong ability to find optimal solutions, it can be used for optimization selection of product distribution channels problem. When the information of potential profits which a new product will make in certain selling locations is gathered, ant colony algorithm can be used to obtain the highest sales revenue, if the sales channels and marketing expenses cap are known. The flow chart based on ant colony algorithm was presented. And finally it was proved by case analysis that,ant colony optimization algorithm has effectiveness and good convergence speed,when it is used for the issue of selling channels selection.%鉴于蚁群算法具有较强的发现优选解的能力,将其用于产品销售渠道的优选问题中.当企业收集到某种新产品在可能销售地点的销售收益时,可以利用蚁群算法,求得在已知产品销售渠道和销售支出费用上限的约束条件下的最大销售收益.提出了基于蚁群算法的问题流程图,以实例分析证明了蚁群算法在产品销售渠道优选问题上,具有一定的有效性和较快的收敛速度.
Institute of Scientific and Technical Information of China (English)
谢颖; 李吉兴; 杨忠学; 张岩
2015-01-01
To optimize the structural parameters of the 4-pole 7. 5 kW line-start permanent magnet synchro-nous motor, an improved binary genetic ant colony algorithm which combined the advantages of genetic al-gorithm with ant colony algorithm and solved the problem of continuous space optimization was used. The basic idea of binary genetic ant colony algorithm and its features were presented, and the specific imple-mentation method of binary genetic ant colony algorithm in motor optimization design was mainly discussed. Language was used to realize the algorithm and results of simulation and calculation were obtained to prove its global convergence property. Finite element method was used to simulate the optimized electromagnetic design. The result slows that the optimization design of motor based on improved binary genetic ant colony algorithm may effectively improve the starting and running performance of the motor.%针对电机的优化设计问题，采用一种改进的二进制遗传蚁群算法，对一台4极7．5 kW的自起动永磁同步电动机的结构参数进行优化，该算法结合遗传算法和蚁群算法各自的优点，并且能解决连续空间优化问题。介绍了改进二进制遗传蚁群算法的基本思想及其特点，重点论述该算法在电机优化设计中的具体实现方法。采用编程语言实现该算法，通过大量的仿真计算验证算法的全局收敛能力。利用有限元方法对优化后的电磁设计方案进行仿真，结果表明该算法可以使自起动永磁同步电动机得到较好的优化，有可能提高电机的起动性能和运行性能。
Job Shop Scheduling Based on Improved Ant Colony Algorithm%基于改进蚁群算法的作业车间调度
Institute of Scientific and Technical Information of China (English)
王硕; 顾幸生
2012-01-01
提出了一种改进的蚁群算法,应用于经典的作业车间调度问题.编码采用基于机器的编码可以控制冗余解的数量,但同时会产生不可行解.本研究提出了控制不可行解产生的策略,同时对已出现的不可行解问题,在尽量保留种群基因的前提下,改变解的形式加以利用.在丰富了种群的多样性的同时解决了不可行解的问题.采用自适应参数法则,使参数的变化顺应种群发展过程各个阶段的需要.在一定代数的迭代后,通过改变某些参数跳出局部最优,从而达到了较好的搜索效果.%The improved ant colony algorithm is proposed and applied to solving the job shop scheduling problem. Using machine based coding, redundancy solution is perfectly limited. However, infeasible solutions can be generated by such coding method. In this paper, strategies to limit the infeasible solutions are put forward and the infeasible solution is transformed into feasible solution at the same time. Such strategies not only preserve the population in rich diversity, but also solved the problem of infeasible solutions. Adaptive parameter laws are issued to make the parameters changing every moment, which met the demands of population at all stages of evolution. After certain iterations, the algorithm may get out of local optimal value by merely changing some parameters. Finally, better searching results have been achieved.
Ant larval demand reduces aphid colony growth rates in an ant-aphid interaction
Cook, James M.; Leather, Simon R; Oliver, Tom H.
2012-01-01
Ants often form mutualistic interactions with aphids, soliciting honeydew in return for protective services. Under certain circumstances, however, ants will prey upon aphids. In addition, in the presence of ants aphids may increase the quantity or quality of honeydew produced, which is costly. Through these mechanisms, ant attendance can reduce aphid colony growth rates. However, it is unknown whether demand from within the ant colony can affect the ant-aphid interaction. In a factorial exper...
Clarifying Cutting and Sewing Processes with Due Windows Using an Effective Ant Colony Optimization
Rong-Hwa Huang; Shun-Chi Yu
2013-01-01
The cutting and sewing process is a traditional flow shop scheduling problem in the real world. This two-stage flexible flow shop is often commonly associated with manufacturing in the fashion and textiles industry. Many investigations have demonstrated that the ant colony optimization (ACO) algorithm is effective and efficient for solving scheduling problems. This work applies a novel effective ant colony optimization (EACO) algorithm to solve two-stage flexible flow shop scheduling problems...
An Improved Multi-Objective Ant Colony Optimization Algorithm%一种改进的多目标蚁群优化算法
Institute of Scientific and Technical Information of China (English)
黄坤; 吴俊
2011-01-01
For the characteristics of multi-objective optimization problems is proposed for multi-objective optimization problems ant colony algorithm.Definition of evolutionary algorithms selected from the population when a certain number of individual sources as the center of pheromone diffusion,more than the distance between the centers of intervals;group of other individuals in accordance with the distance from the nearest source of the principle of individual ownership in one of the Pheromone diffusion source;each spread source of pheromone pheromone diffusion algorithm in accordance with the individual to obtain from the center of the pheromone;each generation groups in the center point to the next generation population retained to ensure convergence and maintenance groups Diversity.Finally,multi-objective knapsack problem to test algorithm performance,and with the MOA and the NSGA-II algorithm was simulated compared.Results show that the search efficiency,the true Pareto front approximation to good effect,to obtain the spread of a wide range of solutions,is a multi-objective optimization problem solving and effective method.%提出了一种改进的多目标优化问题的蚁群算法.算法选择进化算法的定义的时候,种群中一定数量的个体信息来源作为中心的扩散,多个中心点之间有一定的距离;群体中的其他个体按照离源个体最近的距离的原则归属于其中一个信息素扩散源;按照信息素扩散算法,每一信息素扩散源中的个体获得源于中心点的信息素;保留每一代群体中的中心点到下一代种群中,确保了收敛性和维护种群的多样性.最后利用多目标背包问题来测试算法的性能,并与MOA和NSGA-II算法进行了分析比较.结果表明,该搜索效率高,向真实Pareto前沿逼近效果好,得到传播的多种解决方案,是一个多目标优化问题的解决和有效的方法.
Implementation of Travelling Salesman Problem Using ant Colony Optimization
Directory of Open Access Journals (Sweden)
Gaurav Singh,
2014-04-01
Full Text Available Within the Artificial Intelligence community, there is great need for fast and accurate traversal algorithms, specifically those that find a path from a start to goal with minimum cost. Cost can be distance, time, money, energy, etc. Travelling salesman problem (TSP is a combinatorial optimization problem. TSP is the most intensively studied problem in the area of optimization. Ant colony optimization (ACO is a population-based metaheuristic that can be used to find approximate solutions to difficult optimization problems. There have been many efforts in the past to provide time efficient solutions for the problem, both exact and approximate. This paper demonstrates the implementation of TSP using ant colony optimization(ACO.The solution to this problem enjoys wide applicability in a variety of practical fields.TSP in its purest form has several applications such as planning, logistics, and manufacture of microchips, military and traffic.
Generating and prioritizing optimal paths using ant colony optimization
Directory of Open Access Journals (Sweden)
Mukesh Mann
2015-03-01
Full Text Available The assurance of software reliability partially depends on testing. Numbers of approaches for software testing are available with their proclaimed advantages and limitations, but accessibility of any one of them is a subject dependent. Time is a critical factor in deciding cost of any project. A deep insight has shown that executing test cases are time consuming and tedious activity. Thus stress has been given to develop algorithms which can suggest better pathways for testing. One such algorithm called Path Prioritization -Ant Colony Optimization (PP-ACO has been suggested in this paper which is inspired by real Ant's foraging behavior to generate optimal paths sequence of a decision to decision (DD path of a graph. The algorithm does full path coverage and suggests the best optimal sequences of path in path testing and prioritizes them according to path strength.
Solving Optimal Path Problem Based on Improved Ant Colony Algorithm%基于改进型蚁群算法的最优路径问题求解
Institute of Scientific and Technical Information of China (English)
张志协; 曹阳
2012-01-01
如何高效的向用户提供最优路径是蚁群算法大规模应用于导航系统的关键问题,针对现有最优路径问题研究中蚁群算法收敛速度慢及容易发生停滞的缺点,利用A*算法的启发式信息改进蚁群算法的路径选择策略,加快算法收敛速度.同时引入遗传算法的双种群策略和蚁群系统信息素更新策略,增加全局搜索能力,避免算法出现停滞现象.仿真实验结果表明,该改进算法具有较好的稳定性和全局优化性,且收敛速度较快.%Efficient optimal path is the key issue of the Ant Colony Algorithm used in road traffic navigation system. Aiming at the problem of slow convergence and stagnation phenomenon of Ant Colony Algorithm, this paper introduce path selection strategy which is based on the heuristic factor of A* algorithm to speed up the convergence. Meanwhile the Double-population strategy of the Genetic algorithm and the pheromone update rules of the Ant Colony System (ACS) are introduced in the algorithm, which avoid stagnation of the algorithm of the algorithm and speed up convergence. The results of the simulation experiment show that the improved algorithm has good stability, global optimization and fast convergence.
Institute of Scientific and Technical Information of China (English)
张程程; 康维新
2015-01-01
To solve the problems of slow convergence speed, easily falling into local optimal solution in optimization algorithm of dynamic route guidance, an improved quantum ant colony algorithm ( IQACA) is proposed in this pa-per. Firstly, a dynamic road network model considering road intersection and road-consuming and a time optimal path model are established. Taking the quantum ant colony optimization algorithm strategy as reference, the im-proved quantum ant colony algorithm increases the probability amplitude density by narrowing the range of the qubit phase. Hadamard gate mutation mechanism is introduced to make the changes of the qubit probability amplitude' s position and size, which expands population diversity and increases the searching probability of globally optimal so-lution. IQACA is applied to the dynamic route guidance of an actual road network, and is analyzed comparing with the ant-colony algorithm and quantum ant colony algorithm. The simulation shows that IQACA is applicable to sol-ving the problem of time optimal path, which not only has good convergence performance but also obtains the time optimal route quickly.%针对动态路径诱导中寻优算法收敛速度慢,易陷入局部最优解的不足,提出了一种改进的量子蚁群算法( IQA-CA). 首先,建立了考虑交叉口和路段耗费的动态路网模型,并建立了时间最优路径模型. 借鉴量子蚁群算法的寻优策略,改进的量子蚁群算法通过将量子比特相位取值范围缩小的方法,提高概率幅的密度;采用Hadamard门变异机制,实现量子比特概率幅值的位置和大小的变化,扩大了种群多样性,增加了全局最优解搜索的概率. 将IQACA算法应用到实际路网的动态路径诱导中,并与蚁群算法、量子蚁群算法进行对比分析,实验结果表明,改进的IQACA算法适用于求解时间最优路径问题,不仅具有很好的收敛性能还能够较快的得出时间最优路径.
Institute of Scientific and Technical Information of China (English)
高静; 朱永利; 李丽芬
2011-01-01
With the high demand of real-time and reliability of monitoring system of power transmission lines, an ant colony algorithm with negative information growth is presented. Each node is no need to maintain the global information, but the only number must be given. The timer is set for the current not feasible and congestion nodes, nodes can participate in routing when the timer is overtime. The simulation results show that the proposed algorithm can obtain better performance than basic Ant Colony algorithm.%针对输电线路监测系统对无线传感器网络实时性和可靠性要求较高的特点，提出了一种用于线路监测传感网络的带信息素负增长的蚁群算法。该算法中不需要网络节点维护全局信息，但需要赋予唯一的编号。启发函数计及了链路的时延、收包率和距离汇聚节点的跳数，并经过试验增加了以参数a的不同选取可以调整跳数在整个选择过程中所占的重要程度。算法还为当前不可行和拥塞节点设置定时器，超时后可重新参与路由选择。仿真结果表明，相比基本蚁群算法，该算法能够找到具有更好性能的路由。
A Survey Paper on Solving TSP using Ant Colony Optimization on GPU
Directory of Open Access Journals (Sweden)
Khushbu Khatri
2014-12-01
Full Text Available Ant Colony Optimization (ACO is meta-heuristic algorithm inspired from nature to solve many combinatorial optimization problem such as Travelling Salesman Problem (TSP. There are many versions of ACO used to solve TSP like, Ant System, Elitist Ant System, Max-Min Ant System, Rank based Ant System algorithm. For improved performance, these methods can be implemented in parallel architecture like GPU, CUDA architecture. Graphics Processing Unit (GPU provides highly parallel and fully programmable platform. GPUs which have many processing units with an off-chip global memory can be used for general purpose parallel computation. This paper presents a survey on different solving TSP using ACO on GPU.
Institute of Scientific and Technical Information of China (English)
李明
2011-01-01
将蚁群优化算法引入土地资源规划,构建基于蚁群算法的资源规划数学模型,克服了传统优化模型的缺陷,选择张家口地区进行实证分析,得出了该地区最佳的土地利用结构优化方案.%This paper introduces the ant colony optimization into the land resources planning, constructs a mathematic model of resources planning based on the ant colony algorithm, overcomes the shortcomings of traditional optimization model, conducts an empirical analysis of Zhangjiakou region, and proposes the best optimization model of land use structure.
Km. Shweta; Alka Singh
2013-01-01
Ant Colony optimization has proved suitable to solve a wide range of combinatorial optimization(or NP-hard) problems as the Travelling Salesman Problem (TSP). The first step of ACO algorithm is to setthe parameters that drive the algorithm. The parameter has an important impact on the performance of theant colony algorithm. The basic parameters that are used in ACO algorithms are; the relative importance (orweight) of pheromone, the relative importance of heuristics value, initial pheromone v...
Institute of Scientific and Technical Information of China (English)
刘景巍; 张迪; 唐向辉; 曲大鹏
2014-01-01
蚁群算法是一种成功的启发式算法，但在解决TSP问题时存在着收敛速度慢和易陷入局部最优解的问题。本文针对这两个问题，提出了定期交流和模范带头学习模型，前者是在蚂蚁每走过一定城市后，进行学习交流，选出所走路径相对较短的蚂蚁进行信息素影响，从而加快总体的收敛速度；后者是当所有蚂蚁都旅行一圈后，选出最优秀的蚂蚁，在其走过的路径上释放大量信息素，对下一周期蚂蚁的旅行进行引导，避免陷入局部最优解。实验结果表明新算法在求解质量上比传统蚁群算法有了明显提高。本文也通过实验分析了蚂蚁数量等参数对算法性能的影响。%Ant colony algorithm is a successful heuristic algorithm, but it has two disadvantages in solving Traveling Salesman Problem (TSP), that is slow convergence and easy to fall into local optima ion. In this paper, the authors propose a regular exchange model and an exemplary model of learning. The former is that each ant walking in certain cities, learning exchanges, the path chosen by the relatively short walk pheromone ant influence, thus speeding up the overall speed of convergence; the latter is that when all the ants are traveling around after selection of the most outstanding ants, release large amounts of pheromone on its path traversed for the next cycle of ants traveling to boot, to avoid falling into local optima. After comparison with the conventional ant colony algorithm found that the new algorithm has been significantly improved in the solution quality. The paper analyzes the influence of the parameters, such as ant population, on the algorithm performance.
Enhanced Bee Colony Algorithm for Complex Optimization Problems
Directory of Open Access Journals (Sweden)
S.Suriya
2012-01-01
Full Text Available Optimization problems are considered to be one kind of NP hard problems. Usually heuristic approaches are found to provide solutions for NP hard problems. There are a plenty of heuristic algorithmsavailable to solve optimization problems namely: Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, etc. The basic Bee Colony algorithm, a population based search algorithm, is analyzed to be a novel tool for complex optimization problems. The algorithm mimics the food foraging behavior of swarmsof honey bees. This paper deals with a modified fitness function of Bee Colony algorithm. The effect of problem dimensionality on the performance of the algorithms will be investigated. This enhanced Bee Colony Optimization will be evaluated based on the well-known benchmark problems. The testing functions like Rastrigin, Rosenbrock, Ackley, Griewank and Sphere are used to evaluavate the performance of the enhanced Bee Colony algorithm. The simulation will be developed on MATLAB.
Using Ant Colony Optimization for Routing in VLSI Chips
Arora, Tamanna; Moses, Melanie
2009-04-01
Rapid advances in VLSI technology have increased the number of transistors that fit on a single chip to about two billion. A frequent problem in the design of such high performance and high density VLSI layouts is that of routing wires that connect such large numbers of components. Most wire-routing problems are computationally hard. The quality of any routing algorithm is judged by the extent to which it satisfies routing constraints and design objectives. Some of the broader design objectives include minimizing total routed wire length, and minimizing total capacitance induced in the chip, both of which serve to minimize power consumed by the chip. Ant Colony Optimization algorithms (ACO) provide a multi-agent framework for combinatorial optimization by combining memory, stochastic decision and strategies of collective and distributed learning by ant-like agents. This paper applies ACO to the NP-hard problem of finding optimal routes for interconnect routing on VLSI chips. The constraints on interconnect routing are used by ants as heuristics which guide their search process. We found that ACO algorithms were able to successfully incorporate multiple constraints and route interconnects on suite of benchmark chips. On an average, the algorithm routed with total wire length 5.5% less than other established routing algorithms.
Routing in Ad Hoc Network Using Ant Colony Optimization
Khanpara, Pimal; Valiveti, Sharada; Kotecha, K.
The ad hoc networks have dynamic topology and are infrastructure less. So it is required to implement a new network protocol for providing efficient end to end communication based on TCP/IP structure. There is a need to re-define or modify the functions of each layer of TCP/IP model to provide end to end communication between nodes. The mobility of the nodes and the limited resources are the main reason for this change. The main challenge in ad hoc networks is routing. Due to the mobility of the nodes in the ad hoc networks, routing becomes very difficult. Ant based algorithms are suitable for routing in ad hoc networks due to its dynamic nature and adaptive behavior. There are number of routing algorithms based on the concept of ant colony optimizations. It is quite difficult to determine the best ant based algorithm for routing as these algorithms perform differently under various circumstances such as the traffic distribution and network topology. In this paper, the overview of such routing algorithms is given.
Nest- and colony-mate recognition in polydomous colonies of meat ants ( Iridomyrmex purpureus)
van Wilgenburg, E.; Ryan, D.; Morrison, P.; Marriott, P. J.; Elgar, M. A.
2006-07-01
Workers of polydomous colonies of social insects must recognize not only colony-mates residing in the same nest but also those living in other nests. We investigated the impact of a decentralized colony structure on colony- and nestmate recognition in the polydomous Australian meat ant ( Iridomyrmex purpureus). Field experiments showed that ants of colonies with many nests were less aggressive toward alien conspecifics than those of colonies with few nests. In addition, while meat ants were almost never aggressive toward nestmates, they were frequently aggressive when confronted with an individual from a different nest within the same colony. Our chemical analysis of the cuticular hydrocarbons of workers using a novel comprehensive two-dimensional gas chromatography technique that increases the number of quantifiable compounds revealed both colony- and nest-specific patterns. Combined, these data indicate an incomplete transfer of colony odor between the nests of polydomous meat ant colonies.
Ant Colony Optimization (ACO) refers to the family of algorithms inspired by the behavior of real ants and used to solve combinatorial problems such as the Traveling Salesman Problem (TSP).Optimal Foraging Theory (OFT) is an evolutionary principle wherein foraging organisms or insect parasites seek ...
Institute of Scientific and Technical Information of China (English)
张兴国; 周东健; 李成浩
2014-01-01
Aiming at the TSP problem,in order to research the optimal path planning for mobile robot,a new algo-rithm based on ant colony algorithm combined with particle swarm algorithm( PAAAA)has been proposed. Firstly, using the particle swarm optimization to search the global path,the second best solution is obtained. Then,after dis-tributing the pheromones on the second best solution paths,using ant colony algorithm to finish accurate searching. Last,the optimal solution of path planning is achieved. The simulation result shows that PAAA is better than single ant colony algorithm or single particle swarm optimization.%基于TSP问题，提出了一种基于粒子群-蚁群算法相互融合的综合优化算法对移动机器人路径规划问题进行研究。通过粒子群算法对全局路径实施粗略搜索，获得部分次优解，在获得次优解的路径上进行信息素分布，再采用蚁群算法进行精确搜索，得到路径规划的最优解。实验结果表明：粒子群-蚁群融合优化算法在路径寻优上优于蚁群算法及粒子群算法。
Binary-Coding-Based Ant Colony Optimization and Its Convergence
Institute of Scientific and Technical Information of China (English)
Tian-Ming Bu; Song-Nian Yu; Hui-Wei Guan
2004-01-01
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 ρ is under the condition of ρ≥ 1,the algorithm can promise to converge at the optimal,whereas if 0 ＜ρ＜ 1,it does not.
Routing in Wireless Sensor Networks Using an Ant Colony Optimization (ACO) Router Chip
Dervis Karaboga; Selcuk Okdem
2009-01-01
Wireless Sensor Networks consisting of nodes with limited power are deployed to gather useful information from the field. In WSNs it is critical to collect the information in an energy efficient manner. Ant Colony Optimization, a swarm intelligence based optimization technique, is widely used in network routing. A novel routing approach using an Ant Colony Optimization algorithm is proposed for Wireless Sensor Networks consisting of stable nodes. Illustrative examples, detailed descriptions a...
Continuous function optimization using hybrid ant colony approach with orthogonal design scheme
Zhang, J.; Chen, W.; Zhong, J.; Tan, X.; Li, Y.
2006-01-01
A hybrid Orthogonal Scheme Ant Colony Optimization (OSACO) algorithm for continuous function optimization (CFO) is presented in this paper. The methodology integrates the advantages of Ant Colony Optimization (ACO) and Orthogonal Design Scheme (ODS). OSACO is based on the following principles: a) each independent variable space (IVS) of CFO is dispersed into a number of random and movable nodes; b) the carriers of pheromone of ACO are shifted to the nodes; c) solution path can be obtained by ...
The Logistics Distribution Route research of Ant Colony Algorithm%基于蚁群算法的应急物资调度系统的研究与开发
Institute of Scientific and Technical Information of China (English)
符志强; 罗丹丹
2014-01-01
In recent years, large-scale natural disasters and public health events are becoming more frequent invasion of the world with the industrialization and city urbanization intensifies. In this paper, ant colony algorithm (Ant Colony Optimization, ACO) is used in emergency supplies scheduling system to create an efficient emergency rescue system. System mainly supplies module management, city information management, vehicle management and call emergency function. The system adopts modular de-sign and has the advantage of clear structure and easy extension.%近年来，随着工业化及城市化进程的加剧，各种大规模自然灾害、公共卫生事件正越来越频繁地侵袭着我们生存的世界。该文以蚁群算法(Ant Colony Optimization，ACO)和应急物资调度系统相结合，创造一个高效的紧急救援系统。系统主要有物资模块管理，城市信息管理，配送车辆管理，紧急呼救等功能。系统采用模块化设计，采用轻量级企业框架(struts2+spring3+hibernate3)开发，具有结构清晰，易于扩展的优点。
A New Version of the Ant-Miner Algorithm Discovering Unordered Rule Sets
Smaldon, James; Freitas, Alex A
2006-01-01
The Ant-Miner algorithm, first proposed by Parpinelli and colleagues, applies an ant colony optimization heuristic to the classification task of data mining to discover an ordered list of classification rules. In this paper we present a new version of the Ant-Miner algorithm, which we call Unordered Rule Set Ant-Miner, that produces an unordered set of classification rules. The proposed version was evaluated against the original Ant-Miner algorithm in six public-domain da...
Mobile Anonymous Trust Based Routing Using Ant Colony Optimization
Directory of Open Access Journals (Sweden)
R. Kalpana
2012-01-01
Full Text Available Problem statement: Ad hoc networks are susceptible to malicious attacks through denial of services, traffic analysis and spoofing. The security of the ad hoc routing protocol depends upon encryption, authentication, anonymity and trust factors. End-to-end security of data is provided by encryption and authentication, topology information of the nodes can be obtained by studying traffic and routing data. This security problem of ad hoc network is addressed by the use of anonymity mechanisms and trust levels. Identification information like traffic flow, network topology, paths from malicious attackers is hidden in anonymous networks. Similarly, trust plays a very important role in the intermediate node selection in ad hoc networks. Trust is essential as selfish and malicious nodes not only pose a security issue but also decreases the Quality of Service. Approach: In this study, a routing to address anonymous routing with a trust which improves the overall security of the ad hoc network was proposed. A new approach for an on demand ad-hoc routing algorithm, which was based on swarm intelligence. Ant colony algorithms were a subset of swarm intelligence and considered the ability of simple ants to solve complex problems by cooperation. The interesting point was, that the ants do not need any direct communication for the solution process, instead they communicate by stigmergy. The notion of stigmergy means the indirect communication of individuals through modifying their environment. Several algorithms which were based on ant colony problems were introduced in recent years to solve different problems, e.g., optimization problems. Results and Conclusion: It is observed that the overall security in the network improves when the trust factor is considered. It is seen that non performing nodes are not considered due to the proposed ACO technique.
Protein folding in hydrophobic-polar lattice model: a flexible ant colony optimization approach
Hu, X-M.; Zhang, J.(High Energy Physics Division, Argonne National Laboratory, Argonne, IL, USA); Xiao, J.; Li, Y.
2008-01-01
This paper proposes a flexible ant colony (FAC) algorithm for solving protein folding problems based on the hydrophobic-polar square lattice model. Collaborations of novel pheromone and heuristic strategies in the proposed algorithm make it more effective in predicting structures of proteins compared with other state-of-the-art algorithms.
Protein folding in hydrophobic-polar lattice model: a flexible ant-colony optimization approach.
Hu, Xiao-Min; Zhang, Jun; Xiao, Jing; Li, Yun
2008-01-01
This paper proposes a flexible ant colony (FAC) algorithm for solving protein folding problems based on the hydrophobic-polar square lattice model. Collaborations of novel pheromone and heuristic strategies in the proposed algorithm make it more effective in predicting structures of proteins compared with other state-of-the-art algorithms. PMID:18537736
Ant Colony Optimization ACO For The Traveling Salesman Problem TSP Using Partitioning
Alok Bajpai; Raghav Yadav
2015-01-01
Abstract An ant colony optimization is a technique which was introduced in 1990s and which can be applied to a variety of discrete combinatorial optimization problem and to continuous optimization. The ACO algorithm is simulated with the foraging behavior of the real ants to find the incremental solution constructions and to realize a pheromone laying-and-following mechanism. This pheromone is the indirect communication among the ants. In this paper we introduces the partitioning technique ba...
Blackboard Mechanism Based Ant Colony Theory for Dynamic Deployment of Mobile Sensor Networks
Institute of Scientific and Technical Information of China (English)
Guang-ping Qi; Ping Song; Ke-jie Li
2008-01-01
A novel bionic swarm intelligence algorithm, called ant colony algorithm based on a blackboard mechanism, is proposed to solve the autonomy and dynamic deployment of mobiles sensor networks effectively. A blackboard mechanism is introduced into the system for making pheromone and completing the algorithm. Every node, which can be looked as an ant, makes one information zone in its memory for communicating with other nodes and leaves pheromone, which is created by ant itself in nature. Then ant colony theory is used to find the optimization scheme for path planning and deployment of mobile Wireless Sensor Network (WSN). We test the algorithm in a dynamic and unconfigurable environment. The results indicate that the algorithm can reduce the power consumption by 13% averagely, enhance the efficiency of path planning and deployment of mobile WSN by 15% averagely.
Multiple ant-bee colony optimization for load balancing in packet-switched networks
Mehdi Kashefi Kia; Nasser Nemat bakhsh; Reza Askari Moghadam
2011-01-01
One of the important issues in computer networks is “Load Balancing” which leads to efficient use of the network resources. To achieve a balanced network it is necessary to find different routes between the source and destination. In the current paper we propose a new approach to find different routes using swarm intelligence techniques and multi colony algorithms. In the proposed algorithm that is an improved version of MACO algorithm, we use different colonies of ants and bees and appoint t...
Remote Sensing Image Feature Extracting Based Multiple Ant Colonies Cooperation
Directory of Open Access Journals (Sweden)
Zhang Zhi-long
2014-02-01
Full Text Available This paper presents a novel feature extraction method for remote sensing imagery based on the cooperation of multiple ant colonies. First, multiresolution expression of the input remote sensing imagery is created, and two different ant colonies are spread on different resolution images. The ant colony in the low-resolution image uses phase congruency as the inspiration information, whereas that in the high-resolution image uses gradient magnitude. The two ant colonies cooperate to detect features in the image by sharing the same pheromone matrix. Finally, the image features are extracted on the basis of the pheromone matrix threshold. Because a substantial amount of information in the input image is used as inspiration information of the ant colonies, the proposed method shows higher intelligence and acquires more complete and meaningful image features than those of other simple edge detectors.
Ant colony based routing in wireless sensor networks
Directory of Open Access Journals (Sweden)
Varnika Bains
2012-09-01
Full Text Available Wireless Sensor Networks comprises of small sensor nodes communicating with each other in a network topology which keeps on varying. The wireless sensor nodes also known as motes have limited energy resources along with constraints on its computational and storage capabilities. Due to these restrictions coupled with its dynamic topology, routing in Wireless Sensor Networks (WSN is a very challenging task. Routing protocols for WSN’s have to ensure reliable multi-hop communication under these conditions. A wide range of adhoc routing algorithms are available for WSN’s. In this paper an adaptation of Ant Colony Optimization (ACO technique is demonstrated for network routing. This approach belongs to the class of routing algorithms inspired by the behavior of the ant colonies in locating and storing food. The effectiveness of the heuristic algorithm is supported by the performance evaluations. PROWLER, a MATLAB based probabilistic wireless network simulator is used for the calculations. This simulator simulates the transmissions including collisions in ad-hoc radio networks, and the operation of the MAC-layer. The performance metrics are evaluated on RMASE, an application built in PROWLER.
云环境下基于改进蚁群算法的网络路由优化%Network Routing Optimization in Cloud Based on Improved Ant Colony Algorithm
Institute of Scientific and Technical Information of China (English)
刘伯红; 赵浚尧; 王刚; 阎英
2013-01-01
Aimed at diversified and complex network structure environment in cloud, an ant colony optimization algorithm is proposed to improve the network routing quanlity. Based on the original intelligent optimization ant colony algorithm, the new algorithm adopts an network node review mechanism which can judge the network node is online or not in real time, and then select the optimal solution network routing path. Simulation result shows that improved algorithm can effectively improve the network routing quanlity when partial network path has no effect caused by the variability of the network nodes in the network, and so solve some network congestion problems.%针对云计算多元化复杂的网络结构环境,提出一种旨在改善网络路由的蚁群优化算法.新算法在原有蚁群算法智能寻优的基础上,加入网络节点在网审查机制,实时判断网络节点是否在网,选择最优解路径.仿真实验表明,改进算法能有效的改善因为网络节点在网情况的多变性而造成的部分路径失效的情况,进而缓解网络拥塞.
基于量子蚁群算法的机器人联盟问题研究%Study on Robot Coalition Problem Based on Quantum-inspired Ant Colony Algorithm
Institute of Scientific and Technical Information of China (English)
李絮; 刘争艳
2013-01-01
@@@@Combining the thought of quantum evolution algorithm with ant colony algorithm, a novel quantum ant colony algorithm for robot coalition is proposed. To avoid the search falling into local optimum, the strategy of multi-group parallel search and quan?tum crossover are designed to improve the information structure of population. In addition, a new pheromone representation is de?signed. Simulation results show that the algorithm has fast convergence speed and global optimal ability.%将量子进化算法与蚁群算法思想相融合,提出一种求解机器人联盟问题的量子蚁群算法.为了避免搜索陷入局部最优,采用多种群并行搜索及量子交叉策略,以改善种群信息结构;算法中还设计了一种新的信息素表示方式.仿真实验结果表明,该算法具有较快的收敛速度和全局寻优能力.
基于蚁群优化算法的 SAR 图像舰船检测研究%Study on SAR image ship detection based on ant colony optimization algorithm
Institute of Scientific and Technical Information of China (English)
李琳琳; 王纪奎; 宋艳芳
2014-01-01
边缘检测作为提取图像边缘的重要方法在舰船检测中占有重要位置。采用蚁群优化算法通过调整动态阈值进行边缘检测。与传统边缘检测算子和小波变换算法对比采用蚁群优化算法进行舰船检测大大的减少了计算时间和代价，同时有效地提取了SAR图像的舰船目标和结构信息，保证了检测结果的准确性。蚁群优化算法在处理图像边缘检测等离散优化问题上具有很大的优越性，在图像处理中具有广阔的应用前景。%Edge detection as an important image edge extraction method plays an important part in ship detection .Ant Colony Optimization algorithm was adopted in edge detection by adjusting threshold dynamically.Compared with the traditional edge detection operators and wavelet transform, ant colony optimization algorithm greatly reduces the ship detection computing time and cost,and extracts the targets and ship structure information in SAR image,ensures result accuracy effectively.Ant Colony Optimization algorithm has a lot of advantages in dealing with image edge detection and so on discrete optimization problems,it performs very broad prospect in image processing .
Electricity Consumption Prediction Based on SVR with Ant Colony Optimization
Haijiang Wang; Shanlin Yang
2013-01-01
Accurate forecasting of electric load has always been the most important issues in the electricity industry, particularly for developing countries. Due to the various influences, electric load forecasting reveals highly nonlinear characteristics. This paper creates a system for power load forecasting using support vector machine and ant colony optimization. The method of colony optimization is employed to process large amount of data and eliminate. The SVR model with ant colony optimization i...
Institute of Scientific and Technical Information of China (English)
谭冠政; 贺欢; SLOMAN Aaron
2007-01-01
A novel method for the real-time globally optimal path planning of mobile robots is proposed based on the ant colony system (ACS) algorithm. This method includes three steps: the first step is utilizing the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is utilizing the Dijkstra algorithm to find a sub-optimal collision-free path,and the third step is utilizing the ACS algorithm to optimize the location of the sub-optimal path so as to generate the globally optimal path. The result of computer simulation experiment shows that the proposed method is effective and can be used in the real-time path planning of mobile robots. It has been verified that the proposed method has better performance in convergence speed, solution variation, dynamic convergence behavior, and computational efficiency than the path planning method based on the genetic algorithm with elitist model.
Institute of Scientific and Technical Information of China (English)
叶青; 熊伟清; 李纲
2011-01-01
针对二元蚁群算法在求解多目标问题时难以同时得到多个解和难以得到Pareto曲面的缺陷,使用多种群策略,改善算法的全局搜索能力,引入环境评价/奖励因子和蚁群混合行为搜索机制,提出了多种群混合行为二元蚁群算法.通过对几个不同带约束多目标函数的测试,实验结果表明该算法在保证全局搜索能力的基础上,拥有很好的多目标求解能力.%Aiming at solving the drawbacks of the original binary ant colony algorithm on multi-objective optimization problems:easy to fall into the local optimization and difficult to get the Pareto optimal solutions,Multi-Population Binary Ant colony algorithm with Concrete Behaviors(MPBACB) is proposed.This algorithm introduces multi-population method to ensure the global optimization ability, and uses environmental evaluation/reward model to improve the searching efficiency.Furthermore, concrete ant behaviors are defined to stabilize the performance of the algorithm.Experimental results on several constrained multi-objective functions prove that the algorithm ensures the good global search ability,and has better effect on the multi-objective problems.
Energy Aware Simple Ant Routing Algorithm for Wireless Sensor Networks
2015-01-01
Network lifetime is one of the most prominent barriers in deploying wireless sensor networks for large-scale applications because these networks employ sensors with nonrenewable scarce energy resources. Sensor nodes dissipate most of their energy in complex routing mechanisms. To cope with limited energy problem, we present EASARA, an energy aware simple ant routing algorithm based on ant colony optimization. Unlike most algorithms, EASARA strives to avoid low energy routes and optimizes the ...
Directory of Open Access Journals (Sweden)
Xuemei Sun
2015-01-01
Full Text Available Degree constrained minimum spanning tree (DCMST refers to constructing a spanning tree of minimum weight in a complete graph with weights on edges while the degree of each node in the spanning tree is no more than d (d ≥ 2. The paper proposes an improved multicolony ant algorithm for degree constrained minimum spanning tree searching which enables independent search for optimal solutions among various colonies and achieving information exchanges between different colonies by information entropy. Local optimal algorithm is introduced to improve constructed spanning tree. Meanwhile, algorithm strategies in dynamic ant, random perturbations ant colony, and max-min ant system are adapted in this paper to optimize the proposed algorithm. Finally, multiple groups of experimental data show the superiority of the improved algorithm in solving the problems of degree constrained minimum spanning tree.
Institute of Scientific and Technical Information of China (English)
黄震; 罗中良; 黄时慰
2015-01-01
A hybrid ant colony algorithm was proposed.Because,ant colony algorithm used to solve the vehicle routing problem with time windows (VRPTW)is easy to fall into local optimum,and the quality of initial population in genetic algorithm affects the effectiveness of the algorithm directly.Firstly,the al-gorithm introduces the factors of time windows into node selection probability formula of ant colony algo-rithm to get the initial population.Secondly,the crossover and the mutation were operated to get a better path for the initial population.Applying Matlab environment for hybrid algorithm simulation,the effects on the vehicle utilization and path planning is obvious.It shows the algorithm is efficient,and can avoid falling into local optimum.%针对带时间窗车辆路径问题求解时蚁群算法存在容易陷入局部最优，而遗传算法初始种群的优劣对算法有效性存在直接影响，提出一种混合蚁群优化算法。算法首先在蚁群算法的节点选择概率公式中引入时间窗因素，以得到初始种群，然后通过遗传算法的交叉算子和变异算子对初始种群中的较优路径进行交叉和变异操作，从而得到更优的路径。通过 Matlab 环境下对文中混合算法进行仿真实验，在车辆利用率和路径规划上效果明显，表明了算法的高效性，同时混合算法可以避免陷入局部最优。
Comparative Research on Typical Routing Protocols Based on Ant Colony Algorithm%基于蚁群算法的典型路由协议的比较研究
Institute of Scientific and Technical Information of China (English)
郭彦芳
2015-01-01
Aiming at Ad hoc network changing topology and basic ant colony easy to lose multiple solutions,this paper proposes the combining method of ant colony algorithm and DSR,AODV and DSDV,called ant⁃DSR,ant⁃AODV and ant⁃DSDV after improving the next hop node selection.The improved ant colony algorithm is used to find the optimal path.The end to end delay,throughput,routing overhead,and hop count performance parameters are analyzed and compared in such two scenarios as node rate and pause time. The simulation results show that the first routing protocol is more adaptable to the ant colony algorithm to improve performance compared with on⁃demand routing protocols,but it increases routing overhead and requires more calculation to find the optimal path in each node.%针对Ad hoc网络拓扑结构的多变和基本蚁群算法易失去多解的情况，在对算法的节点选择进行改进后，提出把蚁群算法与DSR、AODV和DSDV相结合，即ant⁃DSR、ant⁃AODV和ant⁃DSDV。利用改进的蚁群算法寻找最优路径，在节点速率、停留时间这2种不同场景下分析比较了端到端时延、吞吐量、路由开销和跳数等参数的性能。仿真结果表明，先应式路由协议比按需路由协议在提高性能上更适合于蚁群算法，但却增加了路由开销，并且每个节点产生最优路径时需要更多的计算。
Institute of Scientific and Technical Information of China (English)
杨名
2013-01-01
物流网络优化中普遍存在着多目标优化的问题.传统的多目标优化算法容易陷入局部最优,采用了多种群相关的蚁群算法求解多目标物流网络优化问题,两个种群分别针对总运费最小和最大单程距离最小两个优化目标,考虑蚁群算法的收敛速度,采用遗传算法对蚁群算法的多个初始参数进行优化选择.实验结果证明,该模型算法可以有效迅速地求得最佳路径,为决策者提供多个可选择的优化方案,避免局部最优解.%In this paper, In order to solve the optimization problem of multi-objective logistics networks, we adopted the the ant colony algorithm in correlation with two colonies, respectively being the two optimization objectives of minimizing total transportation cost and maximizing the distance of a single travel. Then considering the speed of convergence of the ant colony algorithm, we used the genetic algorithm to optimize and select its initial parameters. Through an experiment, we found that this model and algorithm could quickly derive the optimal path,, provide multiple options for the decision-makers and avoid local optimal solutions.
Institute of Scientific and Technical Information of China (English)
范杰; 彭舰; 黎红友
2011-01-01
There is no corresponding standard of valuation for cloud computing at home for the moment, so the price directly affects the network's availability and profits. Traditional Ant Colony Optimization (ACO) algorithm can simulate the load of network, but it has some disadvantages in solving the problem of network congestion and load balance. As these problems above, an improved ACO algorithm was proposed. This algorithm considered the factor of price for network load when valuing. It utilized demand elasticity theory and controlled the load of network through price indirectly. And it solved the problem of low availability or decreased profits caused by computing congestion or resource unemployment. The experimental results indicate that the improved ACO algorithm achieves load balance of the whole network. Therefore, the lifetime of network system is prolonged and more profits can be made.%目前,国内并没有相应的云计算计价标准,价格高低直接影响网络利用率和收益.传统蚁群算法(AC0)能模拟网络负载情况,但在解决网络拥塞和负载均衡问题方面存在不足.针对以上问题,提出一种改进的蚁群算法,在计价时考虑价格因素对网络负载的影响,利用需求弹性理论,以价格手段间接控制网络负载,辅助解决计算拥塞或计算资源闲置导致的网络利用率低下和收益减少问题.实验表明,改进后的算法使整体网络负载均衡,延长了网络寿命并获得一定利润.
Institute of Scientific and Technical Information of China (English)
吴斌; 唐洪; 王素荣; 王兴志; 徐立明; 徐正华
2012-01-01
针对东部某碳酸盐岩油藏裂缝发育,在开发后期油藏内部裂缝系统油水分布复杂,储层有利发育区的确定存在多解性的情况,采用蚁群算法对该区裂缝进行识别和预测。笔者参照岩心和成像测井资料对蚂蚁参数值调优,系统描述了研究区裂缝的空间展布特征,即裂缝体系呈网状结构,NW向、NNE向及近EW向的三组裂缝簇占绝对优势。再利用钻井漏失量、生产资料及成像测井对裂缝预测结果进行可靠性分析,表明利用蚁群算法识别及预测裂缝的方法切实可行,能为分析剩余油分布规律提供技术支持。蚁群算法作为一种新兴的仿生学算法,在利用地震资料定量预测裂缝方面有较大的发展潜力。%A fracture-developed carbonate oil field in the east has a complicate oil and gas distribution in facture network at late development stage, which leads to multiplicity of favorable reservoir estimation. In view of problems above, ant colony algorithm is adapted to recognition and predict facture in this field. The ant parameters are optimized on the basis of core and image log data, and the spatial distribution feature of fracture is described, as a reticulate structure with three dominant clusters of fracture (NW, NNE ~ NE). Drilling leakage, production data and image log are then used for reliability analysis of fracture predict, which presents that ant colony algorithm is a practicable methodology to recognition fracture and provides a support for remaining oil distribution analysis. As a booming bionic algorithm, ant colony algorithm has great potential for quantitative fracture predict with seismic materials.
Institute of Scientific and Technical Information of China (English)
孙伟; 王宜雷; 王慧; 曾盈
2012-01-01
Mathematical model of product structure optimization was established according to characteristics of process of coal preparation plant, and steps of optimizing the product structure by ant colony algorithm were given. Nantun Coal Preparation Plant was used for example to simulate, and the best yields of products under various constraints were obtained. The simulation results showed the feasibility of ant colony algorithm for product structure optimization of coal preparation plant.%根据选煤厂生产流程特点建立了产品结构优化数学模型,给出了应用蚁群算法优化产品结构的步骤；并以南屯选煤厂为例进行仿真,得到了满足各种约束条件下的各产品的最佳产量.仿真结果说明了蚁群算法在选煤厂产品结构优化中应用的可行性.
Orthogonal Methods Based Ant Colony Search for Solving Continuous Optimization Problems
Institute of Scientific and Technical Information of China (English)
Xiao-Min Hu; Jun Zhang; Yun Li
2008-01-01
Research into ant colony algorithms for solving continuous optimization problems forms one of the most significant and promising areas in swarm computation. Although traditional ant algorithms are designed for combinatorial optimization, they have shown great potential in solving a wide range of optimization problems, including continuous optimization. Aimed at solving continuous problems effectively, this paper develops a novel ant algorithm termed "continuous orthogonal ant colony" (COAC), whose pheromone deposit mechanisms would enable ants to search for solutions collaboratively and effectively. By using the orthogonal design method, ants in the feasible domain can explore their chosen regions rapidly and efficiently. By implementing an "adaptive regional radius" method, the proposed algorithm can reduce the probability of being trapped in local optima and therefore enhance the global search capability and accuracy. An elitist strategy is also employed to reserve the most valuable points. The performance of the COAC is compared with two other ant algorithms for continuous optimization -- API and CACO by testing seventeen functions in the continuous domain. The results demonstrate that the proposed COAC algorithm outperforms the others.
Discovering Unordered Rule Sets for Mixed Variables Using an Ant-Miner Algorithm
Nalini, C; Balasubramanie, P
2008-01-01
This work proposes a data mining algorithm called Unordered Rule Sets using a continuous Ant-Miner algorithm. The goal of this work is to extract classification rules from data. Swarm intelligence (SI) is a technique whereby rules may be discovered through the study of collective behavior in decentralized, self-organized systems, such as ants. The Ant-Miner algorithm, first proposed by Parpinelli and his colleagues (2002), applies an ant colony optimization (ACO) heuristic to the classificati...
Ant Colony Optimisation for Backward Production Scheduling
Directory of Open Access Journals (Sweden)
Leandro Pereira dos Santos
2012-01-01
Full Text Available The main objective of a production scheduling system is to assign tasks (orders or jobs to resources and sequence them as efficiently and economically (optimised as possible. Achieving this goal is a difficult task in complex environment where capacity is usually limited. In these scenarios, finding an optimal solution—if possible—demands a large amount of computer time. For this reason, in many cases, a good solution that is quickly found is preferred. In such situations, the use of metaheuristics is an appropriate strategy. In these last two decades, some out-of-the-shelf systems have been developed using such techniques. This paper presents and analyses the development of a shop-floor scheduling system that uses ant colony optimisation (ACO in a backward scheduling problem in a manufacturing scenario with single-stage processing, parallel resources, and flexible routings. This scenario was found in a large food industry where the corresponding author worked as consultant for more than a year. This work demonstrates the applicability of this artificial intelligence technique. In fact, ACO proved to be as efficient as branch-and-bound, however, executing much faster.
A Nested Hybrid Ant Colony Algorithm for Hybrid Production Scheduling Problems%求解混杂生产调度问题的嵌套混合蚁群算法
Institute of Scientific and Technical Information of China (English)
李艳君; 吴铁军
2003-01-01
The validity of the ant colony algorithm has been demonstrated as a powerful tool to solve the optimization problems. This technique is used to solve difficult combinatorial optimization problems but is seldom used for continuous space search due to its biological background. A nested hybrid ant colony algorithm is proposed in this paper to solve the complicated production scheduling problem with hybrid variable structures, and a novel optimal path pheromone update algorithm is suggested to promote search efficiency. Computer simulation results show that the proposed method is more effective than genetic algorithms as a kind of evolutionary algorithms in solving such kind of difficult problems.%蚁群算法作为解决优化问题的有力工具,它的有效性已经得到了证明.由于其生物学背景,基本蚁群算法被设计来求解复杂的排序类型组合优化问题,在连续空间优化问题的求解方面研究很少.本文提出一种嵌套混合蚁群算法,用于解决具有混杂变量类型的复杂生产调度问题,在一种新的最佳路径信息素更新算法的基础上,提高了搜索效率.计算机仿真结果表明,本文提出的方法在求解此类问题上性能优于另一种基于进化计算的有效方法--遗传算法.
A Hybrid Ant Colony Optimization for the Prediction of Protein Secondary Structure
Institute of Scientific and Technical Information of China (English)
Chao CHEN; Yuan Xin TIAN; Xiao Yong ZOU; Pei Xiang CAI; Jin Yuan MO
2005-01-01
Based on the concept of ant colony optimization and the idea of population in genetic algorithm, a novel global optimization algorithm, called the hybrid ant colony optimization (HACO), is proposed in this paper to tackle continuous-space optimization problems. It was compared with other well-known stochastic methods in the optimization of the benchmark functions and was also used to solve the problem of selecting appropriate dilation efficiently by optimizing the wavelet power spectrum of the hydrophobic sequence of protein, which is thc key step on using continuous wavelet transform (CWT) to predict a-helices and connecting peptides.
MAS Equipped with Ant Colony Applied into Dynamic Job Shop Scheduling
Kang, Kai; Zhang, Ren Feng; Yang, Yan Qing
This paper presents a methodology adopting the new structure of MAS(multi-agent system) equipped with ACO(ant colony optimization) algorithm for a better schedule in dynamic job shop. In consideration of the dynamic events in the job shop arriving indefinitely schedules are generated based on tasks with ant colony algorithm. Meanwhile, the global objective is taken into account for the best solution in the actual manufacturing environment. The methodology is tested on a simulated job shop to determine the impact with the new structure.
Ant-colony heuristic algorithm for no-idle flow shop scheduling problem%求解零空闲流水线调度问题的改进蚁群算法
Institute of Scientific and Technical Information of China (English)
张风荣; 段俊华; 庞荣波; 韩红燕
2011-01-01
This paper proposed an advanced ant colony algorithm for no-idle flow shop problem (NIFS) with makespan criterion. Improved the pheromone density initialization and updating rules by using a hybrid heuristic strategy, adoped a new state transition rule to construct an ant-sequence. Then, presented an local search algorithm based on the speed-up technology for inserting neighborhood structure for overcoming algorithm into the local convergence. And the simulation experiments show that the presented algorithm is effective and superiority in finding optimal or near optimal solutions.%针对零空闲流水线调度问题的最大完工时间,提出一种改进蚁群算法.该算法改进了信息素密度的初始化方法和更新规则,采用新的状态转移策略构建新解,结合快速插入邻域局部搜索算法,解决蚁群算法易陷入局部收敛的缺点,从而提高算法的搜索效率.基于典型算例的仿真实验,表明了改进算法具有高效性和优越性.
Brief Announcement: Distributed Task Allocation in Ant Colonies
Dornhaus, Anna; Lynch, Nancy; Radeva, Tsvetomira; Su, and Hsin-Hao
2015-01-01
International audience A common problem in both distributed computing and insect biology is designing a model that accurately captures the behavior of a given distributed system or an ant colony, respectively. While the challenges involved in modeling computer systems and ant colonies are quite different from each other, a common approach is to explore multiple variations of different models and compare the results in terms of the simplicity of the model and the quality of the results. We ...
Determining the Optimum Section of Tunnels Using Ant Colony Optimization
S. Talatahari
2013-01-01
Ant colony optimization is developed to determine optimum cross sections of tunnel structures. Tunnel structures are expensive infrastructures in terms of material, construction, and maintenance and the application of optimization methods has a great role in minimizing their costs. This paper presents the formulation of objective function and constraints of the problem for the first time, and the ant colony optimization, as a developed metaheuristic approach, has been used to solve the proble...
Experiment Study of Entropy Convergence of Ant Colony Optimization
Pang, Chao-Yang; Wang, Chong-Bao; Hu, Ben-Qiong
2009-01-01
Ant colony optimization (ACO) has been applied to the field of combinatorial optimization widely. But the study of convergence theory of ACO is rare under general condition. In this paper, the authors try to find the evidence to prove that entropy is related to the convergence of ACO, especially to the estimation of the minimum iteration number of convergence. Entropy is a new view point possibly to studying the ACO convergence under general condition. Key Words: Ant Colony Optimization, Conv...
Institute of Scientific and Technical Information of China (English)
张寒野; 凌建忠
2016-01-01
分别以遗传算法与蚁群算法对23个站位的海洋渔业资源调查路径进行规划,寻找其最优路径.求解结果表明,遗传算法和蚁群算法都能找到同样的最短路径,比实际路径缩短了8.32%的里程.蚁群算法求得的平均路径长度小于遗传算法,但所耗时间比遗传算法多一倍左右.%Searching for the optimal path is one of the most important combinatorial optimization problems. Since this problem belongs to NP-hard problems,an exact algorithm could not solve the large-scale problems in time,some metaheuristic approaches have been used to solve it in recent years.Genetic algorithm works in a way similar to the process of natural evolution,such as inheritance,mutation,selection and crossover.A basic GA starts with a randomly generated population of candidate solutions.After the evolution of several generations,the optimal solution for the problem is obtained.Ant colony optimization algorithm is to mimic the movements of ants.Ants leave a trail of pheromones when they search for food,and the pheromone density becomes higher on shorter paths than longer ones.As more ants use a particular trail,the pheromone concentration on it increases,hence attracting more ants.Consequently,all ants follow a best path.This article presented GA and ACO for solving the path planning of 23 stations for a fishing resource survey.The results indicate that both algorithm are able to find out the same shortest path,which is 8.32% shorter than the actual path.The average path length obtained by ACO is less than that by GA,but it takes nearly twice as long.It is suggested that ACO has better convergence and more acurate calculation results,as well as GA is suitable for fastsolving and roughly estimating the problems.
Improved Ant Algorithms for Software Testing Cases Generation
Directory of Open Access Journals (Sweden)
Shunkun Yang
2014-01-01
Full Text Available Existing ant colony optimization (ACO for software testing cases generation is a very popular domain in software testing engineering. However, the traditional ACO has flaws, as early search pheromone is relatively scarce, search efficiency is low, search model is too simple, positive feedback mechanism is easy to porduce the phenomenon of stagnation and precocity. This paper introduces improved ACO for software testing cases generation: improved local pheromone update strategy for ant colony optimization, improved pheromone volatilization coefficient for ant colony optimization (IPVACO, and improved the global path pheromone update strategy for ant colony optimization (IGPACO. At last, we put forward a comprehensive improved ant colony optimization (ACIACO, which is based on all the above three methods. The proposed technique will be compared with random algorithm (RND and genetic algorithm (GA in terms of both efficiency and coverage. The results indicate that the improved method can effectively improve the search efficiency, restrain precocity, promote case coverage, and reduce the number of iterations.
Institute of Scientific and Technical Information of China (English)
李思; 宋珺琤; 沙立成
2009-01-01
提出一种基于风险度评价和改进蚁群算法的配电网灵活规划方法.将规划年的预测负荷、电价及导线价格等参数的变化由原来的离散状态转变为多场景区间,从而将电网规划中的单一不确定性问题转化为多个确定性问题.在求解不确定性问题时,提出一种基于云模型的改进蚁群算法,通过定性关联规则推理对蚁群算法中信息素参数ρ和信息素强度Q进行定性控制和动态选取,根据算法进化情况自适应更新支路信息素,有效克服了传统蚁群算法易陷入局部最优解及收敛速度慢的问题.应用改进蚁群算法,依次获得各个场景的规划方案,并以风险度最小为标准确定鲁棒性最优的规划方案,实现电网的灵活规划.算例分析验证了所提方法的有效性.%An "algorithm of power distribution network flexible planning was proposed based on risk assessment and improved ant colony algorithm. The parameters of object year, such as load, electrovalence, lead price were changed from discrete state to a section of multi-scenario. The uncertainty problem of planning was changed into several confirmed problems. An adaptive ant colony algorithm based on cloud model was presented. The parameters ρ and Q of ant colony algorithm are qualitatively controlled and dynamically selected by employing the uncertain qualitative association rule inference based on cloud modeL The pheromone of branches is adaptive updated according to the process of the algorithm. The adaptive algorithm overcomes the main disadvantage of being inclined to local convergence and slow convergence speed of traditional ant colony algorithm. The adaptive algorithm is applied in confirmed distribution network structure planning. The scheme of each scenario was obtained and the optimum robust planning scheme was made with considering the minimum cost expectation scheme. Then the distribution network flexible planning could be realized. The instance
Institute of Scientific and Technical Information of China (English)
方昕
2012-01-01
The heuristic information is the key in the geographic information system(GIS). Ant Colony Algorithm(ACO) easily leads to local optimal solution in solving shortest path problem. To overcome this shortcoming, improved ACO with heuristic information is proposed. The ACO introduces heuristic information to guide ants can fast converge to global optimal solution in the initialization of the ant colony. And, the ACO uses dual population search and improves state transition operator for the balance of global and local search capabilities to effectively improve the performance and increase the diversity. There use C++ programming of Visual Studio 2005. net. The results show that this algorithm can not only effectively solve the shortest path problem of GIS, but also has high convergence precision.%启发信息是地理信息系统(GIS)中的关键,针对蚁群算法易陷入局部最优的缺陷,提出一种带有启发信息的改进蚁群算法.该算法在初始化蚁群时引入启发信息指引蚂蚁快速收敛于全局最优解,为平衡全局与局部搜索能力,也改进状态转移概率算子,从而有效提高算法性能,增加种群多样性.实验以Visual Studi02005中C++编程实现仿真,结果表明此算法不但能有效求解GIS的最短路径,而且改进的算法能快速地收敛且精度高.
Institute of Scientific and Technical Information of China (English)
龙运军; 陈宇宁; 陈英武; 邢立宁
2013-01-01
This paper proposes a multiple satellites imaging icheduling strategy based on Integrated Index Petri Net(IIPN) and hybrid ant colony algorithm, Index information dealing with concurrent observation of multiple satellites and conflict of using satellite resource are introduced into transition, which can also reflect energy and memory constraints, making the problem description more visual and complete. An ant colony algorithm combining with local search is designed to resolve the problem. Index information is integrated in heuristic information for guiding the ants for global search. Local search technique is to accelerate convergence. Experimental results show that the method of can solve the multiple imaging satellites scheduling problem effectively by reaching balance between global search technique and local search technique which is to accelerate convergence.%提出一种基于综合指标Petri网和混合蚁群算法的多星成像调度策略.在综合指标Petri网变迁中引入指标信息,处理多星并发观测和卫星资源竞争关系、反映卫星能量和存储等约束,使得问题描述更直观和完备.设计一种嵌入局部搜索技术的蚁群优化算法,通过启发式信息综合变迁中的指标,引导蚂蚁进行全局搜索.仿真实例结果表明,该策略能有效求解多墨成像调度问题,实现全局搜索和快速收敛的平衡.
A Novel Parser Design Algorithm Based on Artificial Ants
Maiti, Deepyaman; Acharya, Ayan; Konar, Amit; Ramadoss, Janarthanan
2008-01-01
This article presents a unique design for a parser using the Ant Colony Optimization algorithm. The paper implements the intuitive thought process of human mind through the activities of artificial ants. The scheme presented here uses a bottom-up approach and the parsing program can directly use ambiguous or redundant grammars. We allocate a node corresponding to each production rule present in the given grammar. Each node is connected to all other nodes (representing other production rules),...
An Ant Colony Optimization Based Dimension Reduction Method for High-Dimensional Datasets
Institute of Scientific and Technical Information of China (English)
Ying Li; Gang Wang; Huiling Chen; Lian Shi; Lei Qin
2013-01-01
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.
A Graph-based Ant Colony Optimization Approach for Integrated Process Planning and Scheduling
Institute of Scientific and Technical Information of China (English)
Jinfeng Wang; Xiaoliang Fan; Chaowei Zhang; Shuting Wan
2014-01-01
This paper considers an ant colony optimization algorithm based on AND/OR graph for integrated process planning and scheduling (IPPS). General y, the process planning and scheduling are studied separately. Due to the complexity of manufacturing system, IPPS combining both process planning and scheduling can depict the real situation of a manufacturing system. The IPPS is represented on AND/OR graph consisting of nodes, and undirected and directed arcs. The nodes denote operations of jobs, and undirected/directed arcs denote possible visiting path among the nodes. Ant colony goes through the necessary nodes on the graph from the starting node to the end node to obtain the optimal solution with the objective of minimizing makespan. In order to avoid local convergence and low convergence, some improved strategy is incorporated in the standard ant colony optimiza-tion algorithm. Extensive computational experiments are carried out to study the influence of various parameters on the system performance.
Institute of Scientific and Technical Information of China (English)
庞龙; 陆金桂
2012-01-01
Optimizing the order picking is a useful way to improve the efficiency of automated warehouses. According to analyzing the process and characteristics of picking in automated warehouses, a new mathematic model is proposed for automated warehouses. Firstly, we make an excellent initial population by the ant colony algorithm, and then optimize and solve the model with the genetic algorithms. The simulation results show that the model is feasible, and the mix of the ant colony and genetic algorithms is not only feasible but also accelerate the speed of the algorithm, and then improve the efficiency of order picking.%合理优化货物的拣选路径是提高自动化立体仓库运行效率的一种有效方法.通过分析自动化立体仓库拣选作业的工作流程与特点,为自动化仓库拣选作业建立优化数学模型,首先利用蚁群算法生成优异的初始种群,然后通过遗传算法对该数学模型进行优化求解.仿真结果表明该模型是可行的,蚁群遗传算法的混合不仅得到更精确的结果而且加速了算法的求解速度,从而能够改善拣选作业的效率.
Institute of Scientific and Technical Information of China (English)
叶青; 熊伟清; 江宝钏
2011-01-01
为了在最小化综合成本的同时尽量均衡企业的生产负荷以及为水平型制造协作联盟(HMCA)订单分配的管理工作提供依据,设计多种群混合行为二元蚁群算法,用于求解 HMCA 订单分配的多目标模型.该方法在二元蚁群算法的堆础上引入区域划分、环境评价与奖励策略,以弥补二元蚁群算法难以同时寻找多个解的缺陷,通过引入中心扰动行为,进一步提高求解质量.实验结果表明,该算法可以保证分布性,且求解质量较高.%In order to provide the basis for the management of allocating orders in Horizontal Manufacturing Collaborative Alliance(HMCA), this paper designs an algorithm named Multi-population Binary Ant Colony Algorithm with Hybrid Behaviors(MPBAHB) to minimize the comprehensive cost and balance the production loads among the selected manufacturing enterprises.Based on Binary Ant Colony Algorithm (BACA), two strategies of zoning and environmental evaluation/reward are introduced to conquer the drawback of original BACA of difficult to get multiple solutions.And a searching behavior named "central disturbing" is introduced to BACA, so as to strengthen the searching ability.Experimental results prove that the algorithm can get better solutions while keeping the distribution of Pareto front.
基于粗糙集和蚁群算法的机器人路径规划研究%Path Planning of Robot Based on Rough Sets and Ant Colony Algorithm
Institute of Scientific and Technical Information of China (English)
卢宇凡; 张莉
2012-01-01
针对移动机器人路径规划的难题,提出了一种基于粗糙集理论和蚁群算法混合的机器人路径规划方法,用来提高机器人路径规划的速度和精确度.首先利用粗糙集理论获得机器人路径的决策规则,建立初始决策表,并在其基础上利用粗糙集理论进行化简,获得最小决策表,从中提出最小决策规则,然后利用所得的最小决策规则得出可行路径的集合.最后利用蚁群算法对这个种群进行优化,获得最优行走路径在所建立的栅格环境中进行仿真实验,并与基础蚁群算法进行比较,仿真结果表明,改进后的算法简单、有效,收敛速度快,具有良好的搜索功能,验证了改进算法在机器人路径规划问题中快速有效性.%The mobile robot path planning problem is a very challenging problem in robotics. In this paper, hybrid method of rough sets and Ant Colony Algorithm is presented to raise the speed and accuracy of path planning of robot. Firstly, the decision rules are obtained based on rough set theory,and the initial table is established and is simplified according to the rough set theory. Secondly the minimal decision table from which the minimal decision rules are drawn is obtained finally. And then a series of available paths are produced by training the obtained minimal decide rule. Finally, the population of paths is optimized by using ant colony algorithms, and simulations are done in a raster environment, and the excellent path is got. And compare with the basic ant colony algorithm, simulation results show that the improved algorithm is simple and effective,convergence is fast and has good search capability,and also show that the hybrid method is available in raising the speed of path planning of robot.
Institute of Scientific and Technical Information of China (English)
黎自强; 田茁君; 王奕首; 岳本贤
2012-01-01
带平衡约束圆形Packing问题属于NP-hard问题,求解困难.提出一种求解该问题的快速启发式并行蚁群算法.首先提出一种启发式方法:在轮盘赌选择定序的概率公式中增加质量因子和外围逆时针排列定位待布圆,并用它构造出多样性种群个体(相交圆数不超过3的布局方案).然后将蚁群优化与并行搜索相结合,使种群个体快速收敛到最优解或迭代出存在少量干涉的近似最优解(1～3个相交圆).若为后者,则基于物理模型用最速下降法将其快速调整成最优解.所采用的启发式方法、并行蚁群搜索机制和快速调整策略有机结合提高了算法的搜索精度和效率.数值实验表明该算法在性能指标上优于已存在的算法.%Circles packing problem with equilibrium constraints is difficult to solve due to its NP-hard nature. A fast heuristic parallel ant colony algorithm is proposed for this problem. Both circular radius and mass are taken as the probability factors of the roulette selection and the circles are located by arranging round existing circles in peripheral with counter-clockwise movement. Its diverse population individuals (no more than 3 circles are overlapped in each one) are constructed through the proposed heuristic method. The ant colony optimization combined with parallel search mechanism is adopted to obtain an optimal solution or an approximate optimal solution with 1-3 overlapping circles. The steepest descent method based on physical model is used to adjust the approximate optimal solution into the optimal one without overlapping. The combination of heuristic strategy, ant colony search mechanism in parallel, and fast adjustment strategy can improve the computational precision and efficiency of the proposed algorithm. The experiment results show that the proposed algorithm is superior to the existing algorithms in performance.
Institute of Scientific and Technical Information of China (English)
王力军; 田静; 李健; 闻涛; 洪涛
2011-01-01
本文算法在建立组播树时,采用双种蚁群算法,一组从源结点向目的结点搜索,另一组从目的结点向源结点搜索.蚂蚁搜索路径时根据QoS参数影响度的大小修改信息素更新规则,从而建立满足多QoS约束的最优组播树.QoS参数影响度的确认通过正交实验统计方法,根据要搜索路径的规模,选择合适的正交表.实验证明该算法能有效的利用各QoS资源,较快的得到较优解.%The Dual Population Ant Colony Algorithm is used to establish the multicast tree. One group searches from source to destination and the other from destination to source. The pheromone update rule is modified by the effect of QoS parameters and the optimal tree which meets QoS parameters is found. The effect of every QoS parameter is confirmed by orthogonal experiment of statistical. According to the scope of the search paths, a suitable orthogonal table of the orthogonal experiment is implemented. The experimental results show that the proposed algorithm can make full use of QoS resources and get the better result rapidly.
Institute of Scientific and Technical Information of China (English)
杜芳华; 冀俊忠; 吴晨生; 吴金源
2014-01-01
半监督文本分类中已标记数据与未标记数据分布不一致，可能导致分类器性能较低。为此，提出一种利用蚁群聚集信息素浓度的半监督文本分类算法。将聚集信息素与传统的文本相似度计算相融合，利用Top-k策略选取出未标记蚂蚁可能归属的种群，依据判断规则判定未标记蚂蚁的置信度，采用随机选择策略，把置信度高的未标记蚂蚁加入到对其最有吸引力的训练种群中。在标准数据集上与朴素贝叶斯算法和EM算法进行对比实验，结果表明，该算法在精确率、召回率以及F1度量方面都取得了更好的效果。%There are many algorithms based on data distribution to effectively solve semi-supervised text categorization. However,they may perform badly when the labeled data distribution is different from the unlabeled data. This paper presents a semi-supervised text classification algorithm based on aggregation pheromone, which is used for species aggregation in real ants and other insects. The proposed method,which has no assumption regarding the data distribution, can be applied to any kind of data distribution. In light of aggregation pheromone,colonies that unlabeled ants may belong to are selected with a Top-k strategy. Then the confidence of unlabeled ants is determined by a judgment rule. Unlabeled ants with higher confidence are added into the most attractive training colony by a random selection strategy. Compared with Naïve Bayes and EM algorithm,the experiments on benchmark dataset show that this algorithm performs better on precision,recall and Macro F1.
Institute of Scientific and Technical Information of China (English)
胡仁兵; 翼俊忠; 张鸿勋; 刘椿年
2009-01-01
Aiming at the characteristics of dynamic Bayesian transition networks, this paper proposes a structure learning algorithm based on Ant Colony Optimization(ACO) named ACO-DBN-2S by extending the static Bayesian networks structure leaning algorithm I-ACO-B. In ACO-DBN-2S, ants select arcs from the inter-ares between time slices before from the intra-arcs in one slice, and the interval optimization strategy is improved by decreasing the times of optimizatien operation. A number of experiments under standard datasets demonstrate the algorithm can handle large data, and the precision and speed of learning are improved.%针对动态贝叶斯转移网络的特点,以I-ACO-B为基础,提出基于蚁群优化的分步构建转移网络的结构学习算法ACO-DBN-2S.算法将转移网络的结构学习分为时间片之间和时间片内2个步骤进行,通过改进隔代优化策略,减少无效优化次数.标准数据集下的大量实验结果证明,该算法能更有效地处理大规模数据,学习精度和速度有较大改进.
Convergence results for continuous-time dynamics arising in ant colony optimization
Bliman, Pierre-Alexandre; Bhaya, Amit; Kaszkurewicz, Eugenius; Jayadeva
2014-01-01
This paper studies the asymptotic behavior of several continuous-time dynamical systems which are analogs of ant colony optimization algorithms that solve shortest path problems. Local asymptotic stability of the equilibrium corresponding to the shortest path is shown under mild assumptions. A complete study is given for a recently proposed model called EigenAnt: global asymptotic stability is shown, and the speed of convergence is calculated explicitly and shown to be proportional to the dif...
ACOustic: A Nature-Inspired Exploration Indicator for Ant Colony Optimization
Rafid Sagban; Ku Ruhana Ku-Mahamud; Muhamad Shahbani Abu Bakar
2015-01-01
A statistical machine learning indicator, ACOustic, is proposed to evaluate the exploration behavior in the iterations of ant colony optimization algorithms. This idea is inspired by the behavior of some parasites in their mimicry to the queens’ acoustics of their ant hosts. The parasites’ reaction results from their ability to indicate the state of penetration. The proposed indicator solves the problem of robustness that results from the difference of magnitudes in the distance’s matrix, esp...
Effective ANT based Routing Algorithm for Data Replication in MANETs
Directory of Open Access Journals (Sweden)
N.J. Nithya Nandhini
2013-12-01
Full Text Available In mobile ad hoc network, the nodes often move and keep on change its topology. Data packets can be forwarded from one node to another on demand. To increase the data accessibility data are replicated at nodes and made as sharable to other nodes. Assuming that all mobile host cooperative to share their memory and allow forwarding the data packets. But in reality, all nodes do not share the resources for the benefits of others. These nodes may act selfishly to share memory and to forward the data packets. This paper focuses on selfishness of mobile nodes in replica allocation and routing protocol based on Ant colony algorithm to improve the efficiency. The Ant colony algorithm is used to reduce the overhead in the mobile network, so that it is more efficient to access the data than with other routing protocols. This result shows the efficiency of ant based routing algorithm in the replication allocation.
Ant colonies prefer infected over uninfected nest sites.
Pontieri, Luigi; Vojvodic, Svjetlana; Graham, Riley; Pedersen, Jes Søe; Linksvayer, Timothy A
2014-01-01
During colony relocation, the selection of a new nest involves exploration and assessment of potential sites followed by colony movement on the basis of a collective decision making process. Hygiene and pathogen load of the potential nest sites are factors worker scouts might evaluate, given the high risk of epidemics in group-living animals. Choosing nest sites free of pathogens is hypothesized to be highly efficient in invasive ants as each of their introduced populations is often an open network of nests exchanging individuals (unicolonial) with frequent relocation into new nest sites and low genetic diversity, likely making these species particularly vulnerable to parasites and diseases. We investigated the nest site preference of the invasive pharaoh ant, Monomorium pharaonis, through binary choice tests between three nest types: nests containing dead nestmates overgrown with sporulating mycelium of the entomopathogenic fungus Metarhizium brunneum (infected nests), nests containing nestmates killed by freezing (uninfected nests), and empty nests. In contrast to the expectation pharaoh ant colonies preferentially (84%) moved into the infected nest when presented with the choice of an infected and an uninfected nest. The ants had an intermediate preference for empty nests. Pharaoh ants display an overall preference for infected nests during colony relocation. While we cannot rule out that the ants are actually manipulated by the pathogen, we propose that this preference might be an adaptive strategy by the host to "immunize" the colony against future exposure to the same pathogenic fungus. PMID:25372856
Ant colonies prefer infected over uninfected nest sites.
Directory of Open Access Journals (Sweden)
Luigi Pontieri
Full Text Available During colony relocation, the selection of a new nest involves exploration and assessment of potential sites followed by colony movement on the basis of a collective decision making process. Hygiene and pathogen load of the potential nest sites are factors worker scouts might evaluate, given the high risk of epidemics in group-living animals. Choosing nest sites free of pathogens is hypothesized to be highly efficient in invasive ants as each of their introduced populations is often an open network of nests exchanging individuals (unicolonial with frequent relocation into new nest sites and low genetic diversity, likely making these species particularly vulnerable to parasites and diseases. We investigated the nest site preference of the invasive pharaoh ant, Monomorium pharaonis, through binary choice tests between three nest types: nests containing dead nestmates overgrown with sporulating mycelium of the entomopathogenic fungus Metarhizium brunneum (infected nests, nests containing nestmates killed by freezing (uninfected nests, and empty nests. In contrast to the expectation pharaoh ant colonies preferentially (84% moved into the infected nest when presented with the choice of an infected and an uninfected nest. The ants had an intermediate preference for empty nests. Pharaoh ants display an overall preference for infected nests during colony relocation. While we cannot rule out that the ants are actually manipulated by the pathogen, we propose that this preference might be an adaptive strategy by the host to "immunize" the colony against future exposure to the same pathogenic fungus.
Institute of Scientific and Technical Information of China (English)
周涛; 熊珍琦; 姚为; 秦英
2016-01-01
In order to reduce the elastic deformation of thin-walled workpieces with poor rigidity,when it is under the clamping force of tooling during the manufacturing process,a method of flexible tooling layout opti⁃mization for the thin-walled workpieces based on the improved ant colony algorithm is presented. Tooling layout optimization of positioning/supporting array is realized by combining finite element analysis with ant colony-ge⁃netic hybrid algorithm. The verification of examples shows that the optimal state of the topological configuration and distribution density of positioning/supporting array in the flexible tooling system can be achieved through opti⁃mization by the improved genetic-ant colony algorithm,thus obtaining the optimal flexible tooling layout for the thin-walled workpieces. The maximum deformation of the thin-walled workpieces can be reduced by 47%.%为解决薄壁件刚性差，在制造过程中易因工装夹紧力产生弹性变形等问题，提出了一种基于改进蚁群算法的薄壁件柔性工装布局优化方法，对薄壁件的柔性工装进行了布局优化。该方法通过有限元分析与蚁群/遗传混合算法相结合的方法进行了工装定位/支承阵列的布局优化。实例验证表明，采用改进遗传蚁群算法对工装布局进行优化，可使柔性工装系统中定位/支承阵列布局的拓扑形态和分布密度处于最优状态，使薄壁件最大变形量缩小47%。
Institute of Scientific and Technical Information of China (English)
刘永; 王新华; 邢长明; 王硕
2011-01-01
针对当前云计算环境中节点规模巨大,单个节点资源配置较低,寻找有效计算资源效率不高的缺点,文中在Coogle公司的Map/Reduce框架上提出了两个基于蚁群优化的资源调度策略ACO1和ACO2,并在这两个资源调度策略中引入双向蚂蚁机制.在该双向蚂蚁机制中蚂蚁通过相互交流,能够快速地发现合适的虚拟机资源,从而使得Master节点能够快速地为用户任务分配虚拟机.实验结果表明这两个利用了双向蚂蚁机制的资源调度策略显著减少了为用户任务寻找虚拟机的时间,从而使得用户任务能够更快地获得虚拟机,保证用户作业能够按时完成.%It presents two resources scheduling algorithms which are named ACO1 and ACO2 respectively for the cloud computing because of the disadvantage that the scale of nodes is huge, the configuration of nodes is not high and the efficiency of finding nodes is low. The two resources scheduling algorithms are based on ant colony algorithm and Map/Reduce frame which belongs to Google' s company. And two-way ant mechanism is introduced into the two resources scheduling algorithms. In the mechanism the ants can find the virtual machines which perform the tasks fast by the communication of ants so that the Master node can assign the virtual machines to the tasks fast. The experimental result demonstrates that the time to find virtual machines which perform the tasks by AC01 and ACO2 reduces observably, which advantage of the two-way ant mechanism so that it reduces the time to assign the virtual machines to the tasks and assures the users' job can be completed on time.
基于蚁群算法的航空发动机PID参数优化%PID Parameter Tuning of Aero Engine Based on Ant Colony Algorithm
Institute of Scientific and Technical Information of China (English)
傅强
2011-01-01
提出一种基于蚁群算法的PID参数优化控制算法,对航空发动机的双变量解耦控制方法进行了研究.蚁群算法采用分布式并行计算机制,易于与PID控制方法结合,优化后的控制器克服了传统的PID控制参数不易整定的缺点,且控制器结构简单规范、动态和静态性能良好,具有较强的鲁棒性.仿真结果表明该控制系统实现了解耦控制,对航空发动机模型参数在大范围内的变化均有良好的控制效果.%A PID parameter tuning decoupling method based on ant colony algorithm was researched for aero engine binary control system in this paper.Ant colony algorithm as a heuristic bionic optimization algorithm can be effectively applied to PID control method.The improved controller has overcome the default of PID parameter tuning in traditional ways.The improved PID controller which has simple structure and good dynamic and static performance was improved with a strong robustness at the end of the paper.This method can be applied to the decoupling control of the multivariable system like the aero engine.Simulation results demonstrate the effectiveness of the incompletely decoupled adaptive controller and have good effects for engine control.
Institute of Scientific and Technical Information of China (English)
庞清乐; 刘新允
2011-01-01
为了克服神经网络财务危机预警方法收敛慢、不收敛和网络结构难以确定等缺陷,提出了基于蚁群算法的改进神经网络财务危机预警方法.将神经网络模型的结构和参数进行编码,利用蚁群算法确定若干个神经网络模型的结构和参数,然后通过评价函数得到神经网络的最佳结构,最后通过BP算法训练该神经网络,得到神经网络财务危机预警模型.验证结果表明,该模型结构简单、预警精度高.%In order to overcome the defects of the neural network early-warning system for enterprise financial distress including slow convergence, non-convergence and difficulty in determining network structure, the improved neural network early-warning method for enterprise financial distress based on ant colony algorithm is presented. First, the structure and parameters of the neural network is encoded.Some structures and initiating parameters of neural network are obtained by the ant colony algorithm.Then, the optical structure of neural network is determined by means of the evaluation function. Finally,the neural network is trained through BP algorithm and the neural network early-warning model for enterprise financial distress is got. The experiment results show that the model has simple structure and lower error rate.
Institute of Scientific and Technical Information of China (English)
刘羽; 熊壬浩
2016-01-01
为了提高蛋白质折叠结构预测的求解效率，针对2D HP 格点模型，研究蚁群 ACO（Ant Colony Optimization）算法在该问题上的应用。采用四元组表示绝对的折叠方向，并建立构象和解的一一对应关系。通过实验对算法各阶段的常用策略、方法进行比较分析。为了防止搜索陷入停滞，引入位置信息素停滞比和序列信息素停滞比两个参数，使用一种新的停滞检测机制。实验结果表明，改进的算法在保证预测质量的前提下，显著地提升了收敛速度。%Aiming at 2D HP lattice model we studied the application of ant colony optimisation algorithm on protein folding structure prediction in order to improve the efficiency of its solution.We used the quadruple to express absolute folding direction,and established the one-to-one correspondence between conformation and solution.Through experiment we made the comparative analyses on common strategies and methods in each stage of the algorithm.To prevent the search from going to stagnation,we introduced two parameters,the position pheromone stagnation ratio and the sequence pheromone stagnation ratio,and applied a new stagnation detection mechanism as well. Experimental results showed that the improved algorithm remarkably accelerated the convergence speed on the premise of ensuring prediction quality.
A Preliminary Study of Automatic Delineation of Eyes on CT Images Using Ant Colony Optimization
Institute of Scientific and Technical Information of China (English)
LI Yong-jie; XIE Wei-fu; YAO De-zhong
2007-01-01
Eyes are important organs-at-risk (OARs) that should be protected during the radiation treatment of those head tumors. Correct delineation of the eyes on CT images is one of important issues for treatment planning to protect the eyes as much as possible. In this paper, we propose a new method, named ant colony optimization (ACO), to delineate the eyes automatically.In the proposed algorithm, each ant tries to find a closed path, and some pheromone is deposited on the visited path when the ant finds a path. After all ants finish a circle, the best ant will lay some pheromone to enforce the best path. The proposed algorithm is verified on several CT images, and the preliminary results demonstrate the feasibility of ACO for the delineation problem.
Institute of Scientific and Technical Information of China (English)
董张卓; 李哲; 赵元鹏
2013-01-01
Reactive load history information cannot be effectively applied by traditional reactive compensation classification method, so it exists the over-compensation and lack-compensation phenomenon easily. It proposes a optimization model for effective use of reactive load history information to determine the classification capacity. The model is solved using the improved ant colony algorithm. The pheromone is corrected in time by setting a pheromone threshold. Searching in vertical and horizontal way, the efficiency of ants' search is improved. Solution in that algorithm is effectively protected from local optimum, and the efficiency is increased several times.%传统确定无功分级补偿容量的方法不能有效利用负荷历史信息,容易出现过补或欠补现象.建立了有效利用历史无功负荷来求解无功补偿分级容量的优化模型,采用蚁群算法求解,对蚁群算法进行了改进.通过设定信息素的修正阈值,适时对信息素进行修正;通过纵向和横行的搜索方式,提高蚂蚁搜索的效率；算法能更好地避免陷入局部最优,执行效率数倍提高.
M. A. El-dosuky
2013-01-01
Large-scale problems are nonlinear problems that need metaheuristics, or global optimization algorithms. This paper reviews nature-inspired metaheuristics, then it introduces a framework named Competitive Ant Colony Optimization inspired by the chemical communications among insects. Then a case study is presented to investigate the proposed framework for large-scale global optimization.
Multi-view 3D scene reconstruction using ant colony optimization techniques
International Nuclear Information System (INIS)
This paper presents a new method performing high-quality 3D object reconstruction of complex shapes derived from multiple, calibrated photographs of the same scene. The novelty of this research is found in two basic elements, namely: (i) a novel voxel dissimilarity measure, which accommodates the elimination of the lighting variations of the models and (ii) the use of an ant colony approach for further refinement of the final 3D models. The proposed reconstruction procedure employs a volumetric method based on a novel projection test for the production of a visual hull. While the presented algorithm shares certain aspects with the space carving algorithm, it is, nevertheless, first enhanced with the lightness compensating image comparison method, and then refined using ant colony optimization. The algorithm is fast, computationally simple and results in accurate representations of the input scenes. In addition, compared to previous publications, the particular nature of the proposed algorithm allows accurate 3D volumetric measurements under demanding lighting environmental conditions, due to the fact that it can cope with uneven light scenes, resulting from the characteristics of the voxel dissimilarity measure applied. Besides, the intelligent behavior of the ant colony framework provides the opportunity to formulate the process as a combinatorial optimization problem, which can then be solved by means of a colony of cooperating artificial ants, resulting in very promising results. The method is validated with several real datasets, along with qualitative comparisons with other state-of-the-art 3D reconstruction techniques, following the Middlebury benchmark. (paper)
Srinivasan, Thenmozhi; Palanisamy, Balasubramanie
2015-01-01
Clusters of high-dimensional data techniques are emerging, according to data noisy and poor quality challenges. This paper has been developed to cluster data using high-dimensional similarity based PCM (SPCM), with ant colony optimization intelligence which is effective in clustering nonspatial data without getting knowledge about cluster number from the user. The PCM becomes similarity based by using mountain method with it. Though this is efficient clustering, it is checked for optimization using ant colony algorithm with swarm intelligence. Thus the scalable clustering technique is obtained and the evaluation results are checked with synthetic datasets. PMID:26495413
Distribution system minimum loss reconfiguration in the Hyper-Cube Ant Colony Optimization framework
Energy Technology Data Exchange (ETDEWEB)
Carpaneto, Enrico; Chicco, Gianfranco [Dipartimento di Ingegneria Elettrica, Politecnico di Torino, corso Duca degli Abruzzi 24, I-10129 Torino (Italy)
2008-12-15
This paper presents an original application of the Ant Colony Optimization concepts to the optimal reconfiguration of distribution systems, with the objective of minimizing the distribution system losses in the presence of a set of structural and operational constraints. The proposed algorithm starts from the current configuration of the system and proceeds by progressively introducing variations in the configuration according to local and global heuristic rules developed within the Hyper-Cube Ant Colony Optimization framework. Results of numerical tests carried out on a classical system and on a large real urban distribution system are presented to show the effectiveness of the proposed approach. (author)
Srinivasan, Thenmozhi; Palanisamy, Balasubramanie
2015-01-01
Clusters of high-dimensional data techniques are emerging, according to data noisy and poor quality challenges. This paper has been developed to cluster data using high-dimensional similarity based PCM (SPCM), with ant colony optimization intelligence which is effective in clustering nonspatial data without getting knowledge about cluster number from the user. The PCM becomes similarity based by using mountain method with it. Though this is efficient clustering, it is checked for optimization using ant colony algorithm with swarm intelligence. Thus the scalable clustering technique is obtained and the evaluation results are checked with synthetic datasets. PMID:26495413
Runtime analysis of ant colony optimization on dynamic shortest path problems
DEFF Research Database (Denmark)
Lissovoi, Andrei; Witt, Carsten
2015-01-01
A simple ACO algorithm called lambda-MMAS for dynamic variants of the single-destination shortest paths problem is studied by rigorous runtime analyses. Building upon previous results for the special case of 1-MMAS, it is studied to what extent an enlarged colony using lambda ants per vertex helps...... in tracking an oscillating optimum. It is shown that easy cases of oscillations can be tracked by a constant number of ants. However, the paper also identifies more involved oscillations that with overwhelming probability cannot be tracked with any polynomial-size colony. Finally, parameters of...
Runtime analysis of ant colony optimization on dynamic shortest path problems
DEFF Research Database (Denmark)
Lissovoi, Andrei; Witt, Carsten
2013-01-01
A simple ACO algorithm called λ-MMAS for dynamic variants of the single-destination shortest paths problem is studied by rigorous runtime analyses. Building upon previous results for the special case of 1-MMAS, it is studied to what extent an enlarged colony using $\\lambda$ ants per vertex helps in...... tracking an oscillating optimum. It is shown that easy cases of oscillations can be tracked by a constant number of ants. However, the paper also identifies more involved oscillations that with overwhelming probability cannot be tracked with any polynomial-size colony. Finally, parameters of dynamic...
Analysis on Three Parallel Models of Ant Colony Algorithm%蚁群算法的三种并行模型分析
Institute of Scientific and Technical Information of China (English)
王磊; 曹菡; 王长缨
2011-01-01
在单机多核下分别构造基于OpenMP和MPI的并行蚁群算法模型,在多核集群机下构造基于MPI和MPI+OpenMP的并行蚁群算法模型,并提出动态蚁群择优策略及分段周期交流策略.基于实际路网的路径寻优问题对上述模型进行比较,实验结果表明,在单机多核下,基于MPI的模型与基于OpenMP的模型相比,运行时间短,加速比高,在多核集群机下,基于MPI+OpenMP的混合模型相比基于MPI的模型,在进程数较多时仍具有较高的加速比.%This paper constructs parallel model based on Open Multi-processing(OpenMP) and parallel model based on Message Passing Interface(MPI) in multi-core environment, and constructs parallel model based on MPI and parallel model based on MPI+OpenMP in the multi-core clusters. The preferred strategy of dynamic ant colony and the strategy of sub-cycle exchange are presented. Finding optimized path of road is used to compare the three models. Experimental results show that MPI-based model run faster and get higher speedup than OpenMP-based model. In multi-core environment, hybrid model of MPI+OpenMP gets higher speedup than MPI-based model with more processes.
Application of ant colony algorithm in technology innovation diffusion model%蚁群算法在技术创新扩散模型中的应用研究
Institute of Scientific and Technical Information of China (English)
许智宏; 高静静; 胡浩
2012-01-01
Technological innovation makes enterprises more competitive and technological innovation diffusion in enterprises has greatly promoted the economic development. Therefore, studies on the laws of technological innovation diffusion have great significance for economic development. This paper has proposed a more realistic modified ant colony algorithm with multi-pheromone and multi-evaporation rate by combining with the evolution of technological innovation diffusion. It tries to apply ant colony optimization to simulate the process of technological innovation diffusion, to analyze the diffusion law and to predict trends of enterprises' technological innovation. Then the system simulates the process of technological innovation diffusion with specific economic data. The simulation results show that ant colony algorithm has fine possibility and reliability in the application of innovation diffusion model.%技术创新使企业在市场竞争中赢得竞争优势,而技术创新在企业间的扩散极大地促进了经济的发展,因此对技术创新扩算规律的研究对经济发展有着重要意义.通过结合区域创新系统中技术创新扩散的演变过程,提出了一种更切合实际的多信息素、多衰减系数的改进型蚁群算法,首次将其用于仿真技术创新的扩散过程,尝试利用蚁群算法仿真区域创新系统中的技术创新扩散过程,以得到企业的动态技术变革路线图,分析企业的技术扩散规律及预测企业的技术变革趋势.并用具体的经济数据进行实验,很好地证明了蚁群算法在技术创新扩散模型研究中应用的可能性和可靠性.
Action Decision Strategy of Simulation Robot Fish Based on Ant Colony Algorithm%基于蚁群算法的仿真机器鱼动作决策策略
Institute of Scientific and Technical Information of China (English)
张纯; 邓彦松
2011-01-01
针对仿真机器鱼非对抗赛和对抗赛情况,为使求解结果在既不依赖初始路线的选择,也不需要外界的特定干预的情况下,实现鱼快速、准确的调整,分别提出2种基于蚁群算法的动作决策策略.基于蚁群算法的分支界限法,判断机器鱼关键物理量所在分支,自主确定当前时刻的鱼的速度和角速度档位的最优组合；而基于蚁群算法的动态规划法,在每个周期内,根据机器鱼反馈回来的动态变量及时进行自主调整.以上2种方法经2D仿真平台验证结果表明:机器鱼可根据该策略调整路径,实现速度和方向的组合优化,以最短的时间和距离找到目标点.这说明基于蚁群算法的2种动作决策策略具有很强的适应能力,满足仿真机器鱼对于动作决策的要求.%Aiming at the non-match and match situations of simulation robot fish, in order to solve the result is neither dependent on the choice of the initial line does not need outside intervention-specific circumstances, to achieve the fish fast, accurate adjustment, this paper proposed two kinds of ant colony algorithms action decision strategy. Ant colony algorithm based on branch bound method judges the key physical machine where the fish branch, self-determined speed and angular velocity in the moment and optimal combined speed of the fish with angular velocity of the fish; In each cycle, the ant colony algorithm based on dynamic programming make self adjustment according to the dynamics of the robot fish immediate feedback. Examples of the above two methods used by the 2D simulation platform validation results showed, robot fish can be adjusted based on the policy path, to achieve optima) combination of speed and direction, the shortest time and distance to find the target point. This shows that based on ant colony algorithm's two kinds of action decision strategy has a strong ability to adapt effectively to meet the simulation of robot fish for action
Institute of Scientific and Technical Information of China (English)
朱伟; 徐克林; 孙禹; 高丽
2011-01-01
A fusion algorithm of Petri net and ant colony was put forward to achieve global optimization to logistics distribution route. An expanded Petri net model was introduced and its enable rule was defined. The concepts of dynamic token and static token were proposed, and their data structures as well as characteristics of network behavior were given. The dynamic token carries information such as transition sequence and transition cost and so on, while the static token only records the transition information of the best dynamic token. The probability selection rule of ant colony algorithm is improved by adding the constraint test of distribution into the selection rule, and the transitions which do not conform to the distribution restraint are excluded by the selection probability of zero. The setting mode of tabu list for ant colony algorithm is changed by substituting the traditional tabu with the sharing one, which not only guarantees the entire net traversal of distribution path but also accelerates the resolving speed. Example comparison shows that the optimal or nearly optimal solution for the logistics distribution route problem can be quickly obtained by fusion of expanded Petri net and ant colony algorithm.%摘 要:为实现物流配送路径的全局优化,提出Petri网融合蚁群算法.引入一种扩展Petri网模型并介绍了它的使能规则,提出“动态托肯”和“静态托肯”的概念,介绍了它们的数据结构及其在网络运行时的行为特征:动态托肯携带各自的变迁序列及变迁成本等信息,静态托肯记录库所中最优动态托肯的变迁特征.改进了蚁群算法的概率选择规则,在选择规则中加入配送约束检验因子,对不符合配送约束的变迁以概率0将其排除.改变了禁忌表的设置方式,以蚁群共享禁忌表替代传统禁忌表,既保证了对配送路径的全网遍历,又加快了问题的解算速度.算例比对说明:用Petri网融合蚁
Institute of Scientific and Technical Information of China (English)
宋智玲; 贾小珠
2011-01-01
How to discover communities automatically has great significance in the study of structure, function and behavior of complex network. Based on ant colony algorithm to optimize the node computational performance, a new method of identifying similar nodes is provided by using clustering technology.%如何在复杂网络中自动地发现社团,对于研究复杂网络的结构、功能和行为有着非常重要的意义.在聚类技术的基础上,提出了一种基于蚁群算法识别相似结点的方法,以优化结点的计算性能.
Structural link prediction based on ant colony approach in social networks
Sherkat, Ehsan; Rahgozar, Maseud; Asadpour, Masoud
2015-02-01
As the size and number of online social networks are increasing day by day, social network analysis has become a popular issue in many branches of science. The link prediction is one of the key rolling issues in the analysis of social network's evolution. As the size of social networks is increasing, the necessity for scalable link prediction algorithms is being felt more. The aim of this paper is to introduce a new unsupervised structural link prediction algorithm based on the ant colony approach. Recently, ant colony approach has been used for solving some graph problems. Different kinds of networks are used for testing the proposed approach. In some networks, the proposed scalable algorithm has the best result in comparison to other structural unsupervised link prediction algorithms. In order to evaluate the algorithm results, methods like the top- n precision, area under the Receiver Operating Characteristic (ROC) and Precision-Recall curves are carried out on real-world networks.
Institute of Scientific and Technical Information of China (English)
符志强; 刘磊安
2014-01-01
随着物流业的快速发展，配送路径优化成为研究热点，而路径优化是NP难问题，传统的算法不能在有限的时间内给出最优解。使用蚁群算法并对其参数进行优化，从而解决车辆配送路径优化问题，使得配送路径实时最优化，并应用在物流配送系统中，降低物流配送成本和企业经营成本。基于蚁群算法的物流配送系统采用模块化设计，实现对物流管理、数据统计、货物配送、实时生成最优路径的功能。系统具有结构清晰，易于扩展的优点。%With the rapid development of the logistics industry, distribution optimization has become a research hotspot. Path optimization is a NP hard problem and traditional algorithms can not be given an optimal solution in finite time. Uses ant colony optimization and adjusts the parameters to solve the optimization of vehicle routing problem. It can reduce logistics cost and the operation cost of enterprises. Designs logistics distribution system based on ant colony algorithm based on modular. The system can give the service of logistics management, data statistics, goods distribution, real-time generation of optimal path. The system has the advantages of clear structure and easy to ex-pand.
Research of BP neural network optimizing method based on Ant Colony Algorithm%一种基于蚁群算法的BP神经网络优化方法研究
Institute of Scientific and Technical Information of China (English)
王沥; 邝育军
2012-01-01
BP 神经网络是人工神经网络中应用最广泛的一种多层前馈神经网络。针对它容易陷入局部极小值及隐层节点大多利用经验试凑来确定的缺点，本文提出了一种基于蚁群算法的BP神经网络结构及参数优化方法，利用蚁群算法的全局寻优能力克服BP神经网络存在的不足。最后，将该方法用于短时交通流预测，实验结果表明：利用蚁群算法优化神经网络是有效的，预测结果也有较高精度。%BP neural network is the most widely used multilayer feedforward artificial neural networks, however,it is vulnerable to be trapped in local minimum and there is no systematic method to determine the number of hidden layer nodes thus usually done empirically. This paper introduces a method to optimize the structure and parameters of BP neural network which integrates ant colony algorithm with BP neural network to overcome shortcomings of traditional BP neural networks. The proposed method has been applied in short-term traffic flow forecasting. Simulation results demonstrate that the new BP neural network based on ant colony algorithm is more effective and can provide higher precision in traffic flow forecasting.
Institute of Scientific and Technical Information of China (English)
孙艳梅; 都文和; 冯昌浩; 刘道森; 卢俊国; 崔全领; 苗凤娟; 宋志章
2013-01-01
Aiming at the drawback of temperature drift of the pressure sensor,a temperature compensation method of RBF Neural Networks based on ant colony clustering is proposed. Based on the feature of parallel search optimum of the ant colony algorithm and a dynamic method to adjust the parameter of evaporation coefficient,the center of each basis function of RBF can be defined by using a new proposed clustering algorithm;in order to simplify the structure of RBF network, we use a pruning method to remove those hidden units. The simulation results showed that the method has the features of small error,high precision and a good compensation effect for the pressure sensor’s tem-perature drift.%针对压力传感器在应用中存在温度漂移这一缺点，提出了一种基于蚁群聚类算法的RBF( Radial Basis Function)神经网络温度补偿方法。利用蚁群算法的并行寻优特征和一种自适应调整挥发系数的方法作为聚类算法来确定RBF神经网络的基函数的位置，并通过裁减的方法约简隐层的神经元达到简化网络结构的目的。通过仿真可以看出，该算法具有误差小，精度高等优点，对压力传感器的温度漂移有较好的补偿效果。
Application of Modified Ant Colony Optimization (MACO for Multicast Routing Problem
Directory of Open Access Journals (Sweden)
Sudip Kumar Sahana
2016-04-01
Full Text Available It is well known that multicast routing is combinatorial problem finds the optimal path between source destination pairs. Traditional approaches solve this problem by establishment of the spanning tree for the network which is mapped as an undirected weighted graph. This paper proposes a Modified Ant Colony Optimization (MACO algorithm which is based on Ant Colony System (ACS with some modification in the configuration of starting movement and in local updation technique to overcome the basic limitations of ACS such as poor initialization and slow convergence rate. It is shown that the proposed Modified Ant Colony Optimization (MACO shows better convergence speed and consumes less time than the conventional ACS to achieve the desired solution.
Institute of Scientific and Technical Information of China (English)
霍军周; 李广强; 滕弘飞
2006-01-01
The problem of the layout design of a satellite module (LDSM) belongs to NP-hard problem in mathematics;one effectie way to sole this problem is to explore hybrid eolutionary algorithms. Based on the framework of parallel genetic algorithm (PGA), a parallel genetic-Powell-ant colony hybrid algorithm (PGPAHA) is gien, in which the migration moment of sub-populations is decided according to their conergence rate, and Powell method is taken as one of operator of PGA to accelerate their conergence rate at the later period of the searching, then ant colony optimization (ACO) algorithm is used to enhance the computational accuracy. The gien algorithm is used to sole optimal layout design of a simplified international commercial communication satellite module. The numerical experiment results show that the gien algorithm is superior to the PGA in computational precision, efficiency and stability.%卫星舱布局优化设计问题数学上属NP-hard问题. 较有效的求解途径之一是研究混合算法,为此构造了并行混合PGA-Powell-蚁群算法(简称PGPAHA). 该算法以并行遗传算法为基本框架,根据各子群体收敛速率的快慢来决定它们之间迁移的时机,在收敛后期加入Powell法作为并行遗传算法的算子来加快收敛速度,并利用蚁群优化算法提高计算的精度. 最后应用该算法求解了简化的三维带性能约束的国际商用通讯卫星的卫星舱布局设计问题,数值实验结果表明,该算法与并行遗传算法(PGA)相比,在计算精度、计算效率及计算稳定性方面较优.
Parallelization Strategies for Ant Colony Optimisation on GPUs
Cecilia, Jose M; Ujaldon, Manuel; Nisbet, Andy; Amos, Martyn
2011-01-01
Ant Colony Optimisation (ACO) is an effective population-based meta-heuristic for the solution of a wide variety of problems. As a population-based algorithm, its computation is intrinsically massively parallel, and it is there- fore theoretically well-suited for implementation on Graphics Processing Units (GPUs). The ACO algorithm comprises two main stages: Tour construction and Pheromone update. The former has been previously implemented on the GPU, using a task-based parallelism approach. However, up until now, the latter has always been implemented on the CPU. In this paper, we discuss several parallelisation strategies for both stages of the ACO algorithm on the GPU. We propose an alternative data-based parallelism scheme for Tour construction, which fits better on the GPU architecture. We also describe novel GPU programming strategies for the Pheromone update stage. Our results show a total speed-up exceeding 28x for the Tour construction stage, and 20x for Pheromone update, and suggest that ACO is a po...
多叉树蚁群算法及在区位选址中的应用研究%An Ant Colony Algorithm Based on Multi-way Tree for Optimal Site Location
Institute of Scientific and Technical Information of China (English)
赵元; 张新长; 康停军
2011-01-01
Site location by brute-force method is difficult for optimization due to massive spatial data and huge solution space under the constraint condition of multi-objective and large spatial resolutions. In this study, an improved ant colony optimization (ACO) based on multi-way tree is introduced to solve site location problem. Better solutions can be obtained swiftly according to the density of pheromone the ants leave on the search paths constructed in nested subspaces divided by means of the multi-way tree algorithm. First, the algorithm derived from ACO is aiming to search for an optimal path in space regardless of initial distribution, based on the behavior of ants seeking a path at a specific probability. Second, the multi-way tree algorithm's growth rate between search size and spatial scale is logarithmic, so the cost of searching increases slowly as the size of its input grows. The study area, located in Guangzhou city, is a densely populated region. The raster layers have a resolution of 92 m×92 m with a size of 512 × 512 pixels. This optimization problem consists of two factors:population distribution and spatial distance. Comparison experiment between ACO based on multi-way tree and the simple search algorithm indicates that this method can produce closely related results with a greater convergence rate and spend less computing time. In conclusion,the proposed algorithm is important and suitable for solving site search problems.%本文提出了基于多叉树蚁群算法(ant colony optimization based on multi-way tree)的区位选址优化方法.在多目标和大型空间尺度约束条件下,地理区位选址的解决方案组合呈现海量规模、空间搜索量庞大,难以求出理想解.基于多叉树的蚁群算法对地理空间进行多叉树划分,在多叉树的层上构造蚂蚁路径(ant path),让蚂蚁在多叉树的搜索路径上逐步留下信息素,借助信息素的通讯来间接协作获得理想的候选解.采用该方法用于
A Novel Parser Design Algorithm Based on Artificial Ants
Maiti, Deepyaman; Konar, Amit; Ramadoss, Janarthanan
2008-01-01
This article presents a unique design for a parser using the Ant Colony Optimization algorithm. The paper implements the intuitive thought process of human mind through the activities of artificial ants. The scheme presented here uses a bottom-up approach and the parsing program can directly use ambiguous or redundant grammars. We allocate a node corresponding to each production rule present in the given grammar. Each node is connected to all other nodes (representing other production rules), thereby establishing a completely connected graph susceptible to the movement of artificial ants. Each ant tries to modify this sentential form by the production rule present in the node and upgrades its position until the sentential form reduces to the start symbol S. Successful ants deposit pheromone on the links that they have traversed through. Eventually, the optimum path is discovered by the links carrying maximum amount of pheromone concentration. The design is simple, versatile, robust and effective and obviates ...
ACOustic: A Nature-Inspired Exploration Indicator for Ant Colony Optimization.
Sagban, Rafid; Ku-Mahamud, Ku Ruhana; Abu Bakar, Muhamad Shahbani
2015-01-01
A statistical machine learning indicator, ACOustic, is proposed to evaluate the exploration behavior in the iterations of ant colony optimization algorithms. This idea is inspired by the behavior of some parasites in their mimicry to the queens' acoustics of their ant hosts. The parasites' reaction results from their ability to indicate the state of penetration. The proposed indicator solves the problem of robustness that results from the difference of magnitudes in the distance's matrix, especially when combinatorial optimization problems with rugged fitness landscape are applied. The performance of the proposed indicator is evaluated against the existing indicators in six variants of ant colony optimization algorithms. Instances for travelling salesman problem and quadratic assignment problem are used in the experimental evaluation. The analytical results showed that the proposed indicator is more informative and more robust. PMID:25954768
Response Ant Colony Optimization of End Milling Surface Roughness
Ahmed N. Abd Alla; M. M. Noor; K. Kadirgama
2010-01-01
Metal cutting processes are important due to increased consumer demands for quality metal cutting related products (more precise tolerances and better product surface roughness) that has driven the metal cutting industry to continuously improve quality control of metal cutting processes. This paper presents optimum surface roughness by using milling mould aluminium alloys (AA6061-T6) with Response Ant Colony Optimization (RACO). The approach is based on Response Surface Method (RSM) and Ant C...
Institute of Scientific and Technical Information of China (English)
雷秀娟; 黄旭; 吴爽; 郭玲
2012-01-01
由于PPI网络数据的无尺度和小世界特性,使得目前对此类数据的聚类算法效果不理想.根据PPI网络的拓扑结构特性,本文提出了一种基于连接强度的蚁群优化(Joint Strength based Ant Colony Optimization,JSACO)聚类算法,该算法引入了连接强度的概念对蚁群聚类算法中的拾起/放下规则加以改进,以连接强度作为拾起规则,对结点进行聚类,并根据放下规则放弃部分不良数据,产生最终聚类结果.最后采用了MIPS数据库中的PPI数据进行实验,将JSACO算法与PPI网络数据的其他聚类算法进行比较,聚类结果表明JSACO算法正确率高,时间开销低.%Due to the sale-free and small-world characters of Protein-Protein Interaction (PPI) network data,current clustering algorithms did not perform well.According to the topological structural characters of PP1 networks,this paper proposed an ant colony optimization clustering algorithm based on joint strength (JSACO).This method modified the pickup/drop rules of ACO algorithm by means of introducing the concept of joint strength,which regarded the joint strength as pickup rule to cluster the protein nodes.In addition,the protein nodes which had the low joint strength were abandoned in accordance with drop rule and the final clustering result was obtained.Finally the PP1 data in MIPS database was used to test the algorithm and the clustering result was compared with other PPI clustering methods.The simulation results show that JSACO algorithm performs better in terms of precision value and consumes less time.
Institute of Scientific and Technical Information of China (English)
章正伟
2015-01-01
Double screw compressor have excellent stability performance and have been used widely in the mining industry. To meet the requirements of production capacity, using ant colony algorithm to planning it’ s machining process procedure by study processing technology of the end cover. In order to guarantee the feasibility of processing scheme, apply the processing constraint and introduce the new“or” type constraint. Finally the algorithm structure of ant colony algorithm is completed by implement the process element sifting module and two heuristic methods in process select module. The key machining process element is find out by study the assembly process of this product. Then the process constraint model of end cover is established and input into the ACO algorithm. So the computer aided process planning is completed. And the effective-ness of this algorithm is proved by contract with the human experience machining plan.%双螺杆压缩机以其优良的运行平稳性在矿用工业中被推广应用，为满足产能要求，在研究其端盖加工工艺基础上，采用蚁群算法研究双螺杆压缩机端盖加工工艺规程规划方案。研究了实际加工情况，引入工艺约束关系矩阵并提出“或”型工艺约束，针对此复杂问题编制了加工要素选择模块，并采用启发式要素选择规则，改进了蚁群算法的结构。在充分研究端盖-腔体装配过程后，锁定了端盖的关键加工要素。在此基础上编制了零件的工艺约束模型，代入算法求解，实现了加工工艺规程规划的自动化，并通过与人为经验工艺设计进行对比，说明了此算法的有效性并具有更高的经济性。
Institute of Scientific and Technical Information of China (English)
张洁; 张朋; 刘国宝
2013-01-01
针对带非等效并行机的作业车间生产调度问题,以制造系统的生产成本、准时交货率等为目标,构建生产调度多目标模型.利用蚁群算法在求解复杂优化问题方面的优越性,建立调度问题与蚁群并行搜索的映射关系,将调度过程分成任务分派和任务排序两个阶段,每个阶段分别设计蚁群优化算法,并将两阶段寻优蚂蚁有机结合,构建一种具有继承关系的两阶段蚁群并行搜索算法,可以大大提高获得较优解的概率,并且压缩求解空间,快速获得较优解.通过均匀试验和统计分析确定算法的关键参数组合,将两阶段蚁群算法应用不同规模的8组算例.结果表明,无论是优化结果还是计算效率,两阶蚁群算法均优于改进的遗传算法.将所提出两阶段蚁群算法应用于实际车间的生产调度,减少了生产过程中工序间等待时间和缩短了产品交付周期.%The job shop scheduling problem with unrelated parallel machines is investigated. Multiple objectives such as production cost and on time delivery rate for manufacturing system are taken into account in the proposed scheduling model. Considering the superiority of ant colony algorithm in solving the complex optimization problem, the mapping relationship between scheduling problem and ant colony parallel search is structured. The schedule process consists of two stages: tasks assignment and task sequencing. For each stage, the ant colony optimization is designed respectively so that a two-stage ant colony system(TSACS) with inheritance relationship is proposed. It can compress the solution space and improve the solving speed. Key parameters of TSACS are identified through the uniform experiment and statistical analysis. Computational experiments of 8 examples with different sizes are conducted. The results indicate that the proposed TSACA significantly outperforms the improved genetic algorithm in both optimization results and
Ant Colonies Prefer Infected over Uninfected Nest Sites
Luigi Pontieri; Svjetlana Vojvodic; Riley Graham; Jes Søe Pedersen; Linksvayer, Timothy A
2014-01-01
During colony relocation, the selection of a new nest involves exploration and assessment of potential sites followed by colony movement on the basis of a collective decision making process. Hygiene and pathogen load of the potential nest sites are factors worker scouts might evaluate, given the high risk of epidemics in group-living animals. Choosing nest sites free of pathogens is hypothesized to be highly efficient in invasive ants as each of their introduced populations is often an open n...
Channeler Ant Model: 3 D segmentation of medical images through ant colonies
International Nuclear Information System (INIS)
In this paper the Channeler Ant Model (CAM) and some results of its application to the analysis of medical images are described. The CAM is an algorithm able to segment 3 D structures with different shapes, intensity and background. It makes use of virtual and colonies and exploits their natural capabilities to modify the environment and communicate with each other by pheromone deposition. Its performance has been validated with the segmentation of 3 D artificial objects and it has been already used successfully in lung nodules detection on Computer Tomography images. This work tries to evaluate the CAM as a candidate to solve the quantitative segmentation problem in Magnetic Resonance brain images: to evaluate the percentage of white matter, gray matter and cerebrospinal fluid in each voxel.
Ant-cuckoo colony optimization for feature selection in digital mammogram.
Jona, J B; Nagaveni, N
2014-01-15
Digital mammogram is the only effective screening method to detect the breast cancer. Gray Level Co-occurrence Matrix (GLCM) textural features are extracted from the mammogram. All the features are not essential to detect the mammogram. Therefore identifying the relevant feature is the aim of this work. Feature selection improves the classification rate and accuracy of any classifier. In this study, a new hybrid metaheuristic named Ant-Cuckoo Colony Optimization a hybrid of Ant Colony Optimization (ACO) and Cuckoo Search (CS) is proposed for feature selection in Digital Mammogram. ACO is a good metaheuristic optimization technique but the drawback of this algorithm is that the ant will walk through the path where the pheromone density is high which makes the whole process slow hence CS is employed to carry out the local search of ACO. Support Vector Machine (SVM) classifier with Radial Basis Kernal Function (RBF) is done along with the ACO to classify the normal mammogram from the abnormal mammogram. Experiments are conducted in miniMIAS database. The performance of the new hybrid algorithm is compared with the ACO and PSO algorithm. The results show that the hybrid Ant-Cuckoo Colony Optimization algorithm is more accurate than the other techniques. PMID:24783812
Soleimani-Pouri, Mohammad; Rezvanian, Alireza; Meybodi, Mohammad Reza
2013-01-01
Interaction between users in online social networks plays a key role in social network analysis. One on important types of social group is full connected relation between some users, which known as clique structure. Therefore finding a maximum clique is essential for some analysis. In this paper, we proposed a new method using ant colony optimization algorithm and particle swarm optimization algorithm. In the proposed method, in order to attain better results, it is improved process of pherom...
Ant colony optimization applied to route planning using link travel time predictions
Claes, Rutger; Holvoet, Tom
2011-01-01
Finding the shortest path in a road network is a well known problem. Various proven static algorithms such as Dijkstra and A* are extensively evaluated and implemented. When confronted with dynamic costs, such as link travel time predictions, alternative route planning algorithms have to be applied. This paper applies Ant Colony Optimization combined with link travel time predictions to find routes that reduce the time spend by travels by taking into account link travel time predictions. The ...
Institute of Scientific and Technical Information of China (English)
孔凡光; 何建华; 唐奎
2013-01-01
Based on the problem of target assignment in BVR(Beyond Visual Range)air combat, a Shuffled Frog Leaping Algo-rithm(SFLA)and Ant Colony Algorithm(ACA)fusion is presented. A model of decision-making in BVR is built up by taking target threat evaluation as the criterion. According to the characteristic of BVR, a special coding process for frog is presented. An improved SFLA based on mutation idea in Differential Evolution(DE)is proposed, and the aberrance operator for ACA is embedded to reduce the search time. Since shuffled frog leaping algorithm has the capability of taking a global searching rapidly and ant colony algorithm has the positive feedback feature, the fusion algorithms use the SFLA to build optimized group at its initial stage, and then use ACA to search the exact answer at the later stage. With Matlab, simulations are implemented. The simu-lation results show that this method can give a reasonable target allocation plan effectively.%针对未来超视距条件下的多机协同空战，提出了一种基于混合蛙跳融合蚁群算法的目标分配方法。以目标威胁评估值为准则建立空战决策模型，根据空战决策特点对青蛙粒子进行特殊编码处理，在混合蛙跳算法局部搜索过程中加入自适应差分扰动机制、在蚁群算法中引入变异算子以减少算法搜索时间。融合算法利用混合蛙跳算法快速的全局搜索能力生成初始优化解群，利用蚁群算法具有正反馈的特点求精确解，利用Matlab仿真。仿真结果表明该方法能够快速有效地给出合理的目标分配方案。
Bait distribution among multiple colonies of Pharaoh ants (hymenoptera: Formicidae).
Oi, D H; Vail, K M; Williams, D F
2000-08-01
Pharaoh ant, Monomorium pharaonis (L.), infestations often consist of several colonies located at different nest sites. To achieve control, it is desirable to suppress or eliminate the populations of a majority of these colonies. We compared the trophallactic distribution and efficacy of two ant baits, with different modes of action, among groups of four colonies of Pharaoh ants. Baits contained either the metabolic-inhibiting active ingredient hydramethylnon or the insect growth regulator (IGR) pyriproxyfen. Within 3 wk, the hydramethylnon bait reduced worker and brood populations by at least 80%, and queen reductions ranged between 73 and 100%, when nests were in proximity (within 132 cm) to the bait source. However, these nest sites were reoccupied by ants from other colonies located further from the bait source. The pyriproxyfen bait was distributed more thoroughly to all nest locations with worker populations gradually declining by 73% at all nest sites after 8 wk. Average queen reductions ranged from 31 to 49% for all nest sites throughout the study. Even though some queens survived, brood reductions were rapid in the pyriproxyfen treatment, with reductions of 95% at all locations by week 3. Unlike the metabolic inhibitor, the IGR did not kill adult worker ants quickly, thus, more surviving worker ants were available to distribute the bait to all colonies located at different nest sites. Thus, from a single bait source, the slow-acting bait toxicant provided gradual, but long-term control, whereas the fast-acting bait toxicant provided rapid, localized control for a shorter duration. PMID:10985038
Apply Local Clustering Method to Improve the Running Speed of Ant Colony Optimization
Pang, Chao-Yang; Hu, Wei; Li, Xia; Hu, Be-Qiong
2009-01-01
Ant Colony Optimization (ACO) has time complexity O(t*m*N*N), and its typical application is to solve Traveling Salesman Problem (TSP), where t, m, and N denotes the iteration number, number of ants, number of cities respectively. Cutting down running time is one of study focuses, and one way is to decrease parameter t and N, especially N. For this focus, the following method is presented in this paper. Firstly, design a novel clustering algorithm named Special Local Clustering algorithm (SLC...
Institute of Scientific and Technical Information of China (English)
付杰; 周国华
2011-01-01
蚁群优化算法应用于复杂问题的求解是非常耗时的.文章在MATLAB环境下实现了一个基于GPU+CPU的并行MAX-MIN蚁群系统,并将其应用于旅行商问题的求解.让全部蚂蚁共享一个伪随机数矩阵,一个信息素矩阵,一个禁忌矩阵和一个概率矩阵,并运用了一个全新的基于这些矩阵的随机选择算法-AIR(All-In-Roulette).文章还介绍了如何使用这些矩阵来构造并行蚁群优化算法,并与相应串行算法进行了比较.计算结果表明新的并行算法比相应串行算法要高效很多.%Ant colony optimization is computationally expensive when it comes to complex problems. This paper presents and implements a parallel MAX-MIN Ant System(MMAS) based on a GPU＋CPU hardware platform under the MATLAB environment solve Traveling Salesman Problem(TSP). The key idea is to let all ants share only one pseudorandom number matrix, one pheromone matrix, one taboo matrix, and one probability matrix. A new selection approach based on those matrices, named AIR(All-In-Roulette) has been used. The main contribution of this paper is the description of how to design parallel MMAS based on those ideas and the comparison to the relevant sequential version. The computational results show that our parallel algorithm is much more efficient than the sequential version.
Ant colony Optimization: A Solution of Load balancing in Cloud
Directory of Open Access Journals (Sweden)
Ratan Mishra
2012-05-01
Full Text Available As the cloud computing is a new style of computing over internet. It has many advantages along with some crucial issues to be resolved in order to improve reliability of cloud environment. These issues are related with the load management, fault tolerance and different security issues in cloud environment. In this paper the main concern is load balancing in cloud computing. The load can be CPU load, memory capacity, delay or network load. Load balancing is the process of distributing the load among various nodes of adistributed system to improve both resource utilization and job response time while also avoiding a situation where some of the nodes are heavily loaded while other nodes are idle or doing very little work. Load balancing ensures that all the processor in the system or every node in the network does approximately the equal amount of work at any instant of time. Many methods to resolve this problem has been came into existence like Particle Swarm Optimization, hash method, genetic algorithms and severalscheduling based algorithms are there. In this paper we are proposing a method based on Ant Colony optimization to resolve the problem of load balancing in cloud environment.
Reliability optimization using multiobjective ant colony system approaches
International Nuclear Information System (INIS)
The multiobjective ant colony system (ACS) meta-heuristic has been developed to provide solutions for the reliability optimization problem of series-parallel systems. This type of problems involves selection of components with multiple choices and redundancy levels that produce maximum benefits, and is subject to the cost and weight constraints at the system level. These are very common and realistic problems encountered in conceptual design of many engineering systems. It is becoming increasingly important to develop efficient solutions to these problems because many mechanical and electrical systems are becoming more complex, even as development schedules get shorter and reliability requirements become very stringent. The multiobjective ACS algorithm offers distinct advantages to these problems compared with alternative optimization methods, and can be applied to a more diverse problem domain with respect to the type or size of the problems. Through the combination of probabilistic search, multiobjective formulation of local moves and the dynamic penalty method, the multiobjective ACSRAP, allows us to obtain an optimal design solution very frequently and more quickly than with some other heuristic approaches. The proposed algorithm was successfully applied to an engineering design problem of gearbox with multiple stages
Study on ant colony optimization for fuel loading pattern problem
International Nuclear Information System (INIS)
Modified ant colony optimization (ACO) was applied to the in-core fuel loading pattern (LP) optimization problem to minimize the power peaking factor (PPF) in the modeled 1/4 symmetry PWR core. Loading order was found to be important in ACO. Three different loading orders with and without the adjacent effect between fuel assemblies (FAs) were compared, and it was found that the loading order from the central core is preferable because many selections of FAs to be inserted are available in the core center region. LPs were determined from pheromone trail and heuristic information, which is a priori knowledge based on the feature of the problem. Three types of heuristic information were compared to obtain the desirable performance of searching LPs with low PPF. Moreover, mutation operation, such as the genetic algorithm (GA), was introduced into the ACO algorithm to avoid searching similar LPs because heuristic information used in ACO tends to localize the searching space in the LP problem. The performance of ACO with some improvement was compared with those of simulated annealing and GA. In conclusion, good performance can be achieved by setting proper heuristic information and mutation operation parameter in ACO. (author)
Institute of Scientific and Technical Information of China (English)
王春香; 郭晓妮
2011-01-01
In order to improve the efficiency of holes NC machining, with the shortest path to holes machining as the objective function, a hybrid algorithm (HA) integrated genetic algorithm (GA) with ant colony optimization (ACO) for solving holes machining path optimization was studied. The GA was run first and then ACO in the hybrid algorithm. A new strategy called GSA was proposed aiming at the key link in the "HA" that converted genetic solution from GA into information pheromone to distribute in ACO. The new matrix was taken by the GSA, which was formed by the combination of the former 90% of individual from genetic solution and 10% of individual by random generation as the basis of transformation of pheromone value. The best combination of genetic operators in GA was also discussed. The experimental results show that with the traditional processing route by numbers compared, by the HA using optimal combination operator and GSA transformation strategy, the length can be shortened for 70. 9% , and has higher precision than a single genetic algorithm. The NC machining efficiency of holes can be obviously improved theoretically.%为了提高孔群的数控加工效率,以孔群加工路径最短为目标函数,采用遗传蚁群混合算法对孔群加工路径规划问题进行研究.该混合优化算法的前期采用遗传算法、后期采用蚁群算法.在遗传算法向蚁群算法转换过程中,提出一种GSA遗传解到信息素转化策略.该策略以在遗传解endpop中选取前90％个个体和再随机产生的10％个个体合并后组成的新矩阵作为信息素值的转化依据；同时探讨了遗传算法中遗传算子的最佳组合问题.实例计算结果表明:与传统分批按编号加工的路径相比较,采用最佳组合算子和GSA转化策略后的遗传蚁群混合算法求解问题所获得的孔群加工路径缩短了70.9％,比单一遗传算法具有更高的求解精度,理论上可以明显地提高孔群的数控加工效率.
Ant colony optimization analysis on overall stability of high arch dam basis of field monitoring.
Lin, Peng; Liu, Xiaoli; Chen, Hong-Xin; Kim, Jinxie
2014-01-01
A dam ant colony optimization (D-ACO) analysis of the overall stability of high arch dams on complicated foundations is presented in this paper. A modified ant colony optimization (ACO) model is proposed for obtaining dam concrete and rock mechanical parameters. A typical dam parameter feedback problem is proposed for nonlinear back-analysis numerical model based on field monitoring deformation and ACO. The basic principle of the proposed model is the establishment of the objective function of optimizing real concrete and rock mechanical parameter. The feedback analysis is then implemented with a modified ant colony algorithm. The algorithm performance is satisfactory, and the accuracy is verified. The m groups of feedback parameters, used to run a nonlinear FEM code, and the displacement and stress distribution are discussed. A feedback analysis of the deformation of the Lijiaxia arch dam and based on the modified ant colony optimization method is also conducted. By considering various material parameters obtained using different analysis methods, comparative analyses were conducted on dam displacements, stress distribution characteristics, and overall dam stability. The comparison results show that the proposal model can effectively solve for feedback multiple parameters of dam concrete and rock material and basically satisfy assessment requirements for geotechnical structural engineering discipline. PMID:25025089
Ant Colony Optimization Analysis on Overall Stability of High Arch Dam Basis of Field Monitoring
Directory of Open Access Journals (Sweden)
Peng Lin
2014-01-01
Full Text Available A dam ant colony optimization (D-ACO analysis of the overall stability of high arch dams on complicated foundations is presented in this paper. A modified ant colony optimization (ACO model is proposed for obtaining dam concrete and rock mechanical parameters. A typical dam parameter feedback problem is proposed for nonlinear back-analysis numerical model based on field monitoring deformation and ACO. The basic principle of the proposed model is the establishment of the objective function of optimizing real concrete and rock mechanical parameter. The feedback analysis is then implemented with a modified ant colony algorithm. The algorithm performance is satisfactory, and the accuracy is verified. The m groups of feedback parameters, used to run a nonlinear FEM code, and the displacement and stress distribution are discussed. A feedback analysis of the deformation of the Lijiaxia arch dam and based on the modified ant colony optimization method is also conducted. By considering various material parameters obtained using different analysis methods, comparative analyses were conducted on dam displacements, stress distribution characteristics, and overall dam stability. The comparison results show that the proposal model can effectively solve for feedback multiple parameters of dam concrete and rock material and basically satisfy assessment requirements for geotechnical structural engineering discipline.
蚁群算法求解混合流水车间分批调度问题%Batch scheduling problem of hybrid flow shop based on ant colony algorithm
Institute of Scientific and Technical Information of China (English)
宋代立; 张洁
2013-01-01
为解决混合流水车间分批调度问题,提出一种三级递阶结构的蚁群算法.算法中,第一级蚁群算法设计了一种批量大小动态结合的柔性分批策略,完成产品的批次划分；第二级蚁群算法考虑工件在各设备的加工时间和设备可用能力,设计蚂蚁设备间的转移概率,完成工序约束下各批次的设备选择；第三级蚁群算法考虑同一设备上批次顺序相关的换批时间,设计蚂蚁批次间的转移概率,完成各设备的批次排序.通过实例仿真,分别对分批算法和混合流水车间调度算法性能进行比较分析和评价,结果表明了算法的有效性和优越性.最后从生产实际出发给出算例,验证了算法的有效性和对生产实践的指导作用.%To solve the problem of hybrid flow shop batch scheduling,a novel Ant Colony Optimization (ACO) algorithm with three level hierarchical structures was proposed.A flexible batching strategy was put forward in the first level of ACO to complete the batch partition of products.By considering the process time and available capacity of equipment in second level of ACO,the transition probability between ant equipments was designed,and the equipment selection of each batch under process constraint was fulfilled.Through considering the relevant batch time of batch sequence on same equipment,the transfer probabilities of ants between machines were designed in third level of ACO,and batch scheduling of equipment was completed.Performance of batch algorithm and hybrid flow shop scheduling algorithm were evaluated respectively through simulation experiment,and the results demonstrated the feasibility and effectiveness of proposed algorithm.An example from the practical production was addressed to express the guidance for production practice.
Institute of Scientific and Technical Information of China (English)
李菲; 王书锋; 冯冬青
2011-01-01
Dynamic adaptive weighted polymorphic ant colony algorithm was applied to minimize the makespan on a single batch-processing machine with non-identical job sizes. The algorithm introduced the different types of ant colonies, each colony had a different updating mechanism, the transition probabilities and the pheromone value update of ant colony was redesigned for the problem. The algorithm was more accordant with the ants' information processing mechanism, which combined the local search with the global search to improve its convergence and searching ability. In the experiment, different levels of instances are simulated and the results show the efficiency of dynamic adaptive weighted polymorphic ant colony algorithm.%针对差异工件的单机批调度问题,提出了动态自适应加权多态蚁群算法对最大完工时间进行优化,该算法引入了不同种类的蚁群,每种蚁群都有不同的信息素调控机制,并根据批调度问题对不同种类的蚁群状态转移概率和信息素更新机制进行了改进,同时将局域搜索与全局搜索相结合,从而更符合蚁群的真实信息处理机制.对不同规模的算例进行了仿真,结果验证了该算法的有效性和可行性.
Ant Colony Approach to Predict Amino Acid Interaction Networks
Gaci, Omar; Balev, Stefan
2009-01-01
In this paper we introduce the notion of protein interaction network. This is a graph whose vertices are the proteins amino acids and whose edges are the interactions between them. We consider the problem of reconstructing protein's interaction network from its amino acid sequence. An ant colony approach is used to solve this problem.
DATA MINING UNTUK KLASIFIKASI PELANGGAN DENGAN ANT COLONY OPTIMIZATION
Directory of Open Access Journals (Sweden)
Maulani Kapiudin
2007-01-01
Full Text Available In this research the system for potentially customer classification is designed by extracting rule based classification from raw data with certain criteria. The searching process uses customer database from a bank with data mining technic by using ant colony optimization. A test based on min_case_per_rule variety and phenomene updating were done on a certain period of time. The result are group of customer class which base on rules built by ant and by modifying the pheromone updating, the area of the case is getting bigger. Prototype of the software is coded with C++ 6 version. The customer database master is created by using Microsoft Access. This paper gives information about potential customer of bank that can be classified by prototype of the software. Abstract in Bahasa Indonesia : Pada penelitian untuk sistem klasifikasi potensial customer ini didesain dengan melakukan ekstrak rule berdasarkan klasifikasi dari data mentah dengan kriteria tertentu. Proses pencarian menggunakan database pelanggan dari suatu bank dengan teknik data mining dengan ant colony optimization. Dilakukan percobaan dengan min_case_per_rule variety dan phenomene updating pada periode waktu tertentu. Hasilnya adalah sekelompok class pelanggan yang didasarkan dari rules yang dibangun dengan ant dan dengan dimodifikasi dengan pheromone updating, area permasalahan menjadi lebih melebar. Prototype dari software ini menggunakan C++ versi 6. Database pelanggan dibangun dengan Microsoft Access. Paper ini memberikan informasi mengenai potensi pelanggan dari bank, sehingga dapat diklasifikasikan dengan prototype dari software. Kata kunci: ant colony optimization, classification, min_case_per_rule, term, pheromone updating
DEFF Research Database (Denmark)
Offenberg, Hans Joachim; Nielsen, Mogens Gissel; Peng, Renkang
2011-01-01
Weaver ants (Oecophylla spp.) are increasingly being used for biocontrol and are targeted for future production of insect protein in ant farms. An efficient production of live ant colonies may facilitate the utilization of these ants but the production of mature colonies is hampered by the long...... and no transplantation. Thus, in ant nurseries the use of multiple queens during nest founding as well as transplantation of pupae from foreign colonies may be utilised to decrease the time it takes to produce a colony ready for implementation....... time it takes for newly established colonies to grow to a suitable size. In this study we followed the growth of newly founded O. smaragdina colonies with 2, 3 or 4 founding queens during 12 days of development, following the transplantation of 0, 30 or 60 pupae from a mature donor colony. Colony...
Institute of Scientific and Technical Information of China (English)
高晓波
2011-01-01
多目标最小生成树问题是典型的NP问题.针对此问题,提出一种改进的多目标蚁群算法.为获得更好的非劣前端,通过合理选取多个信息素扩散源与扩散策略来避免其早熟收敛,并引入非支配排序算子,提高种群多样性并避免算法过早陷入局部最优解.对比实验结果表明:对于多目标最小生成树问题,该算法是有效的,不但在求解效率和解的质量方面优于相关算法,而且随着问题规模的扩大,算法仍保持较好的性能.%Multi-objective minimum spanning tree problem is a typical NP problem.For this problem, this paper proposed an improved ant colony algorithm for multi-objective, non-inferiority in order to obtain a better front end, by choosing the number of pheromone diffusion source and diffusion strategy avoid premature convergence.And the introduction of mutation operator was to enhance population diversity and avoid falling into local optimal solution algorithm prematurely.Comparison of experimental results shows that for multi-objective minimum spanning tree problem, the algorithm is effective not only in the solution efficiency of the quality of reconciliation is better than related algorithms.With the expansion of scale of the problem and algorithm remains a good performance.
Ant colony system (ACS with hybrid local search to solve vehicle routing problems
Directory of Open Access Journals (Sweden)
Suphan Sodsoon
2016-02-01
Full Text Available This research applied an Ant Colony System algorithm with a Hybrid Local Search to solve Vehicle Routing Problems (VRP from a single depot when the customers’ requirements are known. VRP is an NP-hard optimization problem and has usually been successfully solved optimum by heuristics. A fleet of vehicles of a specific capacity are used to serve a number of customers at minimum cost, without violating the constraints of vehicle capacity. There are meta-heuristic approaches to solve these problems, such as Simulated Annealing, Genetic Algorithm, Tabu Search and the Ant Colony System algorithm. In this case a hybrid local search was used (Cross-Exchange, Or-Opt and 2-Opt algorithm with an Ant Colony System algorithm. The Experimental Design was tested on 7 various problems from the data set online in the OR-Library. There are five different problems in which customers are randomly distributed with the depot in an approximately central location. The customers were grouped into clusters. The results are evaluated in terms of optimal routes using optimal distances. The experimental results are compared with those obtained from meta-heuristics and they show that the proposed method outperforms six meta-heuristics in the literature.
Emigration of a colony of the leaf-cutting ant Acromyrmex heyeri Forel (Hymenoptera, Formicidae
Directory of Open Access Journals (Sweden)
Mariane Aparecida Nickele
2012-09-01
Full Text Available Emigration of a colony of the leaf-cutting ant Acromyrmex heyeri Forel (Hymenoptera, Formicidae. Colony migration is a poorly studied phenomenon in leaf-cutting ants. Here we report on the emigration of a colony of the leaf-cutting ant A. heyeri in Brazil. The colony emigrated to a new location 47.4 m away from the original nest site, possibly because it had undergone considerable stress due to competitive interactions with a colony of Acromyrmex crassispinus.
Emigration of a colony of the leaf-cutting ant Acromyrmex heyeri Forel (Hymenoptera, Formicidae)
Mariane Aparecida Nickele; Marcio Roberto Pie; Wilson Reis Filho
2012-01-01
Emigration of a colony of the leaf-cutting ant Acromyrmex heyeri Forel (Hymenoptera, Formicidae). Colony migration is a poorly studied phenomenon in leaf-cutting ants. Here we report on the emigration of a colony of the leaf-cutting ant A. heyeri in Brazil. The colony emigrated to a new location 47.4 m away from the original nest site, possibly because it had undergone considerable stress due to competitive interactions with a colony of Acromyrmex crassispinus.
Institute of Scientific and Technical Information of China (English)
黄玲; 胡蔚蔚
2016-01-01
In recent years, the"shortage of farmer"problem plays more more strong in our country,and a large number of young workers migrant workers, rural land fallow more and more.China's population aging is serious, the agricultural population is reduced, the labor gap is too large, which leads to the demand of agricultural robot very urgent.With the rapid development of agricultural machinery and automation technology , agricultural robots are constantly developing , which can better adapt to the development of biotechnology, the past the traditional picking methods will be greatly changed, the focus of farmers planting is about to improve.Based on the improved ant colony algorithm, this paper de-signs and plans the 3D path of fruit picking robot walking, and increases the adaptive adjustment function in the process of advancing.Experimental simulation results show that the improved ant colony algorithm based 3D space path planning of fruit and vegetable picking robot is the best way to meet the requirements of the picking robot.%近年来，我国“农民荒”问题越演越烈，大量年轻劳动力外出务工，农村土地荒置越来越多。我国人口高龄化严重，农业人口的减少，劳动力缺口过大，导致对农业机器人的需求极为迫切。随着农业机械和自动化技术的快速发展，农业机器人也在不断发展，其可以更好地适应生物技术种植产业发展，过去传统的采摘方式将会有很大改变，农民种植的侧重点即将改善。为此，基于改进蚁群算法，设计和规划了果蔬采摘机器人行走的三维路径，并增加在前进过程中的自适应调整功能。实验仿真结果表明：基于改进蚁群算法的果蔬采摘移动机器人三维空间路径规划在路径和转弯个数上都做到了最小化，能够很好地满足采摘机器人运行需求。
Institute of Scientific and Technical Information of China (English)
栾宇; 丁文辉; 林天军; 王路仙; 关振群
2011-01-01
In order to improve the buckling stability, this paper employs the methodology of solving salesman traveling problems by ant colony algorithm to optimize the stacking-sequence of the filament wounded case.First, this paper explains the essence of stacking sequence optimization is adjusting the stiffness distribution along the thickness direction to improve the bending stiffness and the load path, thru which the stability of the composite structure can be enhanced.Then, an automatic grouping and coding technique is proposed, which adapts the classic ant colony algorithm to the process technics constraint and improves the computational efficiency by reducing the number of cities.The validity and the superiority of the method are verified via a classic example.Finally, the program developed has been successfully used to improve the buckling stability of a composite case.%为了提高纤维复合材料缠绕壳体结构的屈曲稳定性,本文将蚁群算法(ACA)求解旅行商问题(TSP)的方法引入到缠绕层顺序优化的计算中.阐明铺层顺序优化的本质是调整沿板厚方向的刚度分布以提高版的抗弯刚度,同时改善荷载的传递路径,以降低由偏心而产生的附加弯矩,从而提高结构的抗屈曲能力;扩展了经典蚁群算法,提出一种分组编解码方法,既可描述缠绕工艺对铺层排列组合的约束,又可降低城市数目以提高优化效率;通过与经典算例的比较,验证了本文方法的有效性和优越性;并研发了复合材料缠绕壳体铺层顺序优化程序系统,实现了壳体结构的屈曲稳定性优化设计.
Institute of Scientific and Technical Information of China (English)
史巧硕; 马岱; 米少华
2012-01-01
Since microRNA has important adjusting and controlling function in the organism system, it is crucial to predict it in a quick and effective way. Taking account of microRNA's precursor: pre-microRNA's sequences and structure characters, this paper puts forward a microRNA prediction method based on the combination of Ant Colony Algorithm and Support Vector Machine. Through learning and testing both of the known positive pre-microRNA database selected from Sanger center and the negative dataset extracted from Refseq sequences in human protein area from UCSE database, along with a comparison with the other two machine learning method of J48 and RBF neural networks, the experiment result shows that the accuracy of pre-microRNA prediction through the combination of Ant Colony Algorithm and Support Vector Machine is higher than RBF neural network and J48. Therefore, this prediction method can facilitate experimental identification of pre-microRNAs.%由于microRNA在生物体系统中起着重要的调控功能,对microRNA进行快速有效的预测很有必要.本文通过使用蚁群算法和支持向量机相结合的思想,结合microRNA的前体pre-miRNA序列特征和结构特征,构造了一种microRNA的预测方法.通过采集Sanger和UCSE数据库中的人类阳性和部分阴性数据集进行学习和测试,同时使用J48和BP神经网络两种机器学习方法进行对比,实验结果显示,使用蚁群算法和支持向量机的方法预测pre-miRNA的识别率达97.471％,与另外两种方法相对比,识别率分别提高了8.736％和10.575％,预测的准确性有显著提高.
Applying Data Clustering Feature to Speed Up Ant Colony Optimization
Directory of Open Access Journals (Sweden)
Chao-Yang Pang
2014-01-01
Full Text Available Ant colony optimization (ACO is often used to solve optimization problems, such as traveling salesman problem (TSP. When it is applied to TSP, its runtime is proportional to the squared size of problem N so as to look less efficient. The following statistical feature is observed during the authors’ long-term gene data analysis using ACO: when the data size N becomes big, local clustering appears frequently. That is, some data cluster tightly in a small area and form a class, and the correlation between different classes is weak. And this feature makes the idea of divide and rule feasible for the estimate of solution of TSP. In this paper an improved ACO algorithm is presented, which firstly divided all data into local clusters and calculated small TSP routes and then assembled a big TSP route with them. Simulation shows that the presented method improves the running speed of ACO by 200 factors under the condition that data set holds feature of local clustering.
BWR Fuel Lattice Design Using an Ant Colony Model
Energy Technology Data Exchange (ETDEWEB)
Montes, Jose L.; Ortiz, Juan J. [Instituto Nacional de Investigaciones Nucleares, Depto. de Sistemas Nucleares, Carretera Mexico Toluca S/N. La Marquesa Ocoyoacac. 52750, Estado de Mexico (Mexico); Francois, Juan L.; Martin-del-Campo, Cecilia [Depto. de Sistemas Energeticos, Facultad de Ingenieria, Universidad Nacional Autonoma de Mexico Paseo Cuauhnahuac 8532. Jiutepec, Mor. 62550 (Mexico)
2008-07-01
This paper deals with one of the steps of the nuclear fuel design: the radial fuel lattice design. It can be seen as a combinatorial optimization problem for determining the optimal 2D fuel rods enrichment and gadolinia distribution. In order to solve this optimization problem, the ant colony system technique is proposed. The main idea of the ant colony approach consists of emulating the real ant colony behaviour in their searching for minimum paths between two given points, usually between the nest and a food source. In this case, the environment where the artificial ants move is the space defined by the discrete possible values of Gd{sub 2}O{sub 3} contents, the U{sup 235} enrichment, and the valid locations inside the 10x10 BWR fuel lattice array. In order to assess any candidate fuel lattice in the optimization process, the HELIOS neutronic transport code is used. The results obtained in the application of the implemented model show that the proposed technique is a powerful tool to tackle this step of the fuel design. (authors)
BWR Fuel Lattice Design Using an Ant Colony Model
International Nuclear Information System (INIS)
This paper deals with one of the steps of the nuclear fuel design: the radial fuel lattice design. It can be seen as a combinatorial optimization problem for determining the optimal 2D fuel rods enrichment and gadolinia distribution. In order to solve this optimization problem, the ant colony system technique is proposed. The main idea of the ant colony approach consists of emulating the real ant colony behaviour in their searching for minimum paths between two given points, usually between the nest and a food source. In this case, the environment where the artificial ants move is the space defined by the discrete possible values of Gd2O3 contents, the U235 enrichment, and the valid locations inside the 10x10 BWR fuel lattice array. In order to assess any candidate fuel lattice in the optimization process, the HELIOS neutronic transport code is used. The results obtained in the application of the implemented model show that the proposed technique is a powerful tool to tackle this step of the fuel design. (authors)
A Hybrid Artificial Bee Colony Algorithm for the Service Selection Problem
Changsheng Zhang; Bin Zhang
2014-01-01
To tackle the QoS-based service selection problem, a hybrid artificial bee colony algorithm called h-ABC is proposed, which incorporates the ant colony optimization mechanism into the artificial bee colony optimization process. In this algorithm, a skyline query process is used to filter the candidates related to each service class, which can greatly shrink the search space in case of not losing good candidates, and a flexible self-adaptive varying construct graph is designed to model the sea...
Multiple ant-bee colony optimization for load balancing in packet-switched networks
Kashefikia, Mehdi; Moghadam, Reza Askari
2011-01-01
One of the important issues in computer networks is "Load Balancing" which leads to efficient use of the network resources. To achieve a balanced network it is necessary to find different routes between the source and destination. In the current paper we propose a new approach to find different routes using swarm intelligence techniques and multi colony algorithms. In the proposed algorithm that is an improved version of MACO algorithm, we use different colonies of ants and bees and appoint these colony members as intelligent agents to monitor the network and update the routing information. The survey includes comparison and critiques of MACO. The simulation results show a tangible improvement in the aforementioned approach.
MULTIPLE ANT-BEE COLONY OPTIMIZATION FOR LOAD BALANCING IN PACKET-SWITCHED NETWORKS
Directory of Open Access Journals (Sweden)
Mehdi Kashefi Kia
2011-10-01
Full Text Available One of the important issues in computer networks is “Load Balancing” which leads to efficient use of the network resources. To achieve a balanced network it is necessary to find different routes between the source and destination. In the current paper we propose a new approach to find different routes using swarm intelligence techniques and multi colony algorithms. In the proposed algorithm that is an improved version of MACO algorithm, we use different colonies of ants and bees and appoint these colony members as intelligent agents to monitor the network and update the routing information. The survey includes comparison and critiques of MACO. The simulation results show a tangible improvement in the aforementioned approach.
Institute of Scientific and Technical Information of China (English)
周书敬; 潘靖
2011-01-01
The volatile coefficient was adjusted according to the defect of slow convergence rate and trapping in local optimum of Ant Colony Algorithm ( ACA). Volatile coefficient was initially endowed with a greater value to make the ants search for the better path, later, was decreased and self-adjusted by degrees to avoid the local convergence and obtain the global optimal path. The improved algorithm was used for the optimization design of concrete - filled steel tubular structure and the design model was established, in which the design parameter is section' s character and the aiming function is the minimal cost. The pure flexural and axial - compressed members of concrete - filled steel tubular structure were used as the examples which were optimized for the design model. The results were compared with the ones of improved Genetic Algorithm in literate [4]. The results showed that the column and the beam obtained the global optimal solution after 58 and 52 iterations separately. The algorithm jumped out of the analysis of the complicated - interactive mechanism between steel tube and confined - concrete, was simple and efficient.%针对基本蚁群算法收敛速度慢和容易陷入局部最优的不足,在算法初期赋予挥发系数一个较大的初始值,使蚂蚁搜索到较优路径；后期不断减小和自调整挥发系数,避免局部收敛,在搜索到的较优路径中获得全局最优路径.将改进后的蚁群算法应用到钢管混凝土构件的优化设计中,建立了以梁、柱构件截面特征为设计变量,造价最低为目标函数的优化设计模型.以钢管混凝土纯弯、轴压构件为例,进行模型优化分析,并与文献[4]中改进遗传算法的优化结果进行对比.结果表明,柱和梁分别在58次和52次迭代后求得较好的全局最优解,算法跳过了钢管与套箍混凝土之间复杂作用机理的分析,简单高效.
Ant Colonies Do Not Trade-Off Reproduction against Maintenance.
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Boris H Kramer
Full Text Available The question on how individuals allocate resources into maintenance and reproduction is one of the central questions in life history theory. Yet, resource allocation into maintenance on the organismic level can only be measured indirectly. This is different in a social insect colony, a "superorganism" where workers represent the soma and the queen the germ line of the colony. Here, we investigate whether trade-offs exist between maintenance and reproduction on two levels of biological organization, queens and colonies, by following single-queen colonies of the ant Cardiocondyla obscurior throughout the entire lifespan of the queen. Our results show that maintenance and reproduction are positively correlated on the colony level, and we confirm results of an earlier study that found no trade-off on the individual (queen level. We attribute this unexpected outcome to the existence of a positive feedback loop where investment into maintenance (workers increases the rate of resource acquisition under laboratory conditions. Even though food was provided ad libitum, variation in productivity among the colonies suggests that resources can only be utilized and invested into additional maintenance and reproduction by the colony if enough workers are available. The resulting relationship between per-capita and colony productivity in our study fits well with other studies conducted in the field, where decreasing per-capita productivity and the leveling off of colony productivity have been linked to density dependent effects due to competition among colonies. This suggests that the absence of trade-offs in our laboratory study might also be prevalent under natural conditions, leading to a positive association of maintenance, (= growth and reproduction. In this respect, insect colonies resemble indeterminate growing organisms.
Routing in Wireless Sensor Networks Using an Ant Colony Optimization (ACO) Router Chip.
Okdem, Selcuk; Karaboga, Dervis
2009-01-01
Wireless Sensor Networks consisting of nodes with limited power are deployed to gather useful information from the field. In WSNs it is critical to collect the information in an energy efficient manner. Ant Colony Optimization, a swarm intelligence based optimization technique, is widely used in network routing. A novel routing approach using an Ant Colony Optimization algorithm is proposed for Wireless Sensor Networks consisting of stable nodes. Illustrative examples, detailed descriptions and comparative performance test results of the proposed approach are included. The approach is also implemented to a small sized hardware component as a router chip. Simulation results show that proposed algorithm provides promising solutions allowing node designers to efficiently operate routing tasks. PMID:22399947
Routing in Wireless Sensor Networks Using an Ant Colony Optimization (ACO Router Chip
Directory of Open Access Journals (Sweden)
Dervis Karaboga
2009-02-01
Full Text Available Wireless Sensor Networks consisting of nodes with limited power are deployed to gather useful information from the field. In WSNs it is critical to collect the information in an energy efficient manner. Ant Colony Optimization, a swarm intelligence based optimization technique, is widely used in network routing. A novel routing approach using an Ant Colony Optimization algorithm is proposed for Wireless Sensor Networks consisting of stable nodes. Illustrative examples, detailed descriptions and comparative performance test results of the proposed approach are included. The approach is also implemented to a small sized hardware component as a router chip. Simulation results show that proposed algorithm provides promising solutions allowing node designers to efficiently operate routing tasks.
Ant colony optimization approach for test scheduling of system on chip
Institute of Scientific and Technical Information of China (English)
CHEN Ling; PAN Zhong-liang
2009-01-01
It is necessary to perform the test of system on chip, the test scheduling determines the test start and finishing time of every core in the system on chip such that the overall test time is minimized. A new test scheduling approach based on chaotic ant colony algorithm is presented in this paper. The optimization model of test scheduling was studied, the model uses the information such as the scale of test sets of both cores and user defined logic. An approach based on chaotic ant colony algorithm was proposed to solve the optimization model of test scheduling. The test of signal integrity faults such as crosstalk were also investigated when performing the test scheduling. Experimental results on many circuits show that the proposed approach can be used to solve test scheduling problems.
Automated Software Testing Using Metahurestic Technique Based on An Ant Colony Optimization
Srivastava, Praveen Ranjan
2011-01-01
Software testing is an important and valuable part of the software development life cycle. Due to time, cost and other circumstances, exhaustive testing is not feasible that's why there is a need to automate the software testing process. Testing effectiveness can be achieved by the State Transition Testing (STT) which is commonly used in real time, embedded and web-based type of software systems. Aim of the current paper is to present an algorithm by applying an ant colony optimization technique, for generation of optimal and minimal test sequences for behavior specification of software. Present paper approach generates test sequence in order to obtain the complete software coverage. This paper also discusses the comparison between two metaheuristic techniques (Genetic Algorithm and Ant Colony optimization) for transition based testing
International Nuclear Information System (INIS)
Using concepts and principles of the quantum computation, as the quantum bit and superposition of states, coupled with the biological metaphor of a colony of ants, used in the Ant Colony Optimization algorithm (ACO), Wang et al developed the Quantum Ant Colony Optimization (QACO). In this paper we present a modification of the algorithm proposed by Wang et al. While the original QACO was used just for simple benchmarks functions with, at the most, two dimensions, QACOAlfa was developed for application where the original QACO, due to its tendency to converge prematurely, does not obtain good results, as in complex multidimensional functions. Furthermore, to evaluate its behavior, both algorithms are applied to the real problem of identification of accidents in PWR nuclear power plants. (author)
Energy Technology Data Exchange (ETDEWEB)
Silva, Marcio H.; Schirru, Roberto; Medeiros, Jose A.C.C., E-mail: marciohenrique@lmp.ufrj.b, E-mail: schirru@lmp.ufrj.b, E-mail: canedo@lmp.ufrj.b [Coordenacao dos Programas de Pos-Graduacao de Engenharia (COPPE/UFRJ), RJ (Brazil). Programa de Engenharia Nuclear. Lab. de Monitoramento de Processos
2009-07-01
Using concepts and principles of the quantum computation, as the quantum bit and superposition of states, coupled with the biological metaphor of a colony of ants, used in the Ant Colony Optimization algorithm (ACO), Wang et al developed the Quantum Ant Colony Optimization (QACO). In this paper we present a modification of the algorithm proposed by Wang et al. While the original QACO was used just for simple benchmarks functions with, at the most, two dimensions, QACO{sub A}lfa was developed for application where the original QACO, due to its tendency to converge prematurely, does not obtain good results, as in complex multidimensional functions. Furthermore, to evaluate its behavior, both algorithms are applied to the real problem of identification of accidents in PWR nuclear power plants. (author)
Kochem Vendramin, Ana Cristina; Munaretto, Anelise; Regattieri Delgado, Myriam; Carneiro Viana, Aline
2011-01-01
This paper presents a new prediction-based forwarding protocol for the complex and dynamic Delay Tolerant Networks (DTN). The proposed protocol is called GrAnt (Greedy Ant) as it uses a greedy transition rule for the Ant Colony Optimization (ACO) metaheuristic to select the most promising forwarder nodes or to provide the exploitation of good paths previously found. The main motivation for the use of ACO is to take advantage of its population-based search and of the rapid adaptation of its le...
Structural Damage Detection Based on Modal Parameters Using Continuous Ant Colony Optimization
Aditi Majumdar; Bharadwaj Nanda; Dipak Kumar Maiti; Damodar Maity
2014-01-01
A method is presented to detect and quantify structural damages from changes in modal parameters (such as natural frequencies and mode shapes). An inverse problem is formulated to minimize the objective function, defined in terms of discrepancy between the vibration data identified by modal testing and those computed from analytical model, which then solved to locate and assess the structural damage using continuous ant colony optimization algorithm. The damage is formulated as stiffness redu...
Institute of Scientific and Technical Information of China (English)
韦晓; 常相全
2014-01-01
According to the characteristics of emergency logistics under the uncertainty disaster level, this paper uses related method of uncertainty theory to analyze and research the emergency logistics center location issue, builds the center location model adapted to the characteristics of emergency logistics, finally carries on simulation example, uses ant colony algorithm to solve the model and draws relevant conclusions.%针对灾情等级不确定情况下应急物流的特点，本文利用不确定理论的相关方法，就应急物流中转站选址问题进行了相关分析和研究，构建了适应于应急物流特性的中心选址模型。最后进行仿真实例，利用改进蚁群算法求解模型，并得出相关结论。
Institute of Scientific and Technical Information of China (English)
吕立新
2014-01-01
该文在多Sink节点并行的网络结构基础上，基于能量熵理论建立了多Sink节点任务分配控制模型，设计了基于蚁群算法的任务分配控制策略，该方法能够根据Sink节点的当前状态、能耗情况合理分配数据通信和处理任务，均衡各Sink节点的负载，提高网络数据处理效率和网络生存时间。%Parallel Sink node of the network structure is presented in this paper, based on the entropy theory is established based on energy Sink node task assignment control model, design the task assignment control strategy based on ant colony algorithm, this method can according to the current state of the Sink node, the energy consumption situation and reasonable distribution of data communication and processing tasks, and balance the load of each Sink node, improve the efficiency of network data process⁃ing and network survival time.
Route Planning of Unmanned Target Drone Based on Cellular-Ant Colony Algorithm%基于元胞蚂蚁算法的无人靶机航路规划设计
Institute of Scientific and Technical Information of China (English)
刘志强; 陈景彬
2013-01-01
靶机飞行航路设计是实现靶机有效控制，确保高效完成供靶任务的保障。本文通过元胞蚂蚁算法对某型无人靶机飞行航路优化设计进行了研究，分析了实现航路优化应突出解决的问题，并通过仿真实验验证该方法的可行性。% Route planning of unmanned target drone is the basis of its efficient control and ensuring its high-performance of completing target mission. The route planning of an unmanned target drone is studied based on cellular-ant colony algorithm. The main problem of the optimum of the route planning is analyzed. The possibility of this method has been tested by simulation experiments.
Research of Multi-Depot Vehicle Routing Problem by Cellular Ant Algorithm
Directory of Open Access Journals (Sweden)
Yuanzhi Wang
2013-07-01
Full Text Available The Multi-Depot Vehicle Routing Problem (MDVRP is a generalization of SDVRP, in which multiple vehicles start from multiple depots and return to their original depots at the end of their assigned tours. The MDVRP is NP-hard, therefore, the development of heuristic algorithms for this problem class is of primary interest. This paper solves Multi-Depot Vehicle Routing Problem with Cellular Ant Algorithm which is a new optimization method for solving real problems by using both the evolutionary rule of cellular, graph theory and the characteristics of ant colony optimization. The simulation experiment shows that the Cellular Ant Algorithm is feasible and effective for the MDVRP. The clarity and simplicity of the Cellular Ant Algorithm is greatly enhanced to ant colony optimization.
Response Ant Colony Optimization of End Milling Surface Roughness
Directory of Open Access Journals (Sweden)
Ahmed N. Abd Alla
2010-03-01
Full Text Available Metal cutting processes are important due to increased consumer demands for quality metal cutting related products (more precise tolerances and better product surface roughness that has driven the metal cutting industry to continuously improve quality control of metal cutting processes. This paper presents optimum surface roughness by using milling mould aluminium alloys (AA6061-T6 with Response Ant Colony Optimization (RACO. The approach is based on Response Surface Method (RSM and Ant Colony Optimization (ACO. The main objectives to find the optimized parameters and the most dominant variables (cutting speed, feedrate, axial depth and radial depth. The first order model indicates that the feedrate is the most significant factor affecting surface roughness.
Web Mining using Artificial Ant Colonies: A Survey
2014-01-01
Web mining has been very crucial to any organization as it provides useful insights to business patterns. It helps the company to understand its customers better. As the web is growing in pace, so is its importance and hence it becomes all the more necessary to find useful patterns. Here in this paper, web mining using ant colony optimization has been reviewed with some of its experimental results.
Predicting Multicomponent Protein Assemblies Using an Ant Colony Approach
Venkatraman, Vishwesh; Ritchie, David
2011-01-01
National audience Biological processes are often governed by functional modules of large protein assemblies such as the proteasomes and the nuclear pore complex, for example. However, atomic structures can be determined experimentally only for a small fraction of these multicomponent assemblies. In this article, we present an ant colony optimization based approach to predict the structure of large multicomponent complexes. Starting with pair-wise docking predictions, a multigraph consistin...
Reconstructing Amino Acid Interaction Networks by an Ant Colony Approach
Gaci, Omar; Balev, Stefan
2009-01-01
In this paper we introduce the notion of protein interaction network. This is a graph whose vertices are the proteins amino acids and whose edges are the interactions between them. We consider the problem of reconstructing protein's interaction network from its amino acid sequence. We rely on a probability that two amino acids interact as a function of their physico-chemical properties coupled to an ant colony system to solve this problem.
Ant colony optimization for bearings-only maneuvering target tracking in sensors network
Institute of Scientific and Technical Information of China (English)
Benlian XU; Zhiquan WANG; Zhengyi WU
2007-01-01
In this paper, the problem of bearings-only maneuvering target tracking in sensors network is investigated.Two objectives are proposed and optimized by the ant colony optimization (ACO), then two kinds of node searching strategies of the ACO algorithm are presented. On the basis of the nodes determined by the ACO algorithm, the interacting multiple models extended Kalman filter (IMMEKF) for the multi-sensor bearings-only maneuvering target tracking is introduced. Simulation results indicate that the proposed ACO algorithm performs better than the Closest Nodes method.Furthermore, the Strategy 2 of the two given strategies is preferred in terms of the requirement of real time.
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
A novel method based on ant colony optimization (ACO), algorithm for solving the ill-conditioned linear systems of equations is proposed. ACO is a parallelized bionic optimization algorithm which is inspired from the behavior of real ants. ACO algorithm is first introduced, a kind of positive feedback mechanism is adopted in ACO. Then, the solution problem of linear systems of equations was reformulated as an unconstrained optimization problem for solution by an ACO algorithm. Finally, the ACO with other traditional methods is applied to solve a kind of multi-dimensional Hilbert ill-conditioned linear equations. The numerical results demonstrate that ACO is effective, robust and recommendable in solving ill-conditioned linear systems of equations.
Ant colony optimization approach to estimate energy demand of Turkey
International Nuclear Information System (INIS)
This paper attempts to shed light on the determinants of energy demand in Turkey. Energy demand model is first proposed using the ant colony optimization (ACO) approach. It is multi-agent systems in which the behavior of each ant is inspired by the foraging behavior of real ants to solve optimization problem. ACO energy demand estimation (ACOEDE) model is developed using population, gross domestic product (GDP), import and export. All equations proposed here are linear and quadratic. QuadraticACOEDE provided better-fit solution due to fluctuations of the economic indicators. The ACOEDE model plans the energy demand of Turkey until 2025 according to three scenarios. The relative estimation errors of the ACOEDE model are the lowest when they are compared with the Ministry of Energy and Natural Resources (MENR) projection
An Improved Heuristic Ant-Clustering Algorithm
Institute of Scientific and Technical Information of China (English)
Yunfei Chen; Yushu Liu; Jihai Zhao
2004-01-01
An improved heuristic ant-clustering algorithm(HAC)is presented in this paper. A device of ＇memory bank＇ is proposed,which can bring forth heuristic knowledge guiding ant to move in the bi-dimension grid space.The device experiments on real data sets and synthetic data sets.The results demonstrate that HAC has superiority in misclassification error rate and runtime over the classical algorithm.
Institute of Scientific and Technical Information of China (English)
胡中华; 赵敏; 刘世豪; 章婷
2012-01-01
To solve the problem of Unmanned Aerial Vehicle（UAV）route planning, Adaptive Ant Golony Optlmiza- tion（AACO）algorithm was proposed. Different from the global search mode of standard Ant Colony Optimizatio （ACO）, local search mode was adopted by AACO. Based on the relative position of starting node and destination node, one of the appropriate search mode in four was selected, and transition probabilities of each candidate node were calculated. The next node was selected according to the roulette principle. The simulation result showed that AACO algorithm had advantages such as few search nodes, quick speed and so on. It could reduce flight path cost and computing time. In addition, AACO could also avoided singular flight path segment, thus the attained practical flight path could fly was guaranteed. Therefore,the performance of AACO was much better than standard ACO.%为求解无人飞行器航迹规划问题，提出自适应蚁群算法，区别于标准蚁群算法的全部搜索模式，该算法采用局部搜索模式。首先根据起始节点与目标节点的相对位置关系选择相应的搜索模式，然后计算各个待选节点的转移概率，最后按照轮盘赌规则选择下一个节点。仿真结果表明，自适应蚁群算法具有搜寻节点数少、速度快等优点，在降低了航迹代价的同时，减小了计算时间。此外，自适应蚁群算法可以避免奇异航迹段的出现，从而保证所获的航迹实际可飞，表明所提算法整体性能明显较标准蚁群算法优异。
Institute of Scientific and Technical Information of China (English)
刘薇
2016-01-01
Appling the ant colony algorithm mathematical model to the risk early-warning for critical point of food supply chains, fusion optimization ability for ant colony algorithm and topology characteristic for the food supply chains, the simulation results show that the risk early-warning of ant colony algorithm mathematical methods can better highlights food supply chain topology structure characteristics, reveal risks and ways of harm degree from food supply chain structure rule.%将蚁群算法数学模型应用于食品供应链关键点的风险预警分析，融合了蚁群算法的寻优能力与食品供应链的拓扑结构特征。仿真结果表明：蚁群算法模型的风险预警方法能更好地凸显食品供应链的拓扑结构特点，较好地从食品供应链结构的规律中揭示风险存在的方式以及危害程度。
Ant Colony Optimization: Implementace a testování biologicky inspirované optimalizační metody
Havlík, Michal
2015-01-01
Havlík, M. Ant Colony Optimization: Implementation and testing of bio-inspired optimization method. Diploma thesis. Brno, 2015. This thesis deals with the implementation and testing of algorithm Ant Colony Optimization as a representative of the family of bio-inspired opti-mization methods. A given algorithm is described, analyzed and subsequently put into context with the problems which can be solved. Based on the collec-ted information is designed implementation that solves the Traveling sa...
A Stochastic Inversion Method for Potential Field Data: Ant Colony Optimization
Liu, Shuang; Hu, Xiangyun; Liu, Tianyou
2014-07-01
Simulating natural ants' foraging behavior, the ant colony optimization (ACO) algorithm performs excellently in combinational optimization problems, for example the traveling salesman problem and the quadratic assignment problem. However, the ACO is seldom used to inverted for gravitational and magnetic data. On the basis of the continuous and multi-dimensional objective function for potential field data optimization inversion, we present the node partition strategy ACO (NP-ACO) algorithm for inversion of model variables of fixed shape and recovery of physical property distributions of complicated shape models. We divide the continuous variables into discrete nodes and ants directionally tour the nodes by use of transition probabilities. We update the pheromone trails by use of Gaussian mapping between the objective function value and the quantity of pheromone. It can analyze the search results in real time and promote the rate of convergence and precision of inversion. Traditional mapping, including the ant-cycle system, weaken the differences between ant individuals and lead to premature convergence. We tested our method by use of synthetic data and real data from scenarios involving gravity and magnetic anomalies. The inverted model variables and recovered physical property distributions were in good agreement with the true values. The ACO algorithm for binary representation imaging and full imaging can recover sharper physical property distributions than traditional linear inversion methods. The ACO has good optimization capability and some excellent characteristics, for example robustness, parallel implementation, and portability, compared with other stochastic metaheuristics.
The Role of Non-Foraging Nests in Polydomous Wood Ant Colonies
Samuel Ellis; Robinson, Elva J. H.
2015-01-01
A colony of red wood ants can inhabit more than one spatially separated nest, in a strategy called polydomy. Some nests within these polydomous colonies have no foraging trails to aphid colonies in the canopy. In this study we identify and investigate the possible roles of non-foraging nests in polydomous colonies of the wood ant Formica lugubris. To investigate the role of non-foraging nests we: (i) monitored colonies for three years; (ii) observed the resources being transported between non...
TABU SEARCH SEBAGAI LOCAL SEARCH PADA ALGORITMA ANT COLONY UNTUK PENJADWALAN FLOWSHOP
Directory of Open Access Journals (Sweden)
Iwan Halim Sahputra
2009-01-01
Full Text Available Ant colony optimization (ACO is one of the meta-heuristic methods developed for finding solutions to optimization problems such as scheduling. Local search method is one part of the ACO which determines the quality of the resulting solution. In this paper, Tabu Search was proposed as a method of local search in ACO to solve the problem of flowshop scheduling. The purpose of this scheduling was to minimize the makespan. Makespan and computation time of the proposed method were compared to the ACO that implemented Job-Index as local search method. Using proposed algorithm, makespan values obtained were not significantly different than solutions of ACO using Job-Index method, and had computation time shorter. Abstract in Bahasa Indonesia: Ant colony optimization (ACO adalah salah satu metode meta-heuristic yang dikembangkan untuk mencari solusi bagi permasalahan optimasi seperti penjadwalan. Metode local search merupakan salah satu bagian dari ACO yang menentukan kualitas solusi yang dihasilkan. Dalam makalah ini Tabu Search diusulkan sebagai metode local search dalam algoritma ACO untuk menyelesaikan masalah penjadwalan flowshop. Tujuan dari penjadwalan ini adalah untuk meminimalkan makespan. Hasil makespan dan computation time dari metode usulan ini akan dibandingkan dengan algoritma ACO yang menggunakan Job-Index sebagai metode local search. Dengan menggunakan algoritma Tabu Search sebagai local search didapat nilai makespan yang tidak berbeda secara signifikan dibandingkan yang menggunakan metode Job-Index, dengan kelebihan computation time yang lebih singkat. Kata kunci: Tabu Search, Ant Colony Algorithm, Local Search, Penjadwalan Flowshop.
Liu, Shuang; Hu, Xiangyun; Liu, Tianyou; Xi, Yufei; Cai, Jianchao; Zhang, Henglei
2015-01-01
The ant colony optimisation algorithm has successfully been used to invert for surface magnetic data. However, the resolution of the distributions of the recovered physical property for deeply buried magnetic sources is not generally very high because of geophysical ambiguities. We use three approaches to deal with this problem. First, the observed surface magnetic data are taken together with the three-component borehole magnetic anomalies to recover the distributions of the physical properties. This cooperative inversion strategy improves the resolution of the inversion results in the vertical direction. Additionally, as the ant colony tours the discrete nodes, we force it to visit the nodes with physical properties that agree with the drilled lithologies. These lithological constraints reduce the non-uniqueness of the inversion problem. Finally, we also implement a K-means cluster analysis for the distributions of the magnetic cells after each iteration, in order to separate the distributions of magnetisation intensity instead of concentrating the distribution in a single area. We tested our method using synthetic data and found that all tests returned favourable results. In the case study of the Mengku iron-ore deposit in northwest China, the recovered distributions of magnetisation are in good agreement with the locations and shapes of the magnetite orebodies as inferred by drillholes. Uncertainty analysis shows that the ant colony algorithm is robust in the presence of noise and that the proposed approaches significantly improve the quality of the inversion results.
Ant Colony Based Node Disjoint Hybrid Multi-path Routing for Mobile Ad Hoc Network
Directory of Open Access Journals (Sweden)
B. Kalaavathi
2008-01-01
Full Text Available Mobile Ad hoc Networks are characterized by multi-hop wireless links, without any infrastructure and frequent node mobility. A class of ant colony based routing protocols has recently gained attention because of their adaptability to the network changes. AntHocNet is ant colony based hybrid algorithm, which combines reactive path setup with proactive path probing, maintenance and improvement. Multi-path routing represents a promising routing method for mobile ad hoc network. Multi-path routing achieves load balancing and is more resilient to route failures. This research introduces node disjoint multi-path property to AntHocNet routing algorithm. A virtual class room is one that can be established by using mobile devices and whose members can be dynamically added or removed. The implementation of virtual class room for lesson handling and query discussion using mobile ad hoc network is analyzed. The data are spread among the N (here N = 3 node disjoint routes from the beginning of the data transmission session. The performance metrics, average end-to-end delay, packet delivery ratio and load balancing have been analyzed for various pause times. The average end-to-end delay and packet delivery ratio have not varied significantly. Optimal load distribution is achieved by spreading the load among different node disjoint routes.
A Multi-pipe Path Planning by Modified Ant Colony Optimization
Institute of Scientific and Technical Information of China (English)
QU Yan-feng; JIANG Dan; LIU Bin
2011-01-01
Path planning in 3D geometry space is used to find an optimal path in the restricted environment, according to a certain evaluation criteria. To solve the problem of long searching time and slow solving speed in 3D path planning, a modified ant colony optimization is proposed in this paper. Firstly, the grid method for environment modeling is adopted. Heuristic information is connected with the planning space. A semi-iterative global pheromone update mechanism is proposed. Secondly, the optimal ants mutate the paths to improve the diversity of the algorithm after a defined iterative number. Thirdly, co-evolutionary algorithm is used. Finally, the simulation result shows the effectiveness of the proposed algorithm in solving the problem of 3D pipe path planning.
Directory of Open Access Journals (Sweden)
Antonio Carlos Da-Silva
2012-07-01
Leafcutter ants (Atta sexdens rubropilosa (Forel 1908 have an elaborate social organization, complete with caste divisions. Activities carried out by specialist groups contribute to the overall success and survival of the colony when it is confronted with environmental challenges such as dehydration. Ants detect variations in humidity inside the nest and react by activating several types of behavior that enhance water uptake and decrease water loss, but it is not clear whether or not a single caste collects water regardless of the cost of bringing this resource back to the colony. Accordingly, we investigated water collection activities in three colonies of Atta sexdens rubropilosa experimentally exposed to water stress. Specifically, we analyzed whether or not the same ant caste foraged for water, regardless of the absolute energetic cost (distance of transporting this resource back to the colony. Our experimental design offered water sources at 0 m, 1 m and 10 m from the nest. We studied the body size of ants near the water sources from the initial offer of water (time = 0 to 120 min, and tested for specialization. We observed a reduction in the average size and variance of ants that corroborated the specialization hypothesis. Although the temporal course of specialization changed with distance, the final outcome was similar among distances. Thus, we conclude that, for this species, a specialist (our use of the word “specialist” does not mean exclusive task force is responsible for collecting water, regardless of the cost of transporting water back to the colony.
Yi-Huei Chen; Robinson, Elva J. H.
2014-01-01
Climate change may affect ecosystems and biodiversity through the impacts of rising temperature on species' body size. In terms of physiology and genetics, the colony is the unit of selection for ants so colony size can be considered the body size of a colony. For polydomous ant species, a colony is spread across several nests. This study aims to clarify how climate change may influence an ecologically significant ant species group by investigating thermal effects on wood ant colony size. The...
Bus Network Modeling Using Ant Algorithms
Directory of Open Access Journals (Sweden)
Sepideh Eshragh
2010-02-01
Full Text Available Bus transit network modeling is a complex and combinatorial problem. The main purpose of this paper is to apply a contemporary method for designing a bus transit network with the objective of achieving optimum results. The method is called Ant Algorithms, a Meta Heuristic method, which has been applied to optimization problems in transportation with noticeable success. The description of the algorithm, as well as the main methodology and computations, is presented in this paper. Furthermore, a case study using Ant Algorithms applied to the city of Ghazvin, one of the most important suburbs of Tehran, Iran, is presented.
Ant System Algorithm Research and Its Applications
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
In this paper, systematic review on Ant System (AS) algorithm research and application is made, and the authors works of introducing As algorithm into continuous space application are summarized. Then the applicability characters of AS in continuous space optimization problems are also discussed.
Ant colony optimisation for resource searching in dynamic peer-to-peer grids
Krynicki, Kamil; Jaén Martínez, Francisco Javier; Mocholi Agües, Jose Antonio
2014-01-01
The applicability of peer-to-peer (p2p) in the domain of grid computing has been an important subject over the past years. Nevertheless, the sole merger between p2p and the concept of grid is not sufficient to guarantee non-trivial efficiency. Some claim that ant colony optimisation (ACO) algorithms might provide a definite answer to this question. However, the use of ACO in grid networks causes several problems. The first and foremost stems out of the fact that ACO algorithms usually perform...
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金浩; 刘维宁
2014-01-01
鉴于梯式轨道在实际应用过程中，需要同时优化钢轨振动加速度和道床振动加速度，利用MO-FHACO（多目标觅食-返巢机制连续域蚁群算法）对其进行双目标优化。利用四端参数法建立对应的梯式轨道数学模型，将1~80 Hz频段钢轨振动加速度级和道床振动加速度级作为优化目标，枕下减振垫刚度和数量作为设计变量。经过MO-FHACO双目标优化，成功得到梯式轨道双目标优化解。结果表明，采用MO-FHACO对梯式轨道进行多目标优化具有工程应用价值。%In order to optimize rail vibration acceleration and foundation vibration acceleration for the ladder track, MO-FHACO(multi-objective foraging-homing ant colony algorithm)is employed in this paper. The ladder track is sim-plified using four-pole parameter method. Vibration acceleration level of rail and vibration acceleration level of foundation ranging from 1 Hz to 80 Hz are set objective functions. Stiffness and number of sleeper bearings are set designing variables. After several optimizations by MO-FHACO, optimization solutions of bi-objective for the ladder track are obtained. Results show that, it’s an efficient method for multi-objective optimization of the ladder track with MO-FHACO.
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孙志刚
2013-01-01
In order to improve the forecasting accuracy of logistics demand,this paper puts forward a logistics demand forecasting model based on least support vector machines optirmized by ant colony op timization algorithm (ACOLSSVM).Firstly,the data of logistics demand are reconstructed,and then the complex nonlinear change rule of logistics demand is explained through LSSVM,and the parameters of LSSVM model are optimized by ACO,and lastly,the performance of mode are tested by logistics demand data.The simulation results show that ACO-LSSVM has improved the forecasting accuracy of logistics demand,and which is an effective method for logistics demand forecasting.%为了提高物流需求预测精度,针对物流需求的复杂变化特性,提出一种蚁群算法ACO)优化最小二乘支持向量机的(LSSVM)的物流需求预测模型(ACO-LSSVM).首先对物流需求数据进行重构,然后采用LSSVMY刻画物流需求的复杂非线性变化特性,并通过ACO算法优化选择LSSVM参数,采用物流需求预测实例对ACO-LSSVM性能进行测试.结果表明,ACO-LSSVM提高了物流需求预测精度,是一种有效的物流需求预测方法.
基于蚁群算法的停车场车位引导问题研究%Research on Parking Guidance Based on Improved Ant Colony Algorithm
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黄小珂
2012-01-01
In order to solve the problem of parking guidance in modern large scale parking lot more efficiently,a parking lot structure model is built in this paper;the concept of node busy factor is introduced,a constrained mathematical model of the optimal path is mentioned,and the problem of parking guidance to be translated into solving for the optimal path based on the actual situation.Moreover,improved the heuristic function and pheromone update rule of ant colony algorithm,which is used to get the optimal path of the parking guidance.Finally,the best parking space and the guiding paths out of parking lot are given out by the simulation.This solution provides guidance for the parking,and the efficiency of the parking lot usage is improved.%根据停车场实际情况建立了停车场结构模型,引入了节点繁忙因子的概念,提出了带约束的最优路径数学模型,将车位引导问题转化为对网络中最优路径的求解,并对基本蚁群算法的启发函数、信息素更新规则进行改进,将改进后的算法用于停车场车位引导问题中最优路径的求解。最后通过仿真实验找出了最优车位及存取车路线,为进出停车场的车辆提供引导,提高了停车场的使用效率。
Institute of Scientific and Technical Information of China (English)
王佳
2011-01-01
在无线传感器网络(Wireless Sensor Networks,简写为WSN)系统中增加了移动Agent,能提高系统的整体性能及工作效率.参考Sink节点的功能及特点,移动Agent具有收集和简单处理信息数据并将其传输到上一级单位的功能,它的移动功能提高了网络的连通性、传感覆盖率及使用寿命等性能.采用传感覆盖率高,能耗相对较小的正六边形网格划分方法部署传感器节点.Sink节点只能在通信范围内收集信息,根据这一特点,在此网络结构中利用蚁群优化算法为移动Agent寻找移动路径,从而达到有效改善网络性能的目的.仿真结果说明了此方法的有效性及特点.%For the purpose of performance improvement of WSN ( Wireless Sensor Networks), mobile agent were considered, which are a kind of special nodes-sinks in charge of data collection in WSN. Mobility of agent can improve the performance of whole system, such as connectedness, sense coverage, and energy efficient. An effective structure of regular hexagonal grid plot is chosen, which is suitable for improving connectedness and sense coverage. A route planning for mobile agent is proposed by applying a well designed ant colony optimization algorithms. Finally, the advantages and effectiveness of mobile agent in WSN are illustrated by an example.
McGlynn, Terry
2010-01-01
Serial monodomy is the nesting behavior in which a colony of animals maintains multiple nests for its exclusive use, occupying one nest at a time. Among serially monodomous ants, the availability of unoccupied nests reduces the probability and costs of army ant attacks. It has been proposed that nest odors mediate serial monodomy in the gypsy ant, Aphaenogaster araneoides Emery (Hymenoptera: Formicidae), and that colonies avoid returning to previously occupied nests that harbor colony odors. ...
Institute of Scientific and Technical Information of China (English)
高冬
2011-01-01
UNA secondary structure prediction is an important research field in bioinformatics. A new method is presented to predict RNA secondary structure based on hybrid ant colony system and genetic algorithm. Hie relationship information between different stems is used to generate the initial population and the accumulated pheromone information is used to construct new secondary structure. Then the folding pathway is simulated, including such processes as construction of the heuristic information, the rule of initializing the pheromone, the mechanism of choosing the initial and next stem and the strategy of updating the pheromone between two different steins. And a new crossover strategy is proposed. By testing the RNA sequences with known structures, experiment result shows that this algorithm improves the prediction accuracy compared with genetic algorithm.%RNA二级结构预测是生物信息学的重要研究领域.本文提出一种新的基于混合蚁群遗传算法的RNA二级结构预测方法.充分利用茎区和茎区之间的关系信息和累积的信息,通过蚁群算法产生初始种群和新的个体,进而替换遗传算法中的变异算子.构造蚁群算法中的启发式信息、初始信息素矩阵、下一茎区的选取规则和信息素的更新机制,给出遗传算法中交叉算子的交叉策略.最后通过测试已知二级结构的RNA序列,实验结果表明,该方法相对于遗传算法不仅节省程序运行的时间,而且可提高预测的准确性.
Institute of Scientific and Technical Information of China (English)
张涛; 胡佳研; 李福娟; 张玥杰
2012-01-01
of large scale NP-hard combinatorial optimization problem which is difficult to be solved by the accurate algorithms effectively. As a meta-heuristics algorithm, ant colony optimization algorithm (ACO) has strong global search ability and the ability to find a better solution. Thus, we select the ACO as the algorithm for the AAP problem.Considering the link time constraints and link airport constraints between two consecutive flight strings, and considering the total flying time constraints of the aircrafts, we propose the concept of virtual flight string and construct a mixed integer programming model. To solve this model, we propose an ACO algorithm according to the characteristics of the problem. In this algorithm we adopt the pheromone updating strategy of rank-based version of ant system (ASRank) and MAX-MIN ant system ( MMAS). The ASRank can make the search space more close to the optimal solution while the MMAS make the algorithm avoid stagnation in the process of searching.For testing the validity of our model and algorithm, we use the practical data of one airline to do the experiments and test some important parameters of the algorithm. The numerical results show that the best goal value obtained by our method is 3.75% less than the best goal value obtained by manual method, the utilization rate of the aircrafts is improved and the total link time is effectively decreased.
JOB SHOP METHODOLOGY BASED ON AN ANT COLONY
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OMAR CASTRILLON
2009-01-01
Full Text Available The purpose of this study is to reduce the total process time (Makespan and to increase the machines working time, in a job shop environment, using a heuristic based on ant colony optimization. This work is developed in two phases: The first stage describes the identification and definition of heuristics for the sequential processes in the job shop. The second stage shows the effectiveness of the system in the traditional programming of production. A good solution, with 99% efficiency is found using this technique.
Applying Data Clustering Feature to Speed Up Ant Colony Optimization
Chao-Yang Pang; Ben-Qiong Hu; Jie Zhang; Wei Hu; Zheng-Chao Shan
2013-01-01
Ant colony optimization (ACO) is often used to solve optimization problems, such as traveling salesman problem (TSP). When it is applied to TSP, its runtime is proportional to the squared size of problem $N$ so as to look less efficient. The following statistical feature is observed during the authors’ long-term gene data analysis using ACO: when the data size $N$ becomes big, local clustering appears frequently. That is, some data cluster tightly in a small area and form a class, and the cor...
An ant colony optimization method for generalized TSP problem
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Jinhui Yang; Xiaohu Shi; Maurizio Marchese; Yanchun Liang
2008-01-01
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 field. By considering the group influence, an improved method is further improved. To avoid locking into local minima, a mutation process and a local searching technique are also introduced into this method. Numerical 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.
AGNOSCO - Identification of Infected Nodes with artificial Ant Colonies
Hilker, Michael
2008-01-01
If a computer node is infected by a virus, worm or a backdoor, then this is a security risk for the complete network structure where the node is associated. Existing Network Intrusion Detection Systems (NIDS) provide a certain amount of support for the identification of such infected nodes but suffer from the need of plenty of communication and computational power. In this article, we present a novel approach called AGNOSCO to support the identification of infected nodes through the usage of artificial ant colonies. It is shown that AGNOSCO overcomes the communication and computational power problem while identifying infected nodes properly.
Multi-Constrained Dynamic QoS Multicast Routing Design Using Ant Colony System
Institute of Scientific and Technical Information of China (English)
GUI Zhi-bo; WU Xiao-quan
2005-01-01
In this paper, an Ant Colony System (AC) based heuristic algorithm is presented to find the multi-constrained dynamic Quality of Service (QoS) multicast routing. We also explore the scalability of the AC algorithm and multicast tree by using "Pull" mode instead of "Push" mode, and the improvement on the time complexity of AC algorithm by using a new data structure, I.e., a pointer array instead of the previous "matrix" structure. Our extensive tests show that the presented algorithm can find the global optimum or suboptimum, and has a good scalability with dynamic adaptation to the change of multicast group, and gives better performance in terms of the total cost than other two algorithms.
A Dynamic Job Shop Scheduling Method Based on Ant Colony Coordination System
Institute of Scientific and Technical Information of China (English)
ZHU Qiong; WU Li-hui; ZHANG Jie
2009-01-01
Due to the stubborn nature of dynamic job shop scheduling problem, a novel ant colony coordination mechanism is proposed in this paper to search for an optimal schedule in dynamic environment. In ant colony coordination mechanism, the dynamic .job shop is composed of several autonomous ants. These ants coordinate with each other by simulating the ant foraging behavior of spreading pheromone on the trails, by which they can make information available globally, and further more guide ants make optimal decisions. The proposed mechanism is tested by several instances and the results confirm the validity of it.
An ant colony optimization algorithm for home security robot path planning%一种家庭安保机器人运动路径规划的蚁群算法
Institute of Scientific and Technical Information of China (English)
龙珑; 邓伟; 胡秦斌; 黄智海
2012-01-01
由于家庭居住环境复杂，家庭安保机器人导航问题难于解决。使用传统蚁群算法，家庭安保机器人容易陷入搜索家庭环境局部极值的困境，无法找出在复杂环境下家庭最优的运动路径。因此，家庭安保机器人设计方案引入混沌理论改良局部个体的质量，利用混沌扰动，能够避免家庭机器人陷入搜索家庭环境局部极值的困境，由最初的混沌行为过渡到群体智能行为，使家庭安保机器人找到最优的运动路径。经仿真实验表明，在复杂的家庭环境下，家庭安保机器人也可以安全避障。%Because domestic living environment is very complex, and family security robot navigation problems are very difficult to solve. It is difficult problem to research family security robot navigates in a complicated environment. If security robot uses the traditional colony ant algorithm, family security robot would search family environment in local extremes trouble, and could not find out the best path under the complex environment family. Therefore, this paper family security robot algorithm introduces chaos theo- ry to improve the quality of local individual, thus to avoid family robot to fall into searching family environment in local extremes trouble, and the initial chaos behavior transition swarms intelligence behavior, eventually family security robot find the optimal path of the movement. The simulation experiment shows that the robot can succeed avoid obstacle in complex family environment
Institute of Scientific and Technical Information of China (English)
何春林
2015-01-01
A large database object interaction information scheduling is the key to improve the stability and throughput of network database system structure, using the UDP protocol to transport real-time large object data scheduling, improve the transmission performance of large object data, key to avoid delay and lost frames. This paper presents an interactive infor-mation scheduling algorithm of ant colony and congestion based on adjusting step, take the business of each component of a response and constant in the frequency domain, then take the nearest integer, realize data decompression process according-ly, according to the HList returned to IC Compiler for task scheduling, according to the time sequence information of each node using double threshold decision, design conditions, the sender needs to send data, the time axis is divided into adja-cent to each task, smoothing window model, information model construction scheduling adaptive step adjustment, the simu-lation results show that this algorithm can effectively improve the scheduling performance of interactive information, save the task overhead, shorten interactive information scheduling delay, it can reduce the bit error rate.%大数据库对象交互信息调度是提高网络数据库系统结构的稳定性和吞吐量的关键,通过采用UDP协议传输实时大对象数据调度,提高大对象数据的传输性能,是避免延时和丢帧的关键.提出一种基于蚁群拥挤度跃阶调整的交互信息调度算法,根据HList返回到IC Compiler中进行任务调度,根据每个节点的时序信息,采用双阈值判决条件设计,发送方需要发送数据时,时间轴划分成各个相邻的任务匹配平滑窗口模型,构建跃阶调整的自适应信息调度模型,仿真结果表明,本文算法能有效提高交互信息调度性能,节省了任务开销,缩短交互信息调度延时,降低误码率.
Dynamic scheduling study on engineering machinery of clusters using multi-agent system ant algorithm
Gao, Qiang; Wang, Hongli; Guo, Long; Xiang, Jianping
2005-12-01
In the process of road surface construction, dispatchers' scheduling was experiential and blindfold in some degree and static scheduling restricted the continuity of the construction. Serious problems such as labor holdup, material awaiting and scheduling delay could occur when the old scheduling technique was used. This paper presents ant colony algorithm based on MAS that has the abilities of intelligentized modeling and dynamic scheduling. MAS model deals with single agent's communication and corresponding in engineering machinery of clusters firstly, next we apply ant colony algorithm to solve dynamic scheduling in the plant. Ant colony algorithm can optimize the match of agents and make the system dynamic balance. The effectiveness of the proposed method is demonstrated with MATLAB simulations.
Operations planning for agricultural harvesters using ant colony optimization
Directory of Open Access Journals (Sweden)
A. Bakhtiari
2013-07-01
Full Text Available An approach based on ant colony optimization for the generation for optimal field coverage plans for the harvesting operations using the optimal track sequence principle B-patterns was presented. The case where the harvester unloads to a stationary facility located out of the field area, or in the field boundary, was examined. In this operation type there are capacity constraints to the load that a primary unit, or a harvester in this specific case, can carry and consequently, it is not able to complete the task of harvesting a field area and therefore it has to leave the field area, to unload, and return to continue the task one or more times. Results from comparing the optimal plans with conventional plans generated by operators show reductions in the in-field nonworking distance in the range of 19.3-42.1% while the savings in the total non-working distance were in the range of 18-43.8%. These savings provide a high potential for the implementation of the ant colony optimization approach for the case of harvesting operations that are not supported by transport carts for the out-of-the-field removal of the crops, a practice case that is normally followed in developing countries, due to lack of resources.
Ant colony optimization as a method for strategic genotype sampling.
Spangler, M L; Robbins, K R; Bertrand, J K; Macneil, M; Rekaya, R
2009-06-01
A simulation study was carried out to develop an alternative method of selecting animals to be genotyped. Simulated pedigrees included 5000 animals, each assigned genotypes for a bi-allelic single nucleotide polymorphism (SNP) based on assumed allelic frequencies of 0.7/0.3 and 0.5/0.5. In addition to simulated pedigrees, two beef cattle pedigrees, one from field data and the other from a research population, were used to test selected methods using simulated genotypes. The proposed method of ant colony optimization (ACO) was evaluated based on the number of alleles correctly assigned to ungenotyped animals (AK(P)), the probability of assigning true alleles (AK(G)) and the probability of correctly assigning genotypes (APTG). The proposed animal selection method of ant colony optimization was compared to selection using the diagonal elements of the inverse of the relationship matrix (A(-1)). Comparisons of these two methods showed that ACO yielded an increase in AK(P) ranging from 4.98% to 5.16% and an increase in APTG from 1.6% to 1.8% using simulated pedigrees. Gains in field data and research pedigrees were slightly lower. These results suggest that ACO can provide a better genotyping strategy, when compared to A(-1), with different pedigree sizes and structures. PMID:19220227
Colony location algorithm for assignment problems
Institute of Scientific and Technical Information of China (English)
Dingwei WANG
2004-01-01
A novel algorithm called Colony Location Algorithm (CLA) is proposed. It mimics the phenomena in biotic conmunity that colonies of species could be located in the places most suitable to their growth. The factors working on the species location such as the nutrient of soil, resource competition between species, growth and decline process, and effect on environment were considered in CLA via the nutrient function, growth and decline rates, environment evaluation and fertilization strategy.CLA was applied to solve the classical assignment problems. The computation results show that CLA can achieve the optimal solution with higher possibility and shorter running time.
Adaptive tracking and compensation of laser spot based on ant colony optimization
Yang, Lihong; Ke, Xizheng; Bai, Runbing; Hu, Qidi
2009-05-01
Because the effect of atmospheric scattering and atmospheric turbulence on laser signal of atmospheric absorption,laser spot twinkling, beam drift and spot split-up occur ,when laser signal transmits in the atmospheric channel. The phenomenon will be seriously affects the stability and the reliability of laser spot receiving system. In order to reduce the influence of atmospheric turbulence, we adopt optimum control thoughts in the field of artificial intelligence, propose a novel adaptive optical control technology-- model-free optimized adaptive control technology, analyze low-order pattern wave-front error theory, in which an -adaptive optical system is employed to adjust errors, and design its adaptive structure system. Ant colony algorithm is the control core algorithm, which is characteristic of positive feedback, distributed computing and greedy heuristic search. . The ant colony algorithm optimization of adaptive optical phase compensation is simulated. Simulation result shows that, the algorithm can effectively control laser energy distribution, improve laser light beam quality, and enhance signal-to-noise ratio of received signal.
Institute of Scientific and Technical Information of China (English)
蔡庭; 黄善国; 李新; 尹珊; 张杰; 顾畹仪
2012-01-01
With the constant expansion of the optical networking and network architecture flattening process accelerated, network transmission reliability and timeliness are facing new challenges, which brought a new challenge the survivability routing algorthrn. In order to realize dynamic survivable mapping of IP routing to WDM based opticla networks, with strong robustness and memory capacity of the ant colony optimization algorithm, a novel pheromone structure and pheromone update mechanism is proposed and survivable constraint information is also introduced to improve the probability mechanism of routing selection. The survivable mapping considers the cut sets of all single node and the dynamic characteristics of the IP over WDM network. Compared with other similar algorithm, the algorithm is no longer an alternate route set and the physical topology of the storage network itself huge cut-set relationship, under the premise of ensuring network survivability effect by cut-set relaxation conditions effectively reduce the computation lime complexity to improve the convergence rale to adapt to the real-time reauirements of the network. Simulation results show that the proposed algorithm can effectively improve the performance of survivable mapping, network resource utilization efficiency and the blocking rale compared with the shortest path algorithm.%随着光网络规模的不断扩大以及网络体系结构的扁平化过程加速,网络传输的可靠性和实时性以及生存性路面算法本身面临新的挑战本文在单点割集的松弛生存性约束条件下,结合动态业务下的IP over WDM光网络的特点.借助蚁群优化算法的强鲁棒性和记忆能力,通过改变与调整蚁群优化算法结构中的信息素结构与其更新机制,在动态路由选择过程中,引入网络可生存性约束信息改进路由选择概率计算机制,来实现IP over WDM光网络动态生存性映射的路由策略,与其他同类算法相比,该算法不再
Biomedical Image Edge Detection using an Ant Colony Optimization Based on Artificial Neural Networks
Directory of Open Access Journals (Sweden)
Javad Rahebi
2011-12-01
Full Text Available Ant colony optimization (ACO is the algorithm that has inspired from natural behavior of ants life, which the ants leaved pheromone to search food on the ground. In this paper, ACO is introduced for resolving the edge detection in the biomedical image. Edge detection method based on ACO is able to create a matrix pheromone that shows information of available edge in each location of edge pixel which is created based on the movements of a number of ants on the biomedical image. Moreover, the movements of these ants are created by local fluctuation of biomedical image intensity values. The detected edge biomedical images have low quality rather than detected edge biomedical image resulted of a classic mask and won’t result application of these masks to edge detection biomedical image obtained of ACO. In proposed method, we use artificial neuralnetwork with supervised learning along with momentum to improve edge detection based on ACO. The experimental results shows that make use neural network are very effective in edge detection based on ACO.
Bus Network Modeling Using Ant Algorithms
Sepideh Eshragh; Shahriar Afandizadeh Zargari; Ardeshir Faghri; Earl Rusty Lee
2010-01-01
Bus transit network modeling is a complex and combinatorial problem. The main purpose of this paper is to apply a contemporary method for designing a bus transit network with the objective of achieving optimum results. The method is called Ant Algorithms, a Meta Heuristic method, which has been applied to optimization problems in transportation with noticeable success. The description of the algorithm, as well as the main methodology and computations, is presented in this paper. Furthermore, ...
Structural Damage Detection Based on Modal Parameters Using Continuous Ant Colony Optimization
Directory of Open Access Journals (Sweden)
Aditi Majumdar
2014-01-01
Full Text Available A method is presented to detect and quantify structural damages from changes in modal parameters (such as natural frequencies and mode shapes. An inverse problem is formulated to minimize the objective function, defined in terms of discrepancy between the vibration data identified by modal testing and those computed from analytical model, which then solved to locate and assess the structural damage using continuous ant colony optimization algorithm. The damage is formulated as stiffness reduction factor. The study indicates potentiality of the developed code to solve a wide range of inverse identification problems.
Ant Colony Optimization In Multi-Agent Systems With NetLogo
Directory of Open Access Journals (Sweden)
Mustafa Tüker
2013-02-01
Full Text Available Multi-agent systems (MAS offer an effective way to model and solve complex optimization problems. In this study, MAS and ant colonies have been used together to solve the Travelling Salesmen Problem (TSP. System simulation has been realized with NetLogo which is an agent-based programming environment. It has been explained in detail with code examples that how to use NetLogo for modeling and simulation of the problem. Algorithm has been tested for different numbers of nodes and obtained results have been discussed.
Integration of GPS and DinSAR for Deformation Monitoring Based on Ant Colony Optimization
Shi, Guoqiang; He, Xiufeng; Xiao, Ruya
2014-11-01
To acquire three-dimensional earth surface deformation, a measurement method based on ant colony optimization (ACO) is proposed. It highly integrates high-accuracy GPS observations from sparse ground points with InSAR line-of-sight (LOS) direction information. Two constraints, GPS and DInSAR observations, are employed in constructing the energy function whose minimum value will be searched by the ACO operated in continuous space. Compared with conventional interpolation algorithms, the proposed method increases the three-dimensional deformation observation accuracy, especially showing the improvement in the up direction.
DEFF Research Database (Denmark)
Ouagoussounon, Issa; Sinzogan, Antonio; Offenberg, Joachim;
2013-01-01
Oecophylla ants are currently used for biological control in fruit plantations in Australia, Asia and Africa and for protein production in Asia. To further improve the technology and implement it on a large scale, effective and fast production of live colonies is desirable. Early colony development...... capita brood production by the resident queen, triggered by the adopted pupae. Thus pupae transplantation may be used to shorten the time it takes to produce weaver ant colonies in ant nurseries, and may in this way facilitate the implementation of weaver ant biocontrol in West Africa....
A nuclear reactor core fuel reload optimization using artificial ant colony connective networks
Energy Technology Data Exchange (ETDEWEB)
Lima, Alan M.M. de [Universidade Federal do Rio de Janeiro, PEN/COPPE - UFRJ, Ilha do Fundao s/n, CEP 21945-970 Rio de Janeiro (Brazil)], E-mail: alanmmlima@yahoo.com.br; Schirru, Roberto [Universidade Federal do Rio de Janeiro, PEN/COPPE - UFRJ, Ilha do Fundao s/n, CEP 21945-970 Rio de Janeiro (Brazil)], E-mail: schirru@lmp.ufrj.br; Carvalho da Silva, Fernando [Universidade Federal do Rio de Janeiro, PEN/COPPE - UFRJ, Ilha do Fundao s/n, CEP 21945-970 Rio de Janeiro (Brazil)], E-mail: fernando@con.ufrj.br; Medeiros, Jose Antonio Carlos Canedo [Universidade Federal do Rio de Janeiro, PEN/COPPE - UFRJ, Ilha do Fundao s/n, CEP 21945-970 Rio de Janeiro (Brazil)], E-mail: canedo@lmp.ufrj.br
2008-09-15
The core of a nuclear Pressurized Water Reactor (PWR) may be reloaded every time the fuel burn-up is such that it is not more possible to maintain the reactor operating at nominal power. The nuclear core fuel reload optimization problem consists in finding a pattern of burned-up and fresh-fuel assemblies that maximize the number of full operational days. This is an NP-Hard problem, meaning that complexity grows exponentially with the number of fuel assemblies in the core. Moreover, the problem is non-linear and its search space is highly discontinuous and multi-modal. Ant Colony System (ACS) is an optimization algorithm based on artificial ants that uses the reinforcement learning technique. The ACS was originally developed to solve the Traveling Salesman Problem (TSP), which is conceptually similar to the nuclear core fuel reload problem. In this work a parallel computational system based on the ACS, called Artificial Ant Colony Networks is introduced to solve the core fuel reload optimization problem.
A nuclear reactor core fuel reload optimization using artificial ant colony connective networks
International Nuclear Information System (INIS)
The core of a nuclear Pressurized Water Reactor (PWR) may be reloaded every time the fuel burn-up is such that it is not more possible to maintain the reactor operating at nominal power. The nuclear core fuel reload optimization problem consists in finding a pattern of burned-up and fresh-fuel assemblies that maximize the number of full operational days. This is an NP-Hard problem, meaning that complexity grows exponentially with the number of fuel assemblies in the core. Moreover, the problem is non-linear and its search space is highly discontinuous and multi-modal. Ant Colony System (ACS) is an optimization algorithm based on artificial ants that uses the reinforcement learning technique. The ACS was originally developed to solve the Traveling Salesman Problem (TSP), which is conceptually similar to the nuclear core fuel reload problem. In this work a parallel computational system based on the ACS, called Artificial Ant Colony Networks is introduced to solve the core fuel reload optimization problem
Power Efficient Resource Allocation for Clouds Using Ant Colony Framework
Chimakurthi, Lskrao
2011-01-01
Cloud computing is one of the rapidly improving technologies. It provides scalable resources needed for the ap- plications hosted on it. As cloud-based services become more dynamic, resource provisioning becomes more challenging. The QoS constrained resource allocation problem is considered in this paper, in which customers are willing to host their applications on the provider's cloud with a given SLA requirements for performance such as throughput and response time. Since, the data centers hosting the applications consume huge amounts of energy and cause huge operational costs, solutions that reduce energy consumption as well as operational costs are gaining importance. In this work, we propose an energy efficient mechanism that allocates the cloud resources to the applications without violating the given service level agreements(SLA) using Ant colony framework.
DETECTION OF MASSES IN MAMMOGRAM IMAGES USING ANT COLONY OPTIMIZATION
Directory of Open Access Journals (Sweden)
Varsha Patankar
2014-04-01
Full Text Available This paper proposes the advances in edge detection techniques, which is used for the mammogram images for cancer diagnosis. It compares the evaluation of edge detection with the proposed method ant colony optimization. The study shows that the edge detection technique is applied on the mammogram images because it will clearly identify the masses in mammogram images. This will help to identify the type of cancer at the early stage. ACO edge detector is best in detecting the edges when compared to the other edge detectors. The quality of various edge detectors is calculated based on the parameters such as Peak signal to noise ratio (PSNR and Mean square error (MSE.
Adamu Murtala Zungeru; Kah Phooi Seng; Li-Minn Ang; Wai Chong Chia
2013-01-01
The main problem for event gathering in wireless sensor networks (WSNs) is the restricted communication range for each node. Due to the restricted communication range and high network density, event forwarding in WSNs is very challenging and requires multihop data forwarding. Currently, the energy-efficient ant based routing (EEABR) algorithm, based on the ant colony optimization (ACO) metaheuristic, is one of the state-of-the-art energy-aware routing protocols. In this paper, we propose thre...
Improving the cAnt-MinerPB Classification Algorithm
Medland, Matthew; Otero, Fernando E. B.; Freitas, Alex A
2012-01-01
Ant Colony Optimisation (ACO) has been successfully applied to the classification task of data mining in the form of Ant-Miner. A new extension of Ant-Miner, called cAnt-MinerPB, uses the ACO procedure in a different fashion. The main difference is that the search in cAnt-MinerPB is optimised to find the best list of rules, whereas in Ant-Miner the search is optimised to find the best individual rule at each step of the sequential covering, producing a list of best rules. We aim to improve cA...
Hybrid real-code ant colony optimisation for constrained mechanical design
Pholdee, Nantiwat; Bureerat, Sujin
2016-01-01
This paper proposes a hybrid meta-heuristic based on integrating a local search simplex downhill (SDH) method into the search procedure of real-code ant colony optimisation (ACOR). This hybridisation leads to five hybrid algorithms where a Monte Carlo technique, a Latin hypercube sampling technique (LHS) and a translational propagation Latin hypercube design (TPLHD) algorithm are used to generate an initial population. Also, two numerical schemes for selecting an initial simplex are investigated. The original ACOR and its hybrid versions along with a variety of established meta-heuristics are implemented to solve 17 constrained test problems where a fuzzy set theory penalty function technique is used to handle design constraints. The comparative results show that the hybrid algorithms are the top performers. Using the TPLHD technique gives better results than the other sampling techniques. The hybrid optimisers are a powerful design tool for constrained mechanical design problems.
COLONY INSULARITY THROUGH QUEEN CONTROL ON WORKER SOCIAL MOTIVATION IN ANTS
We investigated the relative contribution of the queen and workers to colony nestmate recognition cues and on colony territoriality in the ant Camponotus fellah. Workers were either individually isolated, preventing contact with both queen and workers (Colony Deprived, CD), kept in queenless groups,...
Institute of Scientific and Technical Information of China (English)
李英俊; 陈志祥
2012-01-01
One algorithm structure of an ant colony optimization(ACO) for solving multi -periods continuous and mixed integer programming problem was first designed herein and then its application in the single-level multi -period capacitated dynamic lot-sizing problem(CLSP) was introduced. The algorithm was based on the model characteristics of CLSP and improvements of traditional ant colony algorithm. Compared with other algorithms of other literature, the algorithm presented herein performs better than that the traditional genetic algorithm and the hybrid of simulated annealing penalty and genetic algorithm do;it has higher ability of obtaining optimal value. The application results show the method is feasible and effective for solving this kind problem.%设计了一个用于求解具有多时段连续与整数混合规划问题的算法结构,并以单级多时段多资源约束的生产批量问题(CLSP)的模型为背景进行了应用研究,根据此类问题的特点设计了新颖的蚁群算法,阐明了算法的具体实现过程.通过对其他文献中的例子进行计算和结果比较,表明提出的改进蚁群算法在寻优方面比退火惩罚混合遗传算法和传统的遗传算法要好,验证了所提算法对解决此类问题的可行性和适用性.
Ji, Junzhong; Song, Xiangjing; Liu, Chunnian; Zhang, Xiuzhen
2013-08-01
Community structure detection in complex networks has been intensively investigated in recent years. In this paper, we propose an adaptive approach based on ant colony clustering to discover communities in a complex network. The focus of the method is the clustering process of an ant colony in a virtual grid, where each ant represents a node in the complex network. During the ant colony search, the method uses a new fitness function to percept local environment and employs a pheromone diffusion model as a global information feedback mechanism to realize information exchange among ants. A significant advantage of our method is that the locations in the grid environment and the connections of the complex network structure are simultaneously taken into account in ants moving. Experimental results on computer-generated and real-world networks show the capability of our method to successfully detect community structures.
AntStar: Enhancing Optimization Problems by Integrating an Ant System and A⁎ Algorithm
Directory of Open Access Journals (Sweden)
Mohammed Faisal
2016-01-01
Full Text Available Recently, nature-inspired techniques have become valuable to many intelligent systems in different fields of technology and science. Among these techniques, Ant Systems (AS have become a valuable technique for intelligent systems in different fields. AS is a computational system inspired by the foraging behavior of ants and intended to solve practical optimization problems. In this paper, we introduce the AntStar algorithm, which is swarm intelligence based. AntStar enhances the optimization and performance of an AS by integrating the AS and A⁎ algorithm. Applying the AntStar algorithm to the single-source shortest-path problem has been done to ensure the efficiency of the proposed AntStar algorithm. The experimental result of the proposed algorithm illustrated the robustness and accuracy of the AntStar algorithm.
Cui, Xiao-Yan; Huo, Zhong-Gang; Xin, Zhong-Hua; Tian, Xiao; Zhang, Xiao-Dong
2013-07-01
Three-dimensional (3D) copying of artificial ears and pistol printing are pushing laser three-dimensional copying technique to a new page. Laser three-dimensional scanning is a fresh field in laser application, and plays an irreplaceable part in three-dimensional copying. Its accuracy is the highest among all present copying techniques. Reproducibility degree marks the agreement of copied object with the original object on geometry, being the most important index property in laser three-dimensional copying technique. In the present paper, the error of laser three-dimensional copying was analyzed. The conclusion is that the data processing to the point cloud of laser scanning is the key technique to reduce the error and increase the reproducibility degree. The main innovation of this paper is as follows. On the basis of traditional ant colony optimization, rational ant colony optimization algorithm proposed by the author was applied to the laser three-dimensional copying as a new algorithm, and was put into practice. Compared with customary algorithm, rational ant colony optimization algorithm shows distinct advantages in data processing of laser three-dimensional copying, reducing the error and increasing the reproducibility degree of the copy. PMID:24059192
Acharya, Ayan; Konar, Amit; Janarthanan, Ramadoss
2008-01-01
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, the deposition of pheromone on different parts of the tour of a particular ant is always kept unvarying. Thus 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 p...
Institute of Scientific and Technical Information of China (English)
朱铁欣; 董桂菊; 颜丙学; 郭凯敏; 谢学刚; 郭志强
2016-01-01
In response to the problems of agricultural robot path planning bad real-time and stability,artificial potential field, elite sorting method combined with Memetic algorithm is adopted.This algorithm initialize the path population with potential field method,optimizes the sorting of each generation ants path.And also updates the pheromone according to the superiority of the ants path.At the same time, with the help of the pheromone of the elite ants, and using crossover and mutation operation of the memetic algorithm on each generation path,so as accelerate the convergence speed of the al-gorithm, improves the stability of it.The simulation results show that the improved algorithm of the optimal path length on average increased by 12 .56%,the convergence generation increased by 55 .86%, the algorithm time increased by 65 . 3%,and the optimal solution percentage increased by 40%,this shows that the mentioned algorithm can plan the optimal path in a quick and efficient way, improving the efficiency of agricultural robot.%针对农业机器人路径规划实时性和稳定性差的问题，采用人工势场法，并结合 Memetic 算法与精英排序法优化基本蚁群算法。该算法用势场法获得路径初始化种群，对每代路径进行 Memetic 算法中的交叉组合操作，将每代蚂蚁产生的路径分别进行优化排序，根据蚂蚁路径的优劣程度，对信息素进行更新；同时，加入精英小组蚂蚁产生的信息素，从而加快了算法的收敛速度，提高了算法的稳定性。实验表明：改进后算法的平均最优路径长度提高了12．56％，收敛代数提高55．86％，算法用时提高了65．3％，最优解百分比增加了40％。本算法能够快速有效地规划出最优路径，提高了农业机器人的工作效率。
A Study of Different Quality Evaluation Functions in the cAnt-MinerPB Classification Algorithm
Medland, Matthew; Otero, Fernando E. B.
2012-01-01
Ant colony optimization (ACO) algorithms for classification in general employ a sequential covering strategy to create a list of classification rules. A key component in this strategy is the selection of the rule quality function, since the algorithm aims at creating one rule at a time using an ACO-based procedure to search the best rule. Recently, an improved strategy has been proposed in the cAnt-MinerPB algorithm, where an ACO-based procedure is used to create a complete list of rules inst...
A Novel Polymorphic Ant Colony -Based Clustering Mechanism for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Min Xiang
2012-10-01
Full Text Available In wireless sensor networks, sensor nodes are extremely power constrained, so energy efficient clustering mechanism is mainly considered in the network topology management. A new clustering mechanism based on the polymorphic ant colony (PAC is designed for dynamically controlling the networks clustering structure. According to different functions, the nodes of the networks are respectively defined as the queen ant, the scout ant and worker ant. Based on the calculated cost function and real-time pheromone, the queen ant restructures an optimum clustering structure. Furthermore, the worker ants and the scout ants can send or receive sensing data with optional communication path based on their pheromones. With the mechanism, the energy consumption in inter-cluster and intra-cluster communication for the worker ants and scout ants can be reduced. The simulation results demonstrate that the proposed mechanism can effectively remodel the clustering structure and improve the energy efficiency of the networks.
Ant colony optimization and neural networks applied to nuclear power plant monitoring
Energy Technology Data Exchange (ETDEWEB)
Santos, Gean Ribeiro dos; Andrade, Delvonei Alves de; Pereira, Iraci Martinez, E-mail: gean@usp.br, E-mail: delvonei@ipen.br, E-mail: martinez@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)
2015-07-01
A recurring challenge in production processes is the development of monitoring and diagnosis systems. Those systems help on detecting unexpected changes and interruptions, preventing losses and mitigating risks. Artificial Neural Networks (ANNs) have been extensively used in creating monitoring systems. Usually the ANNs created to solve this kind of problem are created by taking into account only parameters as the number of inputs, outputs, and hidden layers. The result networks are generally fully connected and have no improvements in its topology. This work intends to use an Ant Colony Optimization (ACO) algorithm to create a tuned neural network. The ACO search algorithm will use Back Error Propagation (BP) to optimize the network topology by suggesting the best neuron connections. The result ANN will be applied to monitoring the IEA-R1 research reactor at IPEN. (author)
Chang, Yung-Chia; Li, Vincent C.; Chiang, Chia-Ju
2014-04-01
Make-to-order or direct-order business models that require close interaction between production and distribution activities have been adopted by many enterprises in order to be competitive in demanding markets. This article considers an integrated production and distribution scheduling problem in which jobs are first processed by one of the unrelated parallel machines and then distributed to corresponding customers by capacitated vehicles without intermediate inventory. The objective is to find a joint production and distribution schedule so that the weighted sum of total weighted job delivery time and the total distribution cost is minimized. This article presents a mathematical model for describing the problem and designs an algorithm using ant colony optimization. Computational experiments illustrate that the algorithm developed is capable of generating near-optimal solutions. The computational results also demonstrate the value of integrating production and distribution in the model for the studied problem.
Ant colony optimization and neural networks applied to nuclear power plant monitoring
International Nuclear Information System (INIS)
A recurring challenge in production processes is the development of monitoring and diagnosis systems. Those systems help on detecting unexpected changes and interruptions, preventing losses and mitigating risks. Artificial Neural Networks (ANNs) have been extensively used in creating monitoring systems. Usually the ANNs created to solve this kind of problem are created by taking into account only parameters as the number of inputs, outputs, and hidden layers. The result networks are generally fully connected and have no improvements in its topology. This work intends to use an Ant Colony Optimization (ACO) algorithm to create a tuned neural network. The ACO search algorithm will use Back Error Propagation (BP) to optimize the network topology by suggesting the best neuron connections. The result ANN will be applied to monitoring the IEA-R1 research reactor at IPEN. (author)
Peker, Musa; ŞEN, Baha; KUMRU, Pınar Yıldız
2012-01-01
Owing to its complexity, the traveling salesman problem (TSP) is one of the most intensively studied problems in computational mathematics. The TSP is defined as the provision of minimization of total distance, cost, and duration by visiting the n number of points only once in order to arrive at the starting point. Various heuristic algorithms used in many fields have been developed to solve this problem. In this study, a solution was proposed for the TSP using the ant colony system and...
Improved Ant Algorithms for Software Testing Cases Generation
Shunkun Yang; Tianlong Man; Jiaqi Xu
2014-01-01
Existing ant colony optimization (ACO) for software testing cases generation is a very popular domain in software testing engineering. However, the traditional ACO has flaws, as early search pheromone is relatively scarce, search efficiency is low, search model is too simple, positive feedback mechanism is easy to porduce the phenomenon of stagnation and precocity. This paper introduces improved ACO for software testing cases generation: improved local pheromone update strategy for ant colony...