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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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.
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
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
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
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
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
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.
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
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.
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
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.
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
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
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
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.
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.
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
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
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
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
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.
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
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.
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
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.
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
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
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
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
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.
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
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
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
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
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.
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)
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
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
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.
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
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.
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
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.
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
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%.
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.
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.
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.
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
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
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
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.
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
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.
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.
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.
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
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
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
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。
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
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
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
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.
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
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
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.
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...
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
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
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....
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
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
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...
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
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
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
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
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
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.
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
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.
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
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
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
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
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
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
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.
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.
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
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...
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
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.
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
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
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
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
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
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
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
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
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
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.
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
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
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
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.
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.
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
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.