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...
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...
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...
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.
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...
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...
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...
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.
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.
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.
Reliability optimization using multiobjective ant colony system approaches
The multiobjective ant colony system (ACS) meta-heuristic has been developed to provide solutions for the reliability optimization problem of series-parallel systems. This type of problems involves selection of components with multiple choices and redundancy levels that produce maximum benefits, and is subject to the cost and weight constraints at the system level. These are very common and realistic problems encountered in conceptual design of many engineering systems. It is becoming increasingly important to develop efficient solutions to these problems because many mechanical and electrical systems are becoming more complex, even as development schedules get shorter and reliability requirements become very stringent. The multiobjective ACS algorithm offers distinct advantages to these problems compared with alternative optimization methods, and can be applied to a more diverse problem domain with respect to the type or size of the problems. Through the combination of probabilistic search, multiobjective formulation of local moves and the dynamic penalty method, the multiobjective ACSRAP, allows us to obtain an optimal design solution very frequently and more quickly than with some other heuristic approaches. The proposed algorithm was successfully applied to an engineering design problem of gearbox with multiple stages
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...
Distribution system minimum loss reconfiguration in the Hyper-Cube Ant Colony Optimization framework
Carpaneto, Enrico; Chicco, Gianfranco [Dipartimento di Ingegneria Elettrica, Politecnico di Torino, corso Duca degli Abruzzi 24, I-10129 Torino (Italy)
2008-12-15
This paper presents an original application of the Ant Colony Optimization concepts to the optimal reconfiguration of distribution systems, with the objective of minimizing the distribution system losses in the presence of a set of structural and operational constraints. The proposed algorithm starts from the current configuration of the system and proceeds by progressively introducing variations in the configuration according to local and global heuristic rules developed within the Hyper-Cube Ant Colony Optimization framework. Results of numerical tests carried out on a classical system and on a large real urban distribution system are presented to show the effectiveness of the proposed approach. (author)
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.
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
A Dynamic Job Shop Scheduling Method Based on Ant Colony Coordination System
ZHU Qiong; WU Li-hui; ZHANG Jie
2009-01-01
Due to the stubborn nature of dynamic job shop scheduling problem, a novel ant colony coordination mechanism is proposed in this paper to search for an optimal schedule in dynamic environment. In ant colony coordination mechanism, the dynamic .job shop is composed of several autonomous ants. These ants coordinate with each other by simulating the ant foraging behavior of spreading pheromone on the trails, by which they can make information available globally, and further more guide ants make optimal decisions. The proposed mechanism is tested by several instances and the results confirm the validity of it.
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. ...
Peinado, Alberto; Ortiz, Andrés; Munilla, Jorge
2013-01-01
In this work, we present a distributed system designed for road traffic management. The system is inspired by the behavior of the ant colonies. The distributed design responds to the particular limitations of an emergency situation; mainly, the fixed infrastructures are out of service because no energy supply is available. The implementation is based on the VANET facilities complemented with passive RFID tags or GPS localization. The vehicles can use the information of previous vehic...
Ant colony optimization approach for test scheduling of system on chip
CHEN Ling; PAN Zhong-liang
2009-01-01
It is necessary to perform the test of system on chip, the test scheduling determines the test start and finishing time of every core in the system on chip such that the overall test time is minimized. A new test scheduling approach based on chaotic ant colony algorithm is presented in this paper. The optimization model of test scheduling was studied, the model uses the information such as the scale of test sets of both cores and user defined logic. An approach based on chaotic ant colony algorithm was proposed to solve the optimization model of test scheduling. The test of signal integrity faults such as crosstalk were also investigated when performing the test scheduling. Experimental results on many circuits show that the proposed approach can be used to solve test scheduling problems.
Ant Colony System for a Fuzzy Adjacent Multiple-Level Warehouse Layout Problem
ZHANG Qiang; YU Ying-zi; LAI K K
2006-01-01
A warehouse layout problem where the warehouse has more than one level and both the distance from the cell to the receive/exit bay and demand of item types are fuzzy variables is proposed. The problem is to find a layout with the minimum transportation cost subject to adjacency and other constraints. A fuzzy expected value model is given and an ant colony system is designed to solve the problem. Computational results indicate the efficiency and effectiveness of the method.
Ant colony system (ACS with hybrid local search to solve vehicle routing problems
Suphan Sodsoon
2016-02-01
Full Text Available This research applied an Ant Colony System algorithm with a Hybrid Local Search to solve Vehicle Routing Problems (VRP from a single depot when the customers’ requirements are known. VRP is an NP-hard optimization problem and has usually been successfully solved optimum by heuristics. A fleet of vehicles of a specific capacity are used to serve a number of customers at minimum cost, without violating the constraints of vehicle capacity. There are meta-heuristic approaches to solve these problems, such as Simulated Annealing, Genetic Algorithm, Tabu Search and the Ant Colony System algorithm. In this case a hybrid local search was used (Cross-Exchange, Or-Opt and 2-Opt algorithm with an Ant Colony System algorithm. The Experimental Design was tested on 7 various problems from the data set online in the OR-Library. There are five different problems in which customers are randomly distributed with the depot in an approximately central location. The customers were grouped into clusters. The results are evaluated in terms of optimal routes using optimal distances. The experimental results are compared with those obtained from meta-heuristics and they show that the proposed method outperforms six meta-heuristics in the literature.
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
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.
无
2005-01-01
A novel method based on ant colony optimization (ACO), algorithm for solving the ill-conditioned linear systems of equations is proposed. ACO is a parallelized bionic optimization algorithm which is inspired from the behavior of real ants. ACO algorithm is first introduced, a kind of positive feedback mechanism is adopted in ACO. Then, the solution problem of linear systems of equations was reformulated as an unconstrained optimization problem for solution by an ACO algorithm. Finally, the ACO with other traditional methods is applied to solve a kind of multi-dimensional Hilbert ill-conditioned linear equations. The numerical results demonstrate that ACO is effective, robust and recommendable in solving ill-conditioned linear systems of equations.
Ant Colony Optimization In Multi-Agent Systems With NetLogo
Mustafa Tüker
2013-02-01
Full Text Available Multi-agent systems (MAS offer an effective way to model and solve complex optimization problems. In this study, MAS and ant colonies have been used together to solve the Travelling Salesmen Problem (TSP. System simulation has been realized with NetLogo which is an agent-based programming environment. It has been explained in detail with code examples that how to use NetLogo for modeling and simulation of the problem. Algorithm has been tested for different numbers of nodes and obtained results have been discussed.
Optimization of travel salesman problem using the ant colony system and Greedy search
In this paper we present some results obtained during the development of optimization systems that can be used to design refueling and patterns of control rods in a BWR. These systems use ant colonies and Greedy search. The first phase of this project is to be familiar with these optimization techniques applied to the problem of travel salesman problem (TSP). The utility of TSP study is that, like the refueling design and pattern design of control rods are problems of combinative optimization. Even, the similarity with the problem of the refueling design is remarkable. It is presented some results for the TSP with the 32 state capitals of Mexico country. (Author)
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.
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.
A New Constructive Method for Electric Power System Reconfiguration Using Ant Colony
Habib HAMDAOUI
2008-06-01
Full Text Available This electric power distribution system delivers power to the customers from a set of distribution substations. While the transmission lines are configured in a meshed network, the distribution feeders are configured radially in almost all cases. The proposed problem in this work is to determine the optimal topology among a various alternatives. This problem is known as a problem of total investment-cost minimization, subject to power constraints. In fact, the paper addresses an ant colony met-heuristic optimization method to solve this combinatorial problem. Due to the variation of demand, the reconfiguration may be considered in two different situations: in the system planning or system design stage. The proposed met-heuristic determines the minimal investment-cost system configuration during the considered study period to satisfy power transit constraints. The algorithm of ant colony approach (ACA is required to identify the optimal combination of adding or cut off feeders with different parameters for the new topology design.
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.
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 fuel reloads for a BWR using the ant colony system
In this work some results obtained during the development of optimization systems are presented, which are employees for the fuel reload design in a BWR. The systems use the ant colony optimization technique. As first instance, a system is developed that was adapted at travel salesman problem applied for the 32 state capitals of Mexican Republic. The purpose of this implementation is that a similarity exists with the design of fuel reload, since the two problems are of combinatorial optimization with decision variables that have similarity between both. The system was coupled to simulator SIMULATE-3, obtaining good results when being applied to an operation cycle in equilibrium for reactors of nuclear power plant of Laguna Verde. (Author)
Reliability worth applied to transmission expansion planning based on ant colony system
Leite da Silva, Armando M.; Rezende, Leandro S. [Institute of Electric Systems and Energy, Federal University of Itajuba, UNIFEI (Brazil); da Fonseca Manso, Luiz A.; de Resende, Leonidas C. [Department of Electrical Engineering, Federal University of Sao Joao del Rei, UFSJ (Brazil)
2010-12-15
This paper proposes a new methodology to solve transmission expansion planning (TEP) problems in power system, based on the metaheuristic ant colony optimisation (ACO). The TEP problem includes the search for the least cost solution, bearing in mind investment cost and reliability worth. Reliability worth is considered through the assessment of the interruption costs represented by the index LOLC - loss of load cost. The focus of this work is the development of a tool for the multi-stage planning of transmission systems and how reliability aspects can influence on the decision-making process. The applications of the proposed methodology are illustrated through case studies carried out using a test system and a real sub-transmission network. (author)
3D sensor placement strategy using the full-range pheromone ant colony system
Shuo, Feng; Jingqing, Jia
2016-07-01
An optimized sensor placement strategy will be extremely beneficial to ensure the safety and cost reduction considerations of structural health monitoring (SHM) systems. The sensors must be placed such that important dynamic information is obtained and the number of sensors is minimized. The practice is to select individual sensor directions by several 1D sensor methods and the triaxial sensors are placed in these directions for monitoring. However, this may lead to non-optimal placement of many triaxial sensors. In this paper, a new method, called FRPACS, is proposed based on the ant colony system (ACS) to solve the optimal placement of triaxial sensors. The triaxial sensors are placed as single units in an optimal fashion. And then the new method is compared with other algorithms using Dalian North Bridge. The computational precision and iteration efficiency of the FRPACS has been greatly improved compared with the original ACS and EFI method.
Multi-Constrained Dynamic QoS Multicast Routing Design Using Ant Colony System
GUI Zhi-bo; WU Xiao-quan
2005-01-01
In this paper, an Ant Colony System (AC) based heuristic algorithm is presented to find the multi-constrained dynamic Quality of Service (QoS) multicast routing. We also explore the scalability of the AC algorithm and multicast tree by using "Pull" mode instead of "Push" mode, and the improvement on the time complexity of AC algorithm by using a new data structure, I.e., a pointer array instead of the previous "matrix" structure. Our extensive tests show that the presented algorithm can find the global optimum or suboptimum, and has a good scalability with dynamic adaptation to the change of multicast group, and gives better performance in terms of the total cost than other two algorithms.
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.
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....
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...
Fuel lattice design in a boiling water reactor using an ant-colony-based system
Montes, Jose Luis, E-mail: joseluis.montes@inin.gob.mx [Instituto Nacional de Investigaciones Nucleares, Carr. Mexico-Toluca S/N, La Marquesa, Ocoyoacac, Estado de Mexico, CP 52750 (Mexico); Facultad de Ciencias, Universidad Autonoma del Estado de Mexico (Mexico); Francois, Juan-Luis, E-mail: juan.luis.francois@gmail.com [Departamento de Sistemas Energeticos, Facultad de Ingenieria, Universidad Nacional Autonoma de Mexico, Paseo Cuauhnahuac 8532, Jiutepec, Mor., CP 62550 (Mexico); Ortiz, Juan Jose, E-mail: juanjose.ortiz@inin.gob.mx [Instituto Nacional de Investigaciones Nucleares, Carr. Mexico-Toluca S/N, La Marquesa, Ocoyoacac, Estado de Mexico, CP 52750 (Mexico); Martin-del-Campo, Cecilia, E-mail: cecilia.martin.del.campo@gmail.com [Departamento de Sistemas Energeticos, Facultad de Ingenieria, Universidad Nacional Autonoma de Mexico, Paseo Cuauhnahuac 8532, Jiutepec, Mor., CP 62550 (Mexico); Perusquia, Raul, E-mail: raul.perusquia@inin.gob.mx [Instituto Nacional de Investigaciones Nucleares, Carr. Mexico-Toluca S/N, La Marquesa, Ocoyoacac, Estado de Mexico, CP 52750 (Mexico)
2011-06-15
Research highlights: > We present an ant-colony-based system for BWR fuel lattice design and optimization. > Assessment of candidate solutions at 0.0 MWd/kg {sup 235}U seems to have a limited scope. > Suitable heuristic rules enable more realistic fuel lattice designs. > The election of the objective has a large impact in CPU time. > ACS enables an important decrease of the initial average U-235 enrichment. - Abstract: This paper presents a new approach to deal with the boiling water reactor radial fuel lattice design. The goal is to optimize the distribution of both, the fissionable material, and the reactivity control poison material inside the fuel lattice at the beginning of its life. An ant-colony-based system was used to search for either: the optimum location of the poisoned pin inside the lattice, or the U{sup 235} enrichment and Gd{sub 2}O{sub 3} concentrations. In the optimization process, in order to know the parameters of the candidate solutions, the neutronic simulator CASMO-4 transport code was used. A typical 10 x 10 BWR fuel lattice with an initial average U{sup 235} enrichment of 4.1%, used in the current operation of Laguna Verde Nuclear Power Plant was taken as a reference. With respect to that reference lattice, it was possible to decrease the average U{sup 235} enrichment up to 3.949%, this obtained value represents a decrease of 3.84% with respect to the reference U{sup 235} enrichment; whereas, the k-infinity was inside the {+-}100 pcm's range, and there was a difference of 0.94% between the local power peaking factor and the lattice reference value. Particular emphasis was made on defining the objective function which is used for making the assessment of candidate solutions. In a typical desktop personal computer, about four hours of CPU time were necessary for the algorithm to fulfill the goals of the optimization process. The results obtained with the application of the implemented system showed that the proposed approach represents a
Fuel lattice design in a boiling water reactor using an ant-colony-based system
Research highlights: → We present an ant-colony-based system for BWR fuel lattice design and optimization. → Assessment of candidate solutions at 0.0 MWd/kg 235U seems to have a limited scope. → Suitable heuristic rules enable more realistic fuel lattice designs. → The election of the objective has a large impact in CPU time. → ACS enables an important decrease of the initial average U-235 enrichment. - Abstract: This paper presents a new approach to deal with the boiling water reactor radial fuel lattice design. The goal is to optimize the distribution of both, the fissionable material, and the reactivity control poison material inside the fuel lattice at the beginning of its life. An ant-colony-based system was used to search for either: the optimum location of the poisoned pin inside the lattice, or the U235 enrichment and Gd2O3 concentrations. In the optimization process, in order to know the parameters of the candidate solutions, the neutronic simulator CASMO-4 transport code was used. A typical 10 x 10 BWR fuel lattice with an initial average U235 enrichment of 4.1%, used in the current operation of Laguna Verde Nuclear Power Plant was taken as a reference. With respect to that reference lattice, it was possible to decrease the average U235 enrichment up to 3.949%, this obtained value represents a decrease of 3.84% with respect to the reference U235 enrichment; whereas, the k-infinity was inside the ±100 pcm's range, and there was a difference of 0.94% between the local power peaking factor and the lattice reference value. Particular emphasis was made on defining the objective function which is used for making the assessment of candidate solutions. In a typical desktop personal computer, about four hours of CPU time were necessary for the algorithm to fulfill the goals of the optimization process. The results obtained with the application of the implemented system showed that the proposed approach represents a powerful tool to tackle this step of
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. ...
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
Electricity Consumption Prediction Based on SVR with Ant Colony Optimization
Haijiang Wang; Shanlin Yang
2013-01-01
Accurate forecasting of electric load has always been the most important issues in the electricity industry, particularly for developing countries. Due to the various influences, electric load forecasting reveals highly nonlinear characteristics. This paper creates a system for power load forecasting using support vector machine and ant colony optimization. The method of colony optimization is employed to process large amount of data and eliminate. The SVR model with ant colony optimization i...
Ant Colony Optimization for Mapping, Scheduling and Placing in Reconfigurable Systems
Ferrandi, Fabrizio; Lanzi, Pier Luca; Pilato, Christian; Sciuto, Donatella; Tumeo, Antonino
2013-06-24
Modern heterogeneous embedded platforms, com- posed of several digital signal, application specific and general purpose processors, also include reconfigurable devices support- ing partial dynamic reconfiguration. These devices can change the behavior of some of their parts during execution, allowing hardware acceleration of more sections of the applications. Never- theless, partial dynamic reconfiguration imposes severe overheads in terms of latency. For such systems, a critical part of the design phase is deciding on which processing elements (mapping) and when (scheduling) executing a task, but also how to place them on the reconfigurable device to guarantee the most efficient reuse of the programmable logic. In this paper we propose an algorithm based on Ant Colony Optimization (ACO) that simultaneously executes the scheduling, the mapping and the linear placing of tasks, hiding reconfiguration overheads through prefetching. Our heuristic gradually constructs solutions and then searches around the best ones, cutting out non-promising areas of the design space. We show how to consider the partial dynamic reconfiguration constraints in the scheduling, placing and mapping problems and compare our formulation to other heuristics that address the same problems. We demonstrate that our proposal is more general and robust, and finds better solutions (16.5% in average) with respect to competing solutions.
Peker, Musa; ŞEN, Baha; KUMRU, Pınar Yıldız
2012-01-01
Owing to its complexity, the traveling salesman problem (TSP) is one of the most intensively studied problems in computational mathematics. The TSP is defined as the provision of minimization of total distance, cost, and duration by visiting the n number of points only once in order to arrive at the starting point. Various heuristic algorithms used in many fields have been developed to solve this problem. In this study, a solution was proposed for the TSP using the ant colony system and...
Kronauer, Daniel J C; O'Donnell, Sean; Boomsma, Jacobus J;
2011-01-01
queen mating frequencies, and therefore among the lowest degrees of colony relatedness, occur in Apis honeybees and army ants of the subfamilies Aenictinae, Ecitoninae, and Dorylinae, suggesting that common life history features such as reproduction by colony fission and male biased numerical sex......-ratios have convergently shaped these mating systems. Here we show that ponerine army ants of the genus Simopelta, which are distantly related but similar in general biology to other army ants, have strictly monandrous queens. Preliminary data suggest that workers reproduce in queenright colonies, which is in...
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.
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...
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.
Brief Announcement: Distributed Task Allocation in Ant Colonies
Dornhaus, Anna; Lynch, Nancy; Radeva, Tsvetomira; Su, and Hsin-Hao
2015-01-01
International audience A common problem in both distributed computing and insect biology is designing a model that accurately captures the behavior of a given distributed system or an ant colony, respectively. While the challenges involved in modeling computer systems and ant colonies are quite different from each other, a common approach is to explore multiple variations of different models and compare the results in terms of the simplicity of the model and the quality of the results. We ...
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.
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.
This article is based on a previous study made by Bris, Chatelet and Yalaoui [Bris R, Chatelet E, Yalaoui F. New method to minimise the preventive maintenance cost of series-parallel systems. Reliab Eng Syst Saf 2003;82:247-55]. They use genetic algorithm to minimize preventive maintenance cost problem for the series-parallel systems. We propose to improve their results developing a new method based on another technique, the Ant Colony Optimization (ACO). The resolution consists in determining the solution vector of system component inspection periods, T P. Those calculations were applied within the programming tool Matlab. Thus, highly interesting results and improvements of previous studies were obtained
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 ...
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 methodology for obtaining the control rods patterns in a BWR using systems based on ants colonies
In this work the AZCATL-PBC system based on a technique of ants colonies for the search of control rods patterns of those reactors of the Nuclear Power station of Laguna Verde (CNLV) is presented. The technique was applied to a transition cycle and one of balance. For both cycles they were compared the kef values obtained with a Haling calculation and the control rods pattern proposed by AZCATL-PBC for a burnt one fixed. It was found that the methodology is able to extend the length of the cycle with respect to the Haling prediction, maintaining sure to the reactor. (Author)
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
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
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
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.
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.
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...
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...
Tippachon, Wiwat; Rerkpreedapong, Dulpichet [Department of Electrical Engineering, Kasetsart University, 50 Phaholyothin Rd., Ladyao, Jatujak, Bangkok 10900 (Thailand)
2009-07-15
This paper presents a multiobjective optimization methodology to optimally place switches and protective devices in electric power distribution networks. Identifying the type and location of them is a combinatorial optimization problem described by a nonlinear and nondifferential function. The multiobjective ant colony optimization (MACO) has been applied to this problem to minimize the total cost while simultaneously minimize two distribution network reliability indices including system average interruption frequency index (SAIFI) and system interruption duration index (SAIDI). Actual distribution feeders are used in the tests, and test results have shown that the algorithm can determine the set of optimal nondominated solutions. It allows the utility to obtain the optimal type and location of devices to achieve the best system reliability with the lowest cost. (author)
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)
Azcatl-CRP: An ant colony-based system for searching full power control rod patterns in BWRs
Ortiz, Juan Jose [Dpto. Sistemas Nucleares, ININ, Carr. Mexico-Toluca Km. 36.5, Salazar, Edo. de Mexico (Mexico)]. E-mail: jjortiz@nuclear.inin.mx; Requena, Ignacio [Dpto. Ciencias Computacion e I.A. ETSII Informatica, University of Granada, C. Daniel Saucedo Aranda s/n, 18071 Granada (Spain)]. E-mail: requena@decsai.ugr.es
2006-01-15
We show a new system named AZCATL-CRP to design full power control rod patterns in BWRs. Azcatl-CRP uses an ant colony system and a reactor core simulator for this purpose. Transition and equilibrium cycles of Laguna Verde Nuclear Power Plant (LVNPP) reactor core in Mexico were used to test Azcatl-CRP. LVNPP has 109 control rods grouped in four sequences and currently uses control cell core (CCC) strategy in its fuel reload design. With CCC method only one sequence is employed for reactivity control at full power operation. Several operation scenarios are considered, including core water flow variation throughout the cycle, target different axial power distributions and Haling conditions. Azcatl-CRP designs control rod patterns (CRP) taking into account safety aspects such as k {sub eff} core value and thermal limits. Axial power distributions are also adjusted to a predetermined power shape.
Azcatl-CRP: An ant colony-based system for searching full power control rod patterns in BWRs
We show a new system named AZCATL-CRP to design full power control rod patterns in BWRs. Azcatl-CRP uses an ant colony system and a reactor core simulator for this purpose. Transition and equilibrium cycles of Laguna Verde Nuclear Power Plant (LVNPP) reactor core in Mexico were used to test Azcatl-CRP. LVNPP has 109 control rods grouped in four sequences and currently uses control cell core (CCC) strategy in its fuel reload design. With CCC method only one sequence is employed for reactivity control at full power operation. Several operation scenarios are considered, including core water flow variation throughout the cycle, target different axial power distributions and Haling conditions. Azcatl-CRP designs control rod patterns (CRP) taking into account safety aspects such as k eff core value and thermal limits. Axial power distributions are also adjusted to a predetermined power shape
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...
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)
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...
Hassan Mohammed Mustafa
2010-11-01
Full Text Available Investigational analysis and evaluation of cooperative learning phenomenon is an interdisciplinary and challenging educational research issue. Educationalists have been interesting in modeling of human's cooperative learning to investigate its analogy with some learning aspects of observed social insect behavior. Specifically, this paper presents realistic modeling inspired from interdisciplinary integrated fields of ecology, education ,and animal behavior learning sciences. Presented modeling considers cooperative behavioral learning at ant colony system (ACS. That's motivated by qualitative simulation results obtained after running of an ACS algorithm searching for optimal solution of Travelling Salesman Problem (TSP. In the context of computational intelligence ; cooperative ACS algorithm reaches optimal TSP solution analogously to convergence process of Hebbian coincidence learning paradigm. Moreover, suggested mathematical modeling presents diversity of positive interdependence aspect observed during human's interactive cooperative learning. Interestingly, presented analysis and evaluation of mathematically modeled practical insights of adopted phenomenon, may shed light on promising future enhancement of cooperative learning performance.
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.
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...
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.
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
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...
Polyethism in a colony of artificial ants
Marriott, Chris
2011-01-01
We explore self-organizing strategies for role assignment in a foraging task carried out by a colony of artificial agents. Our strategies are inspired by various mechanisms of division of labor (polyethism) observed in eusocial insects like ants, termites, or bees. Specifically we instantiate models of caste polyethism and age or temporal polyethism to evaluated the benefits to foraging in a dynamic environment. Our experiment is directly related to the exploration/exploitation trade of in machine learning.
Ant colony optimization and constraint programming
Solnon, Christine
2013-01-01
Ant colony optimization is a metaheuristic which has been successfully applied to a wide range of combinatorial optimization problems. The author describes this metaheuristic and studies its efficiency for solving some hard combinatorial problems, with a specific focus on constraint programming. The text is organized into three parts. The first part introduces constraint programming, which provides high level features to declaratively model problems by means of constraints. It describes the main existing approaches for solving constraint satisfaction problems, including complete tree search
Ant Colony Optimisation for Backward Production Scheduling
Leandro Pereira dos Santos
2012-01-01
Full Text Available The main objective of a production scheduling system is to assign tasks (orders or jobs to resources and sequence them as efficiently and economically (optimised as possible. Achieving this goal is a difficult task in complex environment where capacity is usually limited. In these scenarios, finding an optimal solution—if possible—demands a large amount of computer time. For this reason, in many cases, a good solution that is quickly found is preferred. In such situations, the use of metaheuristics is an appropriate strategy. In these last two decades, some out-of-the-shelf systems have been developed using such techniques. This paper presents and analyses the development of a shop-floor scheduling system that uses ant colony optimisation (ACO in a backward scheduling problem in a manufacturing scenario with single-stage processing, parallel resources, and flexible routings. This scenario was found in a large food industry where the corresponding author worked as consultant for more than a year. This work demonstrates the applicability of this artificial intelligence technique. In fact, ACO proved to be as efficient as branch-and-bound, however, executing much faster.
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.
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.
Ant larval demand reduces aphid colony growth rates in an ant-aphid interaction
Cook, James M.; Leather, Simon R; Oliver, Tom H.
2012-01-01
Ants often form mutualistic interactions with aphids, soliciting honeydew in return for protective services. Under certain circumstances, however, ants will prey upon aphids. In addition, in the presence of ants aphids may increase the quantity or quality of honeydew produced, which is costly. Through these mechanisms, ant attendance can reduce aphid colony growth rates. However, it is unknown whether demand from within the ant colony can affect the ant-aphid interaction. In a factorial exper...
DATA MINING UNTUK KLASIFIKASI PELANGGAN DENGAN ANT COLONY OPTIMIZATION
Maulani Kapiudin
2007-01-01
Full Text Available In this research the system for potentially customer classification is designed by extracting rule based classification from raw data with certain criteria. The searching process uses customer database from a bank with data mining technic by using ant colony optimization. A test based on min_case_per_rule variety and phenomene updating were done on a certain period of time. The result are group of customer class which base on rules built by ant and by modifying the pheromone updating, the area of the case is getting bigger. Prototype of the software is coded with C++ 6 version. The customer database master is created by using Microsoft Access. This paper gives information about potential customer of bank that can be classified by prototype of the software. Abstract in Bahasa Indonesia : Pada penelitian untuk sistem klasifikasi potensial customer ini didesain dengan melakukan ekstrak rule berdasarkan klasifikasi dari data mentah dengan kriteria tertentu. Proses pencarian menggunakan database pelanggan dari suatu bank dengan teknik data mining dengan ant colony optimization. Dilakukan percobaan dengan min_case_per_rule variety dan phenomene updating pada periode waktu tertentu. Hasilnya adalah sekelompok class pelanggan yang didasarkan dari rules yang dibangun dengan ant dan dengan dimodifikasi dengan pheromone updating, area permasalahan menjadi lebih melebar. Prototype dari software ini menggunakan C++ versi 6. Database pelanggan dibangun dengan Microsoft Access. Paper ini memberikan informasi mengenai potensi pelanggan dari bank, sehingga dapat diklasifikasikan dengan prototype dari software. Kata kunci: ant colony optimization, classification, min_case_per_rule, term, pheromone updating
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.
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...
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.
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.
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.
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; 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...
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...
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.
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
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.
BWR Fuel Lattice Design Using an Ant Colony Model
Montes, Jose L.; Ortiz, Juan J. [Instituto Nacional de Investigaciones Nucleares, Depto. de Sistemas Nucleares, Carretera Mexico Toluca S/N. La Marquesa Ocoyoacac. 52750, Estado de Mexico (Mexico); Francois, Juan L.; Martin-del-Campo, Cecilia [Depto. de Sistemas Energeticos, Facultad de Ingenieria, Universidad Nacional Autonoma de Mexico Paseo Cuauhnahuac 8532. Jiutepec, Mor. 62550 (Mexico)
2008-07-01
This paper deals with one of the steps of the nuclear fuel design: the radial fuel lattice design. It can be seen as a combinatorial optimization problem for determining the optimal 2D fuel rods enrichment and gadolinia distribution. In order to solve this optimization problem, the ant colony system technique is proposed. The main idea of the ant colony approach consists of emulating the real ant colony behaviour in their searching for minimum paths between two given points, usually between the nest and a food source. In this case, the environment where the artificial ants move is the space defined by the discrete possible values of Gd{sub 2}O{sub 3} contents, the U{sup 235} enrichment, and the valid locations inside the 10x10 BWR fuel lattice array. In order to assess any candidate fuel lattice in the optimization process, the HELIOS neutronic transport code is used. The results obtained in the application of the implemented model show that the proposed technique is a powerful tool to tackle this step of the fuel design. (authors)
BWR Fuel Lattice Design Using an Ant Colony Model
This paper deals with one of the steps of the nuclear fuel design: the radial fuel lattice design. It can be seen as a combinatorial optimization problem for determining the optimal 2D fuel rods enrichment and gadolinia distribution. In order to solve this optimization problem, the ant colony system technique is proposed. The main idea of the ant colony approach consists of emulating the real ant colony behaviour in their searching for minimum paths between two given points, usually between the nest and a food source. In this case, the environment where the artificial ants move is the space defined by the discrete possible values of Gd2O3 contents, the U235 enrichment, and the valid locations inside the 10x10 BWR fuel lattice array. In order to assess any candidate fuel lattice in the optimization process, the HELIOS neutronic transport code is used. The results obtained in the application of the implemented model show that the proposed technique is a powerful tool to tackle this step of the fuel design. (authors)
Nest- and colony-mate recognition in polydomous colonies of meat ants ( Iridomyrmex purpureus)
van Wilgenburg, E.; Ryan, D.; Morrison, P.; Marriott, P. J.; Elgar, M. A.
2006-07-01
Workers of polydomous colonies of social insects must recognize not only colony-mates residing in the same nest but also those living in other nests. We investigated the impact of a decentralized colony structure on colony- and nestmate recognition in the polydomous Australian meat ant ( Iridomyrmex purpureus). Field experiments showed that ants of colonies with many nests were less aggressive toward alien conspecifics than those of colonies with few nests. In addition, while meat ants were almost never aggressive toward nestmates, they were frequently aggressive when confronted with an individual from a different nest within the same colony. Our chemical analysis of the cuticular hydrocarbons of workers using a novel comprehensive two-dimensional gas chromatography technique that increases the number of quantifiable compounds revealed both colony- and nest-specific patterns. Combined, these data indicate an incomplete transfer of colony odor between the nests of polydomous meat ant colonies.
Reconstructing Amino Acid Interaction Networks by an Ant Colony Approach
Gaci, Omar; Balev, Stefan
2009-01-01
In this paper we introduce the notion of protein interaction network. This is a graph whose vertices are the proteins amino acids and whose edges are the interactions between them. We consider the problem of reconstructing protein's interaction network from its amino acid sequence. We rely on a probability that two amino acids interact as a function of their physico-chemical properties coupled to an ant colony system to solve this problem.
Remote Sensing Image Feature Extracting Based Multiple Ant Colonies Cooperation
Zhang Zhi-long
2014-02-01
Full Text Available This paper presents a novel feature extraction method for remote sensing imagery based on the cooperation of multiple ant colonies. First, multiresolution expression of the input remote sensing imagery is created, and two different ant colonies are spread on different resolution images. The ant colony in the low-resolution image uses phase congruency as the inspiration information, whereas that in the high-resolution image uses gradient magnitude. The two ant colonies cooperate to detect features in the image by sharing the same pheromone matrix. Finally, the image features are extracted on the basis of the pheromone matrix threshold. Because a substantial amount of information in the input image is used as inspiration information of the ant colonies, the proposed method shows higher intelligence and acquires more complete and meaningful image features than those of other simple edge detectors.
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.
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.
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...
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
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.
Ant colony optimization approach to estimate energy demand of Turkey
This paper attempts to shed light on the determinants of energy demand in Turkey. Energy demand model is first proposed using the ant colony optimization (ACO) approach. It is multi-agent systems in which the behavior of each ant is inspired by the foraging behavior of real ants to solve optimization problem. ACO energy demand estimation (ACOEDE) model is developed using population, gross domestic product (GDP), import and export. All equations proposed here are linear and quadratic. QuadraticACOEDE provided better-fit solution due to fluctuations of the economic indicators. The ACOEDE model plans the energy demand of Turkey until 2025 according to three scenarios. The relative estimation errors of the ACOEDE model are the lowest when they are compared with the Ministry of Energy and Natural Resources (MENR) projection
An Improved 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.
A Survey Paper on Solving TSP using Ant Colony Optimization on GPU
Khushbu Khatri
2014-12-01
Full Text Available Ant Colony Optimization (ACO is meta-heuristic algorithm inspired from nature to solve many combinatorial optimization problem such as Travelling Salesman Problem (TSP. There are many versions of ACO used to solve TSP like, Ant System, Elitist Ant System, Max-Min Ant System, Rank based Ant System algorithm. For improved performance, these methods can be implemented in parallel architecture like GPU, CUDA architecture. Graphics Processing Unit (GPU provides highly parallel and fully programmable platform. GPUs which have many processing units with an off-chip global memory can be used for general purpose parallel computation. This paper presents a survey on different solving TSP using ACO on GPU.
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
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
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.
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.
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.
Determining the Optimum Section of Tunnels Using Ant Colony Optimization
S. Talatahari
2013-01-01
Ant colony optimization is developed to determine optimum cross sections of tunnel structures. Tunnel structures are expensive infrastructures in terms of material, construction, and maintenance and the application of optimization methods has a great role in minimizing their costs. This paper presents the formulation of objective function and constraints of the problem for the first time, and the ant colony optimization, as a developed metaheuristic approach, has been used to solve the proble...
Experiment Study of Entropy Convergence of Ant Colony Optimization
Pang, Chao-Yang; Wang, Chong-Bao; Hu, Ben-Qiong
2009-01-01
Ant colony optimization (ACO) has been applied to the field of combinatorial optimization widely. But the study of convergence theory of ACO is rare under general condition. In this paper, the authors try to find the evidence to prove that entropy is related to the convergence of ACO, especially to the estimation of the minimum iteration number of convergence. Entropy is a new view point possibly to studying the ACO convergence under general condition. Key Words: Ant Colony Optimization, Conv...
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...
JOB SHOP METHODOLOGY BASED ON AN ANT COLONY
OMAR CASTRILLON
2009-01-01
Full Text Available The purpose of this study is to reduce the total process time (Makespan and to increase the machines working time, in a job shop environment, using a heuristic based on ant colony optimization. This work is developed in two phases: The first stage describes the identification and definition of heuristics for the sequential processes in the job shop. The second stage shows the effectiveness of the system in the traditional programming of production. A good solution, with 99% efficiency is found using this technique.
AGNOSCO - Identification of Infected Nodes with artificial Ant Colonies
Hilker, Michael
2008-01-01
If a computer node is infected by a virus, worm or a backdoor, then this is a security risk for the complete network structure where the node is associated. Existing Network Intrusion Detection Systems (NIDS) provide a certain amount of support for the identification of such infected nodes but suffer from the need of plenty of communication and computational power. In this article, we present a novel approach called AGNOSCO to support the identification of infected nodes through the usage of artificial ant colonies. It is shown that AGNOSCO overcomes the communication and computational power problem while identifying infected nodes properly.
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
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
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.
Tasks Scheduling using Ant Colony Optimization
A. P. Shanthi
2012-01-01
Full Text Available Problem statement: Efficient scheduling of the tasks to heterogeneous processors for any application is critical in order to achieve high performance. Finding a feasible schedule for a given task set to a set of heterogeneous processors without exceeding the capacity of the processors, in general, is NP-Hard. Even if there are many conventional approaches available, people have been looking at unconventional approaches for solving this problem. This study uses a paradigm using Ant Colony Optimisation (ACO for arriving at a schedule. Approach: An attempt is made to arrive at a feasible schedule of a task set on heterogeneous processors ensuring load balancing across the processors. The heterogeneity of the processors is modelled by assuming different utilisation times for the same task on different processors. ACO, a bio-inspired computing paradigm, is used for generating the schedule. Results: For a given instance of the problem, ten runs are conducted based on an ACO algorithm and the average wait time of all tasks is computed. Also the average utilisation of each processor is calculated. For the same instance, the two parameters: average wait time of tasks and utilisation of processors are computed using the First Come First Served (FCFS. The results are tabulated and compared and it is found that ACO performs better than the FCFS with respect to the wait time. Although the processor utilisation is more for some processors using FCFS algorithm, it is found that the load is better balanced among the processors in ACO. There is a marginal increase in the time for arriving at a schedule in ACO compared to FCFS algorithm. Conclusion: This approach to the tasks assignment problem using ACO performs better with respect to the two parameters used compared to the FCFS algorithm but the time taken to come up with the schedule using ACO is slightly more than that of FCFS.
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.
Ant colonies prefer infected over uninfected nest sites.
Pontieri, Luigi; Vojvodic, Svjetlana; Graham, Riley; Pedersen, Jes Søe; Linksvayer, Timothy A
2014-01-01
During colony relocation, the selection of a new nest involves exploration and assessment of potential sites followed by colony movement on the basis of a collective decision making process. Hygiene and pathogen load of the potential nest sites are factors worker scouts might evaluate, given the high risk of epidemics in group-living animals. Choosing nest sites free of pathogens is hypothesized to be highly efficient in invasive ants as each of their introduced populations is often an open network of nests exchanging individuals (unicolonial) with frequent relocation into new nest sites and low genetic diversity, likely making these species particularly vulnerable to parasites and diseases. We investigated the nest site preference of the invasive pharaoh ant, Monomorium pharaonis, through binary choice tests between three nest types: nests containing dead nestmates overgrown with sporulating mycelium of the entomopathogenic fungus Metarhizium brunneum (infected nests), nests containing nestmates killed by freezing (uninfected nests), and empty nests. In contrast to the expectation pharaoh ant colonies preferentially (84%) moved into the infected nest when presented with the choice of an infected and an uninfected nest. The ants had an intermediate preference for empty nests. Pharaoh ants display an overall preference for infected nests during colony relocation. While we cannot rule out that the ants are actually manipulated by the pathogen, we propose that this preference might be an adaptive strategy by the host to "immunize" the colony against future exposure to the same pathogenic fungus. PMID:25372856
Ant colonies prefer infected over uninfected nest sites.
Luigi Pontieri
Full Text Available During colony relocation, the selection of a new nest involves exploration and assessment of potential sites followed by colony movement on the basis of a collective decision making process. Hygiene and pathogen load of the potential nest sites are factors worker scouts might evaluate, given the high risk of epidemics in group-living animals. Choosing nest sites free of pathogens is hypothesized to be highly efficient in invasive ants as each of their introduced populations is often an open network of nests exchanging individuals (unicolonial with frequent relocation into new nest sites and low genetic diversity, likely making these species particularly vulnerable to parasites and diseases. We investigated the nest site preference of the invasive pharaoh ant, Monomorium pharaonis, through binary choice tests between three nest types: nests containing dead nestmates overgrown with sporulating mycelium of the entomopathogenic fungus Metarhizium brunneum (infected nests, nests containing nestmates killed by freezing (uninfected nests, and empty nests. In contrast to the expectation pharaoh ant colonies preferentially (84% moved into the infected nest when presented with the choice of an infected and an uninfected nest. The ants had an intermediate preference for empty nests. Pharaoh ants display an overall preference for infected nests during colony relocation. While we cannot rule out that the ants are actually manipulated by the pathogen, we propose that this preference might be an adaptive strategy by the host to "immunize" the colony against future exposure to the same pathogenic fungus.
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.
Collective Intelligence for Optimal Power Flow Solution Using Ant Colony Optimization
Boumediène ALLAOUA
2008-12-01
Full Text Available This paper presents the performance ant collective intelligence efficiency for electrical network. Solutions for Optimal Power Flow (OPF problem of a power system deliberate via an ant colony optimization metaheuristic method. The objective is to minimize the total fuel cost of thermal generating units and also conserve an acceptable system performance in terms of limits on generator real and reactive power outputs, bus voltages, shunt capacitors/reactors, transformers tap-setting and power flow of transmission lines. Simulation results on the IEEE 30-bus electrical network show that the ant colony optimization method converges quickly to the global optimum.
Esquivel E, J. [Universidad Autonoma del Estado de Mexico, Facultad de Ingenieria, Cerro de Coatepec s/n, Ciudad Universitaria, 50110 Toluca, Estado de Mexico (Mexico); Ortiz S, J. J. [ININ, Carretera Mexico-Toluca s/n, 52750 Ocoyoacac, Estado de Mexico (Mexico)], e-mail: jaime.es.jaime@gmail.com
2009-10-15
In this work some results obtained during the development of optimization systems are presented, which are employees for the fuel reload design in a BWR. The systems use the ant colony optimization technique. As first instance, a system is developed that was adapted at travel salesman problem applied for the 32 state capitals of Mexican Republic. The purpose of this implementation is that a similarity exists with the design of fuel reload, since the two problems are of combinatorial optimization with decision variables that have similarity between both. The system was coupled to simulator SIMULATE-3, obtaining good results when being applied to an operation cycle in equilibrium for reactors of nuclear power plant of Laguna Verde. (Author)
Adaptive ant colony sliding mode control for nonlinear coupling systems%非线性耦合系统的自适应蚁群滑模控制
梁利华; 陈国民; 张松涛
2011-01-01
针对一类多子系统之间具有强耦合关系的非线性系统,提出自适应蚁群滑模控制方法.依据等效滑模控制理论,建立滑模算法控制结构图,用于解耦非线性耦合系统；根据自适应蚁群算法建立了多变量蚁群算法的图形表示,对滑模控制中的滑模面斜率进行优化,来削弱“抖振”现象.该方法可以用较小的计算量对耦合系统自然解耦,并利用蚁群算法的并行特性来优化参数.将该方法应用到三轴摇摆台控制系统中,仿真结果表明,与其他算法相比,超调量减小50％,上升时间和调节时间也明显减小,且有较好的控制效果和鲁棒性.%An adaptive Ant Colony Sliding Mode Control (ACSMC) approach is proposed for a class of nonlinear systems with multi-coupling subsystems for the first time. The structure diagram was established to decouple the systems of nonlinear coupling system based on the equivalent theory of sliding mode control. A multivariate graphical representation of the ant colony algorithm was given to optimize the surface slope of sliding mode control to eliminate the chattering phenomenon. Precise natural decoupling with smaller amount of calculation in coupling system is achieved and use the parallel characteristics of ant colony algorithm to optimize the parameters is employed. By applying this method to three-axis swing platform control system the simulation results prove that compared with other algorithms the proposed approach decreases overshoot 50% and it also reduces the rising time and the settling time significantly and has better effectiveness and robustness.
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.
Response Ant Colony Optimization of End Milling Surface Roughness
Ahmed N. Abd Alla; M. M. Noor; K. Kadirgama
2010-01-01
Metal cutting processes are important due to increased consumer demands for quality metal cutting related products (more precise tolerances and better product surface roughness) that has driven the metal cutting industry to continuously improve quality control of metal cutting processes. This paper presents optimum surface roughness by using milling mould aluminium alloys (AA6061-T6) with Response Ant Colony Optimization (RACO). The approach is based on Response Surface Method (RSM) and Ant C...
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...
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.
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.
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.
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.
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.
An Ant Colony Optimization Based Dimension Reduction Method for High-Dimensional Datasets
Ying Li; Gang Wang; Huiling Chen; Lian Shi; Lei Qin
2013-01-01
In this paper,a bionic optimization algorithm based dimension reduction method named Ant Colony Optimization -Selection (ACO-S) is proposed for high-dimensional datasets.Because microarray datasets comprise tens of thousands of features (genes),they are usually used to test the dimension reduction techniques.ACO-S consists of two stages in which two well-known ACO algorithms,namely ant system and ant colony system,are utilized to seek for genes,respectively.In the first stage,a modified ant system is used to filter the nonsignificant genes from high-dimensional space,and a number of promising genes are reserved in the next step.In the second stage,an improved ant colony system is applied to gene selection.In order to enhance the search ability of ACOs,we propose a method for calculating priori available heuristic information and design a fuzzy logic controller to dynamically adjust the number of ants in ant colony system.Furthermore,we devise another fuzzy logic controller to tune the parameter (q0) in ant colony system.We evaluate the performance of ACO-S on five microarray datasets,which have dimensions varying from 7129 to 12000.We also compare the performance of ACO-S with the results obtained from four existing well-known bionic optimization algorithms.The comparison results show that ACO-S has a notable ability to generate a gene subset with the smallest size and salient features while yielding high classification accuracy.The comparative results generated by ACO-S adopting different classifiers are also given.The proposed method is shown to be a promising and effective tool for mining high-dimension data and mobile robot navigation.
Ant Colonies Prefer Infected over Uninfected Nest Sites
Luigi Pontieri; Svjetlana Vojvodic; Riley Graham; Jes Søe Pedersen; Linksvayer, Timothy A
2014-01-01
During colony relocation, the selection of a new nest involves exploration and assessment of potential sites followed by colony movement on the basis of a collective decision making process. Hygiene and pathogen load of the potential nest sites are factors worker scouts might evaluate, given the high risk of epidemics in group-living animals. Choosing nest sites free of pathogens is hypothesized to be highly efficient in invasive ants as each of their introduced populations is often an open n...
Bait distribution among multiple colonies of Pharaoh ants (hymenoptera: Formicidae).
Oi, D H; Vail, K M; Williams, D F
2000-08-01
Pharaoh ant, Monomorium pharaonis (L.), infestations often consist of several colonies located at different nest sites. To achieve control, it is desirable to suppress or eliminate the populations of a majority of these colonies. We compared the trophallactic distribution and efficacy of two ant baits, with different modes of action, among groups of four colonies of Pharaoh ants. Baits contained either the metabolic-inhibiting active ingredient hydramethylnon or the insect growth regulator (IGR) pyriproxyfen. Within 3 wk, the hydramethylnon bait reduced worker and brood populations by at least 80%, and queen reductions ranged between 73 and 100%, when nests were in proximity (within 132 cm) to the bait source. However, these nest sites were reoccupied by ants from other colonies located further from the bait source. The pyriproxyfen bait was distributed more thoroughly to all nest locations with worker populations gradually declining by 73% at all nest sites after 8 wk. Average queen reductions ranged from 31 to 49% for all nest sites throughout the study. Even though some queens survived, brood reductions were rapid in the pyriproxyfen treatment, with reductions of 95% at all locations by week 3. Unlike the metabolic inhibitor, the IGR did not kill adult worker ants quickly, thus, more surviving worker ants were available to distribute the bait to all colonies located at different nest sites. Thus, from a single bait source, the slow-acting bait toxicant provided gradual, but long-term control, whereas the fast-acting bait toxicant provided rapid, localized control for a shorter duration. PMID:10985038
A Graph-based Ant Colony Optimization Approach for Integrated Process Planning and Scheduling
Jinfeng Wang; Xiaoliang Fan; Chaowei Zhang; Shuting Wan
2014-01-01
This paper considers an ant colony optimization algorithm based on AND/OR graph for integrated process planning and scheduling (IPPS). General y, the process planning and scheduling are studied separately. Due to the complexity of manufacturing system, IPPS combining both process planning and scheduling can depict the real situation of a manufacturing system. The IPPS is represented on AND/OR graph consisting of nodes, and undirected and directed arcs. The nodes denote operations of jobs, and undirected/directed arcs denote possible visiting path among the nodes. Ant colony goes through the necessary nodes on the graph from the starting node to the end node to obtain the optimal solution with the objective of minimizing makespan. In order to avoid local convergence and low convergence, some improved strategy is incorporated in the standard ant colony optimiza-tion algorithm. Extensive computational experiments are carried out to study the influence of various parameters on the system performance.
Collective Intelligence for Optimal Power Flow Solution Using Ant Colony Optimization
Boumediène ALLAOUA; Abdellah LAOUFI
2008-01-01
This paper presents the performance ant collective intelligence efficiency for electrical network. Solutions for Optimal Power Flow (OPF) problem of a power system deliberate via an ant colony optimization metaheuristic method. The objective is to minimize the total fuel cost of thermal generating units and also conserve an acceptable system performance in terms of limits on generator real and reactive power outputs, bus voltages, shunt capacitors/reactors, transformers tap-setting and power ...
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.
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...
Ant Colony Approach to Predict Amino Acid Interaction Networks
Gaci, Omar; Balev, Stefan
2009-01-01
In this paper we introduce the notion of protein interaction network. This is a graph whose vertices are the proteins amino acids and whose edges are the interactions between them. We consider the problem of reconstructing protein's interaction network from its amino acid sequence. An ant colony approach is used to solve this problem.
Offenberg, Hans Joachim; Nielsen, Mogens Gissel; Peng, Renkang
2011-01-01
Weaver ants (Oecophylla spp.) are increasingly being used for biocontrol and are targeted for future production of insect protein in ant farms. An efficient production of live ant colonies may facilitate the utilization of these ants but the production of mature colonies is hampered by the long...... and no transplantation. Thus, in ant nurseries the use of multiple queens during nest founding as well as transplantation of pupae from foreign colonies may be utilised to decrease the time it takes to produce a colony ready for implementation....... time it takes for newly established colonies to grow to a suitable size. In this study we followed the growth of newly founded O. smaragdina colonies with 2, 3 or 4 founding queens during 12 days of development, following the transplantation of 0, 30 or 60 pupae from a mature donor colony. Colony...
Ant Colony Optimization Control in VAV System%蚁群优化控制在变风量空调系统中的应用
朱栋华; 李肇蕙; 田晓曦
2012-01-01
目的 寻找一种有效地解决系统惯性大、时间滞后及单独采用PID控制器产生效果不佳的方法,实现温度参量的优化控制.方法 根据变风量空调系统的结构,对变风量系统室内温度控制环节采用蚁群优化算法,经过Matlab软件进行仿真,使室内温度控制环节的上升时间、超调量及调整时间得以优化.结果 控制环节优化前PID阶跃响应的动态指标为:上升时间tr=50 s,调整时间ts=276 s,超调量σ=30％；经过蚁群优化的PID阶跃响应的动态指标为:上升时间tr=112.5 s,调整时间ts=88 s,超调量σ=3.5％,上升时间相对增加后调整时间和超调量大幅度减小,室内温度控制环节趋于稳定.结论 蚁群算法改善了控制环节的超调量、调整时间等问题,提高了系统的自适应性,保证了系统的稳定性和准确性.%Looking for a solution which could effectively solve the system's inertia,time lag and poor results that were produced by VAV system using PID controller alone, achieving temperature parameter optimization control. According to the VAV air conditioning system's structure,the ant colony algorithm is applied to the indoor temperature control link of the system along with using Matlab simulation software simulating in order to rise time, overshoot and adustment time can be optimized about the indoor temperature control link. And then,the dynamic indictors before PID step response of the control link optimization is,the rise time tr = 50 s,adustment time ts = 276 s,overshoot σ =30% ;dynamic indictors of PID step response for the ant colony algorithm,rise time tr = 112. 5 s,adustment time ts = 88 s,overshoot σ = 3. 5% . After the rise time increased relatively, adjustment time and overshoot reduced drastically, and the link of indoor temperature control has been stabilized. Therefore, Ant colony optimization algorithm can be further applied to the control aspect of the whole system,improving the self
Optimal Power Flow of the Algerian Electrical Network using an Ant Colony Optimization Method
Tarek BOUKTIR
2005-06-01
Full Text Available This paper presents solution of optimal power flow (OPF problem of a power system via an Ant Colony Optimization Meta-heuristic method. The objective is to minimize the total fuel cost of thermal generating units and also conserve an acceptable system performance in terms of limits on generator real and reactive power outputs, bus voltages, shunt capacitors/reactors, transformers tap-setting and power flow of transmission lines. Simulation results on the Algerian Electrical Network show that the Ant Colony Optimization method converges quickly to the global optimum.
Emigration of a colony of the leaf-cutting ant Acromyrmex heyeri Forel (Hymenoptera, Formicidae
Mariane Aparecida Nickele
2012-09-01
Full Text Available Emigration of a colony of the leaf-cutting ant Acromyrmex heyeri Forel (Hymenoptera, Formicidae. Colony migration is a poorly studied phenomenon in leaf-cutting ants. Here we report on the emigration of a colony of the leaf-cutting ant A. heyeri in Brazil. The colony emigrated to a new location 47.4 m away from the original nest site, possibly because it had undergone considerable stress due to competitive interactions with a colony of Acromyrmex crassispinus.
Emigration of a colony of the leaf-cutting ant Acromyrmex heyeri Forel (Hymenoptera, Formicidae)
Mariane Aparecida Nickele; Marcio Roberto Pie; Wilson Reis Filho
2012-01-01
Emigration of a colony of the leaf-cutting ant Acromyrmex heyeri Forel (Hymenoptera, Formicidae). Colony migration is a poorly studied phenomenon in leaf-cutting ants. Here we report on the emigration of a colony of the leaf-cutting ant A. heyeri in Brazil. The colony emigrated to a new location 47.4 m away from the original nest site, possibly because it had undergone considerable stress due to competitive interactions with a colony of Acromyrmex crassispinus.
Convergence results for continuous-time dynamics arising in ant colony optimization
Bliman, Pierre-Alexandre; Bhaya, Amit; Kaszkurewicz, Eugenius; Jayadeva
2014-01-01
This paper studies the asymptotic behavior of several continuous-time dynamical systems which are analogs of ant colony optimization algorithms that solve shortest path problems. Local asymptotic stability of the equilibrium corresponding to the shortest path is shown under mild assumptions. A complete study is given for a recently proposed model called EigenAnt: global asymptotic stability is shown, and the speed of convergence is calculated explicitly and shown to be proportional to the dif...
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.
Application of Modified Ant Colony Optimization (MACO for Multicast Routing Problem
Sudip Kumar Sahana
2016-04-01
Full Text Available It is well known that multicast routing is combinatorial problem finds the optimal path between source destination pairs. Traditional approaches solve this problem by establishment of the spanning tree for the network which is mapped as an undirected weighted graph. This paper proposes a Modified Ant Colony Optimization (MACO algorithm which is based on Ant Colony System (ACS with some modification in the configuration of starting movement and in local updation technique to overcome the basic limitations of ACS such as poor initialization and slow convergence rate. It is shown that the proposed Modified Ant Colony Optimization (MACO shows better convergence speed and consumes less time than the conventional ACS to achieve the desired solution.
Blackboard Mechanism Based Ant Colony Theory for Dynamic Deployment of Mobile Sensor Networks
Guang-ping Qi; Ping Song; Ke-jie Li
2008-01-01
A novel bionic swarm intelligence algorithm, called ant colony algorithm based on a blackboard mechanism, is proposed to solve the autonomy and dynamic deployment of mobiles sensor networks effectively. A blackboard mechanism is introduced into the system for making pheromone and completing the algorithm. Every node, which can be looked as an ant, makes one information zone in its memory for communicating with other nodes and leaves pheromone, which is created by ant itself in nature. Then ant colony theory is used to find the optimization scheme for path planning and deployment of mobile Wireless Sensor Network (WSN). We test the algorithm in a dynamic and unconfigurable environment. The results indicate that the algorithm can reduce the power consumption by 13% averagely, enhance the efficiency of path planning and deployment of mobile WSN by 15% averagely.
MAS Equipped with Ant Colony Applied into Dynamic Job Shop Scheduling
Kang, Kai; Zhang, Ren Feng; Yang, Yan Qing
This paper presents a methodology adopting the new structure of MAS(multi-agent system) equipped with ACO(ant colony optimization) algorithm for a better schedule in dynamic job shop. In consideration of the dynamic events in the job shop arriving indefinitely schedules are generated based on tasks with ant colony algorithm. Meanwhile, the global objective is taken into account for the best solution in the actual manufacturing environment. The methodology is tested on a simulated job shop to determine the impact with the new structure.
Sequenzierung mit Ant-Colony-Systemen am Beispiel Querverteil-Wagen
Novoa, Clara Maria; Büchter, Hubert
2005-01-01
Dieser Beitrag zeigt die Anwendung des Ant-Colony-System (ACS) Algorithmus auf die Sequenzierung von Querverteil-Wagen in einem Lager. Wir erweitern den Basisalgorithmus der Ant-Colony-Optimierung (ACO) für die Minimierung der Bearbeitungszeit einer Menge von Fahraufträgen für die Querverteil-Wagen. Im Vergleich zu dem Greedy-Algorithmus ist der ACO-Algorithmus wettbewerbsfähig und schnell. In vielen Lagerverwaltungssystemen werden die Fahraufträge nach dem FIFO-Prinzip (First-in-First-out) a...
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.
Ant Colonies Do Not Trade-Off Reproduction against Maintenance.
Boris H Kramer
Full Text Available The question on how individuals allocate resources into maintenance and reproduction is one of the central questions in life history theory. Yet, resource allocation into maintenance on the organismic level can only be measured indirectly. This is different in a social insect colony, a "superorganism" where workers represent the soma and the queen the germ line of the colony. Here, we investigate whether trade-offs exist between maintenance and reproduction on two levels of biological organization, queens and colonies, by following single-queen colonies of the ant Cardiocondyla obscurior throughout the entire lifespan of the queen. Our results show that maintenance and reproduction are positively correlated on the colony level, and we confirm results of an earlier study that found no trade-off on the individual (queen level. We attribute this unexpected outcome to the existence of a positive feedback loop where investment into maintenance (workers increases the rate of resource acquisition under laboratory conditions. Even though food was provided ad libitum, variation in productivity among the colonies suggests that resources can only be utilized and invested into additional maintenance and reproduction by the colony if enough workers are available. The resulting relationship between per-capita and colony productivity in our study fits well with other studies conducted in the field, where decreasing per-capita productivity and the leveling off of colony productivity have been linked to density dependent effects due to competition among colonies. This suggests that the absence of trade-offs in our laboratory study might also be prevalent under natural conditions, leading to a positive association of maintenance, (= growth and reproduction. In this respect, insect colonies resemble indeterminate growing organisms.
Kochem Vendramin, Ana Cristina; Munaretto, Anelise; Regattieri Delgado, Myriam; Carneiro Viana, Aline
2011-01-01
This paper presents a new prediction-based forwarding protocol for the complex and dynamic Delay Tolerant Networks (DTN). The proposed protocol is called GrAnt (Greedy Ant) as it uses a greedy transition rule for the Ant Colony Optimization (ACO) metaheuristic to select the most promising forwarder nodes or to provide the exploitation of good paths previously found. The main motivation for the use of ACO is to take advantage of its population-based search and of the rapid adaptation of its le...
Response Ant Colony Optimization of End Milling Surface Roughness
Ahmed N. Abd Alla
2010-03-01
Full Text Available Metal cutting processes are important due to increased consumer demands for quality metal cutting related products (more precise tolerances and better product surface roughness that has driven the metal cutting industry to continuously improve quality control of metal cutting processes. This paper presents optimum surface roughness by using milling mould aluminium alloys (AA6061-T6 with Response Ant Colony Optimization (RACO. The approach is based on Response Surface Method (RSM and Ant Colony Optimization (ACO. The main objectives to find the optimized parameters and the most dominant variables (cutting speed, feedrate, axial depth and radial depth. The first order model indicates that the feedrate is the most significant factor affecting surface roughness.
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
Implementation of Travelling Salesman Problem Using ant Colony Optimization
Gaurav Singh,
2014-04-01
Full Text Available Within the Artificial Intelligence community, there is great need for fast and accurate traversal algorithms, specifically those that find a path from a start to goal with minimum cost. Cost can be distance, time, money, energy, etc. Travelling salesman problem (TSP is a combinatorial optimization problem. TSP is the most intensively studied problem in the area of optimization. Ant colony optimization (ACO is a population-based metaheuristic that can be used to find approximate solutions to difficult optimization problems. There have been many efforts in the past to provide time efficient solutions for the problem, both exact and approximate. This paper demonstrates the implementation of TSP using ant colony optimization(ACO.The solution to this problem enjoys wide applicability in a variety of practical fields.TSP in its purest form has several applications such as planning, logistics, and manufacture of microchips, military and traffic.
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
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.
Web Mining using Artificial Ant Colonies: A Survey
2014-01-01
Web mining has been very crucial to any organization as it provides useful insights to business patterns. It helps the company to understand its customers better. As the web is growing in pace, so is its importance and hence it becomes all the more necessary to find useful patterns. Here in this paper, web mining using ant colony optimization has been reviewed with some of its experimental results.
Predicting Multicomponent Protein Assemblies Using an Ant Colony Approach
Venkatraman, Vishwesh; Ritchie, David
2011-01-01
National audience Biological processes are often governed by functional modules of large protein assemblies such as the proteasomes and the nuclear pore complex, for example. However, atomic structures can be determined experimentally only for a small fraction of these multicomponent assemblies. In this article, we present an ant colony optimization based approach to predict the structure of large multicomponent complexes. Starting with pair-wise docking predictions, a multigraph consistin...
Using ant colonies for solve the multiprocessor task graph scheduling
Bremang, Appah
2006-01-01
The problem of scheduling a parallel program presented by a weighted directed acyclic graph (DAG) to the set of homogeneous processors for minimizing the completion time of the program has been extensively studied as academic optimization problem which occurs in optimizing the execution time of parallel algorithm with parallel computer.In this paper, we propose an application of the Ant Colony Optimization (ACO) to a multiprocessor scheduling problem (MPSP). In the MPSP, no preemption is allo...
Apriori and Ant Colony Optimization of Association Rules
Anshuman Singh Sadh; Nitin Shukla
2013-01-01
Association Rule mining is one of the important and most popular data mining technique. Association rule mining can be efficiently used in any decision making processor decision based rule generation. In this paper we present an efficient mining based optimization techniques for rule generation. By using apriori algorithm we find the positive and negative association rules. Then we apply ant colony optimization algorithm (ACO) for optimizing the association rules. Our results show the effecti...
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...
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.
Ant colony Optimization: A Solution of Load balancing in Cloud
Ratan Mishra
2012-05-01
Full Text Available As the cloud computing is a new style of computing over internet. It has many advantages along with some crucial issues to be resolved in order to improve reliability of cloud environment. These issues are related with the load management, fault tolerance and different security issues in cloud environment. In this paper the main concern is load balancing in cloud computing. The load can be CPU load, memory capacity, delay or network load. Load balancing is the process of distributing the load among various nodes of adistributed system to improve both resource utilization and job response time while also avoiding a situation where some of the nodes are heavily loaded while other nodes are idle or doing very little work. Load balancing ensures that all the processor in the system or every node in the network does approximately the equal amount of work at any instant of time. Many methods to resolve this problem has been came into existence like Particle Swarm Optimization, hash method, genetic algorithms and severalscheduling based algorithms are there. In this paper we are proposing a method based on Ant Colony optimization to resolve the problem of load balancing in cloud environment.
The Role of Non-Foraging Nests in Polydomous Wood Ant Colonies
Samuel Ellis; Robinson, Elva J. H.
2015-01-01
A colony of red wood ants can inhabit more than one spatially separated nest, in a strategy called polydomy. Some nests within these polydomous colonies have no foraging trails to aphid colonies in the canopy. In this study we identify and investigate the possible roles of non-foraging nests in polydomous colonies of the wood ant Formica lugubris. To investigate the role of non-foraging nests we: (i) monitored colonies for three years; (ii) observed the resources being transported between non...
Antonio Carlos Da-Silva
2012-07-01
Leafcutter ants (Atta sexdens rubropilosa (Forel 1908 have an elaborate social organization, complete with caste divisions. Activities carried out by specialist groups contribute to the overall success and survival of the colony when it is confronted with environmental challenges such as dehydration. Ants detect variations in humidity inside the nest and react by activating several types of behavior that enhance water uptake and decrease water loss, but it is not clear whether or not a single caste collects water regardless of the cost of bringing this resource back to the colony. Accordingly, we investigated water collection activities in three colonies of Atta sexdens rubropilosa experimentally exposed to water stress. Specifically, we analyzed whether or not the same ant caste foraged for water, regardless of the absolute energetic cost (distance of transporting this resource back to the colony. Our experimental design offered water sources at 0 m, 1 m and 10 m from the nest. We studied the body size of ants near the water sources from the initial offer of water (time = 0 to 120 min, and tested for specialization. We observed a reduction in the average size and variance of ants that corroborated the specialization hypothesis. Although the temporal course of specialization changed with distance, the final outcome was similar among distances. Thus, we conclude that, for this species, a specialist (our use of the word “specialist” does not mean exclusive task force is responsible for collecting water, regardless of the cost of transporting water back to the colony.
Yi-Huei Chen; Robinson, Elva J. H.
2014-01-01
Climate change may affect ecosystems and biodiversity through the impacts of rising temperature on species' body size. In terms of physiology and genetics, the colony is the unit of selection for ants so colony size can be considered the body size of a colony. For polydomous ant species, a colony is spread across several nests. This study aims to clarify how climate change may influence an ecologically significant ant species group by investigating thermal effects on wood ant colony size. The...
Generating and prioritizing optimal paths using ant colony optimization
Mukesh Mann
2015-03-01
Full Text Available The assurance of software reliability partially depends on testing. Numbers of approaches for software testing are available with their proclaimed advantages and limitations, but accessibility of any one of them is a subject dependent. Time is a critical factor in deciding cost of any project. A deep insight has shown that executing test cases are time consuming and tedious activity. Thus stress has been given to develop algorithms which can suggest better pathways for testing. One such algorithm called Path Prioritization -Ant Colony Optimization (PP-ACO has been suggested in this paper which is inspired by real Ant's foraging behavior to generate optimal paths sequence of a decision to decision (DD path of a graph. The algorithm does full path coverage and suggests the best optimal sequences of path in path testing and prioritizes them according to path strength.
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...
McGlynn, Terry
2010-01-01
Serial monodomy is the nesting behavior in which a colony of animals maintains multiple nests for its exclusive use, occupying one nest at a time. Among serially monodomous ants, the availability of unoccupied nests reduces the probability and costs of army ant attacks. It has been proposed that nest odors mediate serial monodomy in the gypsy ant, Aphaenogaster araneoides Emery (Hymenoptera: Formicidae), and that colonies avoid returning to previously occupied nests that harbor colony odors. ...
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...
Mobile Anonymous Trust Based Routing Using Ant Colony Optimization
R. Kalpana
2012-01-01
Full Text Available Problem statement: Ad hoc networks are susceptible to malicious attacks through denial of services, traffic analysis and spoofing. The security of the ad hoc routing protocol depends upon encryption, authentication, anonymity and trust factors. End-to-end security of data is provided by encryption and authentication, topology information of the nodes can be obtained by studying traffic and routing data. This security problem of ad hoc network is addressed by the use of anonymity mechanisms and trust levels. Identification information like traffic flow, network topology, paths from malicious attackers is hidden in anonymous networks. Similarly, trust plays a very important role in the intermediate node selection in ad hoc networks. Trust is essential as selfish and malicious nodes not only pose a security issue but also decreases the Quality of Service. Approach: In this study, a routing to address anonymous routing with a trust which improves the overall security of the ad hoc network was proposed. A new approach for an on demand ad-hoc routing algorithm, which was based on swarm intelligence. Ant colony algorithms were a subset of swarm intelligence and considered the ability of simple ants to solve complex problems by cooperation. The interesting point was, that the ants do not need any direct communication for the solution process, instead they communicate by stigmergy. The notion of stigmergy means the indirect communication of individuals through modifying their environment. Several algorithms which were based on ant colony problems were introduced in recent years to solve different problems, e.g., optimization problems. Results and Conclusion: It is observed that the overall security in the network improves when the trust factor is considered. It is seen that non performing nodes are not considered due to the proposed ACO technique.
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...
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...
Applying Data Clustering Feature to Speed Up Ant Colony Optimization
Chao-Yang Pang; Ben-Qiong Hu; Jie Zhang; Wei Hu; Zheng-Chao Shan
2013-01-01
Ant colony optimization (ACO) is often used to solve optimization problems, such as traveling salesman problem (TSP). When it is applied to TSP, its runtime is proportional to the squared size of problem $N$ so as to look less efficient. The following statistical feature is observed during the authors’ long-term gene data analysis using ACO: when the data size $N$ becomes big, local clustering appears frequently. That is, some data cluster tightly in a small area and form a class, and the cor...
An ant colony optimization method for generalized TSP problem
Jinhui Yang; Xiaohu Shi; Maurizio Marchese; Yanchun Liang
2008-01-01
Focused on a variation of the euclidean traveling salesman problem (TSP), namely, the generalized traveling salesman problem (GTSP), this paper extends the ant colony optimization method from TSP to this field. By considering the group influence, an improved method is further improved. To avoid locking into local minima, a mutation process and a local searching technique are also introduced into this method. Numerical results show that the proposed method can deal with the GTSP problems fairly well, and the developed mutation process and local search technique are effective.
Optimal Power Flow of the Algerian Electrical Network using an Ant Colony Optimization Method
Tarek BOUKTIR; Linda SLIMANI
2005-01-01
This paper presents solution of optimal power flow (OPF) problem of a power system via an Ant Colony Optimization Meta-heuristic method. The objective is to minimize the total fuel cost of thermal generating units and also conserve an acceptable system performance in terms of limits on generator real and reactive power outputs, bus voltages, shunt capacitors/reactors, transformers tap-setting and power flow of transmission lines. Simulation results on the Algerian Electrical Network show that...
Statistical Performance Analysis of an Ant-Colony Optimisation Application in S-Net
MacKenzie, K; Hölzenspies, P.K.F.; Hammond, K.; Kirner, R.; Nguyen, V.T.N.; Boekhorst, te, J.; Grelck, C.; Poss, R.; Verstraaten, M.
2013-01-01
We consider an ant-colony optimsation problem implemented on a multicore system as a collection of asynchronous streamprocessing components under the control of the S-NET coordination language. Statistical analysis and visualisation techniques are used to study the behaviour of the application, and this enables us to discover and correct problems in both the application program and the run-time system underlying S-NET.
Binary-Coding-Based Ant Colony Optimization and Its Convergence
Tian-Ming Bu; Song-Nian Yu; Hui-Wei Guan
2004-01-01
Ant colony optimization(ACO for short)is a meta-heuristics for hard combinatorial optimization problems.It is a population-based approach that uses exploitation of positive feedback as well as greedy search.In this paper,genetic algorithm's(GA for short)ideas are introduced into ACO to present a new binary-coding based ant colony optimization.Compared with the typical ACO,the algorithm is intended to replace the problem's parameter-space with coding-space,which links ACO with GA so that the fruits of GA can be applied to ACO directly.Furthermore,it can not only solve general combinatorial optimization problems,but also other problems such as function optimization.Based on the algorithm,it is proved that if the pheromone remainder factor ρ is under the condition of ρ≥ 1,the algorithm can promise to converge at the optimal,whereas if 0 ＜ρ＜ 1,it does not.
Operations planning for agricultural harvesters using ant colony optimization
A. Bakhtiari
2013-07-01
Full Text Available An approach based on ant colony optimization for the generation for optimal field coverage plans for the harvesting operations using the optimal track sequence principle B-patterns was presented. The case where the harvester unloads to a stationary facility located out of the field area, or in the field boundary, was examined. In this operation type there are capacity constraints to the load that a primary unit, or a harvester in this specific case, can carry and consequently, it is not able to complete the task of harvesting a field area and therefore it has to leave the field area, to unload, and return to continue the task one or more times. Results from comparing the optimal plans with conventional plans generated by operators show reductions in the in-field nonworking distance in the range of 19.3-42.1% while the savings in the total non-working distance were in the range of 18-43.8%. These savings provide a high potential for the implementation of the ant colony optimization approach for the case of harvesting operations that are not supported by transport carts for the out-of-the-field removal of the crops, a practice case that is normally followed in developing countries, due to lack of resources.
Ant colony based routing in wireless sensor networks
Varnika Bains
2012-09-01
Full Text Available Wireless Sensor Networks comprises of small sensor nodes communicating with each other in a network topology which keeps on varying. The wireless sensor nodes also known as motes have limited energy resources along with constraints on its computational and storage capabilities. Due to these restrictions coupled with its dynamic topology, routing in Wireless Sensor Networks (WSN is a very challenging task. Routing protocols for WSN’s have to ensure reliable multi-hop communication under these conditions. A wide range of adhoc routing algorithms are available for WSN’s. In this paper an adaptation of Ant Colony Optimization (ACO technique is demonstrated for network routing. This approach belongs to the class of routing algorithms inspired by the behavior of the ant colonies in locating and storing food. The effectiveness of the heuristic algorithm is supported by the performance evaluations. PROWLER, a MATLAB based probabilistic wireless network simulator is used for the calculations. This simulator simulates the transmissions including collisions in ad-hoc radio networks, and the operation of the MAC-layer. The performance metrics are evaluated on RMASE, an application built in PROWLER.
Ant colony optimization as a method for strategic genotype sampling.
Spangler, M L; Robbins, K R; Bertrand, J K; Macneil, M; Rekaya, R
2009-06-01
A simulation study was carried out to develop an alternative method of selecting animals to be genotyped. Simulated pedigrees included 5000 animals, each assigned genotypes for a bi-allelic single nucleotide polymorphism (SNP) based on assumed allelic frequencies of 0.7/0.3 and 0.5/0.5. In addition to simulated pedigrees, two beef cattle pedigrees, one from field data and the other from a research population, were used to test selected methods using simulated genotypes. The proposed method of ant colony optimization (ACO) was evaluated based on the number of alleles correctly assigned to ungenotyped animals (AK(P)), the probability of assigning true alleles (AK(G)) and the probability of correctly assigning genotypes (APTG). The proposed animal selection method of ant colony optimization was compared to selection using the diagonal elements of the inverse of the relationship matrix (A(-1)). Comparisons of these two methods showed that ACO yielded an increase in AK(P) ranging from 4.98% to 5.16% and an increase in APTG from 1.6% to 1.8% using simulated pedigrees. Gains in field data and research pedigrees were slightly lower. These results suggest that ACO can provide a better genotyping strategy, when compared to A(-1), with different pedigree sizes and structures. PMID:19220227
A nuclear reactor core fuel reload optimization using Artificial-Ant-Colony Connective Networks
A Pressurized Water Reactor core must be reloaded every time the fuel burnup reaches a level when it is not possible to sustain nominal power operation. The nuclear core fuel reload optimization consists in finding a burned-up and fresh-fuel-assembly pattern that maximizes the number of full operational days. This problem is NP-hard, meaning that complexity grows exponentially with the number of fuel assemblies in the core. Besides that, the problem is non-linear and its search space is highly discontinual and multimodal. In this work a parallel computational system based on Ant Colony System (ACS) called Artificial-Ant-Colony Networks is introduced to solve the nuclear reactor core fuel reload optimization problem. ACS is a system based on artificial agents that uses the reinforcement learning technique and was originally developed to solve the Traveling Salesman Problem, which is conceptually similar to the nuclear fuel reload problem. (author)
Using Ant Colony Optimization for Routing in VLSI Chips
Arora, Tamanna; Moses, Melanie
2009-04-01
Rapid advances in VLSI technology have increased the number of transistors that fit on a single chip to about two billion. A frequent problem in the design of such high performance and high density VLSI layouts is that of routing wires that connect such large numbers of components. Most wire-routing problems are computationally hard. The quality of any routing algorithm is judged by the extent to which it satisfies routing constraints and design objectives. Some of the broader design objectives include minimizing total routed wire length, and minimizing total capacitance induced in the chip, both of which serve to minimize power consumed by the chip. Ant Colony Optimization algorithms (ACO) provide a multi-agent framework for combinatorial optimization by combining memory, stochastic decision and strategies of collective and distributed learning by ant-like agents. This paper applies ACO to the NP-hard problem of finding optimal routes for interconnect routing on VLSI chips. The constraints on interconnect routing are used by ants as heuristics which guide their search process. We found that ACO algorithms were able to successfully incorporate multiple constraints and route interconnects on suite of benchmark chips. On an average, the algorithm routed with total wire length 5.5% less than other established routing algorithms.
Routing in Ad Hoc Network Using Ant Colony Optimization
Khanpara, Pimal; Valiveti, Sharada; Kotecha, K.
The ad hoc networks have dynamic topology and are infrastructure less. So it is required to implement a new network protocol for providing efficient end to end communication based on TCP/IP structure. There is a need to re-define or modify the functions of each layer of TCP/IP model to provide end to end communication between nodes. The mobility of the nodes and the limited resources are the main reason for this change. The main challenge in ad hoc networks is routing. Due to the mobility of the nodes in the ad hoc networks, routing becomes very difficult. Ant based algorithms are suitable for routing in ad hoc networks due to its dynamic nature and adaptive behavior. There are number of routing algorithms based on the concept of ant colony optimizations. It is quite difficult to determine the best ant based algorithm for routing as these algorithms perform differently under various circumstances such as the traffic distribution and network topology. In this paper, the overview of such routing algorithms is given.
Esquivel E, J.; Ordonez A, A. [Universidad Autonoma del Estado de Mexico, Facultad de Ingenieria, Cerro de Coatepec s/n, Toluca, Estado de Mexico (Mexico); Ortiz S, J. J. [Departamento de Sistemas Nucleares, ININ, Carretera Mexico-Toluca s/n, Ocoyoacac 52750, Estado de Mexico (Mexico)
2008-07-01
In this paper we present some results obtained during the development of optimization systems that can be used to design refueling and patterns of control rods in a BWR. These systems use ant colonies and Greedy search. The first phase of this project is to be familiar with these optimization techniques applied to the problem of travel salesman problem (TSP). The utility of TSP study is that, like the refueling design and pattern design of control rods are problems of combinative optimization. Even, the similarity with the problem of the refueling design is remarkable. It is presented some results for the TSP with the 32 state capitals of Mexico country. (Author)
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...
Ouagoussounon, Issa; Sinzogan, Antonio; Offenberg, Joachim;
2013-01-01
Oecophylla ants are currently used for biological control in fruit plantations in Australia, Asia and Africa and for protein production in Asia. To further improve the technology and implement it on a large scale, effective and fast production of live colonies is desirable. Early colony development...... capita brood production by the resident queen, triggered by the adopted pupae. Thus pupae transplantation may be used to shorten the time it takes to produce weaver ant colonies in ant nurseries, and may in this way facilitate the implementation of weaver ant biocontrol in West Africa....
A graph-based ant colony optimization approach for process planning.
Wang, JinFeng; Fan, XiaoLiang; Wan, Shuting
2014-01-01
The complex process planning problem is modeled as a combinatorial optimization problem with constraints in this paper. An ant colony optimization (ACO) approach has been developed to deal with process planning problem by simultaneously considering activities such as sequencing operations, selecting manufacturing resources, and determining setup plans to achieve the optimal process plan. A weighted directed graph is conducted to describe the operations, precedence constraints between operations, and the possible visited path between operation nodes. A representation of process plan is described based on the weighted directed graph. Ant colony goes through the necessary nodes on the graph to achieve the optimal solution with the objective of minimizing total production costs (TPC). Two cases have been carried out to study the influence of various parameters of ACO on the system performance. Extensive comparative experiments have been conducted to demonstrate the feasibility and efficiency of the proposed approach. PMID:24995355
Automated Software Testing Using Metahurestic Technique Based on An Ant Colony Optimization
Srivastava, Praveen Ranjan
2011-01-01
Software testing is an important and valuable part of the software development life cycle. Due to time, cost and other circumstances, exhaustive testing is not feasible that's why there is a need to automate the software testing process. Testing effectiveness can be achieved by the State Transition Testing (STT) which is commonly used in real time, embedded and web-based type of software systems. Aim of the current paper is to present an algorithm by applying an ant colony optimization technique, for generation of optimal and minimal test sequences for behavior specification of software. Present paper approach generates test sequence in order to obtain the complete software coverage. This paper also discusses the comparison between two metaheuristic techniques (Genetic Algorithm and Ant Colony optimization) for transition based testing
Ortiz S, J.J. [Depto. de Sistemas Nucleares, ININ, 52045 Ocoyoacac, Estado de Mexico (Mexico); Requena R, I. [Universidad de Granada, 18071 Granada (Spain)]. e-mail: jjortiz@nuclear.inin.mx
2003-07-01
In this work the AZCATL-PBC system based on a technique of ants colonies for the search of control rods patterns of those reactors of the Nuclear Power station of Laguna Verde (CNLV) is presented. The technique was applied to a transition cycle and one of balance. For both cycles they were compared the k{sub ef} values obtained with a Haling calculation and the control rods pattern proposed by AZCATL-PBC for a burnt one fixed. It was found that the methodology is able to extend the length of the cycle with respect to the Haling prediction, maintaining sure to the reactor. (Author)
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...
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.
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.
Power Efficient Resource Allocation for Clouds Using Ant Colony Framework
Chimakurthi, Lskrao
2011-01-01
Cloud computing is one of the rapidly improving technologies. It provides scalable resources needed for the ap- plications hosted on it. As cloud-based services become more dynamic, resource provisioning becomes more challenging. The QoS constrained resource allocation problem is considered in this paper, in which customers are willing to host their applications on the provider's cloud with a given SLA requirements for performance such as throughput and response time. Since, the data centers hosting the applications consume huge amounts of energy and cause huge operational costs, solutions that reduce energy consumption as well as operational costs are gaining importance. In this work, we propose an energy efficient mechanism that allocates the cloud resources to the applications without violating the given service level agreements(SLA) using Ant colony framework.
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.
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.
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.
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)
DETECTION OF MASSES IN MAMMOGRAM IMAGES USING ANT COLONY OPTIMIZATION
Varsha Patankar
2014-04-01
Full Text Available This paper proposes the advances in edge detection techniques, which is used for the mammogram images for cancer diagnosis. It compares the evaluation of edge detection with the proposed method ant colony optimization. The study shows that the edge detection technique is applied on the mammogram images because it will clearly identify the masses in mammogram images. This will help to identify the type of cancer at the early stage. ACO edge detector is best in detecting the edges when compared to the other edge detectors. The quality of various edge detectors is calculated based on the parameters such as Peak signal to noise ratio (PSNR and Mean square error (MSE.
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
COLONY INSULARITY THROUGH QUEEN CONTROL ON WORKER SOCIAL MOTIVATION IN ANTS
We investigated the relative contribution of the queen and workers to colony nestmate recognition cues and on colony territoriality in the ant Camponotus fellah. Workers were either individually isolated, preventing contact with both queen and workers (Colony Deprived, CD), kept in queenless groups,...
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.
Ji, Junzhong; Song, Xiangjing; Liu, Chunnian; Zhang, Xiuzhen
2013-08-01
Community structure detection in complex networks has been intensively investigated in recent years. In this paper, we propose an adaptive approach based on ant colony clustering to discover communities in a complex network. The focus of the method is the clustering process of an ant colony in a virtual grid, where each ant represents a node in the complex network. During the ant colony search, the method uses a new fitness function to percept local environment and employs a pheromone diffusion model as a global information feedback mechanism to realize information exchange among ants. A significant advantage of our method is that the locations in the grid environment and the connections of the complex network structure are simultaneously taken into account in ants moving. Experimental results on computer-generated and real-world networks show the capability of our method to successfully detect community structures.
This paper presents Turkey's net electricity energy generation and demand based on economic indicators. Forecasting model for electricity energy generation and demand is first proposed by the ant colony optimization (ACO) approach. It is multi-agent system in which the behavior of each ant is inspired by the foraging behavior of real ants to solve optimization problem. Ant colony optimization electricity energy estimation (ACOEEE) model is developed using population, gross domestic product (GDP), import and export. All equations proposed here are linear electricity energy generation and demand (linearACOEEGE and linear ACOEEDE) and quadratic energy generation and demand (quadraticACOEEGE and quadratic ACOEEDE). Quadratic models for both generation and demand provided better fit solution due to the fluctuations of the economic indicators. The ACOEEGE and ACOEEDE models indicate Turkey's net electricity energy generation and demand until 2025 according to three scenarios. (author)
Toksari, M. Duran [Engineering Faculty, Industrial Engineering Department, Erciyes University, 38039 Kayseri (Turkey)
2009-03-15
This paper presents Turkey's net electricity energy generation and demand based on economic indicators. Forecasting model for electricity energy generation and demand is first proposed by the ant colony optimization (ACO) approach. It is multi-agent system in which the behavior of each ant is inspired by the foraging behavior of real ants to solve optimization problem. Ant colony optimization electricity energy estimation (ACOEEE) model is developed using population, gross domestic product (GDP), import and export. All equations proposed here are linear electricity energy generation and demand (linear{sub A}COEEGE and linear ACOEEDE) and quadratic energy generation and demand (quadratic{sub A}COEEGE and quadratic ACOEEDE). Quadratic models for both generation and demand provided better fit solution due to the fluctuations of the economic indicators. The ACOEEGE and ACOEEDE models indicate Turkey's net electricity energy generation and demand until 2025 according to three scenarios. (author)
Toksari, M. Duran [Engineering Faculty, Industrial Engineering Department, Erciyes University, 38039 Kayseri (Turkey)], E-mail: dtoksari@erciyes.edu.tr
2009-03-15
This paper presents Turkey's net electricity energy generation and demand based on economic indicators. Forecasting model for electricity energy generation and demand is first proposed by the ant colony optimization (ACO) approach. It is multi-agent system in which the behavior of each ant is inspired by the foraging behavior of real ants to solve optimization problem. Ant colony optimization electricity energy estimation (ACOEEE) model is developed using population, gross domestic product (GDP), import and export. All equations proposed here are linear electricity energy generation and demand (linear{sub A}COEEGE and linear ACOEEDE) and quadratic energy generation and demand (quadratic{sub A}COEEGE and quadratic ACOEEDE). Quadratic models for both generation and demand provided better fit solution due to the fluctuations of the economic indicators. The ACOEEGE and ACOEEDE models indicate Turkey's net electricity energy generation and demand until 2025 according to three scenarios.
This paper presents Turkey's net electricity energy generation and demand based on economic indicators. Forecasting model for electricity energy generation and demand is first proposed by the ant colony optimization (ACO) approach. It is multi-agent system in which the behavior of each ant is inspired by the foraging behavior of real ants to solve optimization problem. Ant colony optimization electricity energy estimation (ACOEEE) model is developed using population, gross domestic product (GDP), import and export. All equations proposed here are linear electricity energy generation and demand (linearACOEEGE and linear ACOEEDE) and quadratic energy generation and demand (quadraticACOEEGE and quadratic ACOEEDE). Quadratic models for both generation and demand provided better fit solution due to the fluctuations of the economic indicators. The ACOEEGE and ACOEEDE models indicate Turkey's net electricity energy generation and demand until 2025 according to three scenarios
A Stochastic Inversion Method for Potential Field Data: Ant Colony Optimization
Liu, Shuang; Hu, Xiangyun; Liu, Tianyou
2014-07-01
Simulating natural ants' foraging behavior, the ant colony optimization (ACO) algorithm performs excellently in combinational optimization problems, for example the traveling salesman problem and the quadratic assignment problem. However, the ACO is seldom used to inverted for gravitational and magnetic data. On the basis of the continuous and multi-dimensional objective function for potential field data optimization inversion, we present the node partition strategy ACO (NP-ACO) algorithm for inversion of model variables of fixed shape and recovery of physical property distributions of complicated shape models. We divide the continuous variables into discrete nodes and ants directionally tour the nodes by use of transition probabilities. We update the pheromone trails by use of Gaussian mapping between the objective function value and the quantity of pheromone. It can analyze the search results in real time and promote the rate of convergence and precision of inversion. Traditional mapping, including the ant-cycle system, weaken the differences between ant individuals and lead to premature convergence. We tested our method by use of synthetic data and real data from scenarios involving gravity and magnetic anomalies. The inverted model variables and recovered physical property distributions were in good agreement with the true values. The ACO algorithm for binary representation imaging and full imaging can recover sharper physical property distributions than traditional linear inversion methods. The ACO has good optimization capability and some excellent characteristics, for example robustness, parallel implementation, and portability, compared with other stochastic metaheuristics.
The 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.
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.
A Novel Polymorphic Ant Colony -Based Clustering Mechanism for Wireless Sensor Networks
Min Xiang
2012-10-01
Full Text Available In wireless sensor networks, sensor nodes are extremely power constrained, so energy efficient clustering mechanism is mainly considered in the network topology management. A new clustering mechanism based on the polymorphic ant colony (PAC is designed for dynamically controlling the networks clustering structure. According to different functions, the nodes of the networks are respectively defined as the queen ant, the scout ant and worker ant. Based on the calculated cost function and real-time pheromone, the queen ant restructures an optimum clustering structure. Furthermore, the worker ants and the scout ants can send or receive sensing data with optional communication path based on their pheromones. With the mechanism, the energy consumption in inter-cluster and intra-cluster communication for the worker ants and scout ants can be reduced. The simulation results demonstrate that the proposed mechanism can effectively remodel the clustering structure and improve the energy efficiency of the networks.
A nuclear reactor core fuel reload optimization using artificial ant colony connective networks
Lima, Alan M.M. de [Universidade Federal do Rio de Janeiro, PEN/COPPE - UFRJ, Ilha do Fundao s/n, CEP 21945-970 Rio de Janeiro (Brazil)], E-mail: alanmmlima@yahoo.com.br; Schirru, Roberto [Universidade Federal do Rio de Janeiro, PEN/COPPE - UFRJ, Ilha do Fundao s/n, CEP 21945-970 Rio de Janeiro (Brazil)], E-mail: schirru@lmp.ufrj.br; Carvalho da Silva, Fernando [Universidade Federal do Rio de Janeiro, PEN/COPPE - UFRJ, Ilha do Fundao s/n, CEP 21945-970 Rio de Janeiro (Brazil)], E-mail: fernando@con.ufrj.br; Medeiros, Jose Antonio Carlos Canedo [Universidade Federal do Rio de Janeiro, PEN/COPPE - UFRJ, Ilha do Fundao s/n, CEP 21945-970 Rio de Janeiro (Brazil)], E-mail: canedo@lmp.ufrj.br
2008-09-15
The core of a nuclear Pressurized Water Reactor (PWR) may be reloaded every time the fuel burn-up is such that it is not more possible to maintain the reactor operating at nominal power. The nuclear core fuel reload optimization problem consists in finding a pattern of burned-up and fresh-fuel assemblies that maximize the number of full operational days. This is an NP-Hard problem, meaning that complexity grows exponentially with the number of fuel assemblies in the core. Moreover, the problem is non-linear and its search space is highly discontinuous and multi-modal. Ant Colony System (ACS) is an optimization algorithm based on artificial ants that uses the reinforcement learning technique. The ACS was originally developed to solve the Traveling Salesman Problem (TSP), which is conceptually similar to the nuclear core fuel reload problem. In this work a parallel computational system based on the ACS, called Artificial Ant Colony Networks is introduced to solve the core fuel reload optimization problem.
A nuclear reactor core fuel reload optimization using artificial ant colony connective networks
The core of a nuclear Pressurized Water Reactor (PWR) may be reloaded every time the fuel burn-up is such that it is not more possible to maintain the reactor operating at nominal power. The nuclear core fuel reload optimization problem consists in finding a pattern of burned-up and fresh-fuel assemblies that maximize the number of full operational days. This is an NP-Hard problem, meaning that complexity grows exponentially with the number of fuel assemblies in the core. Moreover, the problem is non-linear and its search space is highly discontinuous and multi-modal. Ant Colony System (ACS) is an optimization algorithm based on artificial ants that uses the reinforcement learning technique. The ACS was originally developed to solve the Traveling Salesman Problem (TSP), which is conceptually similar to the nuclear core fuel reload problem. In this work a parallel computational system based on the ACS, called Artificial Ant Colony Networks is introduced to solve the core fuel reload optimization problem
RESEARCH ON SOLVING TRAVELLING SALESMAN PROBLEM USING RANK BASED ANT SYSTEM ON GPU
Khushbu Khatri; Vinit Kumar Gupta
2015-01-01
Ant Colony Optimization (ACO) is meta-heuristic algorithm inspired from nature to solve many combinatorial optimization problems such as Travelling Salesman Problem (TSP). There are many versions of ACO used to solve TSP like, Ant System, Elitist Ant System, Max-Min Ant System, Rank based Ant System algorithm. For improved performance, these methods can be implemented in parallel architecture like GPU, CUDA architecture. Graphics Processing Unit (GPU) provides highly parallel and fully progra...
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.
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
Enhanced ant colony optimization for inventory routing problem
Wong, Lily; Moin, Noor Hasnah
2015-10-01
The inventory routing problem (IRP) integrates and coordinates two important components of supply chain management which are transportation and inventory management. We consider a one-to-many IRP network for a finite planning horizon. The demand for each product is deterministic and time varying as well as a fleet of capacitated homogeneous vehicles, housed at a depot/warehouse, delivers the products from the warehouse to meet the demand specified by the customers in each period. The inventory holding cost is product specific and is incurred at the customer sites. The objective is to determine the amount of inventory and to construct a delivery routing that minimizes both the total transportation and inventory holding cost while ensuring each customer's demand is met over the planning horizon. The problem is formulated as a mixed integer programming problem and is solved using CPLEX 12.4 to get the lower and upper bound (best integer) for each instance considered. We propose an enhanced ant colony optimization (ACO) to solve the problem and the built route is improved by using local search. The computational experiments demonstrating the effectiveness of our approach is presented.
Applying Data Clustering Feature to Speed Up Ant Colony Optimization
Chao-Yang Pang
2014-01-01
Full Text Available Ant colony optimization (ACO is often used to solve optimization problems, such as traveling salesman problem (TSP. When it is applied to TSP, its runtime is proportional to the squared size of problem N so as to look less efficient. The following statistical feature is observed during the authors’ long-term gene data analysis using ACO: when the data size N becomes big, local clustering appears frequently. That is, some data cluster tightly in a small area and form a class, and the correlation between different classes is weak. And this feature makes the idea of divide and rule feasible for the estimate of solution of TSP. In this paper an improved ACO algorithm is presented, which firstly divided all data into local clusters and calculated small TSP routes and then assembled a big TSP route with them. Simulation shows that the presented method improves the running speed of ACO by 200 factors under the condition that data set holds feature of local clustering.
Information cascade, Kirman's ant colony model, and kinetic Ising model
Hisakado, Masato
2014-01-01
In this paper, we discuss a voting model in which voters can obtain information from a finite number of previous voters. There exist three groups of voters: (i) digital herders and independent voters, (ii) analog herders and independent voters, and (iii) tanh-type herders. In our previous paper, we used the mean field approximation for case (i). In that study, if the reference number r is above three, phase transition occurs and the solution converges to one of the equilibria. In contrast, in the current study, the solution oscillates between the two equilibria, that is, good and bad equilibria. In this paper, we show that there is no phase transition when r is finite. If the annealing schedule is adequately slow from finite r to infinite r, the voting rate converges only to the good equilibrium. In case (ii), the state of reference votes is equivalent to that of Kirman's ant colony model, and it follows beta binomial distribution. In case (iii), we show that the model is equivalent to the finite-size kinetic...
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
Parallelization Strategies for Ant Colony Optimisation on GPUs
Cecilia, Jose M; Ujaldon, Manuel; Nisbet, Andy; Amos, Martyn
2011-01-01
Ant Colony Optimisation (ACO) is an effective population-based meta-heuristic for the solution of a wide variety of problems. As a population-based algorithm, its computation is intrinsically massively parallel, and it is there- fore theoretically well-suited for implementation on Graphics Processing Units (GPUs). The ACO algorithm comprises two main stages: Tour construction and Pheromone update. The former has been previously implemented on the GPU, using a task-based parallelism approach. However, up until now, the latter has always been implemented on the CPU. In this paper, we discuss several parallelisation strategies for both stages of the ACO algorithm on the GPU. We propose an alternative data-based parallelism scheme for Tour construction, which fits better on the GPU architecture. We also describe novel GPU programming strategies for the Pheromone update stage. Our results show a total speed-up exceeding 28x for the Tour construction stage, and 20x for Pheromone update, and suggest that ACO is a po...
Study on ant colony optimization for fuel loading pattern problem
Modified ant colony optimization (ACO) was applied to the in-core fuel loading pattern (LP) optimization problem to minimize the power peaking factor (PPF) in the modeled 1/4 symmetry PWR core. Loading order was found to be important in ACO. Three different loading orders with and without the adjacent effect between fuel assemblies (FAs) were compared, and it was found that the loading order from the central core is preferable because many selections of FAs to be inserted are available in the core center region. LPs were determined from pheromone trail and heuristic information, which is a priori knowledge based on the feature of the problem. Three types of heuristic information were compared to obtain the desirable performance of searching LPs with low PPF. Moreover, mutation operation, such as the genetic algorithm (GA), was introduced into the ACO algorithm to avoid searching similar LPs because heuristic information used in ACO tends to localize the searching space in the LP problem. The performance of ACO with some improvement was compared with those of simulated annealing and GA. In conclusion, good performance can be achieved by setting proper heuristic information and mutation operation parameter in ACO. (author)
Ant colony optimization-based firewall anomaly mitigation engine.
Penmatsa, Ravi Kiran Varma; Vatsavayi, Valli Kumari; Samayamantula, Srinivas Kumar
2016-01-01
A firewall is the most essential component of network perimeter security. Due to human error and the involvement of multiple administrators in configuring firewall rules, there exist common anomalies in firewall rulesets such as Shadowing, Generalization, Correlation, and Redundancy. There is a need for research on efficient ways of resolving such anomalies. The challenge is also to see that the reordered or resolved ruleset conforms to the organization's framed security policy. This study proposes an ant colony optimization (ACO)-based anomaly resolution and reordering of firewall rules called ACO-based firewall anomaly mitigation engine. Modified strategies are also introduced to automatically detect these anomalies and to minimize manual intervention of the administrator. Furthermore, an adaptive reordering strategy is proposed to aid faster reordering when a new rule is appended. The proposed approach was tested with different firewall policy sets. The results were found to be promising in terms of the number of conflicts resolved, with minimal availability loss and marginal security risk. This work demonstrated the application of a metaheuristic search technique, ACO, in improving the performance of a packet-filter firewall with respect to mitigating anomalies in the rules, and at the same time demonstrated conformance to the security policy. PMID:27441151
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.
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
Channeler Ant Model: 3 D segmentation of medical images through ant colonies
In this paper the Channeler Ant Model (CAM) and some results of its application to the analysis of medical images are described. The CAM is an algorithm able to segment 3 D structures with different shapes, intensity and background. It makes use of virtual and colonies and exploits their natural capabilities to modify the environment and communicate with each other by pheromone deposition. Its performance has been validated with the segmentation of 3 D artificial objects and it has been already used successfully in lung nodules detection on Computer Tomography images. This work tries to evaluate the CAM as a candidate to solve the quantitative segmentation problem in Magnetic Resonance brain images: to evaluate the percentage of white matter, gray matter and cerebrospinal fluid in each voxel.
Diversity, prevalence and virulence of fungal entomopathogens in colonies of the ant Formica selysi
Reber A.; Chapuisat M.
2012-01-01
The richness of the parasitic community associated with social insect colonies has rarely been investigated. Moreover, understanding how hosts and pathogens interact in nature is important to interpret results from laboratory experiments. Here, we assessed the diversity, prevalence and virulence of fungal entomopathogens present around and within colonies of the ant Formica selysi. We detected eight fungal species known to be entomopathogenic in soil sampled from the habitat of ants. Six of t...
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...
Ant Colony Optimization Analysis on Overall Stability of High Arch Dam Basis of Field Monitoring
Peng Lin; Xiaoli Liu; Hong-Xin Chen; Jinxie Kim
2014-01-01
A dam ant colony optimization (D-ACO) analysis of the overall stability of high arch dams on complicated foundations is presented in this paper. A modified ant colony optimization (ACO) model is proposed for obtaining dam concrete and rock mechanical parameters. A typical dam parameter feedback problem is proposed for nonlinear back-analysis numerical model based on field monitoring deformation and ACO. The basic principle of the proposed model is the establishment of the objective function o...
Giancarlo Mauri; Citrolo, Andrea G.
2013-01-01
The hydrophobic-polar (HP) model has been widely studied in the field of protein structure prediction (PSP) both for theoretical purposes and as a benchmark for new optimization strategies. In this work we introduce a new heuristics based on Ant Colony Optimization (ACO) and Markov Chain Monte Carlo (MCMC) that we called Hybrid Monte Carlo Ant Colony Optimization (HMCACO). We describe this method and compare results obtained on well known HP instances in the 3 dimensional cubic lattice to tho...
Routing in Wireless Sensor Networks Using an Ant Colony Optimization (ACO) Router Chip
Dervis Karaboga; Selcuk Okdem
2009-01-01
Wireless Sensor Networks consisting of nodes with limited power are deployed to gather useful information from the field. In WSNs it is critical to collect the information in an energy efficient manner. Ant Colony Optimization, a swarm intelligence based optimization technique, is widely used in network routing. A novel routing approach using an Ant Colony Optimization algorithm is proposed for Wireless Sensor Networks consisting of stable nodes. Illustrative examples, detailed descriptions a...