WorldWideScience

Sample records for artificial ant colonies

  1. Polyethism in a colony of artificial ants

    CERN Document Server

    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.

  2. SWARM INTELLIGENCE FROM NATURAL TO ARTIFICIAL SYSTEMS: ANT COLONY OPTIMIZATION

    Directory of Open Access Journals (Sweden)

    O. Deepa

    2016-03-01

    Full Text Available Successful applications coming from biologically inspired algorithm like Ant Colony Optimization (ACO based on artificial swarm intelligence which is inspired by the collective behavior of social insects. ACO has been inspired from natural ants system, their behavior, team coordination, synchronization for the searching of optimal solution and also maintains information of each ant. At present, ACO has emerged as a leading metaheuristic technique for the solution of combinatorial optimization problems which can be used to find shortest path through construction graph. This paper describe about various behavior of ants, successfully used ACO algorithms, applications and current trends. In recent years, some researchers have also focused on the application of ACO algorithms to design of wireless communication network, bioinformatics problem, dynamic problem and multi-objective problem.

  3. Ant colony for TSP

    OpenAIRE

    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...

  4. AGNOSCO - Identification of Infected Nodes with artificial Ant Colonies

    CERN Document Server

    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.

  5. ACO - Ant Colony Optimization

    OpenAIRE

    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...

  6. A nuclear reactor core fuel reload optimization using Artificial-Ant-Colony Connective Networks

    International Nuclear Information System (INIS)

    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)

  7. A nuclear reactor core fuel reload optimization using artificial ant colony connective networks

    International Nuclear Information System (INIS)

    The core of a nuclear Pressurized Water Reactor (PWR) may be reloaded every time the fuel burn-up is such that it is not more possible to maintain the reactor operating at nominal power. The nuclear core fuel reload optimization problem consists in finding a pattern of burned-up and fresh-fuel assemblies that maximize the number of full operational days. This is an NP-Hard problem, meaning that complexity grows exponentially with the number of fuel assemblies in the core. Moreover, the problem is non-linear and its search space is highly discontinuous and multi-modal. Ant Colony System (ACS) is an optimization algorithm based on artificial ants that uses the reinforcement learning technique. The ACS was originally developed to solve the Traveling Salesman Problem (TSP), which is conceptually similar to the nuclear core fuel reload problem. In this work a parallel computational system based on the ACS, called Artificial Ant Colony Networks is introduced to solve the core fuel reload optimization problem

  8. A nuclear reactor core fuel reload optimization using artificial ant colony connective networks

    Energy Technology Data Exchange (ETDEWEB)

    Lima, Alan M.M. de [Universidade Federal do Rio de Janeiro, PEN/COPPE - UFRJ, Ilha do Fundao s/n, CEP 21945-970 Rio de Janeiro (Brazil)], E-mail: alanmmlima@yahoo.com.br; Schirru, Roberto [Universidade Federal do Rio de Janeiro, PEN/COPPE - UFRJ, Ilha do Fundao s/n, CEP 21945-970 Rio de Janeiro (Brazil)], E-mail: schirru@lmp.ufrj.br; Carvalho da Silva, Fernando [Universidade Federal do Rio de Janeiro, PEN/COPPE - UFRJ, Ilha do Fundao s/n, CEP 21945-970 Rio de Janeiro (Brazil)], E-mail: fernando@con.ufrj.br; Medeiros, Jose Antonio Carlos Canedo [Universidade Federal do Rio de Janeiro, PEN/COPPE - UFRJ, Ilha do Fundao s/n, CEP 21945-970 Rio de Janeiro (Brazil)], E-mail: canedo@lmp.ufrj.br

    2008-09-15

    The core of a nuclear Pressurized Water Reactor (PWR) may be reloaded every time the fuel burn-up is such that it is not more possible to maintain the reactor operating at nominal power. The nuclear core fuel reload optimization problem consists in finding a pattern of burned-up and fresh-fuel assemblies that maximize the number of full operational days. This is an NP-Hard problem, meaning that complexity grows exponentially with the number of fuel assemblies in the core. Moreover, the problem is non-linear and its search space is highly discontinuous and multi-modal. Ant Colony System (ACS) is an optimization algorithm based on artificial ants that uses the reinforcement learning technique. The ACS was originally developed to solve the Traveling Salesman Problem (TSP), which is conceptually similar to the nuclear core fuel reload problem. In this work a parallel computational system based on the ACS, called Artificial Ant Colony Networks is introduced to solve the core fuel reload optimization problem.

  9. Biomedical Image Edge Detection using an Ant Colony Optimization Based on Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Javad Rahebi

    2011-12-01

    Full Text Available Ant colony optimization (ACO is the algorithm that has inspired from natural behavior of ants life, which the ants leaved pheromone to search food on the ground. In this paper, ACO is introduced for resolving the edge detection in the biomedical image. Edge detection method based on ACO is able to create a matrix pheromone that shows information of available edge in each location of edge pixel which is created based on the movements of a number of ants on the biomedical image. Moreover, the movements of these ants are created by local fluctuation of biomedical image intensity values. The detected edge biomedical images have low quality rather than detected edge biomedical image resulted of a classic mask and won’t result application of these masks to edge detection biomedical image obtained of ACO. In proposed method, we use artificial neuralnetwork with supervised learning along with momentum to improve edge detection based on ACO. The experimental results shows that make use neural network are very effective in edge detection based on ACO.

  10. Abnormality detection in retinal images using ant colony optimization and artificial neural networks - biomed 2010.

    Science.gov (United States)

    Kavitha, Ganesan; Ramakrishnan, Swaminathan

    2010-01-01

    Optic disc and retinal vasculature are important anatomical structures in the retina of the eye and any changes observed in these structures provide vital information on severity of various diseases. Digital retinal images are shown to provide a meaningful way of documenting and assessing some of the key elements inside the eye including the optic nerve and the tiny retinal blood vessels. In this work, an attempt has been made to detect and differentiate abnormalities of the retina using Digital image processing together with Optimization based segmentation and Artificial Neural Network methods. The retinal fundus images were recorded using standard protocols. Ant Colony Optimization is employed to extract the most significant objects namely the optic disc and blood vessel. The features related to these objects are obtained and corresponding indices are also derived. Further, these features are subjected to classification using Radial Basis Function Neural Networks and compared with conventional training algorithms. Results show that the Ant Colony Optimization is efficient in extracting useful information from retinal images. The features derived are effective for classification of normal and abnormal images using Radial basis function networks compared to other methods. As Optic disc and blood vessels are significant markers of abnormality in retinal images, the method proposed appears to be useful for mass screening. In this paper, the objectives of the study, methodology and significant observations are presented.

  11. Abnormality detection in retinal images using ant colony optimization and artificial neural networks - biomed 2010.

    Science.gov (United States)

    Kavitha, Ganesan; Ramakrishnan, Swaminathan

    2010-01-01

    Optic disc and retinal vasculature are important anatomical structures in the retina of the eye and any changes observed in these structures provide vital information on severity of various diseases. Digital retinal images are shown to provide a meaningful way of documenting and assessing some of the key elements inside the eye including the optic nerve and the tiny retinal blood vessels. In this work, an attempt has been made to detect and differentiate abnormalities of the retina using Digital image processing together with Optimization based segmentation and Artificial Neural Network methods. The retinal fundus images were recorded using standard protocols. Ant Colony Optimization is employed to extract the most significant objects namely the optic disc and blood vessel. The features related to these objects are obtained and corresponding indices are also derived. Further, these features are subjected to classification using Radial Basis Function Neural Networks and compared with conventional training algorithms. Results show that the Ant Colony Optimization is efficient in extracting useful information from retinal images. The features derived are effective for classification of normal and abnormal images using Radial basis function networks compared to other methods. As Optic disc and blood vessels are significant markers of abnormality in retinal images, the method proposed appears to be useful for mass screening. In this paper, the objectives of the study, methodology and significant observations are presented. PMID:20467104

  12. Ant Colony Optimization

    OpenAIRE

    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...

  13. Artificial Ant Species on Solving Optimization Problems

    OpenAIRE

    Pintea, Camelia-M.

    2013-01-01

    During the last years several ant-based techniques were involved to solve hard and complex optimization problems. The current paper is a short study about the influence of artificial ant species in solving optimization problems. There are studied the artificial Pharaoh Ants, Lasius Niger and also artificial ants with no special specificity used commonly in Ant Colony Optimization.

  14. Energy Efficient Routing Protocol for Maximizing the Lifetime in Wsns Using Ant Colony Algorithm and Artificial Immune System

    Directory of Open Access Journals (Sweden)

    Safaa Khudair Leabi

    2016-03-01

    Full Text Available Energy limitations have become fundamental challenge for designing wireless sensor networks. Network lifetime represent the most important and interested metric. Several attempts have been made for efficient utilization of energy in routing techniques. This paper proposes an energy efficient routing technique for maximizing the networks lifetime called swarm intelligence routing. This is achieved by using ant colony algorithm (ACO and artificial immune system (AIS. AIS is used for solving packet LOOP problem and to control route direction. While ACO algorithm is used for determining optimum route for sending data packets. The proposed routing technique seeks for determining the optimum route from nodes towards base station so that energy exhaustion is balanced and lifetime is maximized. Proposed routing technique is compared with Dijkstra routing method. Results show significant increase in network lifetime of about 1.2567.

  15. Ant Colony Optimization for Control

    NARCIS (Netherlands)

    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

  16. Ant Colony Optimization: A Review and Comparison

    OpenAIRE

    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...

  17. Simultaneous feature selection and parameter optimisation using an artificial ant colony: case study of melting point prediction

    Directory of Open Access Journals (Sweden)

    Nigsch Florian

    2008-10-01

    Full Text Available Abstract Background We present a novel feature selection algorithm, Winnowing Artificial Ant Colony (WAAC, that performs simultaneous feature selection and model parameter optimisation for the development of predictive quantitative structure-property relationship (QSPR models. The WAAC algorithm is an extension of the modified ant colony algorithm of Shen et al. (J Chem Inf Model 2005, 45: 1024–1029. We test the ability of the algorithm to develop a predictive partial least squares model for the Karthikeyan dataset (J Chem Inf Model 2005, 45: 581–590 of melting point values. We also test its ability to perform feature selection on a support vector machine model for the same dataset. Results Starting from an initial set of 203 descriptors, the WAAC algorithm selected a PLS model with 68 descriptors which has an RMSE on an external test set of 46.6°C and R2 of 0.51. The number of components chosen for the model was 49, which was close to optimal for this feature selection. The selected SVM model has 28 descriptors (cost of 5, ε of 0.21 and an RMSE of 45.1°C and R2 of 0.54. This model outperforms a kNN model (RMSE of 48.3°C, R2 of 0.47 for the same data and has similar performance to a Random Forest model (RMSE of 44.5°C, R2 of 0.55. However it is much less prone to bias at the extremes of the range of melting points as shown by the slope of the line through the residuals: -0.43 for WAAC/SVM, -0.53 for Random Forest. Conclusion With a careful choice of objective function, the WAAC algorithm can be used to optimise machine learning and regression models that suffer from overfitting. Where model parameters also need to be tuned, as is the case with support vector machine and partial least squares models, it can optimise these simultaneously. The moving probabilities used by the algorithm are easily interpreted in terms of the best and current models of the ants, and the winnowing procedure promotes the removal of irrelevant descriptors.

  18. A hybrid of ant colony optimization and artificial bee colony algorithm for probabilistic optimal placement and sizing of distributed energy resources

    International Nuclear Information System (INIS)

    Highlights: • A probabilistic optimization framework incorporated with uncertainty is proposed. • A hybrid optimization approach combining ACO and ABC algorithms is proposed. • The problem is to deal with technical, environmental and economical aspects. • A fuzzy interactive approach is incorporated to solve the multi-objective problem. • Several strategies are implemented to compare with literature methods. - Abstract: In this paper, a hybrid configuration of ant colony optimization (ACO) with artificial bee colony (ABC) algorithm called hybrid ACO–ABC algorithm is presented for optimal location and sizing of distributed energy resources (DERs) (i.e., gas turbine, fuel cell, and wind energy) on distribution systems. The proposed algorithm is a combined strategy based on the discrete (location optimization) and continuous (size optimization) structures to achieve advantages of the global and local search ability of ABC and ACO algorithms, respectively. Also, in the proposed algorithm, a multi-objective ABC is used to produce a set of non-dominated solutions which store in the external archive. The objectives consist of minimizing power losses, total emissions produced by substation and resources, total electrical energy cost, and improving the voltage stability. In order to investigate the impact of the uncertainty in the output of the wind energy and load demands, a probabilistic load flow is necessary. In this study, an efficient point estimate method (PEM) is employed to solve the optimization problem in a stochastic environment. The proposed algorithm is tested on the IEEE 33- and 69-bus distribution systems. The results demonstrate the potential and effectiveness of the proposed algorithm in comparison with those of other evolutionary optimization methods

  19. Ant- and Ant-Colony-Inspired ALife Visual Art.

    Science.gov (United States)

    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

  20. Ant colony optimization in continuous problem

    Institute of Scientific and Technical Information of China (English)

    YU Ling; LIU Kang; LI Kaishi

    2007-01-01

    Based on the analysis of the basic ant colony optimization and optimum problem in a continuous space,an ant colony optimization (ACO) for continuous problem is constructed and discussed. The algorithm is efficient and beneficial to the study of the ant colony optimization in a continuous space.

  1. Towards a multilevel ant colony optimization

    OpenAIRE

    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....

  2. GRID SCHEDULING USING ENHANCED ANT COLONY ALGORITHM

    OpenAIRE

    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...

  3. Ant Colony Optimization Algorithm for Continuous Domains Based on Position Distribution Model of Ant Colony Foraging

    OpenAIRE

    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...

  4. Exploration adjustment by ant colonies.

    Science.gov (United States)

    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

  5. Optimization of PID Controllers Using Ant Colony and Genetic Algorithms

    CERN Document Server

    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.

  6. Nestmate and kin recognition in interspecific mixed colonies of ants.

    Science.gov (United States)

    Carlin, N F; Hölldobler, B

    1983-12-01

    Recognition of nestmates and discrimination against aliens is the rule in the social insects. The principal mechanism of nestmate recognition in carpenter ants (Camponotus) appears to be odor labels or "discriminators" that originate from the queen and are distributed among, and learned by, all adult colony members. The acquired odor labels are sufficiently powerful to produce indiscriminate acceptance among workers of different species raised together in artificially mixed colonies and rejection of genetic sisters reared by different heterospecific queens. PMID:17776248

  7. Nestmate and kin recognition in interspecific mixed colonies of ants.

    Science.gov (United States)

    Carlin, N F; Hölldobler, B

    1983-12-01

    Recognition of nestmates and discrimination against aliens is the rule in the social insects. The principal mechanism of nestmate recognition in carpenter ants (Camponotus) appears to be odor labels or "discriminators" that originate from the queen and are distributed among, and learned by, all adult colony members. The acquired odor labels are sufficiently powerful to produce indiscriminate acceptance among workers of different species raised together in artificially mixed colonies and rejection of genetic sisters reared by different heterospecific queens.

  8. Recruitment strategies and colony size in ants.

    Directory of Open Access Journals (Sweden)

    Robert Planqué

    Full Text Available Ants use a great variety of recruitment methods to forage for food or find new nests, including tandem running, group recruitment and scent trails. It has been known for some time that there is a loose correlation across many taxa between species-specific mature colony size and recruitment method. Very small colonies tend to use solitary foraging; small to medium sized colonies use tandem running or group recruitment whereas larger colonies use pheromone recruitment trails. Until now, explanations for this correlation have focused on the ants' ecology, such as food resource distribution. However, many species have colonies with a single queen and workforces that grow over several orders of magnitude, and little is known about how a colony's organization, including recruitment methods, may change during its growth. After all, recruitment involves interactions between ants, and hence the size of the colony itself may influence which recruitment method is used--even if the ants' behavioural repertoire remains unchanged. Here we show using mathematical models that the observed correlation can also be explained by recognizing that failure rates in recruitment depend differently on colony size in various recruitment strategies. Our models focus on the build up of recruiter numbers inside colonies and are not based on optimality arguments, such as maximizing food yield. We predict that ant colonies of a certain size should use only one recruitment method (and always the same one rather than a mix of two or more. These results highlight the importance of the organization of recruitment and how it is affected by colony size. Hence these results should also expand our understanding of ant ecology.

  9. Automatic Programming with Ant Colony Optimization

    OpenAIRE

    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.

  10. Ant colony optimization algorithm for continuous domains based on position distribution model of ant colony foraging.

    Science.gov (United States)

    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

  11. Optic disc detection using ant colony optimization

    OpenAIRE

    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...

  12. Implementasi Algoritma Ant Colony System Dalam Menentukan Optimisasi Network Routing .

    OpenAIRE

    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...

  13. Ant Colony Optimisation for Backward Production Scheduling

    Directory of Open Access Journals (Sweden)

    Leandro Pereira dos Santos

    2012-01-01

    Full Text Available The main objective of a production scheduling system is to assign tasks (orders or jobs to resources and sequence them as efficiently and economically (optimised as possible. Achieving this goal is a difficult task in complex environment where capacity is usually limited. In these scenarios, finding an optimal solution—if possible—demands a large amount of computer time. For this reason, in many cases, a good solution that is quickly found is preferred. In such situations, the use of metaheuristics is an appropriate strategy. In these last two decades, some out-of-the-shelf systems have been developed using such techniques. This paper presents and analyses the development of a shop-floor scheduling system that uses ant colony optimisation (ACO in a backward scheduling problem in a manufacturing scenario with single-stage processing, parallel resources, and flexible routings. This scenario was found in a large food industry where the corresponding author worked as consultant for more than a year. This work demonstrates the applicability of this artificial intelligence technique. In fact, ACO proved to be as efficient as branch-and-bound, however, executing much faster.

  14. Optimized Ant Colony Algorithm by Local Pheromone Update

    OpenAIRE

    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...

  15. Runtime analysis of the 1-ANT ant colony optimizer

    DEFF Research Database (Denmark)

    Doerr, Benjamin; Neumann, Frank; Sudholt, Dirk;

    2011-01-01

    The runtime analysis of randomized search heuristics is a growing field where, in the last two decades, many rigorous results have been obtained. First runtime analyses of ant colony optimization (ACO) have been conducted only recently. In these studies simple ACO algorithms such as the 1-ANT...... are investigated. The influence of the evaporation factor in the pheromone update mechanism and the robustness of this parameter w.r.t. the runtime behavior have been determined for the example function OneMax.This work puts forward the rigorous runtime analysis of the 1-ANT on the example functions Leading......Ones and BinVal. With respect to Evolutionary Algorithms (EAs), such analyses were essential to develop methods for the analysis on more complicated problems. The proof techniques required for the 1-ANT, unfortunately, differ significantly from those for EAs, which means that a new reservoir of methods has...

  16. BWR Fuel Lattice Design Using an Ant Colony Model

    International Nuclear Information System (INIS)

    This paper deals with one of the steps of the nuclear fuel design: the radial fuel lattice design. It can be seen as a combinatorial optimization problem for determining the optimal 2D fuel rods enrichment and gadolinia distribution. In order to solve this optimization problem, the ant colony system technique is proposed. The main idea of the ant colony approach consists of emulating the real ant colony behaviour in their searching for minimum paths between two given points, usually between the nest and a food source. In this case, the environment where the artificial ants move is the space defined by the discrete possible values of Gd2O3 contents, the U235 enrichment, and the valid locations inside the 10x10 BWR fuel lattice array. In order to assess any candidate fuel lattice in the optimization process, the HELIOS neutronic transport code is used. The results obtained in the application of the implemented model show that the proposed technique is a powerful tool to tackle this step of the fuel design. (authors)

  17. BWR Fuel Lattice Design Using an Ant Colony Model

    Energy Technology Data Exchange (ETDEWEB)

    Montes, Jose L.; Ortiz, Juan J. [Instituto Nacional de Investigaciones Nucleares, Depto. de Sistemas Nucleares, Carretera Mexico Toluca S/N. La Marquesa Ocoyoacac. 52750, Estado de Mexico (Mexico); Francois, Juan L.; Martin-del-Campo, Cecilia [Depto. de Sistemas Energeticos, Facultad de Ingenieria, Universidad Nacional Autonoma de Mexico Paseo Cuauhnahuac 8532. Jiutepec, Mor. 62550 (Mexico)

    2008-07-01

    This paper deals with one of the steps of the nuclear fuel design: the radial fuel lattice design. It can be seen as a combinatorial optimization problem for determining the optimal 2D fuel rods enrichment and gadolinia distribution. In order to solve this optimization problem, the ant colony system technique is proposed. The main idea of the ant colony approach consists of emulating the real ant colony behaviour in their searching for minimum paths between two given points, usually between the nest and a food source. In this case, the environment where the artificial ants move is the space defined by the discrete possible values of Gd{sub 2}O{sub 3} contents, the U{sup 235} enrichment, and the valid locations inside the 10x10 BWR fuel lattice array. In order to assess any candidate fuel lattice in the optimization process, the HELIOS neutronic transport code is used. The results obtained in the application of the implemented model show that the proposed technique is a powerful tool to tackle this step of the fuel design. (authors)

  18. Implementation of Travelling Salesman Problem Using ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    Gaurav Singh,

    2014-04-01

    Full Text Available Within the Artificial Intelligence community, there is great need for fast and accurate traversal algorithms, specifically those that find a path from a start to goal with minimum cost. Cost can be distance, time, money, energy, etc. Travelling salesman problem (TSP is a combinatorial optimization problem. TSP is the most intensively studied problem in the area of optimization. Ant colony optimization (ACO is a population-based metaheuristic that can be used to find approximate solutions to difficult optimization problems. There have been many efforts in the past to provide time efficient solutions for the problem, both exact and approximate. This paper demonstrates the implementation of TSP using ant colony optimization(ACO.The solution to this problem enjoys wide applicability in a variety of practical fields.TSP in its purest form has several applications such as planning, logistics, and manufacture of microchips, military and traffic.

  19. Ant colony optimization and constraint programming

    CERN Document Server

    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

  20. A Novel Parser Design Algorithm Based on Artificial Ants

    CERN Document Server

    Maiti, Deepyaman; Konar, Amit; Ramadoss, Janarthanan

    2008-01-01

    This article presents a unique design for a parser using the Ant Colony Optimization algorithm. The paper implements the intuitive thought process of human mind through the activities of artificial ants. The scheme presented here uses a bottom-up approach and the parsing program can directly use ambiguous or redundant grammars. We allocate a node corresponding to each production rule present in the given grammar. Each node is connected to all other nodes (representing other production rules), thereby establishing a completely connected graph susceptible to the movement of artificial ants. Each ant tries to modify this sentential form by the production rule present in the node and upgrades its position until the sentential form reduces to the start symbol S. Successful ants deposit pheromone on the links that they have traversed through. Eventually, the optimum path is discovered by the links carrying maximum amount of pheromone concentration. The design is simple, versatile, robust and effective and obviates ...

  1. Ant larval demand reduces aphid colony growth rates in an ant-aphid interaction

    OpenAIRE

    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...

  2. Model Specification Searches Using Ant Colony Optimization Algorithms

    Science.gov (United States)

    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.

  3. Image feature extraction based multiple ant colonies cooperation

    Science.gov (United States)

    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.

  4. Ant Colony Optimization and Hypergraph Covering Problems

    CERN Document Server

    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 ...

  5. Optic disc detection using ant colony optimization

    Science.gov (United States)

    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.

  6. Improved Ant Colony Clustering Algorithm and Its Performance Study

    OpenAIRE

    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 ...

  7. An Improved Ant Colony Routing Algorithm for WSNs

    OpenAIRE

    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...

  8. Enhanced ant colony optimization for multiscale problems

    Science.gov (United States)

    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.

  9. The ant colony metaphor in continuous spaces using boundary search

    OpenAIRE

    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...

  10. Improved Ant Colony Clustering Algorithm and Its Performance Study.

    Science.gov (United States)

    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

  11. Nest- and colony-mate recognition in polydomous colonies of meat ants ( Iridomyrmex purpureus)

    Science.gov (United States)

    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.

  12. Ant Colony Optimization for Capacitated Vehicle Routing Problem

    Directory of Open Access Journals (Sweden)

    H. V. Seow

    2012-01-01

    Full Text Available Problem statement: The Capacitated Vehicle Routing Problem (CVRP is a well-known combinatorial optimization problem which is concerned with the distribution of goods between the depot and customers. It is of economic importance to businesses as approximately 10-20% of the final cost of the goods is contributed by the transportation process. Approach: This problem was tackled using an Ant Colony Optimization (ACO combined with heuristic approaches that act as the route improvement strategies. The proposed ACO utilized a pheromone evaporation procedure of standard ant algorithm in order to introduce an evaporation rate that depends on the solutions found by the artificial ants. Results: Computational experiments were conducted on benchmark data set and the results obtained from the proposed algorithms shown that the application of combination of two different heuristics in the ACO had the capability to improve the ants’ solutions better than ACO embedded with only one heuristic. Conclusion: ACO with swap and 3-opt heuristic has the capability to tackle the CVRP with satisfactory solution quality and run time. It is a viable alternative for solving the CVRP.

  13. An ant colony algorithm on continuous searching space

    Science.gov (United States)

    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.

  14. Remote Sensing Image Feature Extracting Based Multiple Ant Colonies Cooperation

    Directory of Open Access Journals (Sweden)

    Zhang Zhi-long

    2014-02-01

    Full Text Available This paper presents a novel feature extraction method for remote sensing imagery based on the cooperation of multiple ant colonies. First, multiresolution expression of the input remote sensing imagery is created, and two different ant colonies are spread on different resolution images. The ant colony in the low-resolution image uses phase congruency as the inspiration information, whereas that in the high-resolution image uses gradient magnitude. The two ant colonies cooperate to detect features in the image by sharing the same pheromone matrix. Finally, the image features are extracted on the basis of the pheromone matrix threshold. Because a substantial amount of information in the input image is used as inspiration information of the ant colonies, the proposed method shows higher intelligence and acquires more complete and meaningful image features than those of other simple edge detectors.

  15. Ant Colony Optimization for Capacity Problems

    Directory of Open Access Journals (Sweden)

    Tad Gonsalves

    2015-01-01

    Full Text Available This paper deals with the optimization of the capac ity of a terminal railway station using the Ant Colony Optimization algorithm. The capacity of the terminal station is defined as the number of trains that depart from the station in un it interval of time. The railway capacity optimization problem is framed as a typical symmetr ical Travelling Salesman Problem (TSP, with the TSP nodes representing the train arrival / departure events and the TSP total cost representing the total time-interval of the schedul e. The application problem is then optimized using the ACO algorithm. The simulation experiments validate the formulation of the railway capacity problem as a TSP and the ACO algorithm pro duces optimal solutions superior to those produced by the domain experts.

  16. Tuning PID Controller Using Multiobjective Ant Colony Optimization

    OpenAIRE

    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...

  17. Optimization Planning based on Improved Ant Colony Algorithm for Robot

    OpenAIRE

    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

  18. Improvement and Implementation of Best-worst Ant Colony Algorithm

    OpenAIRE

    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. ...

  19. Loading pattern optimization using ant colony algorithm

    International Nuclear Information System (INIS)

    Electricite de France (EDF) operates 58 nuclear power plants (NPP), of the Pressurized Water Reactor type. The loading pattern optimization of these NPP is currently done by EDF expert engineers. Within this framework, EDF R and D has developed automatic optimization tools that assist the experts. LOOP is an industrial tool, developed by EDF R and D and based on a simulated annealing algorithm. In order to improve the results of such automatic tools, new optimization methods have to be tested. Ant Colony Optimization (ACO) algorithms are recent methods that have given very good results on combinatorial optimization problems. In order to evaluate the performance of such methods on loading pattern optimization, direct comparisons between LOOP and a mock-up based on the Max-Min Ant System algorithm (a particular variant of ACO algorithms) were made on realistic test-cases. It is shown that the results obtained by the ACO mock-up are very similar to those of LOOP. Future research will consist in improving these encouraging results by using parallelization and by hybridizing the ACO algorithm with local search procedures. (author)

  20. Loading pattern optimization using ant colony algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Hoareau, Fabrice [EDF R and D, Clamart (France)

    2008-07-01

    Electricite de France (EDF) operates 58 nuclear power plants (NPP), of the Pressurized Water Reactor type. The loading pattern optimization of these NPP is currently done by EDF expert engineers. Within this framework, EDF R and D has developed automatic optimization tools that assist the experts. LOOP is an industrial tool, developed by EDF R and D and based on a simulated annealing algorithm. In order to improve the results of such automatic tools, new optimization methods have to be tested. Ant Colony Optimization (ACO) algorithms are recent methods that have given very good results on combinatorial optimization problems. In order to evaluate the performance of such methods on loading pattern optimization, direct comparisons between LOOP and a mock-up based on the Max-Min Ant System algorithm (a particular variant of ACO algorithms) were made on realistic test-cases. It is shown that the results obtained by the ACO mock-up are very similar to those of LOOP. Future research will consist in improving these encouraging results by using parallelization and by hybridizing the ACO algorithm with local search procedures. (author)

  1. A Hybrid Artificial Bee Colony Algorithm for the Service Selection Problem

    OpenAIRE

    Changsheng Zhang; Bin Zhang

    2014-01-01

    To tackle the QoS-based service selection problem, a hybrid artificial bee colony algorithm called h-ABC is proposed, which incorporates the ant colony optimization mechanism into the artificial bee colony optimization process. In this algorithm, a skyline query process is used to filter the candidates related to each service class, which can greatly shrink the search space in case of not losing good candidates, and a flexible self-adaptive varying construct graph is designed to model the sea...

  2. Electricity Consumption Prediction Based on SVR with Ant Colony Optimization

    OpenAIRE

    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...

  3. An Improved Ant Colony Routing Algorithm for WSNs

    Directory of Open Access Journals (Sweden)

    Tan Zhi

    2015-01-01

    Full Text Available Ant colony algorithm is a classical routing algorithm. And it are used in a variety of application because it is economic and self-organized. However, the routing algorithm will expend huge amounts of energy at the beginning. In the paper, based on the idea of Dijkstra algorithm, the improved ant colony algorithm was proposed to balance the energy consumption of networks. Through simulation and comparison with basic ant colony algorithms, it is obvious that improved algorithm can effectively balance energy consumption and extend the lifetime of WSNs.

  4. Robustness of Ant Colony Optimization to Noise.

    Science.gov (United States)

    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

  5. Ant Colony Optimization for Container Loading Problem

    Directory of Open Access Journals (Sweden)

    H. V. Seow

    2012-01-01

    Full Text Available Problem statement: The Container Loading Problem (CLP considers packing a subset of given rectangular boxes into a rectangular container of fixed dimensions in the most optimum way. This was very important in the logistics industries and warehousing problems, since the cost can be reduced by increasing the space utilization ratio. Approach: This problem was solved in a two-phased Ant Colony Optimization (ACO where a tower building approach was used as the inner heuristic. In the first phase, ACO with its probabilistic decision rule was used to construct a sequence of boxes. The boxes were then arranged into a container with the tower building heuristic in the second phase. The pheromone feedback of ACO using pheromone updating rule helped to improve the solutions. Results: Computational experiments were conducted on benchmark data set and the results obtained from the proposed algorithm are shown to be comparable with other methods from the literatures. Conclusion: ACO has the capability to solve the CLP.

  6. Data transmission optimal routing in WSN using ant colony algorithm

    OpenAIRE

    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.

  7. Incremental Web Usage Mining Based on Active Ant Colony Clustering

    Institute of Scientific and Technical Information of China (English)

    SHEN Jie; LIN Ying; CHEN Zhimin

    2006-01-01

    To alleviate the scalability problem caused by the increasing Web using and changing users' interests, this paper presents a novel Web Usage Mining algorithm-Incremental Web Usage Mining algorithm based on Active Ant Colony Clustering. Firstly, an active movement strategy about direction selection and speed, different with the positive strategy employed by other Ant Colony Clustering algorithms, is proposed to construct an Active Ant Colony Clustering algorithm, which avoid the idle and "flying over the plane" moving phenomenon, effectively improve the quality and speed of clustering on large dataset. Then a mechanism of decomposing clusters based on above methods is introduced to form new clusters when users' interests change. Empirical studies on a real Web dataset show the active ant colony clustering algorithm has better performance than the previous algorithms, and the incremental approach based on the proposed mechanism can efficiently implement incremental Web usage mining.

  8. Experiment Study of Entropy Convergence of Ant Colony Optimization

    OpenAIRE

    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...

  9. A critical analysis of parameter adaptation in ant colony optimization

    OpenAIRE

    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...

  10. Ant Colony Optimization for Train Scheduling: An Analysis

    OpenAIRE

    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.

  11. Ant Colony Optimization for Inferring Key Gene Interactions

    OpenAIRE

    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...

  12. Brief Announcement: Distributed Task Allocation in Ant Colonies

    OpenAIRE

    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 ...

  13. Determining the Optimum Section of Tunnels Using Ant Colony Optimization

    OpenAIRE

    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...

  14. Protein structure optimization with a "Lamarckian" ant colony algorithm.

    Science.gov (United States)

    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

  15. Finding the Minimum Ratio Traveling Salesman Tour by Artificial Ants

    Institute of Scientific and Technical Information of China (English)

    马良; 崔雪丽; 姚俭

    2003-01-01

    Ants of artificial colony are able to generate good solutions to the famous traveling salesman problem (TSP).We propose an artificial ants algorithm for solving the minimum ratio TSP, which is more general than the standard TSP in combinatorial optimization area. In the minimum ratio TSP, another criterion concerning each edge is added, that is,the traveling salesman can have a benefit if he travels from one city to another. The objective is to minimize the ratio between total costs or distances and total benefits. The idea of this type of optimization is in some sense quite similar to that of traditional cost-benefit analysis in management science. Computational results substantiate the solution quality and efficiency of the algorithm.

  16. Ant Colony Algorithm for Solving QoS Routing Problem

    Institute of Scientific and Technical Information of China (English)

    SUN Li-juan; WANG Liang-jun; WANG Ru-chuan

    2004-01-01

    Based on the state transition rule, the local updating rule and the global updating rule of ant colony algorithm, we propose an improved ant colony algorithm of the least-cost quality of service (QoS) unicast routing. The algorithm is used for solving the routing problem with delay, delay jitter, bandwidth, and packet loss-constrained. In the simulation, about 52.33% ants find the successful QoS routing , and converge to the best. It is proved that the algorithm is efficient and effective.

  17. Ant colonies prefer infected over uninfected nest sites.

    Science.gov (United States)

    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

  18. Ant colonies prefer infected over uninfected nest sites.

    Directory of Open Access Journals (Sweden)

    Luigi Pontieri

    Full Text Available During colony relocation, the selection of a new nest involves exploration and assessment of potential sites followed by colony movement on the basis of a collective decision making process. Hygiene and pathogen load of the potential nest sites are factors worker scouts might evaluate, given the high risk of epidemics in group-living animals. Choosing nest sites free of pathogens is hypothesized to be highly efficient in invasive ants as each of their introduced populations is often an open network of nests exchanging individuals (unicolonial with frequent relocation into new nest sites and low genetic diversity, likely making these species particularly vulnerable to parasites and diseases. We investigated the nest site preference of the invasive pharaoh ant, Monomorium pharaonis, through binary choice tests between three nest types: nests containing dead nestmates overgrown with sporulating mycelium of the entomopathogenic fungus Metarhizium brunneum (infected nests, nests containing nestmates killed by freezing (uninfected nests, and empty nests. In contrast to the expectation pharaoh ant colonies preferentially (84% moved into the infected nest when presented with the choice of an infected and an uninfected nest. The ants had an intermediate preference for empty nests. Pharaoh ants display an overall preference for infected nests during colony relocation. While we cannot rule out that the ants are actually manipulated by the pathogen, we propose that this preference might be an adaptive strategy by the host to "immunize" the colony against future exposure to the same pathogenic fungus.

  19. Ant Colony versus Genetic Algorithm based on Travelling Salesman Problem

    Directory of Open Access Journals (Sweden)

    Mohammed Alhanjouri

    2011-05-01

    Full Text Available The travelling salesman problem (TSP is a nondeterministic Polynomial hard problem in combinatorial optimization studied in operations research and theoretical computer science. And to solve this problem we used two popular meta-heuristics techniques that used for optimization tasks; the first one is Ant Colony Optimization (ACO, and the second is Genetic Algorithm (GA. In this work, we try to apply both techniques to solve TSP by using the same dataset and compare between them to determine the best one for travelling salesman problem. for Ant Colony Optimization, we studied the effect of some parameters on the produced results, these parameters as: number of used Ants, evaporation, and number of iterations. On the other hand, we studied the chromosome population, crossover probability, and mutation probability parameters that effect on the Genetic Algorithm results.The comparison between Genetic Algorithm and Ant Colony Optimization is accomplished to state the better one for travelling salesman problem.

  20. An ant colony approach for image texture classification

    Science.gov (United States)

    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.

  1. Ant Colony Search Algorithm for Solving Unit Commitment Problem

    Directory of Open Access Journals (Sweden)

    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.

  2. Channeler Ant Model: 3 D segmentation of medical images through ant colonies

    International Nuclear Information System (INIS)

    In this paper the Channeler Ant Model (CAM) and some results of its application to the analysis of medical images are described. The CAM is an algorithm able to segment 3 D structures with different shapes, intensity and background. It makes use of virtual and colonies and exploits their natural capabilities to modify the environment and communicate with each other by pheromone deposition. Its performance has been validated with the segmentation of 3 D artificial objects and it has been already used successfully in lung nodules detection on Computer Tomography images. This work tries to evaluate the CAM as a candidate to solve the quantitative segmentation problem in Magnetic Resonance brain images: to evaluate the percentage of white matter, gray matter and cerebrospinal fluid in each voxel.

  3. TestAnt: an ant colony system approach to sequential testing under precedence constraints

    OpenAIRE

    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...

  4. Ant Colony Based Approach for Solving FPGA routing

    Directory of Open Access Journals (Sweden)

    Vinay Chopra

    2011-07-01

    Full Text Available This paper is based on an ant colony optimization algorithm (ASDR for solving FPGA routing for a route based routing constraint model in FPGA design architecture. In this approach FPGA routing task is transformed into a Boolean Satisfiabilty (SAT equation with the property that any assignment of input variables that satisfies the equation specifies a valid route. The Satisfiability equation is then modeled as Constraint Satisfaction problem, which helps in reducing procedural programming. Satisfying assignment for particular route will result in a valid routing and absence of a satisfying assignment implies that the layout is unroutable. In second step ant colony optimization algorithm is applied on the Boolean equation for solving routing alternatives utilizing approach of hard combinatorial optimization problems. The experimental results suggest that the developed ant colony optimization algorithm based router has taken extremely short CPU time as compared to classical Satisfiabilty based detailed router (SDR and finds all possible routes even for large FPGA circuits.

  5. An ant colony optimization algorithm for job shop scheduling problem

    OpenAIRE

    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...

  6. Response Ant Colony Optimization of End Milling Surface Roughness

    OpenAIRE

    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...

  7. Introduction to Ant Colony Algorithm and Its Application in CIMS

    Institute of Scientific and Technical Information of China (English)

    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...

  8. PARAMETER ESTIMATION OF VALVE STICTION USING ANT COLONY OPTIMIZATION

    Directory of Open Access Journals (Sweden)

    S. Kalaivani

    2012-07-01

    Full Text Available In this paper, a procedure for quantifying valve stiction in control loops based on ant colony optimization has been proposed. Pneumatic control valves are widely used in the process industry. The control valve contains non-linearities such as stiction, backlash, and deadband that in turn cause oscillations in the process output. Stiction is one of the long-standing problems and it is the most severe problem in the control valves. Thus the measurement data from an oscillating control loop can be used as a possible diagnostic signal to provide an estimate of the stiction magnitude. Quantification of control valve stiction is still a challenging issue. Prior to doing stiction detection and quantification, it is necessary to choose a suitable model structure to describe control-valve stiction. To understand the stiction phenomenon, the Stenman model is used. Ant Colony Optimization (ACO, an intelligent swarm algorithm, proves effective in various fields. The ACO algorithm is inspired from the natural trail following behaviour of ants. The parameters of the Stenman model are estimated using ant colony optimization, from the input-output data by minimizing the error between the actual stiction model output and the simulated stiction model output. Using ant colony optimization, Stenman model with known nonlinear structure and unknown parameters can be estimated.

  9. Global path planning approach based on ant colony optimization algorithm

    Institute of Scientific and Technical Information of China (English)

    WEN Zhi-qiang; CAI Zi-xing

    2006-01-01

    Ant colony optimization (ACO) algorithm was modified to optimize the global path. In order to simulate the real ant colonies, according to the foraging behavior of ant colonies and the characteristic of food, conceptions of neighboring area and smell area were presented. The former can ensure the diversity of paths and the latter ensures that each ant can reach the goal. Then the whole path was divided into three parts and ACO was used to search the second part path. When the three parts pathes were adjusted,the final path was found. The valid path and invalid path were defined to ensure the path valid. Finally, the strategies of the pheromone search were applied to search the optimum path. However, when only the pheromone was used to search the optimum path, ACO converges easily. In order to avoid this premature convergence, combining pheromone search and random search, a hybrid ant colony algorithm(HACO) was used to find the optimum path. The comparison between ACO and HACO shows that HACO can be used to find the shortest path.

  10. Application of ant colony optimization in NPP classification fault location

    International Nuclear Information System (INIS)

    Nuclear Power Plant is a highly complex structural system with high safety requirements. Fault location appears to be particularly important to enhance its safety. Ant Colony Optimization is a new type of optimization algorithm, which is used in the fault location and classification of nuclear power plants in this paper. Taking the main coolant system of the first loop as the study object, using VB6.0 programming technology, the NPP fault location system is designed, and is tested against the related data in the literature. Test results show that the ant colony optimization can be used in the accurate classification fault location in the nuclear power plants. (authors)

  11. AN IMPROVED ANT COLONY ALGORITHM IN CONTINUOUS OPTIMIZATION

    Institute of Scientific and Technical Information of China (English)

    Ling CHEN; Jie SHEN; Ling QIN; Hongjian CHEN

    2003-01-01

    A modified ant colony algorithm for solving optimization problem with continuous parameters is presented. In the method, groups of candidate values of the components are constructed, and each value in the group has its trail information. In each iteration of the ant colony algorithm, the method first chooses initial values of the components using the trail information. Then GA operations of crossover and mutation can determine the values of the components in the solution. Our experimental results on the problem of nonlinear programming show that our method has a much higher convergence speed and stability than those of simulated annealing (SA) and GA.

  12. Core Business Selection Based on Ant Colony Clustering Algorithm

    Directory of Open Access Journals (Sweden)

    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.

  13. Determining the Optimum Section of Tunnels Using Ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    S. Talatahari

    2013-01-01

    Full Text Available Ant colony optimization is developed to determine optimum cross sections of tunnel structures. Tunnel structures are expensive infrastructures in terms of material, construction, and maintenance and the application of optimization methods has a great role in minimizing their costs. This paper presents the formulation of objective function and constraints of the problem for the first time, and the ant colony optimization, as a developed metaheuristic approach, has been used to solve the problem. The results and comparisons based on numerical examples show the efficiency of the algorithm.

  14. Colony life history and lifetime reproductive success of red harvester ant colonies.

    Science.gov (United States)

    Ingram, Krista K; Pilko, Anna; Heer, Jeffrey; Gordon, Deborah M

    2013-05-01

    1. We estimate colony reproductive success, in numbers of offspring colonies arising from a colony's daughter queens, of colonies of the red harvester ant, Pogonomyrmex barbatus. 2. A measure of lifetime reproductive success is essential to understand the relation of ecological factors, phenotype and fitness in a natural population. This was possible for the first time in a natural population of ant colonies using data from long-term study of a population of colonies in south-eastern Arizona, for which ages of all colonies are known from census data collected since 1985. 3. Parentage analyses of microsatellite data from 5 highly polymorphic loci were used to assign offspring colonies to maternal parent colonies in a population of about 265 colonies, ages 1-28 years, sampled in 2010. 4. The estimated population growth rate Ro was 1.69 and generation time was 7.8 years. There was considerable variation among colonies in reproductive success: of 199 possible parent colonies, only 49 (˜ 25%) had offspring colonies on the site. The mean number of offspring colonies per maternal parent colony was 2.94 and ranged from 1 to 8. A parent was identified for the queen of 146 of 247 offspring colonies. There was no evidence for reproductive senescence; fecundity was about the same throughout the 25-30 year lifespan of a colony. 5. There were no trends in the distance or direction of the dispersal of an offspring relative to its maternal parent colony. There was no relationship between the number of gynes produced by a colony in 1 year and the number of offspring colonies subsequently founded by its daughter reproductive females. The results provide the first estimate of a life table for a population of ant colonies and the first estimate of the female component of colony lifetime reproductive success. 6. The results suggest that commonly used measures of reproductive output may not be correlated with realized reproductive success. This is the starting point for future

  15. Ant Colonies Prefer Infected over Uninfected Nest Sites

    OpenAIRE

    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...

  16. Multi-view 3D scene reconstruction using ant colony optimization techniques

    International Nuclear Information System (INIS)

    This paper presents a new method performing high-quality 3D object reconstruction of complex shapes derived from multiple, calibrated photographs of the same scene. The novelty of this research is found in two basic elements, namely: (i) a novel voxel dissimilarity measure, which accommodates the elimination of the lighting variations of the models and (ii) the use of an ant colony approach for further refinement of the final 3D models. The proposed reconstruction procedure employs a volumetric method based on a novel projection test for the production of a visual hull. While the presented algorithm shares certain aspects with the space carving algorithm, it is, nevertheless, first enhanced with the lightness compensating image comparison method, and then refined using ant colony optimization. The algorithm is fast, computationally simple and results in accurate representations of the input scenes. In addition, compared to previous publications, the particular nature of the proposed algorithm allows accurate 3D volumetric measurements under demanding lighting environmental conditions, due to the fact that it can cope with uneven light scenes, resulting from the characteristics of the voxel dissimilarity measure applied. Besides, the intelligent behavior of the ant colony framework provides the opportunity to formulate the process as a combinatorial optimization problem, which can then be solved by means of a colony of cooperating artificial ants, resulting in very promising results. The method is validated with several real datasets, along with qualitative comparisons with other state-of-the-art 3D reconstruction techniques, following the Middlebury benchmark. (paper)

  17. DATA MINING UNTUK KLASIFIKASI PELANGGAN DENGAN ANT COLONY OPTIMIZATION

    Directory of Open Access Journals (Sweden)

    Maulani Kapiudin

    2007-01-01

    Full Text Available In this research the system for potentially customer classification is designed by extracting rule based classification from raw data with certain criteria. The searching process uses customer database from a bank with data mining technic by using ant colony optimization. A test based on min_case_per_rule variety and phenomene updating were done on a certain period of time. The result are group of customer class which base on rules built by ant and by modifying the pheromone updating, the area of the case is getting bigger. Prototype of the software is coded with C++ 6 version. The customer database master is created by using Microsoft Access. This paper gives information about potential customer of bank that can be classified by prototype of the software. Abstract in Bahasa Indonesia : Pada penelitian untuk sistem klasifikasi potensial customer ini didesain dengan melakukan ekstrak rule berdasarkan klasifikasi dari data mentah dengan kriteria tertentu. Proses pencarian menggunakan database pelanggan dari suatu bank dengan teknik data mining dengan ant colony optimization. Dilakukan percobaan dengan min_case_per_rule variety dan phenomene updating pada periode waktu tertentu. Hasilnya adalah sekelompok class pelanggan yang didasarkan dari rules yang dibangun dengan ant dan dengan dimodifikasi dengan pheromone updating, area permasalahan menjadi lebih melebar. Prototype dari software ini menggunakan C++ versi 6. Database pelanggan dibangun dengan Microsoft Access. Paper ini memberikan informasi mengenai potensi pelanggan dari bank, sehingga dapat diklasifikasikan dengan prototype dari software. Kata kunci: ant colony optimization, classification, min_case_per_rule, term, pheromone updating

  18. PRACTICAL APPLICATION OF POPULATION BASED ANT COLONY OPTIMIZATION ALGORITHM

    OpenAIRE

    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.

  19. Ant Colony Approach to Predict Amino Acid Interaction Networks

    OpenAIRE

    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.

  20. Hybrid ant colony algorithm for traveling salesman problem

    Institute of Scientific and Technical Information of China (English)

    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.

  1. Using pleometrosis (multiple queens) and pupae transplantation to boost weaver ant (Oecophylla smaragdina) colony growth in ant nurseries

    DEFF Research Database (Denmark)

    Offenberg, Hans Joachim; Nielsen, Mogens Gissel; Peng, Renkang

    2011-01-01

    Weaver ants (Oecophylla spp.) are increasingly being used for biocontrol and are targeted for future production of insect protein in ant farms. An efficient production of live ant colonies may facilitate the utilization of these ants but the production of mature colonies is hampered by the long...... and no transplantation. Thus, in ant nurseries the use of multiple queens during nest founding as well as transplantation of pupae from foreign colonies may be utilised to decrease the time it takes to produce a colony ready for implementation....... time it takes for newly established colonies to grow to a suitable size. In this study we followed the growth of newly founded O. smaragdina colonies with 2, 3 or 4 founding queens during 12 days of development, following the transplantation of 0, 30 or 60 pupae from a mature donor colony. Colony...

  2. Research on the Perceptual Law of Artificial Ants

    Institute of Scientific and Technical Information of China (English)

    ZHENG Zhaobao

    2005-01-01

    Beginning with the analysis of the behavior of natural ants, this paper illuminates the principle and method that, by adopting image texture energy as pheromone and finding their way on the track of the pheromone, artificial ants have the ability to identify and remember through similar measurement of pheromone. Based on the quantity of experiments, this paper analyzes some factors that influence the ability of artificial ants and draws some conclusions about the law of ant perception.

  3. All-Optical Implementation of the Ant Colony Optimization Algorithm

    Science.gov (United States)

    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.

  4. Emigration of a colony of the leaf-cutting ant Acromyrmex heyeri Forel (Hymenoptera, Formicidae

    Directory of Open Access Journals (Sweden)

    Mariane Aparecida Nickele

    2012-09-01

    Full Text Available Emigration of a colony of the leaf-cutting ant Acromyrmex heyeri Forel (Hymenoptera, Formicidae. Colony migration is a poorly studied phenomenon in leaf-cutting ants. Here we report on the emigration of a colony of the leaf-cutting ant A. heyeri in Brazil. The colony emigrated to a new location 47.4 m away from the original nest site, possibly because it had undergone considerable stress due to competitive interactions with a colony of Acromyrmex crassispinus.

  5. Emigration of a colony of the leaf-cutting ant Acromyrmex heyeri Forel (Hymenoptera, Formicidae)

    OpenAIRE

    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.

  6. Enhanced ANT Colony algorithm for Grid Scheduling

    Directory of Open Access Journals (Sweden)

    D.Maruthanayagam

    2010-11-01

    Full Text Available Grid computing is to make multiple machines that may be in different physical locations, behave like they are one large virtual machine. Grid scheduling environment is a arranging the machines in the course of find it fast called the Ant algorithm. The find for resource in the collection of geographically distributed heterogeneous computing systems and making scheduling decisions, taking into consideration eminence of service. Allocation of resources to a large number of jobs in a grid computing environment presents more difficulty than in network computational environments. Resource and job will have been allocating by resource discovery and filtering, composed of the selection of resources and idea specific scheduling and job submission. This algorithm is evaluated using the simulated execution times for a grid environment. Before starting the grid scheduling, the expected execution time for each task on each machine must be estimated and represented by an Expected Time calculation. The proposed scheduler allocates adopt the system environment freely at runtime. This resource optimally and adaptively in the scalable, dynamic and distribute controlled environment. Conclude of this propose depending upon the performance of the grid systems. Key words: Grid Computing, Job Scheduling, Heuristic Algorithm, Load Balancing, scheduling algorithm simulation, ant algorithm

  7. Solution to the problem of ant being stuck by ant colony routing algorithm

    Institute of Scientific and Technical Information of China (English)

    ZHAO Jing; TONG Wei-ming

    2009-01-01

    Many ant colony routing (ACR) algorithms have been presented in recent years, but few have studied the problem that ants will get stuck with probability in any terminal host when they are searching paths to route packets around a network. The problem has to be faced when designing and implementing the ACR algorithm. This article analyzes in detail the differences between the ACR and the ant colony optimization (ACO). Besides, particular restrictions on the ACR are pointed out and the three causes of ant being-stuck problem are obtained. Furthermore, this article proposes a new ant searching mechanism through dual path-checking and online routing loop removing by every intermediate node an ant visited and the destination host respectively, to solve the problem of ant being stuck and routing loop simultaneously. The result of numerical simulation is abstracted from one real network. Compared with existing two typical ACR algorithms, it shows that the proposed algorithm can settle the problem of ant being stuck and achieve more effective searching outcome for optimization path.

  8. An ant colony algorithm for solving Max-cut problem

    Institute of Scientific and Technical Information of China (English)

    Lin Gao; Yan Zeng; Anguo Dong

    2008-01-01

    Max-cut problem is an NP-complete and classical combinatorial optimization problem that has a wide range of appfications in dif-ferent domains,such as bioinformatics,network optimization,statistical physics,and very large scale integration design.In this paper we investigate the capabilities of the ant colony optimization(ACO)heuristic for solving the Max-cut problem and present an AntCut algo-rithm.A large number of simulation experiments show that the algorithm can solve the Max-cut problem more efficiently and effectively.

  9. Ant Colonies Do Not Trade-Off Reproduction against Maintenance.

    Science.gov (United States)

    Kramer, Boris H; Schrempf, Alexandra; Scheuerlein, Alexander; Heinze, Jürgen

    2015-01-01

    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. PMID:26383861

  10. Ant Colonies Do Not Trade-Off Reproduction against Maintenance.

    Directory of Open Access Journals (Sweden)

    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.

  11. GrAnt: Inferring Best Forwarders from Complex Networks' Dynamics through a Greedy Ant Colony Optimization

    OpenAIRE

    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...

  12. Ant colonies prefer infected over uninfected nest sites

    DEFF Research Database (Denmark)

    Pontieri, Luigi; Vojvodic, Svjetlana; Graham, Riley;

    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...... 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...

  13. Colony Structure and Nest Location of Two Species of Dacetine Ants: Pyramica ohioensis (Kennedy & Schramm) and Pyramica rostrata (Emery) in Maryland (Hymenoptera: Formicidae)

    OpenAIRE

    Richard M. Duffield; Alpert, Gary D.

    2011-01-01

    The discovery of numerous Pyramica ohioensis and P. rostrata colonies living in acorns, as well as the efficient recovery of colonies from artificial nests placed in suitable habitats, opens a new stage in the study of North American dacetine ants. Here we present detailed information, based on 42 nest collections, on the colony structure of these two species. P. ohioensis colonies are smaller than those of P. rostrata. Both species are polygynous, but nests of P. ohioensis contain fewer d...

  14. Advances on image interpolation based on ant colony algorithm.

    Science.gov (United States)

    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

  15. A Hybrid Ant Colony Algorithm for Loading Pattern Optimization

    Science.gov (United States)

    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.

  16. Ant Colony Optimization With Combining Gaussian Eliminations for Matrix Multiplication.

    Science.gov (United States)

    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

  17. AN ANT COLONY ALGORITHM FOR MINIMUM UNSATISFIABLE CORE EXTRACTION

    Institute of Scientific and Technical Information of China (English)

    Zhang Jianmin; Shen Shengyu; Li Sikun

    2008-01-01

    Explaining the causes of infeasibility of Boolean formulas has many practical applications in electronic design automation and formal verification of hardware. Furthermore,a minimum explanation of infeasibility that excludes all irrelevant information is generally of interest. A smallest-cardinality unsatisfiable subset called a minimum unsatisfiable core can provide a succinct explanation of infea-sibility and is valuable for applications. However,little attention has been concentrated on extraction of minimum unsatisfiable core. In this paper,the relationship between maximal satisfiability and mini-mum unsatisfiability is presented and proved,then an efficient ant colony algorithm is proposed to derive an exact or ncarly exact minimum unsatisfiable core based on the relationship. Finally,ex-perimental results on practical benchmarks compared with the best known approach are reported,and the results show that the ant colony algorithm strongly outperforms the best previous algorithm.

  18. Modal parameters estimation using ant colony optimisation algorithm

    Science.gov (United States)

    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.

  19. An improved ant colony algorithm with diversified solutions based on the immune strategy

    OpenAIRE

    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...

  20. Apriori and Ant Colony Optimization of Association Rules

    OpenAIRE

    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...

  1. Antenna synthesis based on the ant colony optimization algorithm

    OpenAIRE

    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...

  2. Reconstructing Amino Acid Interaction Networks by an Ant Colony Approach

    OpenAIRE

    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.

  3. Predicting Multicomponent Protein Assemblies Using an Ant Colony Approach

    OpenAIRE

    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...

  4. Ant colony optimization approach to estimate energy demand of Turkey

    International Nuclear Information System (INIS)

    This paper attempts to shed light on the determinants of energy demand in Turkey. Energy demand model is first proposed using the ant colony optimization (ACO) approach. It is multi-agent systems in which the behavior of each ant is inspired by the foraging behavior of real ants to solve optimization problem. ACO energy demand estimation (ACOEDE) model is developed using population, gross domestic product (GDP), import and export. All equations proposed here are linear and quadratic. QuadraticACOEDE provided better-fit solution due to fluctuations of the economic indicators. The ACOEDE model plans the energy demand of Turkey until 2025 according to three scenarios. The relative estimation errors of the ACOEDE model are the lowest when they are compared with the Ministry of Energy and Natural Resources (MENR) projection

  5. Rationality in collective decision-making by ant colonies.

    Science.gov (United States)

    Edwards, Susan C; Pratt, Stephen C

    2009-10-22

    Economic models of animal behaviour assume that decision-makers are rational, meaning that they assess options according to intrinsic fitness value and not by comparison with available alternatives. This expectation is frequently violated, but the significance of irrational behaviour remains controversial. One possibility is that irrationality arises from cognitive constraints that necessitate short cuts like comparative evaluation. If so, the study of whether and when irrationality occurs can illuminate cognitive mechanisms. We applied this logic in a novel setting: the collective decisions of insect societies. We tested for irrationality in colonies of Temnothorax ants choosing between two nest sites that varied in multiple attributes, such that neither site was clearly superior. In similar situations, individual animals show irrational changes in preference when a third relatively unattractive option is introduced. In contrast, we found no such effect in colonies. We suggest that immunity to irrationality in this case may result from the ants' decentralized decision mechanism. A colony's choice does not depend on site comparison by individuals, but instead self-organizes from the interactions of multiple ants, most of which are aware of only a single site. This strategy may filter out comparative effects, preventing systematic errors that would otherwise arise from the cognitive limitations of individuals.

  6. Cooperation-based Ant Colony Algorithm in WSN

    Directory of Open Access Journals (Sweden)

    Jianbin Xue

    2013-04-01

    Full Text Available This paper proposed a routing algorithm based on ant colony algorithm. The traditional ant colony algorithm updates pheromone according to the path length, to get the shortest path from the initial node to destination node. But MIMO system is different from the SISO system. The distance is farther but the energy is not bigger. Similarly, the closer the distance, the smaller the energy is not necessarily. So need to select the path according to the energy consumption of the path. This paper is based on the energy consumption to update the pheromone which from the cluster head node to the next hop node. Then, can find a path which the communication energy consumption is least. This algorithm can save more energy consumption of the network. The simulation results of MATLAB show that the path chosen by the algorithm is better than the simple ant colony algorithm, and the algorithm can save the network energy consumption better and can prolong the life cycle of the network.

  7. The use of artificial nests by weaver ants: a preliminary field observation

    DEFF Research Database (Denmark)

    Offenberg, Joachim

    2014-01-01

    populations or destroy colonies. The ants, however, show adaptive nesting behavior, which may mitigate storm impact. This study tested whether Oecophylla smaragdina was willing to use plastic bottles as safe artificial nesting sites, and whether adoption of artificial nests was seasonally related to harsh...... weather. Bottles were used for nesting throughout the stormy rainy season in a pomelo plantation with an open canopy, whereas in a mango plantation with a denser canopy the ants, after initial colonisation, left the bottles again at the end of the rainy season, especially in the calmer part...

  8. A Novel Algorithm for Manets using Ant Colony

    Directory of Open Access Journals (Sweden)

    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.

  9. The Role of Non-Foraging Nests in Polydomous Wood Ant Colonies

    OpenAIRE

    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...

  10. Dealing with water deficit in Atta ant colonies: large ants scout for water while small ants transport it

    Directory of Open Access Journals (Sweden)

    Antonio Carlos Da-Silva

    2012-07-01

    Leafcutter ants (Atta sexdens rubropilosa (Forel 1908 have an elaborate social organization, complete with caste divisions. Activities carried out by specialist groups contribute to the overall success and survival of the colony when it is confronted with environmental challenges such as dehydration. Ants detect variations in humidity inside the nest and react by activating several types of behavior that enhance water uptake and decrease water loss, but it is not clear whether or not a single caste collects water regardless of the cost of bringing this resource back to the colony. Accordingly, we investigated water collection activities in three colonies of Atta sexdens rubropilosa experimentally exposed to water stress. Specifically, we analyzed whether or not the same ant caste foraged for water, regardless of the absolute energetic cost (distance of transporting this resource back to the colony. Our experimental design offered water sources at 0 m, 1 m and 10 m from the nest. We studied the body size of ants near the water sources from the initial offer of water (time  =  0 to 120 min, and tested for specialization. We observed a reduction in the average size and variance of ants that corroborated the specialization hypothesis. Although the temporal course of specialization changed with distance, the final outcome was similar among distances. Thus, we conclude that, for this species, a specialist (our use of the word “specialist” does not mean exclusive task force is responsible for collecting water, regardless of the cost of transporting water back to the colony.

  11. The Relationship between Canopy Cover and Colony Size of the Wood Ant Formica lugubris - Implications for the Thermal Effects on a Keystone Ant Species

    OpenAIRE

    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...

  12. An Ant Colony Optimization Algorithm For Job Shop Scheduling Problem

    Directory of Open Access Journals (Sweden)

    Edson Florez

    2013-07-01

    Full Text Available 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 incombinatorial optimization. This paper describes the implementation of an ACO model algorithm known asElitist Ant System (EAS, applied to a combinatorial optimization problem called Job Shop SchedulingProblem (JSSP. We propose a method that seeks to reduce delays designating the operation immediatelyavailable, but considering the operations that lacklittle to be available and have a greater amount ofpheromone. The performance of the algorithm was evaluated for problems of JSSP reference, comparingthe quality of the solutions obtained regarding thebest known solution of the most effective methods.Thesolutions were of good quality and obtained with aremarkable efficiency by having to make a very lownumber of objective function evaluations.

  13. Blind noisy image quality evaluation using a deformable ant colony algorithm

    Science.gov (United States)

    Chen, Li; Huang, Xiaotong; Tian, Jing; Fu, Xiaowei

    2014-04-01

    The objective of blind noisy image quality assessment is to evaluate the quality of the degraded noisy image without the knowledge of the ground truth image. Its performance relies on the accuracy of the noise statistics estimated from homogenous blocks. The major challenge of block-based approaches lies in the block size selection, as it affects the local noise derivation. To tackle this challenge, a deformable ant colony optimization (DACO) approach is proposed in this paper to adaptively adjust the ant size for image block selection. The proposed DACO approach considers that the size of the ant is adjustable during foraging. For the smooth image blocks, more pheromone is deposited, and then the size of ant is increased. Therefore, this strategy enables the ants to have dynamic food-search capability, leading to more accurate selection of homogeneous blocks. Furthermore, the regression analysis is used to obtain image quality score by exploiting the above-estimated noise statistics. Experimental results are provided to justify that the proposed approach outperforms conventional approaches to provide more accurate noise statistics estimation and achieve a consistent image quality evaluation performance for both the artificially generated and real-world noisy images.

  14. Mobile Anonymous Trust Based Routing Using Ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    R. Kalpana

    2012-01-01

    Full Text Available Problem statement: Ad hoc networks are susceptible to malicious attacks through denial of services, traffic analysis and spoofing. The security of the ad hoc routing protocol depends upon encryption, authentication, anonymity and trust factors. End-to-end security of data is provided by encryption and authentication, topology information of the nodes can be obtained by studying traffic and routing data. This security problem of ad hoc network is addressed by the use of anonymity mechanisms and trust levels. Identification information like traffic flow, network topology, paths from malicious attackers is hidden in anonymous networks. Similarly, trust plays a very important role in the intermediate node selection in ad hoc networks. Trust is essential as selfish and malicious nodes not only pose a security issue but also decreases the Quality of Service. Approach: In this study, a routing to address anonymous routing with a trust which improves the overall security of the ad hoc network was proposed. A new approach for an on demand ad-hoc routing algorithm, which was based on swarm intelligence. Ant colony algorithms were a subset of swarm intelligence and considered the ability of simple ants to solve complex problems by cooperation. The interesting point was, that the ants do not need any direct communication for the solution process, instead they communicate by stigmergy. The notion of stigmergy means the indirect communication of individuals through modifying their environment. Several algorithms which were based on ant colony problems were introduced in recent years to solve different problems, e.g., optimization problems. Results and Conclusion: It is observed that the overall security in the network improves when the trust factor is considered. It is seen that non performing nodes are not considered due to the proposed ACO technique.

  15. Scheduling Optimization of Construction Engineering based on Ant Colony Optimized Hybrid Genetic Algorithm

    OpenAIRE

    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...

  16. Modeling of Vector Quantization Image Coding in an Ant Colony System

    Institute of Scientific and Technical Information of China (English)

    LIXia; LUOXuehui; ZHANGJihong

    2004-01-01

    Ant colony algorithm is a newly emerged stochastic searching optimization algorithm in recent years. In this paper, vector quantization image coding is modeled as a stochastic optimization problem in an Ant colony system (ACS). An appropriately adapted ant colony algorithm is proposed for vector quantization codebook design. Experimental results show that the ACS-based algorithm can produce a better codebook and the improvement of Pixel signal-to-noise ratio (PSNR) exceeds 1dB compared with the conventional LBG algorithm.

  17. Serial Monodomy in the Gypsy Ant, Aphaenogaster araneoides: Does Nest Odor Reduction Influence Colony Relocation?

    OpenAIRE

    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. ...

  18. Design of broadband omnidirectional antireflection coatings using ant colony algorithm.

    Science.gov (United States)

    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

  19. Applying Data Clustering Feature to Speed Up Ant Colony Optimization

    OpenAIRE

    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...

  20. An ant colony optimization method for generalized TSP problem

    Institute of Scientific and Technical Information of China (English)

    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.

  1. JOB SHOP METHODOLOGY BASED ON AN ANT COLONY

    Directory of Open Access Journals (Sweden)

    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.

  2. An Ant Colony Optimization Algorithm for Microwave Corrugated Filters Design

    OpenAIRE

    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...

  3. Microsatellites reveal high genetic diversity within colonies of Camponotus ants.

    Science.gov (United States)

    Gertsch, P; Pamilo, P; Varvio, S L

    1995-04-01

    In order to characterize the sociogenetic structure of colonies in the carpenter ants Camponotus herculeanus and C. ligniperda, we have developed microsatellite markers. The three loci studied were either fixed for different alleles in the two species or showed different patterns of polymorphisms. Genotyping of workers and males showed that the broods of C. ligniperda include several matrilines, a rare phenomenon in the genus. Five alleles from a locus polymorphic in both species were sequenced from the respective PCR-products. A part of the length variation appeared to be due to changes outside the repeat sequence, and some PCR products of an equal length had a different number of dinucleotide repeats.

  4. A Hybrid Optimization Algorithm based on Genetic Algorithm and Ant Colony Optimization

    OpenAIRE

    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...

  5. A Dynamic Job Shop Scheduling Method Based on Ant Colony Coordination System

    Institute of Scientific and Technical Information of China (English)

    ZHU Qiong; WU Li-hui; ZHANG Jie

    2009-01-01

    Due to the stubborn nature of dynamic job shop scheduling problem, a novel ant colony coordination mechanism is proposed in this paper to search for an optimal schedule in dynamic environment. In ant colony coordination mechanism, the dynamic .job shop is composed of several autonomous ants. These ants coordinate with each other by simulating the ant foraging behavior of spreading pheromone on the trails, by which they can make information available globally, and further more guide ants make optimal decisions. The proposed mechanism is tested by several instances and the results confirm the validity of it.

  6. Operations planning for agricultural harvesters using ant colony optimization

    Directory of Open Access Journals (Sweden)

    A. Bakhtiari

    2013-07-01

    Full Text Available An approach based on ant colony optimization for the generation for optimal field coverage plans for the harvesting operations using the optimal track sequence principle B-patterns was presented. The case where the harvester unloads to a stationary facility located out of the field area, or in the field boundary, was examined. In this operation type there are capacity constraints to the load that a primary unit, or a harvester in this specific case, can carry and consequently, it is not able to complete the task of harvesting a field area and therefore it has to leave the field area, to unload, and return to continue the task one or more times. Results from comparing the optimal plans with conventional plans generated by operators show reductions in the in-field nonworking distance in the range of 19.3-42.1% while the savings in the total non-working distance were in the range of 18-43.8%. These savings provide a high potential for the implementation of the ant colony optimization approach for the case of harvesting operations that are not supported by transport carts for the out-of-the-field removal of the crops, a practice case that is normally followed in developing countries, due to lack of resources.

  7. Binary-Coding-Based Ant Colony Optimization and Its Convergence

    Institute of Scientific and Technical Information of China (English)

    Tian-Ming Bu; Song-Nian Yu; Hui-Wei Guan

    2004-01-01

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

  8. Ant colony optimization as a method for strategic genotype sampling.

    Science.gov (United States)

    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

  9. Using Ant Colony Optimization for Routing in VLSI Chips

    Science.gov (United States)

    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.

  10. Routing in Ad Hoc Network Using Ant Colony Optimization

    Science.gov (United States)

    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.

  11. Ant system: optimization by a colony of cooperating agents.

    Science.gov (United States)

    Dorigo, M; Maniezzo, V; Colorni, A

    1996-01-01

    An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.

  12. A Survey Paper on Solving TSP using Ant Colony Optimization on GPU

    OpenAIRE

    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...

  13. Solving Integer Programming Problems by Using Artificial Bee Colony Algorithm

    Science.gov (United States)

    Akay, Bahriye; Karaboga, Dervis

    This paper presents a study that applies the Artificial Bee Colony algorithm to integer programming problems and compares its performance with those of Particle Swarm Optimization algorithm variants and Branch and Bound technique presented to the literature. In order to cope with integer programming problems, in neighbour solution production unit, solutions are truncated to the nearest integer values. The experimental results show that Artificial Bee Colony algorithm can handle integer programming problems efficiently and Artificial Bee Colony algorithm can be considered to be very robust by the statistics calculated such as mean, median, standard deviation.

  14. Population and colony structure of the carpenter ant Camponotus floridanus.

    Science.gov (United States)

    Gadau, J; Heinze, J; Hölldobler, B; Schmid, M

    1996-12-01

    The colony and population structure of the carpenter ant, Camponotus floridanus, were investigated by multilocus DNA fingerprinting using simple repeat motifs as probes [e.g. (GATA)4]. The mating frequency of 15 queens was determined by comparing the fingerprint patterns of the queen and 17-33 of her progeny workers. C. floridanus queens are most probably singly mated, i.e. this species is monandrous and monogynous (one queen per colony). C. floridanus occurs in all counties of mainland Florida and also inhabits most of the Key islands in the southern part of Florida. We tested whether the two mainland populations and the island populations are genetically isolated. Wright's FST and Nei's D-value of genetic distance were calculated from intercolonial bandsharing-coefficients. The population of C. floridanus is substructured (FST = 0.19 +/- 0.09) and the highest degree of genetic distance was found between one of the mainland populations and the island populations (D = 0.35). Our fingerprinting technique could successfully be transferred to 12 other Camponotus species and here also revealed sufficient variability to analyse the genetic structure. In three of these species (C. ligniperdus, C. herculeanus and C. gigas) we could determine the mating frequency of the queen in one or two colonies, respectively. PMID:8981768

  15. Population and colony structure of the carpenter ant Camponotus floridanus.

    Science.gov (United States)

    Gadau, J; Heinze, J; Hölldobler, B; Schmid, M

    1996-12-01

    The colony and population structure of the carpenter ant, Camponotus floridanus, were investigated by multilocus DNA fingerprinting using simple repeat motifs as probes [e.g. (GATA)4]. The mating frequency of 15 queens was determined by comparing the fingerprint patterns of the queen and 17-33 of her progeny workers. C. floridanus queens are most probably singly mated, i.e. this species is monandrous and monogynous (one queen per colony). C. floridanus occurs in all counties of mainland Florida and also inhabits most of the Key islands in the southern part of Florida. We tested whether the two mainland populations and the island populations are genetically isolated. Wright's FST and Nei's D-value of genetic distance were calculated from intercolonial bandsharing-coefficients. The population of C. floridanus is substructured (FST = 0.19 +/- 0.09) and the highest degree of genetic distance was found between one of the mainland populations and the island populations (D = 0.35). Our fingerprinting technique could successfully be transferred to 12 other Camponotus species and here also revealed sufficient variability to analyse the genetic structure. In three of these species (C. ligniperdus, C. herculeanus and C. gigas) we could determine the mating frequency of the queen in one or two colonies, respectively.

  16. Research on the mobile robots intelligent path planning based on ant colony algorithm application in manufacturing logistics

    OpenAIRE

    Guo, Yue; SHEN Xuelian; ZHU Zhanfeng

    2014-01-01

    With the development of robotics and artificial intelligence field unceasingly thorough, path planning as an important field of robot calculation has been widespread concern. This paper analyzes the current development of robot and path planning algorithm and focuses on the advantages and disadvantages of the traditional intelligent path planning as well as the path planning. The problem of mobile robot path planning is studied by using ant colony algorithm, and it also provide...

  17. Nest site and weather affect the personality of harvester ant colonies.

    Science.gov (United States)

    Pinter-Wollman, Noa; Gordon, Deborah M; Holmes, Susan

    2012-09-01

    Environmental conditions and physical constraints both influence an animal's behavior. We investigate whether behavioral variation among colonies of the black harvester ant, Messor andrei, remains consistent across foraging and disturbance situations and ask whether consistent colony behavior is affected by nest site and weather. We examined variation among colonies in responsiveness to food baits and to disturbance, measured as a change in numbers of active ants, and in the speed with which colonies retrieved food and removed debris. Colonies differed consistently, across foraging and disturbance situations, in both responsiveness and speed. Increased activity in response to food was associated with a smaller decrease in response to alarm. Speed of retrieving food was correlated with speed of removing debris. In all colonies, speed was greater in dry conditions, reducing the amount of time ants spent outside the nest. While a colony occupied a certain nest site, its responsiveness was consistent in both foraging and disturbance situations, suggesting that nest structure influences colony personality.

  18. A Simple and Efficient Artificial Bee Colony Algorithm

    OpenAIRE

    Yunfeng Xu; Ping Fan; Ling Yuan

    2013-01-01

    Artificial bee colony (ABC) is a new population-based stochastic algorithm which has shown good search abilities on many optimization problems. However, the original ABC shows slow convergence speed during the search process. In order to enhance the performance of ABC, this paper proposes a new artificial bee colony (NABC) algorithm, which modifies the search pattern of both employed and onlooker bees. A solution pool is constructed by storing some best solutions of the current swarm. New can...

  19. Aircraft technology portfolio optimization using ant colony optimization

    Science.gov (United States)

    Villeneuve, Frederic J.; Mavris, Dimitri N.

    2012-11-01

    Technology portfolio selection is a combinatorial optimization problem often faced with a large number of combinations and technology incompatibilities. The main research question addressed in this article is to determine if Ant Colony Optimization (ACO) is better suited than Genetic Algorithms (GAs) and Simulated Annealing (SA) for technology portfolio optimization when incompatibility constraints between technologies are present. Convergence rate, capability to find optima, and efficiency in handling of incompatibilities are the three criteria of comparison. The application problem consists of finding the best technology portfolio from 29 aircraft technologies. The results show that ACO and GAs converge faster and find optima more easily than SA, and that ACO can optimize portfolios with technology incompatibilities without using penalty functions. This latter finding paves the way for more use of ACO when the number of constraints increases, such as in the technology and concept selection for complex engineering systems.

  20. Ant Colony Based Path Planning Algorithm for Autonomous Robotic Vehicles

    Directory of Open Access Journals (Sweden)

    Yogita Gigras

    2012-11-01

    Full Text Available The requirement of an autonomous robotic vehicles demand highly efficient algorithm as well as software. Today’s advanced computer hardware technology does not provide these types of extensive processing capabilities, so there is still a major space and time limitation for the technologies that are available for autonomous robotic applications. Now days, small to miniature mobile robots are required for investigation, surveillance and hazardous material detection for military and industrial applications. But these small sized robots have limited power capacity as well as memory and processing resources. A number of algorithms exist for producing optimal path for dynamically cost. This paper presents a new ant colony based approach which is helpful in solving path planning problem for autonomous robotic application. The experiment of simulation verified its validity of algorithm in terms of time.

  1. The analysis of the convergence of ant colony optimization algorithm

    Institute of Scientific and Technical Information of China (English)

    ZHU Qingbao; WANG Lingling

    2007-01-01

    The ant colony optimization algorithm has been widely studied and many important results have been obtained.Though this algorithm has been applied to many fields.the analysis about its convergence is much less,which will influence the improvement of this algorithm.Therefore,the convergence of this algorithm applied to the traveling salesman problem(TSP)was analyzed in detail.The conclusion that this algorithm will definitely converge to the optimal solution under the condition of 0<q0<1 was proved true.In addition,the influence on its convergence caused by the properties of the closed path,heuristic functions,the pheromone and q0 was analyzed.Based on the above-mentioned,some conclusions about how to improve the speed of its convergence are obtained.

  2. Microsatellites reveal high genetic diversity within colonies of Camponotus ants.

    Science.gov (United States)

    Gertsch, P; Pamilo, P; Varvio, S L

    1995-04-01

    In order to characterize the sociogenetic structure of colonies in the carpenter ants Camponotus herculeanus and C. ligniperda, we have developed microsatellite markers. The three loci studied were either fixed for different alleles in the two species or showed different patterns of polymorphisms. Genotyping of workers and males showed that the broods of C. ligniperda include several matrilines, a rare phenomenon in the genus. Five alleles from a locus polymorphic in both species were sequenced from the respective PCR-products. A part of the length variation appeared to be due to changes outside the repeat sequence, and some PCR products of an equal length had a different number of dinucleotide repeats. PMID:7735528

  3. Automatic fault extraction using a modified ant-colony algorithm

    International Nuclear Information System (INIS)

    The basis of automatic fault extraction is seismic attributes, such as the coherence cube which is always used to identify a fault by the minimum value. The biggest challenge in automatic fault extraction is noise, including that of seismic data. However, a fault has a better spatial continuity in certain direction, which makes it quite different from noise. Considering this characteristic, a modified ant-colony algorithm is introduced into automatic fault identification and tracking, where the gradient direction and direction consistency are used as constraints. Numerical model test results show that this method is feasible and effective in automatic fault extraction and noise suppression. The application of field data further illustrates its validity and superiority. (paper)

  4. DETECTION OF MASSES IN MAMMOGRAM IMAGES USING ANT COLONY OPTIMIZATION

    Directory of Open Access Journals (Sweden)

    Varsha Patankar

    2014-04-01

    Full Text Available This paper proposes the advances in edge detection techniques, which is used for the mammogram images for cancer diagnosis. It compares the evaluation of edge detection with the proposed method ant colony optimization. The study shows that the edge detection technique is applied on the mammogram images because it will clearly identify the masses in mammogram images. This will help to identify the type of cancer at the early stage. ACO edge detector is best in detecting the edges when compared to the other edge detectors. The quality of various edge detectors is calculated based on the parameters such as Peak signal to noise ratio (PSNR and Mean square error (MSE.

  5. A hybrid ant colony algorithm for loading pattern optimization

    International Nuclear Information System (INIS)

    EDF (Electricity of France) operates 58 nuclear power plant (NPP), all of the Pressurized Water Reactor (PWR) type. The loading pattern (LP) optimization of these NPP is currently done by EDF expert engineers. Within this framework, EDF has developed automatic optimization tools that assist the experts. This paper presents firstly a description of the LP optimization problem listing its constraints. Secondly, a study of the search space is performed using the 'landscape fitness analysis' paradigm. Lastly, a hybrid algorithm based on ant colony and a local search method, is introduced to take advantage of the features of the problem. Tests have been performed on realistic cases. This hybrid algorithm has turned out to give very encouraging results when compared to a randomized local search method

  6. Road Network Vulnerability Analysis Based on Improved Ant Colony Algorithm

    Directory of Open Access Journals (Sweden)

    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.

  7. Electromagnetic Wave Propagation Modeling Using the Ant Colony Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    P. Pechac

    2002-09-01

    Full Text Available The Ant Colony Optimization algorithm - a multi-agent approach tocombinatorial optimization problems - is introduced for a simple raytracing performed on only an ordinary bitmap describing atwo-dimensional scenario. This bitmap can be obtained as a simple scanwhere different colors represent different mediums or obstacles. It isshown that using the presented algorithm a path minimizing the wavetraveling time can be found according to the Fermat's principle. Anexample of practical application is a simple ray tracing performed ononly an ordinary scanned bitmap of the city map. Together with theBerg's recursive model a non-line-of-sight path loss could becalculated without any need of building database. In this way thecoverage predictions for urban microcells could become extremely easyand fast to apply.

  8. Ant Colony Algorithm for the Weighted Item Layout Optimization Problem

    CERN Document Server

    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.

  9. Power Efficient Resource Allocation for Clouds Using Ant Colony Framework

    CERN Document Server

    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.

  10. Time-Based Dynamic Trust Model Using Ant Colony Algorithm

    Institute of Scientific and Technical Information of China (English)

    TANG Zhuo; LU Zhengding; LI Kai

    2006-01-01

    The trust in distributed environment is uncertain, which is variation for various factors. This paper introduces TDTM, a model for time-based dynamic trust. Every entity in the distribute environment is endowed with a trust-vector, which figures the trust intensity between this entity and the others. The trust intensity is dynamic due to the time and the inter-operation between two entities, a method is proposed to quantify this change based on the mind of ant colony algorithm and then an algorithm for the transfer of trust relation is also proposed. Furthermore, this paper analyses the influence to the trust intensity among all entities that is aroused by the change of trust intensity between the two entities, and presents an algorithm to resolve the problem. Finally, we show the process of the trusts'change that is aroused by the time' lapse and the inter-operation through an instance.

  11. Wavelet phase estimation using ant colony optimization algorithm

    Science.gov (United States)

    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.

  12. An Ant Colony Optimization Algorithm for Microwave Corrugated Filters Design

    Directory of Open Access Journals (Sweden)

    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.

  13. Ant Colony Optimization and the Minimum Cut Problem

    DEFF Research Database (Denmark)

    Kötzing, Timo; Lehre, Per Kristian; Neumann, Frank;

    2010-01-01

    Ant Colony Optimization (ACO) is a powerful metaheuristic for solving combinatorial optimization problems. With this paper we contribute to the theoretical understanding of this kind of algorithm by investigating the classical minimum cut problem. An ACO algorithm similar to the one that was proved...... successful for the minimum spanning tree problem is studied. Using rigorous runtime analyses we show how the ACO algorithm behaves similarly to Karger and Stein's algorithm for the minimum cut problem as long as the use of pheromone values is limited. Hence optimal solutions are obtained in expected...... polynomial time. On the other hand, we show that high use of pheromones has a negative effect, and the ACO algorithm may get trapped in local optima resulting in an exponential runtime to obtain an optimal solution. This result indicates that ACO algorithms may be inappropriate for finding minimum cuts....

  14. Data Mining using Advanced Ant Colony Optimization Algorithm and Application to Bankruptcy Prediction

    OpenAIRE

    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...

  15. An ant colony optimization algorithm for phylogenetic estimation under the minimum evolution principle

    Directory of Open Access Journals (Sweden)

    Milinkovitch Michel C

    2007-11-01

    Full Text Available Abstract Background Distance matrix methods constitute a major family of phylogenetic estimation methods, and the minimum evolution (ME principle (aiming at recovering the phylogeny with shortest length is one of the most commonly used optimality criteria for estimating phylogenetic trees. The major difficulty for its application is that the number of possible phylogenies grows exponentially with the number of taxa analyzed and the minimum evolution principle is known to belong to the NP MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGacaGaaiaabeqaaeqabiWaaaGcbaWenfgDOvwBHrxAJfwnHbqeg0uy0HwzTfgDPnwy1aaceaGae8xdX7Kaeeiuaafaaa@3888@-hard class of problems. Results In this paper, we introduce an Ant Colony Optimization (ACO algorithm to estimate phylogenies under the minimum evolution principle. ACO is an optimization technique inspired from the foraging behavior of real ant colonies. This behavior is exploited in artificial ant colonies for the search of approximate solutions to discrete optimization problems. Conclusion We show that the ACO algorithm is potentially competitive in comparison with state-of-the-art algorithms for the minimum evolution principle. This is the first application of an ACO algorithm to the phylogenetic estimation problem.

  16. Colony fusion and worker reproduction after queen loss in army ants

    DEFF Research Database (Denmark)

    Kronauer, Daniel J C; Schöning, Caspar; d'Ettorre, Patrizia;

    2010-01-01

    Theory predicts that altruism is only evolutionarily stable if it is preferentially directed towards relatives, so that any such behaviour towards seemingly unrelated individuals requires scrutiny. Queenless army ant colonies, which have anecdotally been reported to fuse with queenright foreign...... colonies, are such an enigmatic case. Here we combine experimental queen removal with population genetics and cuticular chemistry analyses to show that colonies of the African army ant Dorylus molestus frequently merge with neighbouring colonies after queen loss. Merging colonies often have no direct co...

  17. Displacement back analysis for underground engineering based on immunized continuous ant colony optimization

    Science.gov (United States)

    Gao, Wei

    2016-05-01

    The objective function of displacement back analysis for rock parameters in underground engineering is a very complicated nonlinear multiple hump function. The global optimization method can solve this problem very well. However, many numerical simulations must be performed during the optimization process, which is very time consuming. Therefore, it is important to improve the computational efficiency of optimization back analysis. To improve optimization back analysis, a new global optimization, immunized continuous ant colony optimization, is proposed. This is an improved continuous ant colony optimization using the basic principles of an artificial immune system and evolutionary algorithm. Based on this new global optimization, a new displacement optimization back analysis for rock parameters is proposed. The computational performance of the new back analysis is verified through a numerical example and a real engineering example. The results show that this new method can be used to obtain suitable parameters of rock mass with higher accuracy and less effort than previous methods. Moreover, the new back analysis is very robust.

  18. Adaptive tracking and compensation of laser spot based on ant colony optimization

    Science.gov (United States)

    Yang, Lihong; Ke, Xizheng; Bai, Runbing; Hu, Qidi

    2009-05-01

    Because the effect of atmospheric scattering and atmospheric turbulence on laser signal of atmospheric absorption,laser spot twinkling, beam drift and spot split-up occur ,when laser signal transmits in the atmospheric channel. The phenomenon will be seriously affects the stability and the reliability of laser spot receiving system. In order to reduce the influence of atmospheric turbulence, we adopt optimum control thoughts in the field of artificial intelligence, propose a novel adaptive optical control technology-- model-free optimized adaptive control technology, analyze low-order pattern wave-front error theory, in which an -adaptive optical system is employed to adjust errors, and design its adaptive structure system. Ant colony algorithm is the control core algorithm, which is characteristic of positive feedback, distributed computing and greedy heuristic search. . The ant colony algorithm optimization of adaptive optical phase compensation is simulated. Simulation result shows that, the algorithm can effectively control laser energy distribution, improve laser light beam quality, and enhance signal-to-noise ratio of received signal.

  19. Yeasts associated with the infrabuccal pocket and colonies of the carpenter ant Camponotus vicinus.

    Science.gov (United States)

    Mankowski, M E; Morrell, J J

    2004-01-01

    After scanning electron microscopy indicated that the infrabuccal pockets of carpenter ants (Camponotus vicinus) contained numerous yeast-like cells, yeast associations were examined in six colonies of carpenter ants from two locations in Benton County in western Oregon. Samples from the infrabuccal-pocket contents and worker ant exoskeletons, interior galleries of each colony, and detritus and soil around the colonies were plated on yeast-extract/ malt-extract agar augmented with 1 M hydrochloric acid and incubated at 25 C. Yeasts were identified on the basis of morphological characteristics and physiological attributes with the BIOLOG(®) microbial identification system. Yeast populations from carpenter ant nest material and material surrounding the nest differed from those obtained from the infrabuccal pocket. Debaryomyces polymorphus was isolated more often from the infrabuccal pocket than from other material. This species has also been isolated from other ant species, but its role in colony nutrition is unknown. PMID:21148849

  20. Yeasts associated with the infrabuccal pocket and colonies of the carpenter ant Camponotus vicinus.

    Science.gov (United States)

    Mankowski, M E; Morrell, J J

    2004-01-01

    After scanning electron microscopy indicated that the infrabuccal pockets of carpenter ants (Camponotus vicinus) contained numerous yeast-like cells, yeast associations were examined in six colonies of carpenter ants from two locations in Benton County in western Oregon. Samples from the infrabuccal-pocket contents and worker ant exoskeletons, interior galleries of each colony, and detritus and soil around the colonies were plated on yeast-extract/ malt-extract agar augmented with 1 M hydrochloric acid and incubated at 25 C. Yeasts were identified on the basis of morphological characteristics and physiological attributes with the BIOLOG(®) microbial identification system. Yeast populations from carpenter ant nest material and material surrounding the nest differed from those obtained from the infrabuccal pocket. Debaryomyces polymorphus was isolated more often from the infrabuccal pocket than from other material. This species has also been isolated from other ant species, but its role in colony nutrition is unknown.

  1. Solving optimum operation of single pump unit problem with ant colony optimization (ACO) algorithm

    International Nuclear Information System (INIS)

    For pumping stations, the effective scheduling of daily pump operations from solutions to the optimum design operation problem is one of the greatest potential areas for energy cost-savings, there are some difficulties in solving this problem with traditional optimization methods due to the multimodality of the solution region. In this case, an ACO model for optimum operation of pumping unit is proposed and the solution method by ants searching is presented by rationally setting the object function and constrained conditions. A weighted directed graph was constructed and feasible solutions may be found by iteratively searching of artificial ants, and then the optimal solution can be obtained by applying the rule of state transition and the pheromone updating. An example calculation was conducted and the minimum cost was found as 4.9979. The result of ant colony algorithm was compared with the result from dynamic programming or evolutionary solving method in commercial software under the same discrete condition. The result of ACO is better and the computing time is shorter which indicates that ACO algorithm can provide a high application value to the field of optimal operation of pumping stations and related fields.

  2. Intraspecific Variation among Social Insect Colonies: Persistent Regional and Colony-Level Differences in Fire Ant Foraging Behavior.

    Directory of Open Access Journals (Sweden)

    Alison A Bockoven

    Full Text Available Individuals vary within a species in many ecologically important ways, but the causes and consequences of such variation are often poorly understood. Foraging behavior is among the most profitable and risky activities in which organisms engage and is expected to be under strong selection. Among social insects there is evidence that within-colony variation in traits such as foraging behavior can increase colony fitness, but variation between colonies and the potential consequences of such variation are poorly documented. In this study, we tested natural populations of the red imported fire ant, Solenopsis invicta, for the existence of colony and regional variation in foraging behavior and tested the persistence of this variation over time and across foraging habitats. We also reared single-lineage colonies in standardized environments to explore the contribution of colony lineage. Fire ants from natural populations exhibited significant and persistent colony and regional-level variation in foraging behaviors such as extra-nest activity, exploration, and discovery of and recruitment to resources. Moreover, colony-level variation in extra-nest activity was significantly correlated with colony growth, suggesting that this variation has fitness consequences. Lineage of the colony had a significant effect on extra-nest activity and exploratory activity and explained approximately half of the variation observed in foraging behaviors, suggesting a heritable component to colony-level variation in behavior.

  3. Ant colony clustering with fitness perception and pheromone diffusion for community detection in complex networks

    Science.gov (United States)

    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.

  4. Colony Structure and Nest Location of Two Species of Dacetine Ants: Pyramica ohioensis (Kennedy & Schramm and Pyramica rostrata (Emery in Maryland (Hymenoptera: Formicidae

    Directory of Open Access Journals (Sweden)

    Richard M. Duffield

    2011-01-01

    Full Text Available The discovery of numerous Pyramica ohioensis and P. rostrata colonies living in acorns, as well as the efficient recovery of colonies from artificial nests placed in suitable habitats, opens a new stage in the study of North American dacetine ants. Here we present detailed information, based on 42 nest collections, on the colony structure of these two species. P. ohioensis colonies are smaller than those of P. rostrata. Both species are polygynous, but nests of P. ohioensis contain fewer dealate queens than those of P. rostrata. This is the first report of multiple collections of Pyramica colonies nesting in fallen acorns, and of the use of artificial nesting cavities to sample for dacetines in the soil and leaf litter. We describe an artificial cavity nest design that may prove useful in future investigations.

  5. Colony variation in the collective regulation of foraging by harvester ants.

    Science.gov (United States)

    Gordon, Deborah M; Guetz, Adam; Greene, Michael J; Holmes, Susan

    2011-03-01

    This study investigates variation in collective behavior in a natural population of colonies of the harvester ant, Pogonomyrmex barbatus. Harvester ant colonies regulate foraging activity to adjust to current food availability; the rate at which inactive foragers leave the nest on the next trip depends on the rate at which successful foragers return with food. This study investigates differences among colonies in foraging activity and how these differences are associated with variation among colonies in the regulation of foraging. Colonies differ in the baseline rate at which patrollers leave the nest, without stimulation from returning ants. This baseline rate predicts a colony's foraging activity, suggesting there is a colony-specific activity level that influences how quickly any ant leaves the nest. When a colony's foraging activity is high, the colony is more likely to regulate foraging. Moreover, colonies differ in the propensity to adjust the rate of outgoing foragers to the rate of forager return. Naturally occurring variation in the regulation of foraging may lead to variation in colony survival and reproductive success.

  6. The optimal time-frequency atom search based on a modified ant colony algorithm

    Institute of Scientific and Technical Information of China (English)

    GUO Jun-feng; LI Yan-jun; YU Rui-xing; ZHANG Ke

    2008-01-01

    In this paper,a new optimal time-frequency atom search method based on a modified ant colony algorithm is proposed to improve the precision of the traditional methods.First,the discretization formula of finite length time-frequency atom is inferred at length.Second; a modified ant colony algorithm in continuous space is proposed.Finally,the optimal timefrequency atom search algorithm based on the modified ant colony algorithm is described in detail and the simulation experiment is carried on.The result indicates that the developed algorithm is valid and stable,and the precision of the method is higher than that of the traditional method.

  7. A HYBRID OPTIMIZATION ALGORITHM BASED ON GENETIC ALGORITHM AND ANT COLONY OPTIMIZATION

    Directory of Open Access Journals (Sweden)

    Zainudin Zukhri

    2013-09-01

    Full Text Available In optimization problem, Genetic Algorithm (GA and Ant Colony Optimization Algorithm (ACO have been known as good alternative techniques. GA is designed by adopting the natural evolution process, while ACO is inspired by the foraging behaviour of ant species. This paper presents a hybrid GA-ACO for Travelling Salesman Problem (TSP, called Genetic Ant Colony Optimization (GACO. In this method, GA will observe and preserve the fittest ant in each cycle in every generation and only unvisited cities will be assessed by ACO. From experimental result, GACO performance is significantly improved and its time complexity is fairly equal compared to the GA and ACO.

  8. A Novel Polymorphic Ant Colony -Based Clustering Mechanism for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Min Xiang

    2012-10-01

    Full Text Available In wireless sensor networks, sensor nodes are extremely power constrained, so energy efficient clustering mechanism is mainly considered in the network topology management. A new clustering mechanism based on the polymorphic ant colony (PAC is designed for dynamically controlling the networks clustering structure. According to different functions, the nodes of the networks are respectively defined as the queen ant, the scout ant and worker ant. Based on the calculated cost function and real-time pheromone, the queen ant restructures an optimum clustering structure. Furthermore, the worker ants and the scout ants can send or receive sensing data with optional communication path based on their pheromones. With the mechanism, the energy consumption in inter-cluster and intra-cluster communication for the worker ants and scout ants can be reduced. The simulation results demonstrate that the proposed mechanism can effectively remodel the clustering structure and improve the energy efficiency of the networks.

  9. A Comparative Study of Geometric Hopfield Network and Ant Colony Algorithm to Solve Travelling Salesperson Problem

    Directory of Open Access Journals (Sweden)

    Yogeesha C.B

    2014-09-01

    Full Text Available The classical methods have limited scope in practical applications as some of them involve objective functions which are not continuous and/or differentiable. Evolutionary Computation is a subfield of artificial intelligence that involves combinatorial optimization problems. Travelling Salesperson Problem (TSP, which considered being a classic example for Combinatorial Optimization problem. It is said to be NP-Complete problem that cannot be solved conventionally particularly when number of cities increase. So Evolutionary techniques is the feasible solution to such problem. This paper explores an evolutionary technique: Geometric Hopfield Neural Network model to solve Travelling Salesperson Problem. Paper also achieves the results of Geometric TSP and compares the result with one of the existing widely used nature inspired heuristic approach Ant Colony Optimization Algorithms (ACA/ACO to solve Travelling Salesperson Problem.

  10. Construction of Learning Path Using Ant Colony Optimization from a Frequent Pattern Graph

    Directory of Open Access Journals (Sweden)

    Souvik Sengupta

    2011-11-01

    Full Text Available In an e-Learning system a learner may come across multiple unknown terms, which are generally hyperlinked, while reading a text definition or theory on any topic. It becomes even harder when one tries to understand those unknown terms through further such links and they again find some new terms that have new links. As a consequence they get confused where to initiate from and what are the prerequisites. So it is very obvious for the learner to make a choice of what should be learnt before what. In this paper we have taken the data mining based frequent pattern graph model to define the association and sequencing between the words and then adopted the Ant Colony Optimization, an artificial intelligence approach, to derive a searching technique to obtain an efficient and optimized learning path to reach to a unknown term.

  11. Ant colony optimization and neural networks applied to nuclear power plant monitoring

    International Nuclear Information System (INIS)

    A recurring challenge in production processes is the development of monitoring and diagnosis systems. Those systems help on detecting unexpected changes and interruptions, preventing losses and mitigating risks. Artificial Neural Networks (ANNs) have been extensively used in creating monitoring systems. Usually the ANNs created to solve this kind of problem are created by taking into account only parameters as the number of inputs, outputs, and hidden layers. The result networks are generally fully connected and have no improvements in its topology. This work intends to use an Ant Colony Optimization (ACO) algorithm to create a tuned neural network. The ACO search algorithm will use Back Error Propagation (BP) to optimize the network topology by suggesting the best neuron connections. The result ANN will be applied to monitoring the IEA-R1 research reactor at IPEN. (author)

  12. Construction of Learning Path Using Ant Colony Optimization from a Frequent Pattern Graph

    CERN Document Server

    Sengupta, Souvik; Dasgupta, Ranjan

    2012-01-01

    In an e-Learning system a learner may come across multiple unknown terms, which are generally hyperlinked, while reading a text definition or theory on any topic. It becomes even harder when one tries to understand those unknown terms through further such links and they again find some new terms that have new links. As a consequence they get confused where to initiate from and what are the prerequisites. So it is very obvious for the learner to make a choice of what should be learnt before what. In this paper we have taken the data mining based frequent pattern graph model to define the association and sequencing between the words and then adopted the Ant Colony Optimization, an artificial intelligence approach, to derive a searching technique to obtain an efficient and optimized learning path to reach to a unknown term.

  13. Ant colony optimization and neural networks applied to nuclear power plant monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Santos, Gean Ribeiro dos; Andrade, Delvonei Alves de; Pereira, Iraci Martinez, E-mail: gean@usp.br, E-mail: delvonei@ipen.br, E-mail: martinez@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

    2015-07-01

    A recurring challenge in production processes is the development of monitoring and diagnosis systems. Those systems help on detecting unexpected changes and interruptions, preventing losses and mitigating risks. Artificial Neural Networks (ANNs) have been extensively used in creating monitoring systems. Usually the ANNs created to solve this kind of problem are created by taking into account only parameters as the number of inputs, outputs, and hidden layers. The result networks are generally fully connected and have no improvements in its topology. This work intends to use an Ant Colony Optimization (ACO) algorithm to create a tuned neural network. The ACO search algorithm will use Back Error Propagation (BP) to optimize the network topology by suggesting the best neuron connections. The result ANN will be applied to monitoring the IEA-R1 research reactor at IPEN. (author)

  14. Queen movement during colony emigration in the facultatively polygynous ant Pachycondyla obscuricornis

    Science.gov (United States)

    Pezon, Antoine; Denis, Damien; Cerdan, Philippe; Valenzuela, Jorge; Fresneau, Dominique

    2005-01-01

    In ants, nest relocations are frequent but nevertheless perilous, especially for the reproductive caste. During emigrations, queens are exposed to predation and face the risk of becoming lost. Therefore the optimal strategy should be to move the queen(s) swiftly to a better location, while maintaining maximum worker protection at all times in the new and old nests. The timing of that event is a crucial strategic issue for the colony and may depend on queen number. In monogynous colonies, the queen is vital for colony survival, whereas in polygynous colonies a queen is less essential, if not dispensable. We tested the null hypothesis that queen movement occurs at random within the sequence of emigration events in both monogynous and polygynous colonies of the ponerine ant Pachycondyla obscuricornis. Our study, based on 16 monogynous and 16 polygynous colony emigrations, demonstrates for the first time that regardless of the number of queens per colony, the emigration serial number of a queen occurs in the middle of all emigration events and adult ant emigration events, but not during brood transport events. It therefore appears that the number of workers in both nests plays an essential role in the timing of queen movement. Our results correspond to a robust colony-level strategy since queen emigration is related neither to colony size nor to queen number. Such an optimal strategy is characteristic of ant societies working as highly integrated units and represents a new instance of group-level adaptive behaviors in social insect colonies.

  15. Artificial Bee Colony Optimization for Short-Term Hydrothermal Scheduling

    Science.gov (United States)

    Basu, M.

    2014-12-01

    Artificial bee colony optimization is applied to determine the optimal hourly schedule of power generation in a hydrothermal system. Artificial bee colony optimization is a swarm-based algorithm inspired by the food foraging behavior of honey bees. The algorithm is tested on a multi-reservoir cascaded hydroelectric system having prohibited operating zones and thermal units with valve point loading. The ramp-rate limits of thermal generators are taken into consideration. The transmission losses are also accounted for through the use of loss coefficients. The algorithm is tested on two hydrothermal multi-reservoir cascaded hydroelectric test systems. The results of the proposed approach are compared with those of differential evolution, evolutionary programming and particle swarm optimization. From numerical results, it is found that the proposed artificial bee colony optimization based approach is able to provide better solution.

  16. Ant colony Optimization: A Solution of Load balancing in Cloud

    Directory of Open Access Journals (Sweden)

    Ratan Mishra

    2012-05-01

    Full Text Available As the cloud computing is a new style of computing over internet. It has many advantages along with some crucial issues to be resolved in order to improve reliability of cloud environment. These issues are related with the load management, fault tolerance and different security issues in cloud environment. In this paper the main concern is load balancing in cloud computing. The load can be CPU load, memory capacity, delay or network load. Load balancing is the process of distributing the load among various nodes of adistributed system to improve both resource utilization and job response time while also avoiding a situation where some of the nodes are heavily loaded while other nodes are idle or doing very little work. Load balancing ensures that all the processor in the system or every node in the network does approximately the equal amount of work at any instant of time. Many methods to resolve this problem has been came into existence like Particle Swarm Optimization, hash method, genetic algorithms and severalscheduling based algorithms are there. In this paper we are proposing a method based on Ant Colony optimization to resolve the problem of load balancing in cloud environment.

  17. Information cascade, Kirman's ant colony model, and kinetic Ising model

    CERN Document Server

    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...

  18. Reliability optimization using multiobjective ant colony system approaches

    International Nuclear Information System (INIS)

    The multiobjective ant colony system (ACS) meta-heuristic has been developed to provide solutions for the reliability optimization problem of series-parallel systems. This type of problems involves selection of components with multiple choices and redundancy levels that produce maximum benefits, and is subject to the cost and weight constraints at the system level. These are very common and realistic problems encountered in conceptual design of many engineering systems. It is becoming increasingly important to develop efficient solutions to these problems because many mechanical and electrical systems are becoming more complex, even as development schedules get shorter and reliability requirements become very stringent. The multiobjective ACS algorithm offers distinct advantages to these problems compared with alternative optimization methods, and can be applied to a more diverse problem domain with respect to the type or size of the problems. Through the combination of probabilistic search, multiobjective formulation of local moves and the dynamic penalty method, the multiobjective ACSRAP, allows us to obtain an optimal design solution very frequently and more quickly than with some other heuristic approaches. The proposed algorithm was successfully applied to an engineering design problem of gearbox with multiple stages

  19. Parallelization Strategies for Ant Colony Optimisation on GPUs

    CERN Document Server

    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...

  20. Study on ant colony optimization for fuel loading pattern problem

    International Nuclear Information System (INIS)

    Modified ant colony optimization (ACO) was applied to the in-core fuel loading pattern (LP) optimization problem to minimize the power peaking factor (PPF) in the modeled 1/4 symmetry PWR core. Loading order was found to be important in ACO. Three different loading orders with and without the adjacent effect between fuel assemblies (FAs) were compared, and it was found that the loading order from the central core is preferable because many selections of FAs to be inserted are available in the core center region. LPs were determined from pheromone trail and heuristic information, which is a priori knowledge based on the feature of the problem. Three types of heuristic information were compared to obtain the desirable performance of searching LPs with low PPF. Moreover, mutation operation, such as the genetic algorithm (GA), was introduced into the ACO algorithm to avoid searching similar LPs because heuristic information used in ACO tends to localize the searching space in the LP problem. The performance of ACO with some improvement was compared with those of simulated annealing and GA. In conclusion, good performance can be achieved by setting proper heuristic information and mutation operation parameter in ACO. (author)

  1. Improved Ant Colony Optimization Algorithm based Expert System on Nephrology

    Directory of Open Access Journals (Sweden)

    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.

  2. CACONET: Ant Colony Optimization (ACO) Based Clustering Algorithm for VANET.

    Science.gov (United States)

    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

  3. Ant colony optimization-based firewall anomaly mitigation engine.

    Science.gov (United States)

    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

  4. Applying Data Clustering Feature to Speed Up Ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    Chao-Yang Pang

    2014-01-01

    Full Text Available Ant colony optimization (ACO is often used to solve optimization problems, such as traveling salesman problem (TSP. When it is applied to TSP, its runtime is proportional to the squared size of problem N so as to look less efficient. The following statistical feature is observed during the authors’ long-term gene data analysis using ACO: when the data size N becomes big, local clustering appears frequently. That is, some data cluster tightly in a small area and form a class, and the correlation between different classes is weak. And this feature makes the idea of divide and rule feasible for the estimate of solution of TSP. In this paper an improved ACO algorithm is presented, which firstly divided all data into local clusters and calculated small TSP routes and then assembled a big TSP route with them. Simulation shows that the presented method improves the running speed of ACO by 200 factors under the condition that data set holds feature of local clustering.

  5. Enhanced ant colony optimization for inventory routing problem

    Science.gov (United States)

    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.

  6. A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm

    OpenAIRE

    Wenping Zou; Yunlong Zhu; Hanning Chen; Xin Sui

    2010-01-01

    Artificial Bee Colony (ABC) is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents an extended ABC algorithm, namely, the Cooperative Article Bee Colony (CABC), which significantly improves the original ABC in solving complex optimization problems. Clustering is a popular data analysis and data mining technique; therefore, the CABC could be used for solving clustering problems. In this work, first the CABC algorit...

  7. Artificial Bee Colony Optimization for Multiobjective Quadratic Assignment Problem

    OpenAIRE

    Eleyan, Haytham Mohammed

    2015-01-01

    ABSTRACT: Excellent ability of swarm intelligence can be used to solve multi-objective combinatorial optimization problems. Bee colony algorithms are new swarm intelligence techniques inspired from the smart behaviors of real honeybees in their foraging behavior. Artificial bee colony optimization algorithm has recently been applied for difficult real-valued and combinational optimization problems. Multiobjective quadratic assignment problem (mQAP) is a well-known and hard combinational optim...

  8. XOR-based artificial bee colony algorithm for binary optimization

    OpenAIRE

    KIRAN, Mustafa Servet; Gündüz, Mesut

    2012-01-01

    The artificial bee colony (ABC) algorithm, which was inspired by the foraging and dance behaviors of real honey bee colonies, was first introduced for solving numerical optimization problems. When the solution space of the optimization problem is binary-structured, the basic ABC algorithm should be modified for solving this class of problems. In this study, we propose XOR-based modification for the solution-updating equation of the ABC algorithm in order to solve binary optimization pro...

  9. Optimization design of drilling string by screw coal miner based on ant colony algorithm

    Institute of Scientific and Technical Information of China (English)

    ZHANG Qiang; MAO Jun; DING Fei

    2008-01-01

    It took that the weight minimum and drive efficiency maximal were as double optimizing target,the optimization model had built the drilling string,and the optimization solution was used of the ant colony algorithm to find in progress.Adopted a two-layer search of the continuous space ant colony algorithm with overlapping or variation global ant search operation strategy and conjugated gradient partial ant search operation strategy.The experiment indicates that the spiral drill weight reduces 16.77% and transports the efficiency enhance 7.05% through the optimization design,the ant colony algorithm application on the spiral drill optimized design has provided the basis for the system research screw coal mine machine.

  10. Optimization design of drilling string by screw coal miner based on ant colony algorithm

    Institute of Scientific and Technical Information of China (English)

    ZHANG Qiang; MAO Jun; DING Fei

    2008-01-01

    It took that the weight minimum and drive efficiency maximal were as double optimizing target, the optimization model had built the drilling string, and the optimization solution was used of the ant colony algorithm to find in progress. Adopted a two-layer search of the continuous space ant colony algorithm with overlapping or variation global ant search operation strategy and conjugated gradient partial ant search operation strat-egy. The experiment indicates that the spiral drill weight reduces 16.77% and transports the efficiency enhance 7.05% through the optimization design, the ant colony algorithm application on the spiral drill optimized design has provided the basis for the system re-search screw coal mine machine.

  11. Obstacle avoidance planning of space manipulator end-effector based on improved ant colony algorithm.

    Science.gov (United States)

    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

  12. Using nonlinear optical networks for optimization: primer of the ant colony algorithm

    OpenAIRE

    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.

  13. Text clustering based on fusion of ant colony and genetic algorithms

    Institute of Scientific and Technical Information of China (English)

    Yun ZHANG; Boqin FENG; Shouqiang MA; Lianmeng LIU

    2009-01-01

    Focusing on the problem that the ant colony algorithm gets into stagnation easily and cannot fully search in solution space,a text clustering approach based on the fusion of the ant colony and genetic algorithms is proposed.The four parameters that influence the performance of the ant colony algorithm are encoded as chromosomes,thereby the fitness function,selection,crossover and mutation operator are designed to find the combination of optimal parameters through a number of iteration,and then it is applied to text clustering.The simulation.results show that compared with the classical k-means clustering and the basic ant colony clustering algorithm,the proposed algorithm has better performance and the value of F-Measure is enhanced by 5.69%,48.60% and 69.60%,respectively,in 3 test datasets.Therefore,it is more suitable for processing a larger dataset.

  14. A Survey Paper on Solving TSP using Ant Colony Optimization on GPU

    Directory of Open Access Journals (Sweden)

    Khushbu Khatri

    2014-12-01

    Full Text Available Ant Colony Optimization (ACO is meta-heuristic algorithm inspired from nature to solve many combinatorial optimization problem such as Travelling Salesman Problem (TSP. There are many versions of ACO used to solve TSP like, Ant System, Elitist Ant System, Max-Min Ant System, Rank based Ant System algorithm. For improved performance, these methods can be implemented in parallel architecture like GPU, CUDA architecture. Graphics Processing Unit (GPU provides highly parallel and fully programmable platform. GPUs which have many processing units with an off-chip global memory can be used for general purpose parallel computation. This paper presents a survey on different solving TSP using ACO on GPU.

  15. An Approximate Algorithm Combining P Systems and Ant Colony Optimization for Traveling Salesman Problems

    OpenAIRE

    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...

  16. A study into ant colony optimisation, evolutionary computation and constraint programming on binary constraint satisfaction problems.

    OpenAIRE

    Hemert, van, M.C.; Solnon, C.

    2004-01-01

    We compare two heuristic approaches, evolutionary computation and ant colony optimisation, and a complete tree-search approach, constraint programming, for solving binary constraint satisfaction problems. We experimentally show that, if evolutionary computation is far from being able to compete with the two other approaches, ant colony optimisation nearly always succeeds in finding a solution, so that it can actually compete with constraint programming. The resampling ratio is used to provide...

  17. Apply Ant Colony Algorithm to Search All Extreme Points of Function

    OpenAIRE

    Pang, Chao-Yang; Liu, Hui; Li, Xia; Wang, Yun-fei; Hu, Ben-Qiong

    2009-01-01

    To find all extreme points of multimodal functions is called extremum problem, which is a well known difficult issue in optimization fields. Applying ant colony optimization (ACO) to solve this problem is rarely reported. The method of applying ACO to solve extremum problem is explored in this paper. Experiment shows that the solution error of the method presented in this paper is less than 10^-8. keywords: Extremum Problem; Ant Colony Optimization (ACO)

  18. A Dynamic Algorithm for the Longest Common Subsequence Problem using Ant Colony Optimization Technique

    OpenAIRE

    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...

  19. Ant Colony Optimization Analysis on Overall Stability of High Arch Dam Basis of Field Monitoring

    OpenAIRE

    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...

  20. A Hybrid Monte Carlo Ant Colony Optimization Approach for Protein Structure Prediction in the HP Model

    OpenAIRE

    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...

  1. Routing in Wireless Sensor Networks Using an Ant Colony Optimization (ACO) Router Chip

    OpenAIRE

    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...

  2. Clarifying Cutting and Sewing Processes with Due Windows Using an Effective Ant Colony Optimization

    OpenAIRE

    Rong-Hwa Huang; Shun-Chi Yu

    2013-01-01

    The cutting and sewing process is a traditional flow shop scheduling problem in the real world. This two-stage flexible flow shop is often commonly associated with manufacturing in the fashion and textiles industry. Many investigations have demonstrated that the ant colony optimization (ACO) algorithm is effective and efficient for solving scheduling problems. This work applies a novel effective ant colony optimization (EACO) algorithm to solve two-stage flexible flow shop scheduling problems...

  3. Continuous function optimization using hybrid ant colony approach with orthogonal design scheme

    OpenAIRE

    Zhang, J.; Chen, W.; Zhong, J.; Tan, X.; Li, Y.

    2006-01-01

    A hybrid Orthogonal Scheme Ant Colony Optimization (OSACO) algorithm for continuous function optimization (CFO) is presented in this paper. The methodology integrates the advantages of Ant Colony Optimization (ACO) and Orthogonal Design Scheme (ODS). OSACO is based on the following principles: a) each independent variable space (IVS) of CFO is dispersed into a number of random and movable nodes; b) the carriers of pheromone of ACO are shifted to the nodes; c) solution path can be obtained by ...

  4. Diversity, prevalence and virulence of fungal entomopathogens in colonies of the ant Formica selysi

    OpenAIRE

    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...

  5. Ant-Based Phylogenetic Reconstruction (ABPR): A new distance algorithm for phylogenetic estimation based on ant colony optimization

    OpenAIRE

    Karla Vittori; Alexandre C B Delbem; Pereira, Sérgio L

    2008-01-01

    We propose a new distance algorithm for phylogenetic estimation based on Ant Colony Optimization (ACO), named Ant-Based Phylogenetic Reconstruction (ABPR). ABPR joins two taxa iteratively based on evolutionary distance among sequences, while also accounting for the quality of the phylogenetic tree built according to the total length of the tree. Similar to optimization algorithms for phylogenetic estimation, the algorithm allows exploration of a larger set of nearly optimal solutions. We appl...

  6. Application of ant colony optimization to optimal foragaing theory: comparison of simulation and field results

    Science.gov (United States)

    Ant Colony Optimization (ACO) refers to the family of algorithms inspired by the behavior of real ants and used to solve combinatorial problems such as the Traveling Salesman Problem (TSP).Optimal Foraging Theory (OFT) is an evolutionary principle wherein foraging organisms or insect parasites seek ...

  7. Ant Colony Optimization ACO For The Traveling Salesman Problem TSP Using Partitioning

    OpenAIRE

    Alok Bajpai; Raghav Yadav

    2015-01-01

    Abstract An ant colony optimization is a technique which was introduced in 1990s and which can be applied to a variety of discrete combinatorial optimization problem and to continuous optimization. The ACO algorithm is simulated with the foraging behavior of the real ants to find the incremental solution constructions and to realize a pheromone laying-and-following mechanism. This pheromone is the indirect communication among the ants. In this paper we introduces the partitioning technique ba...

  8. Information cascade, Kirman's ant colony model, and kinetic Ising model

    Science.gov (United States)

    Hisakado, Masato; Mori, Shintaro

    2015-01-01

    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 Hisakado and Mori (2011), 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. However, the conclusion is different from mean field approximation. In this paper, we show that the solution oscillates between the two states. A good (bad) equilibrium is where a majority of r select the correct (wrong) candidate. 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 Ising model. If the voters are rational, a simple herding experiment of information cascade is conducted. Information cascade results from the quenching of the kinetic Ising model. As case (i) is the limit of case (iii) when tanh function becomes a step function, the phase transition can be observed in infinite size limit. We can confirm that there is no phase transition when the reference number r is finite.

  9. Pixel-based ant colony algorithm for source mask optimization

    Science.gov (United States)

    Kuo, Hung-Fei; Wu, Wei-Chen; Li, Frederick

    2015-03-01

    Source mask optimization (SMO) was considered to be one of the key resolution enhancement techniques for node technology below 20 nm prior to the availability of extreme-ultraviolet tools. SMO has been shown to enlarge the process margins for the critical layer in SRAM and memory cells. In this study, a new illumination shape optimization approach was developed on the basis of the ant colony optimization (ACO) principle. The use of this heuristic pixel-based ACO method in the SMO process provides an advantage over the extant SMO method because of the gradient of the cost function associated with the rapid and stable searching capability of the proposed method. This study was conducted to provide lithographic engineers with references for the quick determination of the optimal illumination shape for complex mask patterns. The test pattern used in this study was a contact layer for SRAM design, with a critical dimension and a minimum pitch of 55 and 110 nm, respectively. The optimized freeform source shape obtained using the ACO method was numerically verified by performing an aerial image investigation, and the result showed that the optimized freeform source shape generated an aerial image profile different from the nominal image profile and with an overall error rate of 9.64%. Furthermore, the overall average critical shape difference was determined to be 1.41, which was lower than that for the other off-axis illumination exposure. The process window results showed an improvement in exposure latitude (EL) and depth of focus (DOF) for the ACO-based freeform source shape compared with those of the Quasar source shape. The maximum EL of the ACO-based freeform source shape reached 7.4% and the DOF was 56 nm at an EL of 5%.

  10. [Application of rational ant colony optimization to improve the reproducibility degree of laser three-dimensional copy].

    Science.gov (United States)

    Cui, Xiao-Yan; Huo, Zhong-Gang; Xin, Zhong-Hua; Tian, Xiao; Zhang, Xiao-Dong

    2013-07-01

    Three-dimensional (3D) copying of artificial ears and pistol printing are pushing laser three-dimensional copying technique to a new page. Laser three-dimensional scanning is a fresh field in laser application, and plays an irreplaceable part in three-dimensional copying. Its accuracy is the highest among all present copying techniques. Reproducibility degree marks the agreement of copied object with the original object on geometry, being the most important index property in laser three-dimensional copying technique. In the present paper, the error of laser three-dimensional copying was analyzed. The conclusion is that the data processing to the point cloud of laser scanning is the key technique to reduce the error and increase the reproducibility degree. The main innovation of this paper is as follows. On the basis of traditional ant colony optimization, rational ant colony optimization algorithm proposed by the author was applied to the laser three-dimensional copying as a new algorithm, and was put into practice. Compared with customary algorithm, rational ant colony optimization algorithm shows distinct advantages in data processing of laser three-dimensional copying, reducing the error and increasing the reproducibility degree of the copy. PMID:24059192

  11. Kernel feature identification algorithm based on improved ant colony optimization and its application in transient stability assessment

    Energy Technology Data Exchange (ETDEWEB)

    Guan, L.; Zhang, X.; Wang, T. [South China Univ. of Technology, Guangzhou (China). College of Electrical Power

    2009-03-11

    This study presented an optimized ant colony optimization algorithm combined with a K-nearest neighbour (K-NN) classifier. Ant colony optimization is used to simulate the information exchange and cooperation schemes among individual ants in the process of searching for food. The processes are used to simulate positive feedback, distributed computation, and the use of constructive heuristic searches. In this study, each feature was regarded as a node that the ant may visit. Feature selection processes were described as a path-forming process. The weighted sum of the K-NN classification error and a selected feature dimension was used to construct a fitness function for assessing transient stability. A local search loop wa used to remove redundant or strongly-correlated features. The algorithm was verified using a set of artificial test data. The scheme was then used to obtain a security-related kernel feature for an Institute of Electrical and Electronics Engineers (IEEE) 10-unit 39-bus system. The study demonstrated that the proposed scheme accurately assessed transient stability. 14 refs., 6 figs.

  12. Pupae transplantation to boost early colony growth in the weaver ant Oecophylla longinoda Latreille (Hymenoptera: Formicidae)

    DEFF Research Database (Denmark)

    Ouagoussounon, Issa; Sinzogan, Antonio; Offenberg, Joachim;

    2013-01-01

    Oecophylla ants are currently used for biological control in fruit plantations in Australia, Asia and Africa and for protein production in Asia. To further improve the technology and implement it on a large scale, effective and fast production of live colonies is desirable. Early colony development...

  13. Identification of Dynamic Parameters Based on Pseudo-Parallel Ant Colony Optimization Algorithm

    Institute of Scientific and Technical Information of China (English)

    ZHAO Feng-yao; MA Zhen-yue; ZHANG Yun-liang

    2007-01-01

    For the parameter identification of dynamic problems, a pseudo-parallel ant colony optimization (PPACO) algorithm based on graph-based ant system (AS) was introduced. On the platform of ANSYS dynamic analysis, the PPACO algorithm was applied to the identification of dynamic parameters successfully. Using simulated data of forces and displacements, elastic modulus E and damping ratio ξ was identified for a designed 3D finite element model, and the detailed identification step was given. Mathematical example and simulation example show that the proposed method has higher precision, faster convergence speed and stronger antinoise ability compared with the standard genetic algorithm and the ant colony optimization (ACO) algorithms.

  14. Collective Intelligence for Optimal Power Flow Solution Using Ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    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.

  15. Effect of time on colony odour stability in the ant Formica exsecta

    Science.gov (United States)

    Martin, S. J.; Shemilt, S.; Drijfhout, F. P.

    2012-04-01

    Among social insects, maintaining a distinct colony profile allows individuals to distinguish easily between nest mates and non-nest mates. In ants, colony-specific profiles can be encoded within their cuticular hydrocarbons, and these are influenced by both environmental and genetic factors. Using nine monogynous Formica exsecta ant colonies, we studied the stability of their colony-specific profiles at eight time points over a 4-year period. We found no significant directional change in any colony profile, suggesting that genetic factors are maintaining this stability. However, there were significant short-term effects of season that affected all colony profiles in the same direction. Despite these temporal changes, no significant change in the profile variation within colonies was detected: each colony's profile responded in similar manner between seasons, with nest mates maintaining closely similar profiles, distinct from other colonies. These findings imply that genetic factors may help maintain the long-term stability of colony profile, but environmental factors can influence the profiles over shorter time periods. However, environmental factors do not contribute significantly to the maintenance of diversity among colonies, since all colonies were affected in a similar way.

  16. Enhanced Clustering Techniques for Hyper Network Planning using Minimum Spanning Trees and Ant-Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Lamiaa F. Ibrahim

    2011-01-01

    Full Text Available Problem statement: The process of network planning is divided into two sub steps. The first step is determining the location of the Multi Service Access Node (MSAN. The second step is the construction of subscriber network lines from MSAN to subscribers to satisfy optimization criteria and design constraints. Due to the complexity of this process artificial intelligence and clustering techniques have been successfully deployed to solve many problems. The problems of the locations of MSAN, the cabling layout and the computation of optimum cable network layouts have been addressed in this study. The proposed algorithm, Clustering density-Based Spatial of Applications with Noise original, minimal Spanning tree and modified Ant-Colony-Based algorithm (CBSCAN-SPANT, used two clustering algorithms which are density-based and agglomerative clustering algorithm using distances which are shortest paths distance and satisfying the network constraints. This algorithm used wire and wireless technology to serve the subscribers demand and place the switches in a real optimal place. Approach: The density-based Spatial Clustering of Applications with Noise original (DBSCAN algorithm has been modified and a new algorithm (NetPlan algorithm has been proposed by the author in a recent work to solve the first step in the problem of network planning. In the present study, the NetPlan algorithm is modified by introduce the modified Ant-Colony-Based algorithm to find the optimal path between any node and the corresponding MSAN node in the first step of network planning process to determine nodes belonging to each cluster. The second step, in the process of network planning, is also introduced in the present study. For each cluster, the optimal cabling layout from each MSAN to the subscriber premises is determining by introduce the Prime algorithm which construct minimal spanning tree. Results: Experimental results and analysis indicate that the

  17. Ant queens (Hymenoptera: Formicidae) are attracted to fungal pathogens during the initial stage of colony founding

    OpenAIRE

    Brütsch T.; Felden A.; Reber A.; Chapuisat M.

    2014-01-01

    Ant queens that attempt to disperse and found new colonies independently face high mortality risks. The exposure of queens to soil entomopathogens during claustral colony founding may be particularly harmful, as founding queens lack the protection conferred by mature colonies. Here, we tested the hypotheses that founding queens (I) detect and avoid nest sites that are contaminated by fungal pathogens, and (II) tend to associate with other queens to benefit from social immunity when nest sites...

  18. An adaptive ant colony system algorithm for continuous-space optimization problems

    Institute of Scientific and Technical Information of China (English)

    李艳君; 吴铁军

    2003-01-01

    Ant colony algorithms comprise a novel category of evolutionary computation methods for optimization problems, especially for sequencing-type combinatorial optimization problems. An adaptive ant colony algorithm is proposed in this paper to tackle continuous-space optimization problems, using a new objective-function-based heuristic pheromone assignment approach for pheromone update to filtrate solution candidates. Global optimal solutions can be reached more rapidly by self-adjusting the path searching behaviors of the ants according to objective values. The performance of the proposed algorithm is compared with a basic ant colony algorithm and a Square Quadratic Programming approach in solving two benchmark problems with multiple extremes. The results indicated that the efficiency and reliability of the proposed algorithm were greatly improved.

  19. An adaptive ant colony system algorithm for continuous-space optimization problems

    Institute of Scientific and Technical Information of China (English)

    李艳君; 吴铁军

    2003-01-01

    Ant colony algorithms comprise a novel category of evolutionary computation methods for optimization problems, especially for sequencing-type combinatorial optimization problems. An adaptive ant colony algorithm is proposed in this paper to tackle continuous-space optimization problems, using a new objective-function-based heuristic pheromone assignment approach for pheromone update to filtrate solution candidates.Global optimal solutions can be reached more rapidly by self-adjusting the path searching behaviors of the ants according to objective values. The performance of the proposed algorithm is compared with a basic ant colony algorithm and a Square Quadratic Programming approach in solving two benchmark problems with multiple extremes. The results indicated that the efficiency and reliability of the proposed algorithm were greatly improved.

  20. Colony-level impacts of parasitoid flies on fire ants.

    OpenAIRE

    Mehdiabadi, Natasha J; Gilbert, Lawrence E

    2002-01-01

    The red imported fire ant is becoming a global ecological problem, having invaded the United States, Puerto Rico, New Zealand and, most recently, Australia. In its established areas, this pest is devastating natural biodiversity. Early attempts to halt fire ant expansion with pesticides actually enhanced its spread. Phorid fly parasitoids from South America have now been introduced into the United States as potential biological control agents of the red imported fire ant, but the impact of th...

  1. The rewards of restraint in the collective regulation of foraging by harvester ant colonies.

    Science.gov (United States)

    Gordon, Deborah M

    2013-06-01

    Collective behaviour, arising from local interactions, allows groups to respond to changing conditions. Long-term studies have shown that the traits of individual mammals and birds are associated with their reproductive success, but little is known about the evolutionary ecology of collective behaviour in natural populations. An ant colony operates without central control, regulating its activity through a network of local interactions. This work shows that variation among harvester ant (Pogonomyrmex barbatus) colonies in collective response to changing conditions is related to variation in colony lifetime reproductive success in the production of offspring colonies. Desiccation costs are high for harvester ants foraging in the desert. More successful colonies tend to forage less when conditions are dry, and show relatively stable foraging activity when conditions are more humid. Restraint from foraging does not compromise a colony's long-term survival; colonies that fail to forage at all on many days survive as long, over the colony's 20-30-year lifespan, as those that forage more regularly. Sensitivity to conditions in which to reduce foraging activity may be transmissible from parent to offspring colony. These results indicate that natural selection is shaping the collective behaviour that regulates foraging activity, and that the selection pressure, related to climate, may grow stronger if the current drought in their habitat persists.

  2. cAnt-Miner: an ant colony classification algorithm to cope with continuous attributes

    OpenAIRE

    Otero, Fernando E.B.; Freitas, Alex. A.; Johnson, Colin G.

    2008-01-01

    This paper presents an extension to Ant-Miner, named cAnt-Miner (Ant-Miner coping with continuous attributes), which incorporates an entropy-based discretization method in order to cope with continuous attributes during the rule construction process. By having the ability to create discrete intervals for continuous attributes "on-the-fly", cAnt-Miner does not requires a discretization method in a preprocessing step, as Ant-Miner requires. cAnt-Miner has been compared against Ant-Miner in eigh...

  3. A cuckoo-like parasitic moth leads African weaver ant colonies to their ruin.

    Science.gov (United States)

    Dejean, Alain; Orivel, Jérôme; Azémar, Frédéric; Hérault, Bruno; Corbara, Bruno

    2016-01-01

    In myrmecophilous Lepidoptera, mostly lycaenids and riodinids, caterpillars trick ants into transporting them to the ant nest where they feed on the brood or, in the more derived "cuckoo strategy", trigger regurgitations (trophallaxis) from the ants and obtain trophic eggs. We show for the first time that the caterpillars of a moth (Eublemma albifascia; Noctuidae; Acontiinae) also use this strategy to obtain regurgitations and trophic eggs from ants (Oecophylla longinoda). Females short-circuit the adoption process by laying eggs directly on the ant nests, and workers carry just-hatched caterpillars inside. Parasitized colonies sheltered 44 to 359 caterpillars, each receiving more trophallaxis and trophic eggs than control queens. The thus-starved queens lose weight, stop laying eggs (which transport the pheromones that induce infertility in the workers) and die. Consequently, the workers lay male-destined eggs before and after the queen's death, allowing the colony to invest its remaining resources in male production before it vanishes. PMID:27021621

  4. Extreme queen-mating frequency and colony fission in African army ants

    DEFF Research Database (Denmark)

    Kronauer, Daniel J C; Schoning, Caspar; Pedersen, Jes S;

    2004-01-01

    Army ants have long been suspected to represent an independent origin of multiple queen-mating in the social Hymenoptera. Using microsatellite markers, we show that queens of the African army ant Dorylus (Anomma) molestus have the highest absolute (17.3) and effective (17.5) queen......-mating frequencies reported so far for ants. This confirms that obligate multiple queen-mating in social insects is associated with large colony size and advanced social organization, but also raises several novel questions. First, these high estimates place army ants in the range of mating frequencies of honeybees......, which have so far been regarded as odd exceptions within the social Hymenoptera. Army ants and honeybees are fundamentally different in morphology and life history, but are the only social insects known that combine obligate multiple mating with reproduction by colony fission and extremely male...

  5. Skull removal in MR images using a modified artificial bee colony optimization algorithm.

    Science.gov (United States)

    Taherdangkoo, Mohammad

    2014-01-01

    Removal of the skull from brain Magnetic Resonance (MR) images is an important preprocessing step required for other image analysis techniques such as brain tissue segmentation. In this paper, we propose a new algorithm based on the Artificial Bee Colony (ABC) optimization algorithm to remove the skull region from brain MR images. We modify the ABC algorithm using a different strategy for initializing the coordinates of scout bees and their direction of search. Moreover, we impose an additional constraint to the ABC algorithm to avoid the creation of discontinuous regions. We found that our algorithm successfully removed all bony skull from a sample of de-identified MR brain images acquired from different model scanners. The obtained results of the proposed algorithm compared with those of previously introduced well known optimization algorithms such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) demonstrate the superior results and computational performance of our algorithm, suggesting its potential for clinical applications. PMID:25059256

  6. Inverse transient radiation analysis in one-dimensional participating slab using improved Ant Colony Optimization algorithms

    International Nuclear Information System (INIS)

    As a heuristic intelligent optimization algorithm, the Ant Colony Optimization (ACO) algorithm was applied to the inverse problem of a one-dimensional (1-D) transient radiative transfer in present study. To illustrate the performance of this algorithm, the optical thickness and scattering albedo of the 1-D participating slab medium were retrieved simultaneously. The radiative reflectance simulated by Monte-Carlo Method (MCM) and Finite Volume Method (FVM) were used as measured and estimated value for the inverse analysis, respectively. To improve the accuracy and efficiency of the Basic Ant Colony Optimization (BACO) algorithm, three improved ACO algorithms, i.e., the Region Ant Colony Optimization algorithm (RACO), Stochastic Ant Colony Optimization algorithm (SACO) and Homogeneous Ant Colony Optimization algorithm (HACO), were developed. By the HACO algorithm presented, the radiative parameters could be estimated accurately, even with noisy data. In conclusion, the HACO algorithm is demonstrated to be effective and robust, which had the potential to be implemented in various fields of inverse radiation problems. -- Highlights: • The ACO-based algorithms were firstly applied to the inverse transient radiation problem. • Three ACO-based algorithms were developed based on the BACO algorithm for continuous domain problem. • HACO shows a robust performance for simultaneous estimation of the radiative properties

  7. Inverse transient radiation analysis in one-dimensional participating slab using improved Ant Colony Optimization algorithms

    Science.gov (United States)

    Zhang, B.; Qi, H.; Ren, Y. T.; Sun, S. C.; Ruan, L. M.

    2014-01-01

    As a heuristic intelligent optimization algorithm, the Ant Colony Optimization (ACO) algorithm was applied to the inverse problem of a one-dimensional (1-D) transient radiative transfer in present study. To illustrate the performance of this algorithm, the optical thickness and scattering albedo of the 1-D participating slab medium were retrieved simultaneously. The radiative reflectance simulated by Monte-Carlo Method (MCM) and Finite Volume Method (FVM) were used as measured and estimated value for the inverse analysis, respectively. To improve the accuracy and efficiency of the Basic Ant Colony Optimization (BACO) algorithm, three improved ACO algorithms, i.e., the Region Ant Colony Optimization algorithm (RACO), Stochastic Ant Colony Optimization algorithm (SACO) and Homogeneous Ant Colony Optimization algorithm (HACO), were developed. By the HACO algorithm presented, the radiative parameters could be estimated accurately, even with noisy data. In conclusion, the HACO algorithm is demonstrated to be effective and robust, which had the potential to be implemented in various fields of inverse radiation problems.

  8. An Ant Colony Optimization Based Dimension Reduction Method for High-Dimensional Datasets

    Institute of Scientific and Technical Information of China (English)

    Ying Li; Gang Wang; Huiling Chen; Lian Shi; Lei Qin

    2013-01-01

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

  9. The scent of supercolonies: the discovery, synthesis and behavioural verification of ant colony recognition cues

    Directory of Open Access Journals (Sweden)

    Sulc Robert

    2009-10-01

    Full Text Available Abstract Background Ants form highly social and cooperative colonies that compete, and often fight, against other such colonies, both intra- and interspecifically. Some invasive ants take sociality to an extreme, forming geographically massive 'supercolonies' across thousands of kilometres. The success of social insects generally, as well as invasive ants in particular, stems from the sophisticated mechanisms used to accurately and precisely distinguish colonymates from non-colonymates. Surprisingly, however, the specific chemicals used for this recognition are virtually undescribed. Results Here, we report the discovery, chemical synthesis and behavioural testing of the colonymate recognition cues used by the widespread and invasive Argentine ant (Linepithema humile. By synthesizing pure versions of these chemicals in the laboratory and testing them in behavioural assays, we show that these compounds trigger aggression among normally amicable nestmates, but control hydrocarbons do not. Furthermore, behavioural testing across multiple different supercolonies reveals that the reaction to individual compounds varies from colony to colony -- the expected reaction to true colony recognition labels. Our results also show that both quantitative and qualitative changes to cuticular hydrocarbon profiles can trigger aggression among nestmates. These data point the way for the development of new environmentally-friendly control strategies based on the species-specific manipulation of aggressive behaviour. Conclusion Overall, our findings reveal the identity of specific chemicals used for colonymate recognition by the invasive Argentine ants. Although the particular chemicals used by other ants may differ, the patterns reported here are likely to be true for ants generally. As almost all invasive ants display widespread unicoloniality in their introduced ranges, our findings are particularly relevant for our understanding of the biology of these damaging

  10. Performance evaluation of ant colony optimization-based solution strategies on the mixed-model assembly line balancing problem

    Science.gov (United States)

    Akpinar, Sener; Mirac Bayhan, G.

    2014-06-01

    The aim of this article is to compare the performances of iterative ant colony optimization (ACO)-based solution strategies on a mixed-model assembly line balancing problem of type II (MMALBP-II) by addressing some particular features of real-world assembly line balancing problems such as parallel workstations and zoning constraints. To solve the problem, where the objective is to minimize the cycle time (i.e. maximize the production rate) for a predefined number of workstations in an existing assembly line, two ACO-based approaches which differ in the mission assigned to artificial ants are used. Furthermore, each ACO-based approach is conducted with two different pheromone release strategies: global and local pheromone updating rules. The four ACO-based approaches are used for solving 20 representative MMALBP-II to compare their performance in terms of computational time and solution quality. Detailed comparison results are presented.

  11. Edge Detection of Medical Images Using Modified Ant Colony Optimization Algorithm based on Weighted Heuristics

    Directory of Open Access Journals (Sweden)

    Puneet Rai

    2014-02-01

    Full Text Available Ant Colony Optimization (ACO is nature inspired algorithm based on foraging behavior of ants. The algorithm is based on the fact how ants deposit pheromone while searching for food. ACO generates a pheromone matrix which gives the edge information present at each pixel position of image, formed by ants dispatched on image. The movement of ants depends on local variance of image's intensity value. This paper proposes an improved method based on heuristic which assigns weight to the neighborhood. Thus by assigning the weights or priority to the neighboring pixels, the ant decides in which direction it can move. The method is applied on Medical images and experimental results are provided to support the superior performance of the proposed approach and the existing method.

  12. Blochmannia endosymbionts improve colony growth and immune defence in the ant Camponotus fellah

    Directory of Open Access Journals (Sweden)

    Depoix Delphine

    2009-02-01

    Full Text Available Abstract Background Microorganisms are a large and diverse form of life. Many of them live in association with large multicellular organisms, developing symbiotic relations with the host and some have even evolved to form obligate endosymbiosis 1. All Carpenter ants (genus Camponotus studied hitherto harbour primary endosymbiotic bacteria of the Blochmannia genus. The role of these bacteria in ant nutrition has been demonstrated 2 but the omnivorous diet of these ants lead us to hypothesize that the bacteria might provide additional advantages to their host. In this study, we establish links between Blochmannia, growth of starting new colonies and the host immune response. Results We manipulated the number of bacterial endosymbionts in incipient laboratory-reared colonies of Camponotus fellah by administrating doses of an antibiotic (Rifampin mixed in honey-solution. Efficiency of the treatment was estimated by quantitative polymerase chain reaction and Fluorescent in situ hybridization (FISH, using Blochmannia specific primers (qPCR and two fluorescent probes (one for all Eubacterial and other specific for Blochmannia. Very few or no bacteria could be detected in treated ants. Incipient Rifampin treated colonies had significantly lower numbers of brood and adult workers than control colonies. The immune response of ants from control and treated colonies was estimated by inserting nylon filaments in the gaster and removing it after 24 h. In the control colonies, the encapsulation response was positively correlated to the bacterial amount, while no correlation was observed in treated colonies. Indeed, antibiotic treatment increased the encapsulation response of the workers, probably due to stress conditions. Conclusion The increased growth rate observed in non-treated colonies confirms the importance of Blochmannia in this phase of colony development. This would provide an important selective advantage during colony founding, where the colonies

  13. Chaotic Artificial Bee Colony Used for Cluster Analysis

    Science.gov (United States)

    Zhang, Yudong; Wu, Lenan; Wang, Shuihua; Huo, Yuankai

    A new approach based on artificial bee colony (ABC) with chaotic theory was proposed to solve the partitional clustering problem. We first investigate the optimization model including both the encoding strategy and the variance ratio criterion (VRC). Second, a chaotic ABC algorithm was developed based on the Rossler attractor. Experiments on three types of artificial data of different degrees of overlapping all demonstrate the CABC is superior to both genetic algorithm (GA) and combinatorial particle swarm optimization (CPSO) in terms of robustness and computation time.

  14. A Simple and Efficient Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Yunfeng Xu

    2013-01-01

    Full Text Available Artificial bee colony (ABC is a new population-based stochastic algorithm which has shown good search abilities on many optimization problems. However, the original ABC shows slow convergence speed during the search process. In order to enhance the performance of ABC, this paper proposes a new artificial bee colony (NABC algorithm, which modifies the search pattern of both employed and onlooker bees. A solution pool is constructed by storing some best solutions of the current swarm. New candidate solutions are generated by searching the neighborhood of solutions randomly chosen from the solution pool. Experiments are conducted on a set of twelve benchmark functions. Simulation results show that our approach is significantly better or at least comparable to the original ABC and seven other stochastic algorithms.

  15. Dose response of red imported fire ant colonies to Solenopsis invicta virus 3.

    Science.gov (United States)

    Valles, Steven M; Porter, Sanford D

    2015-10-01

    Baiting tests were conducted to evaluate the effect of increasing Solenopsis invicta virus 3 (SINV-3) dose on fire ant colonies. Actively growing early-stage fire ant (Solenopsis invicta Buren) laboratory colonies were pulse-exposed for 24 hours to six concentrations of SINV-3 (10(1), 10(3), 10(5), 10(7), 10(9) genome equivalents/μl) in 1 ml of a 10 % sucrose bait and monitored regularly for two months. SINV-3 concentration had a significant effect on colony health. Brood rating (proportion of brood to worker ants) began to depart from the control group at 19 days for the 10(9) concentration and 26 days for the 10(7) concentration. At 60 days, brood rating was significantly lower among colonies treated with 10(9), 10(7), and 10(5) SINV-3 concentrations. The intermediate concentration, 10(5), appeared to cause a chronic, low-level infection with one colony (n = 9) supporting virus replication. Newly synthesized virus was not detected in any fire ant colonies treated at the 10(1) concentration, indicating that active infections failed to be established at this level of exposure. The highest bait concentration chosen, 10(9), appeared most effective from a control aspect; mean colony brood rating at this concentration (1.1 ± 0.9 at the 60 day time point) indicated poor colony health with minimal brood production. No clear relationship was observed between the quantity of plus genome strand detected and brood rating. Conversely, there was a strong relationship between the presence of the replicative genome strand and declining brood rating, which may serve as a predictor of disease severity. Recommendations for field treatment levels to control fire ants with SINV-3 are discussed. PMID:26162304

  16. A Review on Artificial Bee Colony in MANET

    OpenAIRE

    Richa Kalucha; Deepak Goyal

    2014-01-01

    Swarm intelligence models the collective intelligence in swarms of insects or animals. To solve wide range of problems, many algorithms that simulate these models have been The Artificial Bee Colony (ABC) algorithm is one of the swarm intelligence algorithms based on a particular intelligent behavior of honeybee swarms. ABC was introduced to solve numerous optimization problems. ABC maximizes the lifetime of network and provides an effective multi-path data transmission in efficient manner.

  17. A Multistrategy Optimization Improved Artificial Bee Colony Algorithm

    OpenAIRE

    Wen Liu

    2014-01-01

    Being prone to the shortcomings of premature and slow convergence rate of artificial bee colony algorithm, an improved algorithm was proposed. Chaotic reverse learning strategies were used to initialize swarm in order to improve the global search ability of the algorithm and keep the diversity of the algorithm; the similarity degree of individuals of the population was used to characterize the diversity of population; population diversity measure was set as an indicator to dynamically and ada...

  18. Artificial Bee Colony with Different Mutation Schemes: A comparative study

    OpenAIRE

    Iyad Abu Doush; Hasan, Basima Hani F.; Mohammed Azmi Al-Betar; Eslam Al Maghayreh; Faisal Alkhateeb; Mohammad Hamdan

    2014-01-01

    Artificial Bee Colony (ABC) is a swarm-based metaheuristic for continuous optimization. Recent work hybridized this algorithm with other metaheuristics in order to improve performance. The work in this paper, experimentally evaluates the use of different mutation operators with the ABC algorithm. The introduced operator is activated according to a determined probability called mutation rate (MR). The results on standard benchmark function suggest that the use of this operator improves p...

  19. Solving Multiobjective Optimization Problems Using Artificial Bee Colony Algorithm

    OpenAIRE

    Beiwei Zhang; Hanning Chen; Yunlong Zhu; Wenping Zou

    2011-01-01

    Multiobjective optimization has been a difficult problem and focus for research in fields of science and engineering. This paper presents a novel algorithm based on artificial bee colony (ABC) to deal with multi-objective optimization problems. ABC is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. It uses less control parameters, and it can be efficiently used for solving multimodal and multidimensional optimization problems. Ou...

  20. Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization

    OpenAIRE

    Lianbo Ma; Hanning Chen; Kunyuan Hu; Yunlong Zhu

    2014-01-01

    This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for t...

  1. AntEpiSeeker: detecting epistatic interactions for case-control studies using a two-stage ant colony optimization algorithm

    OpenAIRE

    Liu Xinyu; Wang Yupeng; Robbins Kelly; Rekaya Romdhane

    2010-01-01

    Abstract Background Epistatic interactions of multiple single nucleotide polymorphisms (SNPs) are now believed to affect individual susceptibility to common diseases. The detection of such interactions, however, is a challenging task in large scale association studies. Ant colony optimization (ACO) algorithms have been shown to be useful in detecting epistatic interactions. Findings AntEpiSeeker, a new two-stage ant colony optimization algorithm, has been developed for detecting epistasis in ...

  2. Application of ant colony algorithm in plant leaves classification based on infrared spectroscopy

    Science.gov (United States)

    Guo, Tiantai; Hong, Bo; Kong, Ming; Zhao, Jun

    2014-04-01

    This paper proposes to use ant colony algorithm in the analysis of spectral data of plant leaves to achieve the best classification of different plants within a short time. Intelligent classification is realized according to different components of featured information included in near infrared spectrum data of plants. The near infrared diffusive emission spectrum curves of the leaves of Cinnamomum camphora and Acer saccharum Marsh are acquired, which have 75 leaves respectively, and are divided into two groups. Then, the acquired data are processed using ant colony algorithm and the same kind of leaves can be classified as a class by ant colony clustering algorithm. Finally, the two groups of data are classified into two classes. Experiment results show that the algorithm can distinguish different species up to the percentage of 100%. The classification of plant leaves has important application value in agricultural development, research of species invasion, floriculture etc.

  3. Weapon target assignment problem satisfying expected damage probabilities based on ant colony algorithm

    Institute of Scientific and Technical Information of China (English)

    Wang Yanxia; Qian Longjun; Guo Zhi; Ma Lifeng

    2008-01-01

    A weapon target assignment (WTA) model satisfying expected damage probabilities with an ant colony algorithm is proposed.In order to save armament resource and attack the targets effectively,the strategy of the weapon assignment is that the target with greater threat degree has higher priority to be intercepted.The effect of this WTA model is not maximizing the damage probability but satisfying the whole assignment result.Ant colony algorithm has been successfully used in many fields,especially in combination optimization.The ant colony algorithm for this WTA problem is described by analyzing path selection,pheromone update,and tabu table update.The effectiveness of the model and the algorithm is demonstrated with an example.

  4. A Graph-based Ant Colony Optimization Approach for Integrated Process Planning and Scheduling

    Institute of Scientific and Technical Information of China (English)

    Jinfeng Wang; Xiaoliang Fan; Chaowei Zhang; Shuting Wan

    2014-01-01

    This paper considers an ant colony optimization algorithm based on AND/OR graph for integrated process planning and scheduling (IPPS). General y, the process planning and scheduling are studied separately. Due to the complexity of manufacturing system, IPPS combining both process planning and scheduling can depict the real situation of a manufacturing system. The IPPS is represented on AND/OR graph consisting of nodes, and undirected and directed arcs. The nodes denote operations of jobs, and undirected/directed arcs denote possible visiting path among the nodes. Ant colony goes through the necessary nodes on the graph from the starting node to the end node to obtain the optimal solution with the objective of minimizing makespan. In order to avoid local convergence and low convergence, some improved strategy is incorporated in the standard ant colony optimiza-tion algorithm. Extensive computational experiments are carried out to study the influence of various parameters on the system performance.

  5. Application of Modified Ant Colony Optimization (MACO for Multicast Routing Problem

    Directory of Open Access Journals (Sweden)

    Sudip Kumar Sahana

    2016-04-01

    Full Text Available It is well known that multicast routing is combinatorial problem finds the optimal path between source destination pairs. Traditional approaches solve this problem by establishment of the spanning tree for the network which is mapped as an undirected weighted graph. This paper proposes a Modified Ant Colony Optimization (MACO algorithm which is based on Ant Colony System (ACS with some modification in the configuration of starting movement and in local updation technique to overcome the basic limitations of ACS such as poor initialization and slow convergence rate. It is shown that the proposed Modified Ant Colony Optimization (MACO shows better convergence speed and consumes less time than the conventional ACS to achieve the desired solution.

  6. Blackboard Mechanism Based Ant Colony Theory for Dynamic Deployment of Mobile Sensor Networks

    Institute of Scientific and Technical Information of China (English)

    Guang-ping Qi; Ping Song; Ke-jie Li

    2008-01-01

    A novel bionic swarm intelligence algorithm, called ant colony algorithm based on a blackboard mechanism, is proposed to solve the autonomy and dynamic deployment of mobiles sensor networks effectively. A blackboard mechanism is introduced into the system for making pheromone and completing the algorithm. Every node, which can be looked as an ant, makes one information zone in its memory for communicating with other nodes and leaves pheromone, which is created by ant itself in nature. Then ant colony theory is used to find the optimization scheme for path planning and deployment of mobile Wireless Sensor Network (WSN). We test the algorithm in a dynamic and unconfigurable environment. The results indicate that the algorithm can reduce the power consumption by 13% averagely, enhance the efficiency of path planning and deployment of mobile WSN by 15% averagely.

  7. The relationship between canopy cover and colony size of the wood ant Formica lugubris--implications for the thermal effects on a keystone ant species.

    Directory of Open Access Journals (Sweden)

    Yi-Huei Chen

    Full Text Available 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 strong link between canopy cover and the local temperatures of wood ant's nesting location provides a feasible approach for our study. Our results showed that nests were larger in shadier areas where the thermal environment was colder and more stable compared to open areas. Colonies (sum of nests in a polydomous colony also tended to be larger in shadier areas than in open areas. In addition to temperature, our results supported that food resource availability may be an additional factor mediating the relationship between canopy cover and nest size. The effects of canopy cover on total colony size may act at the nest level because of the positive relationship between total colony size and mean nest size, rather than at the colony level due to lack of link between canopy cover and number of nests per colony. Causal relationships between the environment and the life-history characteristics may suggest possible future impacts of climate change on these species.

  8. 基于异类蚁群的双种群蚁群算法%Dual population ant colony algorithm based on heterogeneous ant colonies

    Institute of Scientific and Technical Information of China (English)

    何雪莉; 张鹏; 马苗; 林杰; 黄鑫

    2009-01-01

    提出一种基于异类蚁群的双种群蚁群(Dual Population Ant Colony Algorithm Based on Heterogeneous Ant Colonies,DPACBH)算法,算法将两种信息素更新机制不同的蚁群分别独立进行进化求解,并定期交换优良解和信息来改善解的多样性,增强跳出局部最优的能力,使算法更容易收敛到全局最优解.以TSP(Travel Salesman Problem)问题为例所进行的计算表明,该算法比基本双种群蚁群算法具有更好的收敛速度和准确性.

  9. ACOustic: A Nature-Inspired Exploration Indicator for Ant Colony Optimization

    OpenAIRE

    Rafid Sagban; Ku Ruhana Ku-Mahamud; Muhamad Shahbani Abu Bakar

    2015-01-01

    A statistical machine learning indicator, ACOustic, is proposed to evaluate the exploration behavior in the iterations of ant colony optimization algorithms. This idea is inspired by the behavior of some parasites in their mimicry to the queens’ acoustics of their ant hosts. The parasites’ reaction results from their ability to indicate the state of penetration. The proposed indicator solves the problem of robustness that results from the difference of magnitudes in the distance’s matrix, esp...

  10. Collective Intelligence for Optimal Power Flow Solution Using Ant Colony Optimization

    OpenAIRE

    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 ...

  11. Dynamic Load Balancing Strategy for Cloud Computing with Ant Colony Optimization

    OpenAIRE

    Ren Gao; Juebo Wu

    2015-01-01

    How to distribute and coordinate tasks in cloud computing is a challenging issue, in order to get optimal resource utilization and avoid overload. In this paper, we present a novel approach on load balancing via ant colony optimization (ACO), for balancing the workload in a cloud computing platform dynamically. Two strategies, forward-backward ant mechanism and max-min rules, are introduced to quickly find out the candidate nodes for load balancing. We formulate pheromone initialization and p...

  12. Convergence results for continuous-time dynamics arising in ant colony optimization

    OpenAIRE

    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...

  13. Dynamics and Control of an Invasive Species: The Case of the Rasberry Crazy Ant Colonies

    OpenAIRE

    Cheathon, Valerie; Flores, Agustin; Suriel, Victor; Talbot, Octavious; Padilla, Dustin; Sarzynska, Marta; Smith, Adrian; Melara, Luis

    2013-01-01

    This project is motivated by the costs related with the documented risks of the introduction of non-native invasive species of plants, animals, or pathogens associated with travel and international trade. Such invasive species often have no natural enemies in their new regions. The spatiotemporal dynamics related to the invasion/spread of Nylanderia fulva, commonly known as the Rasberry crazy ant, are explored via the use of models that focus on the reproduction of ant colonies. A Cellular Au...

  14. A Multi Ant Colony Optimization algorithm for a Mixed Car Assembly Line

    OpenAIRE

    Pulido, Raúl; García Sánchez, Álvaro; Diego, Francisco Javier; Andrés-Romano, Carlos

    2013-01-01

    This paper presents an ant colony optimization algorithm to sequence the mixed assembly lines considering the inventory and the replenishment of components. This is a NP-problem that cannot be solved to optimality by exact methods when the size of the problem growth. Groups of specialized ants are implemented to solve the different parts of the problem. This is intended to differentiate each part of the problem. Different types of pheromone structures are created to identify good car sequence...

  15. Mechanisms of social regulation change across colony development in an ant

    Directory of Open Access Journals (Sweden)

    Liebig Jürgen

    2010-10-01

    Full Text Available Abstract Background Mutual policing is an important mechanism for reducing conflict in cooperative groups. In societies of ants, bees, and wasps, mutual policing of worker reproduction can evolve when workers are more closely related to the queen's sons than to the sons of workers or when the costs of worker reproduction lower the inclusive fitness of workers. During colony growth, relatedness within the colony remains the same, but the costs of worker reproduction may change. The costs of worker reproduction are predicted to be greatest in incipient colonies. If the costs associated with worker reproduction outweigh the individual direct benefits to workers, policing mechanisms as found in larger colonies may be absent in incipient colonies. Results We investigated policing behaviour across colony growth in the ant Camponotus floridanus. In large colonies of this species, worker reproduction is policed by the destruction of worker-laid eggs. We found workers from incipient colonies do not exhibit policing behaviour, and instead tolerate all conspecific eggs. The change in policing behaviour is consistent with changes in egg surface hydrocarbons, which provide the informational basis for policing; eggs laid by queens from incipient colonies lack the characteristic hydrocarbons on the surface of eggs laid by queens from large colonies, making them chemically indistinguishable from worker-laid eggs. We also tested the response to fertility information in the context of queen tolerance. Workers from incipient colonies attacked foreign queens from large colonies; whereas workers from large colonies tolerated such queens. Workers from both incipient and large colonies attacked foreign queens from incipient colonies. Conclusions Our results provide novel insights into the regulation of worker reproduction in social insects at both the proximate and ultimate levels. At the proximate level, our results show that mechanisms of social regulation, such as

  16. Host specificity and colony impacts of the fire ant pathogen, Solenopsis invicta virus 3.

    Science.gov (United States)

    Porter, Sanford D; Valles, Steven M; Oi, David H

    2013-09-01

    An understanding of host specificity is essential before pathogens can be used as biopesticides or self-sustaining biocontrol agents. In order to define the host range of the recently discovered Solenopsis invicta virus 3 (SINV-3), we exposed laboratory colonies of 19 species of ants in 14 genera and 4 subfamilies to this virus. Despite extreme exposure during these tests, active, replicating infections only occurred in Solenopsis invicta Buren and hybrid (S. invicta×S. richteri) fire ant colonies. The lack of infections in test Solenopsis geminata fire ants from the United States indicates that SINV-3 is restricted to the saevissima complex of South American fire ants, especially since replicating virus was also found in several field-collected samples of the black imported fire ant, Solenopsis richteri Forel. S. invicta colonies infected with SINV-3 declined dramatically with average brood reductions of 85% or more while colonies of other species exposed to virus remained uninfected and healthy. The combination of high virulence and high host specificity suggest that SINV-3 has the potential for use as either a biopesticide or a self-sustaining biocontrol agent. PMID:23665158

  17. Application of ant colony optimization approach to severe accident management measures of Maanshan nuclear power plant

    International Nuclear Information System (INIS)

    The first three guidelines in the Maanshan SAMG were respectively evaluated for the effects in the SBO incident. The MAAP5 code was used to simulate the sequence of events and physical phenomena in the plant. The results show that the priority optimization should be carried out at two separated scenarios, i.e. the power recovered prior or after hot-leg creep rupture. The performance indices in the ant colony optimization could be the vessel life and the hydrogen generation from core for ant colony optimization. (author)

  18. Scalable Clustering of High-Dimensional Data Technique Using SPCM with Ant Colony Optimization Intelligence

    Directory of Open Access Journals (Sweden)

    Thenmozhi Srinivasan

    2015-01-01

    Full Text Available Clusters of high-dimensional data techniques are emerging, according to data noisy and poor quality challenges. This paper has been developed to cluster data using high-dimensional similarity based PCM (SPCM, with ant colony optimization intelligence which is effective in clustering nonspatial data without getting knowledge about cluster number from the user. The PCM becomes similarity based by using mountain method with it. Though this is efficient clustering, it is checked for optimization using ant colony algorithm with swarm intelligence. Thus the scalable clustering technique is obtained and the evaluation results are checked with synthetic datasets.

  19. A clustering routing algorithm based on improved ant colony clustering for wireless sensor networks

    Science.gov (United States)

    Xiao, Xiaoli; Li, Yang

    Because of real wireless sensor network node distribution uniformity, this paper presents a clustering strategy based on the ant colony clustering algorithm (ACC-C). To reduce the energy consumption of the head near the base station and the whole network, The algorithm uses ant colony clustering on non-uniform clustering. The improve route optimal degree is presented to evaluate the performance of the chosen route. Simulation results show that, compared with other algorithms, like the LEACH algorithm and the improve particle cluster kind of clustering algorithm (PSC - C), the proposed approach is able to keep away from the node with less residual energy, which can improve the life of networks.

  20. Runtime analysis of ant colony optimization on dynamic shortest path problems

    DEFF Research Database (Denmark)

    Lissovoi, Andrei; Witt, Carsten

    2015-01-01

    A simple ACO algorithm called lambda-MMAS for dynamic variants of the single-destination shortest paths problem is studied by rigorous runtime analyses. Building upon previous results for the special case of 1-MMAS, it is studied to what extent an enlarged colony using lambda ants per vertex helps...... in tracking an oscillating optimum. It is shown that easy cases of oscillations can be tracked by a constant number of ants. However, the paper also identifies more involved oscillations that with overwhelming probability cannot be tracked with any polynomial-size colony. Finally, parameters of...

  1. Sequenzierung mit Ant-Colony-Systemen am Beispiel Querverteil-Wagen

    OpenAIRE

    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...

  2. Application of ant colony optimization approach to severe accident management measures of Maanshan nuclear power plant

    Energy Technology Data Exchange (ETDEWEB)

    Tsai, C.-M.; Wang, S.-J. [Inst. of Nuclear Energy Research, Taiwan (China)

    2011-07-01

    The first three guidelines in the Maanshan SAMG were respectively evaluated for the effects in the SBO incident. The MAAP5 code was used to simulate the sequence of events and physical phenomena in the plant. The results show that the priority optimization should be carried out at two separated scenarios, i.e. the power recovered prior or after hot-leg creep rupture. The performance indices in the ant colony optimization could be the vessel life and the hydrogen generation from core for ant colony optimization. (author)

  3. Runtime analysis of ant colony optimization on dynamic shortest path problems

    DEFF Research Database (Denmark)

    Lissovoi, Andrei; Witt, Carsten

    2013-01-01

    A simple ACO algorithm called λ-MMAS for dynamic variants of the single-destination shortest paths problem is studied by rigorous runtime analyses. Building upon previous results for the special case of 1-MMAS, it is studied to what extent an enlarged colony using $\\lambda$ ants per vertex helps in...... tracking an oscillating optimum. It is shown that easy cases of oscillations can be tracked by a constant number of ants. However, the paper also identifies more involved oscillations that with overwhelming probability cannot be tracked with any polynomial-size colony. Finally, parameters of dynamic...

  4. Distribution system minimum loss reconfiguration in the Hyper-Cube Ant Colony Optimization framework

    Energy Technology Data Exchange (ETDEWEB)

    Carpaneto, Enrico; Chicco, Gianfranco [Dipartimento di Ingegneria Elettrica, Politecnico di Torino, corso Duca degli Abruzzi 24, I-10129 Torino (Italy)

    2008-12-15

    This paper presents an original application of the Ant Colony Optimization concepts to the optimal reconfiguration of distribution systems, with the objective of minimizing the distribution system losses in the presence of a set of structural and operational constraints. The proposed algorithm starts from the current configuration of the system and proceeds by progressively introducing variations in the configuration according to local and global heuristic rules developed within the Hyper-Cube Ant Colony Optimization framework. Results of numerical tests carried out on a classical system and on a large real urban distribution system are presented to show the effectiveness of the proposed approach. (author)

  5. Scalable Clustering of High-Dimensional Data Technique Using SPCM with Ant Colony Optimization Intelligence.

    Science.gov (United States)

    Srinivasan, Thenmozhi; Palanisamy, Balasubramanie

    2015-01-01

    Clusters of high-dimensional data techniques are emerging, according to data noisy and poor quality challenges. This paper has been developed to cluster data using high-dimensional similarity based PCM (SPCM), with ant colony optimization intelligence which is effective in clustering nonspatial data without getting knowledge about cluster number from the user. The PCM becomes similarity based by using mountain method with it. Though this is efficient clustering, it is checked for optimization using ant colony algorithm with swarm intelligence. Thus the scalable clustering technique is obtained and the evaluation results are checked with synthetic datasets. PMID:26495413

  6. A Hybrid Ant Colony Optimization for the Prediction of Protein Secondary Structure

    Institute of Scientific and Technical Information of China (English)

    Chao CHEN; Yuan Xin TIAN; Xiao Yong ZOU; Pei Xiang CAI; Jin Yuan MO

    2005-01-01

    Based on the concept of ant colony optimization and the idea of population in genetic algorithm, a novel global optimization algorithm, called the hybrid ant colony optimization (HACO), is proposed in this paper to tackle continuous-space optimization problems. It was compared with other well-known stochastic methods in the optimization of the benchmark functions and was also used to solve the problem of selecting appropriate dilation efficiently by optimizing the wavelet power spectrum of the hydrophobic sequence of protein, which is thc key step on using continuous wavelet transform (CWT) to predict a-helices and connecting peptides.

  7. MAS Equipped with Ant Colony Applied into Dynamic Job Shop Scheduling

    Science.gov (United States)

    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.

  8. Blending of heritable recognition cues among ant nestmates creates distinct colony gestalt odours but prevents within-colony nepotism

    DEFF Research Database (Denmark)

    van Zweden, Jelle Stijn; Brask, Josefine B.; Christensen, Jan H.;

    2010-01-01

    members to create a Gestalt odour. Although earlier studies have established that hydrocarbon profiles are influenced by heritable factors, transfer among nestmates and additional environmental factors, no studies have quantified these relative contributions for separate compounds. Here, we use the ant...... discrimination or as nestmate recognition cues. These results indicate that heritable compounds are suitable for establishing a genetic Gestalt for efficient nestmate recognition, but that recognition cues within colonies are insufficiently distinct to allow nepotistic kin discrimination....

  9. Multiple ant-bee colony optimization for load balancing in packet-switched networks

    OpenAIRE

    Mehdi Kashefi Kia; Nasser Nemat bakhsh; Reza Askari Moghadam

    2011-01-01

    One of the important issues in computer networks is “Load Balancing” which leads to efficient use of the network resources. To achieve a balanced network it is necessary to find different routes between the source and destination. In the current paper we propose a new approach to find different routes using swarm intelligence techniques and multi colony algorithms. In the proposed algorithm that is an improved version of MACO algorithm, we use different colonies of ants and bees and appoint t...

  10. Colony formation of the western harvester ant in a chronic gamma radiation field

    International Nuclear Information System (INIS)

    A colony of Western harvester ants, Pogonomyrmex occidentalis, became established in a chronically exposed gamma radiation field located on the native short-grass plains of Colorado. The exposure level at the nest site was 18 R/hr. At the end of the colony's first and second seasons the nest mound diam were 25 and 36 cm, respectively. There were no apparent habitat modifications to suggest any avoidance response to the radiation. (U.S.)

  11. An Effect and Analysis of Parameter on Ant Colony Optimization for Solving Travelling Salesman Problem

    OpenAIRE

    Km. Shweta; Alka Singh

    2013-01-01

    Ant Colony optimization has proved suitable to solve a wide range of combinatorial optimization(or NP-hard) problems as the Travelling Salesman Problem (TSP). The first step of ACO algorithm is to setthe parameters that drive the algorithm. The parameter has an important impact on the performance of theant colony algorithm. The basic parameters that are used in ACO algorithms are; the relative importance (orweight) of pheromone, the relative importance of heuristics value, initial pheromone v...

  12. A Preliminary Study of Automatic Delineation of Eyes on CT Images Using Ant Colony Optimization

    Institute of Scientific and Technical Information of China (English)

    LI Yong-jie; XIE Wei-fu; YAO De-zhong

    2007-01-01

    Eyes are important organs-at-risk (OARs) that should be protected during the radiation treatment of those head tumors. Correct delineation of the eyes on CT images is one of important issues for treatment planning to protect the eyes as much as possible. In this paper, we propose a new method, named ant colony optimization (ACO), to delineate the eyes automatically.In the proposed algorithm, each ant tries to find a closed path, and some pheromone is deposited on the visited path when the ant finds a path. After all ants finish a circle, the best ant will lay some pheromone to enforce the best path. The proposed algorithm is verified on several CT images, and the preliminary results demonstrate the feasibility of ACO for the delineation problem.

  13. Orthogonal Methods Based Ant Colony Search for Solving Continuous Optimization Problems

    Institute of Scientific and Technical Information of China (English)

    Xiao-Min Hu; Jun Zhang; Yun Li

    2008-01-01

    Research into ant colony algorithms for solving continuous optimization problems forms one of the most significant and promising areas in swarm computation. Although traditional ant algorithms are designed for combinatorial optimization, they have shown great potential in solving a wide range of optimization problems, including continuous optimization. Aimed at solving continuous problems effectively, this paper develops a novel ant algorithm termed "continuous orthogonal ant colony" (COAC), whose pheromone deposit mechanisms would enable ants to search for solutions collaboratively and effectively. By using the orthogonal design method, ants in the feasible domain can explore their chosen regions rapidly and efficiently. By implementing an "adaptive regional radius" method, the proposed algorithm can reduce the probability of being trapped in local optima and therefore enhance the global search capability and accuracy. An elitist strategy is also employed to reserve the most valuable points. The performance of the COAC is compared with two other ant algorithms for continuous optimization -- API and CACO by testing seventeen functions in the continuous domain. The results demonstrate that the proposed COAC algorithm outperforms the others.

  14. Utilisation of multiple queens and pupae transplantation to boost early colony growth of weaver ants Oecophylla smaragdina

    DEFF Research Database (Denmark)

    Peng, Renkang; Nielsen, Mogens Gissel; Offenberg, Joachim

    2013-01-01

    Weaver ants (Oecophylla smaragdina Fabricius) have been increasingly used as biocontrol agents of insect pests and as insect protein for human food and animals. For either of these purposes, mature ant colonies are essential. However, for a newly established colony to develop to a suitable mature...

  15. A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Wenping Zou

    2010-01-01

    Full Text Available Artificial Bee Colony (ABC is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents an extended ABC algorithm, namely, the Cooperative Article Bee Colony (CABC, which significantly improves the original ABC in solving complex optimization problems. Clustering is a popular data analysis and data mining technique; therefore, the CABC could be used for solving clustering problems. In this work, first the CABC algorithm is used for optimizing six widely used benchmark functions and the comparative results produced by ABC, Particle Swarm Optimization (PSO, and its cooperative version (CPSO are studied. Second, the CABC algorithm is used for data clustering on several benchmark data sets. The performance of CABC algorithm is compared with PSO, CPSO, and ABC algorithms on clustering problems. The simulation results show that the proposed CABC outperforms the other three algorithms in terms of accuracy, robustness, and convergence speed.

  16. Adaptive Edge Detection Using Adjusted ANT Colony Optimization

    Science.gov (United States)

    Davoodianidaliki, M.; Abedini, A.; Shankayi, M.

    2013-09-01

    Edges contain important information in image and edge detection can be considered a low level process in image processing. Among different methods developed for this purpose traditional methods are simple and rather efficient. In Swarm Intelligent methods developed in last decade, ACO is more capable in this process. This paper uses traditional edge detection operators such as Sobel and Canny as input to ACO and turns overall process adaptive to application. Magnitude matrix or edge image can be used for initial pheromone and ant distribution. Image size reduction is proposed as an efficient smoothing method. A few parameters such as area and diameter of travelled path by ants are converted into rules in pheromone update process. All rules are normalized and final value is acquired by averaging.

  17. Biomantling and bioturbation by colonies of the Florida harvester ant, Pogonomyrmex badius.

    Science.gov (United States)

    Tschinkel, Walter R

    2015-01-01

    In much of the world, soil-nesting ants are among the leading agents of biomantling and bioturbation, depositing excavated soil on the surface or in underground chambers. Colonies of the Florida harvester ant, Pogonomyrmex badius excavate a new nest once a year on average, depositing 0.1 to 12 L (3 L average) of soil on the surface. Repeated surveys of a population of about 400 colonies yielded the frequency of moves (approximately once per year), the distance moved (mean 4 m), and the direction moved (random). The area of the soil disc correlated well with the volume and maximum depth of the nest, as determined by excavation and mapping of chambers. The population-wide frequency distribution of disc areas thus yielded the frequency distribution of nest volumes and maximum depths. For each surveyed colony, the volume of soil excavated from six specified depth ranges and deposited on the surface was estimated. These parameters were used in a simulation to estimate the amount of soil mantled over time by the observed population of P. badius colonies. Spread evenly, P. badius mantling would create a soil layer averaging 0.43 cm thick in a millennium, with 10-15% of the soil deriving from depths greater than 1 m. Biomantling by P. badius is discussed in the context of the ant community of which it is a part, and in relation to literature reports of ant biomantling.

  18. Biomantling and bioturbation by colonies of the Florida harvester ant, Pogonomyrmex badius.

    Directory of Open Access Journals (Sweden)

    Walter R Tschinkel

    Full Text Available In much of the world, soil-nesting ants are among the leading agents of biomantling and bioturbation, depositing excavated soil on the surface or in underground chambers. Colonies of the Florida harvester ant, Pogonomyrmex badius excavate a new nest once a year on average, depositing 0.1 to 12 L (3 L average of soil on the surface. Repeated surveys of a population of about 400 colonies yielded the frequency of moves (approximately once per year, the distance moved (mean 4 m, and the direction moved (random. The area of the soil disc correlated well with the volume and maximum depth of the nest, as determined by excavation and mapping of chambers. The population-wide frequency distribution of disc areas thus yielded the frequency distribution of nest volumes and maximum depths. For each surveyed colony, the volume of soil excavated from six specified depth ranges and deposited on the surface was estimated. These parameters were used in a simulation to estimate the amount of soil mantled over time by the observed population of P. badius colonies. Spread evenly, P. badius mantling would create a soil layer averaging 0.43 cm thick in a millennium, with 10-15% of the soil deriving from depths greater than 1 m. Biomantling by P. badius is discussed in the context of the ant community of which it is a part, and in relation to literature reports of ant biomantling.

  19. Item Selection for the Development of Short Forms of Scales Using an Ant Colony Optimization Algorithm

    Science.gov (United States)

    Leite, Walter L.; Huang, I-Chan; Marcoulides, George A.

    2008-01-01

    This article presents the use of an ant colony optimization (ACO) algorithm for the development of short forms of scales. An example 22-item short form is developed for the Diabetes-39 scale, a quality-of-life scale for diabetes patients, using a sample of 265 diabetes patients. A simulation study comparing the performance of the ACO algorithm and…

  20. Improved ant colony optimization for optimal crop and irrigation water allocation by incorporating domain knowledge

    Science.gov (United States)

    An improved ant colony optimization (ACO) formulation for the allocation of crops and water to different irrigation areas is developed. The formulation enables dynamic adjustment of decision variable options and makes use of visibility factors (VFs, the domain knowledge that can be used to identify ...

  1. Studi Penggunaan Algoritma Ant Colony Dalam Pengalokasian Kanal Dinamik Pada Komunikasi Seluler

    OpenAIRE

    Ganda, Beni Afnora

    2015-01-01

    Keterbatasan spektrum frekuensi atau kanal menjadi salah satu masalah pada komunikasi seluler pada saat ini. Untuk itu perlu dicari solusi untuk dapat meminimalkan panggilan yang tidak dapat dilayani atau diblok, yaitu dengan melakukan optimasi pengalokasian kanal. Tugas Akhir ini membahas studi penggunaan algoritma ant colony dalam pengalokasian kanal dinamik pada komunikasi seluler sebagai metode penyelesaiannya. Dengan metode ini diharapkan persentase probailitas blocking menjadi lebih...

  2. Ant Colony Optimization Analysis on Overall Stability of High Arch Dam Basis of Field Monitoring

    Directory of Open Access Journals (Sweden)

    Peng Lin

    2014-01-01

    Full Text Available A dam ant colony optimization (D-ACO analysis of the overall stability of high arch dams on complicated foundations is presented in this paper. A modified ant colony optimization (ACO model is proposed for obtaining dam concrete and rock mechanical parameters. A typical dam parameter feedback problem is proposed for nonlinear back-analysis numerical model based on field monitoring deformation and ACO. The basic principle of the proposed model is the establishment of the objective function of optimizing real concrete and rock mechanical parameter. The feedback analysis is then implemented with a modified ant colony algorithm. The algorithm performance is satisfactory, and the accuracy is verified. The m groups of feedback parameters, used to run a nonlinear FEM code, and the displacement and stress distribution are discussed. A feedback analysis of the deformation of the Lijiaxia arch dam and based on the modified ant colony optimization method is also conducted. By considering various material parameters obtained using different analysis methods, comparative analyses were conducted on dam displacements, stress distribution characteristics, and overall dam stability. The comparison results show that the proposal model can effectively solve for feedback multiple parameters of dam concrete and rock material and basically satisfy assessment requirements for geotechnical structural engineering discipline.

  3. CACO : Competitive Ant Colony Optimization, A Nature-Inspired Metaheuristic For Large-Scale Global Optimization

    OpenAIRE

    M. A. El-dosuky

    2013-01-01

    Large-scale problems are nonlinear problems that need metaheuristics, or global optimization algorithms. This paper reviews nature-inspired metaheuristics, then it introduces a framework named Competitive Ant Colony Optimization inspired by the chemical communications among insects. Then a case study is presented to investigate the proposed framework for large-scale global optimization.

  4. Protein folding in hydrophobic-polar lattice model: a flexible ant colony optimization approach

    OpenAIRE

    Hu, X-M.; Zhang, J.(High Energy Physics Division, Argonne National Laboratory, Argonne, IL, USA); Xiao, J.; Li, Y.

    2008-01-01

    This paper proposes a flexible ant colony (FAC) algorithm for solving protein folding problems based on the hydrophobic-polar square lattice model. Collaborations of novel pheromone and heuristic strategies in the proposed algorithm make it more effective in predicting structures of proteins compared with other state-of-the-art algorithms.

  5. Protein folding in hydrophobic-polar lattice model: a flexible ant-colony optimization approach.

    Science.gov (United States)

    Hu, Xiao-Min; Zhang, Jun; Xiao, Jing; Li, Yun

    2008-01-01

    This paper proposes a flexible ant colony (FAC) algorithm for solving protein folding problems based on the hydrophobic-polar square lattice model. Collaborations of novel pheromone and heuristic strategies in the proposed algorithm make it more effective in predicting structures of proteins compared with other state-of-the-art algorithms. PMID:18537736

  6. Ant colony optimization analysis on overall stability of high arch dam basis of field monitoring.

    Science.gov (United States)

    Lin, Peng; Liu, Xiaoli; Chen, Hong-Xin; Kim, Jinxie

    2014-01-01

    A dam ant colony optimization (D-ACO) analysis of the overall stability of high arch dams on complicated foundations is presented in this paper. A modified ant colony optimization (ACO) model is proposed for obtaining dam concrete and rock mechanical parameters. A typical dam parameter feedback problem is proposed for nonlinear back-analysis numerical model based on field monitoring deformation and ACO. The basic principle of the proposed model is the establishment of the objective function of optimizing real concrete and rock mechanical parameter. The feedback analysis is then implemented with a modified ant colony algorithm. The algorithm performance is satisfactory, and the accuracy is verified. The m groups of feedback parameters, used to run a nonlinear FEM code, and the displacement and stress distribution are discussed. A feedback analysis of the deformation of the Lijiaxia arch dam and based on the modified ant colony optimization method is also conducted. By considering various material parameters obtained using different analysis methods, comparative analyses were conducted on dam displacements, stress distribution characteristics, and overall dam stability. The comparison results show that the proposal model can effectively solve for feedback multiple parameters of dam concrete and rock material and basically satisfy assessment requirements for geotechnical structural engineering discipline. PMID:25025089

  7. Study on Increasing the Accuracy of Classification Based on Ant Colony algorithm

    Science.gov (United States)

    Yu, M.; Chen, D.-W.; Dai, C.-Y.; Li, Z.-L.

    2013-05-01

    The application for GIS advances the ability of data analysis on remote sensing image. The classification and distill of remote sensing image is the primary information source for GIS in LUCC application. How to increase the accuracy of classification is an important content of remote sensing research. Adding features and researching new classification methods are the ways to improve accuracy of classification. Ant colony algorithm based on mode framework defined, agents of the algorithms in nature-inspired computation field can show a kind of uniform intelligent computation mode. It is applied in remote sensing image classification is a new method of preliminary swarm intelligence. Studying the applicability of ant colony algorithm based on more features and exploring the advantages and performance of ant colony algorithm are provided with very important significance. The study takes the outskirts of Fuzhou with complicated land use in Fujian Province as study area. The multi-source database which contains the integration of spectral information (TM1-5, TM7, NDVI, NDBI) and topography characters (DEM, Slope, Aspect) and textural information (Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Second Moment, Correlation) were built. Classification rules based different characters are discovered from the samples through ant colony algorithm and the classification test is performed based on these rules. At the same time, we compare with traditional maximum likelihood method, C4.5 algorithm and rough sets classifications for checking over the accuracies. The study showed that the accuracy of classification based on the ant colony algorithm is higher than other methods. In addition, the land use and cover changes in Fuzhou for the near term is studied and display the figures by using remote sensing technology based on ant colony algorithm. In addition, the land use and cover changes in Fuzhou for the near term is studied and display the figures by using

  8. ACOustic: A Nature-Inspired Exploration Indicator for Ant Colony Optimization.

    Science.gov (United States)

    Sagban, Rafid; Ku-Mahamud, Ku Ruhana; Abu Bakar, Muhamad Shahbani

    2015-01-01

    A statistical machine learning indicator, ACOustic, is proposed to evaluate the exploration behavior in the iterations of ant colony optimization algorithms. This idea is inspired by the behavior of some parasites in their mimicry to the queens' acoustics of their ant hosts. The parasites' reaction results from their ability to indicate the state of penetration. The proposed indicator solves the problem of robustness that results from the difference of magnitudes in the distance's matrix, especially when combinatorial optimization problems with rugged fitness landscape are applied. The performance of the proposed indicator is evaluated against the existing indicators in six variants of ant colony optimization algorithms. Instances for travelling salesman problem and quadratic assignment problem are used in the experimental evaluation. The analytical results showed that the proposed indicator is more informative and more robust. PMID:25954768

  9. Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem

    Science.gov (United States)

    Chen, Wei

    2015-07-01

    In this paper, we discuss the portfolio optimization problem with real-world constraints under the assumption that the returns of risky assets are fuzzy numbers. A new possibilistic mean-semiabsolute deviation model is proposed, in which transaction costs, cardinality and quantity constraints are considered. Due to such constraints the proposed model becomes a mixed integer nonlinear programming problem and traditional optimization methods fail to find the optimal solution efficiently. Thus, a modified artificial bee colony (MABC) algorithm is developed to solve the corresponding optimization problem. Finally, a numerical example is given to illustrate the effectiveness of the proposed model and the corresponding algorithm.

  10. Performance Comparison of Constrained Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Soudeh Babaeizadeh

    2015-06-01

    Full Text Available This study is aimed to evaluate, analyze and compare the performances of available constrained Artificial Bee Colony (ABC algorithms in the literature. In recent decades, many different variants of the ABC algorithms have been suggested to solve Constrained Optimization Problems (COPs. However, to the best of the authors' knowledge, there rarely are comparative studies on the numerical performance of those algorithms. This study is considering a set of well-known benchmark problems from test problems of Congress of Evolutionary Computation 2006 (CEC2006.

  11. A Hybrid Artificial Bee Colony Algorithm for Graph 3-Coloring

    OpenAIRE

    Fister Jr., Iztok; Fister, Iztok; Brest, Janez

    2012-01-01

    The Artificial Bee Colony (ABC) is the name of an optimization algorithm that was inspired by the intelligent behavior of a honey bee swarm. It is widely recognized as a quick, reliable, and efficient methods for solving optimization problems. This paper proposes a hybrid ABC (HABC) algorithm for graph 3-coloring, which is a well-known discrete optimization problem. The results of HABC are compared with results of the well-known graph coloring algorithms of today, i.e. the Tabucol and Hybrid ...

  12. Polygonal Approximation Using an Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Shu-Chien Huang

    2015-01-01

    Full Text Available A polygonal approximation method based on the new artificial bee colony (NABC algorithm is proposed in this paper. In the present method, a solution is represented by a vector, and the objective function is defined as the integral square error between the given curve and its corresponding polygon. The search process, including the employed bee stage, the onlooker bee stage, and the scout bee stage, has been constructed for this specific problem. Most experiments show that the present method when compared with the DE-based method can obtain superior approximation results with less error norm with respect to the original curves.

  13. Improvised Scout Bee Movements in Artificial Bee Colony

    OpenAIRE

    Tarun Kumar Sharma; Millie Pant

    2014-01-01

    In the basic Artificial Bee Colony (ABC) algorithm, if the fitness value associated with a food source is not improved for a certain number of specified trials then the corresponding bee becomes a scout to which a random value is assigned for finding the new food source. Basically, it is a mechanism of pulling out the candidate solution which may be entrapped in some local optimizer due to which its value is not improving. In the present study, we propose two new mechanisms for the movements ...

  14. Artificial Bee Colony with Different Mutation Schemes: A comparative study

    Directory of Open Access Journals (Sweden)

    Iyad Abu Doush

    2014-03-01

    Full Text Available Artificial Bee Colony (ABC is a swarm-based metaheuristic for continuous optimization. Recent work hybridized this algorithm with other metaheuristics in order to improve performance. The work in this paper, experimentally evaluates the use of different mutation operators with the ABC algorithm. The introduced operator is activated according to a determined probability called mutation rate (MR. The results on standard benchmark function suggest that the use of this operator improves performance in terms of convergence speed and quality of final obtained solution. It shows that Power and Polynomial mutations give best results. The fastest convergence was for the mutation rate value (MR=0.2.

  15. An ant colony based algorithm for overlapping community detection in complex networks

    Science.gov (United States)

    Zhou, Xu; Liu, Yanheng; Zhang, Jindong; Liu, Tuming; Zhang, Di

    2015-06-01

    Community detection is of great importance to understand the structures and functions of networks. Overlap is a significant feature of networks and overlapping community detection has attracted an increasing attention. Many algorithms have been presented to detect overlapping communities. In this paper, we present an ant colony based overlapping community detection algorithm which mainly includes ants' location initialization, ants' movement and post processing phases. An ants' location initialization strategy is designed to identify initial location of ants and initialize label list stored in each node. During the ants' movement phase, the entire ants move according to the transition probability matrix, and a new heuristic information computation approach is redefined to measure similarity between two nodes. Every node keeps a label list through the cooperation made by ants until a termination criterion is reached. A post processing phase is executed on the label list to get final overlapping community structure naturally. We illustrate the capability of our algorithm by making experiments on both synthetic networks and real world networks. The results demonstrate that our algorithm will have better performance in finding overlapping communities and overlapping nodes in synthetic datasets and real world datasets comparing with state-of-the-art algorithms.

  16. Harvester ant colony variation in foraging activity and response to humidity.

    Science.gov (United States)

    Gordon, Deborah M; Dektar, Katherine N; Pinter-Wollman, Noa

    2013-01-01

    Collective behavior is produced by interactions among individuals. Differences among groups in individual response to interactions can lead to ecologically important variation among groups in collective behavior. Here we examine variation among colonies in the foraging behavior of the harvester ant, Pogonomyrmex barbatus. Previous work shows how colonies regulate foraging in response to food availability and desiccation costs: the rate at which outgoing foragers leave the nest depends on the rate at which foragers return with food. To examine how colonies vary in response to humidity and in foraging rate, we performed field experiments that manipulated forager return rate in 94 trials with 17 colonies over 3 years. We found that the effect of returning foragers on the rate of outgoing foragers increases with humidity. There are consistent differences among colonies in foraging activity that persist from year to year.

  17. MOEA/D-ACO: a multiobjective evolutionary algorithm using decomposition and AntColony.

    Science.gov (United States)

    Ke, Liangjun; Zhang, Qingfu; Battiti, Roberto

    2013-12-01

    Combining ant colony optimization (ACO) and the multiobjective evolutionary algorithm (EA) based on decomposition (MOEA/D), this paper proposes a multiobjective EA, i.e., MOEA/D-ACO. Following other MOEA/D-like algorithms, MOEA/D-ACO decomposes a multiobjective optimization problem into a number of single-objective optimization problems. Each ant (i.e., agent) is responsible for solving one subproblem. All the ants are divided into a few groups, and each ant has several neighboring ants. An ant group maintains a pheromone matrix, and an individual ant has a heuristic information matrix. During the search, each ant also records the best solution found so far for its subproblem. To construct a new solution, an ant combines information from its group's pheromone matrix, its own heuristic information matrix, and its current solution. An ant checks the new solutions constructed by itself and its neighbors, and updates its current solution if it has found a better one in terms of its own objective. Extensive experiments have been conducted in this paper to study and compare MOEA/D-ACO with other algorithms on two sets of test problems. On the multiobjective 0-1 knapsack problem,MOEA/D-ACO outperforms the MOEA/D with conventional genetic operators and local search on all the nine test instances. We also demonstrate that the heuristic information matrices in MOEA/D-ACO are crucial to the good performance of MOEA/D-ACO for the knapsack problem. On the biobjective traveling salesman problem, MOEA/D-ACO performs much better than the BicriterionAnt on all the 12 test instances. We also evaluate the effects of grouping, neighborhood, and the location information of current solutions on the performance of MOEA/D-ACO. The work in this paper shows that reactive search optimization scheme, i.e., the "learning while optimizing" principle, is effective in improving multiobjective optimization algorithms. PMID:23757576

  18. Ant Colony Algorithm and Simulation for Robust Airport Gate Assignment

    Directory of Open Access Journals (Sweden)

    Hui Zhao

    2014-01-01

    Full Text Available Airport gate assignment is core task for airport ground operations. Due to the fact that the departure and arrival time of flights may be influenced by many random factors, the airport gate assignment scheme may encounter gate conflict and many other problems. This paper aims at finding a robust solution for airport gate assignment problem. A mixed integer model is proposed to formulate the problem, and colony algorithm is designed to solve this model. Simulation result shows that, in consideration of robustness, the ability of antidisturbance for airport gate assignment scheme has much improved.

  19. Ant-cuckoo colony optimization for feature selection in digital mammogram.

    Science.gov (United States)

    Jona, J B; Nagaveni, N

    2014-01-15

    Digital mammogram is the only effective screening method to detect the breast cancer. Gray Level Co-occurrence Matrix (GLCM) textural features are extracted from the mammogram. All the features are not essential to detect the mammogram. Therefore identifying the relevant feature is the aim of this work. Feature selection improves the classification rate and accuracy of any classifier. In this study, a new hybrid metaheuristic named Ant-Cuckoo Colony Optimization a hybrid of Ant Colony Optimization (ACO) and Cuckoo Search (CS) is proposed for feature selection in Digital Mammogram. ACO is a good metaheuristic optimization technique but the drawback of this algorithm is that the ant will walk through the path where the pheromone density is high which makes the whole process slow hence CS is employed to carry out the local search of ACO. Support Vector Machine (SVM) classifier with Radial Basis Kernal Function (RBF) is done along with the ACO to classify the normal mammogram from the abnormal mammogram. Experiments are conducted in miniMIAS database. The performance of the new hybrid algorithm is compared with the ACO and PSO algorithm. The results show that the hybrid Ant-Cuckoo Colony Optimization algorithm is more accurate than the other techniques. PMID:24783812

  20. An artificial bee colony algorithm for the capacitated vehicle routing problem

    DEFF Research Database (Denmark)

    Szeto, W.Y.; Wu, Yongzhong; Ho, Sin C.

    2011-01-01

    This paper introduces an artificial bee colony heuristic for solving the capacitated vehicle routing problem. The artificial bee colony heuristic is a swarm-based heuristic, which mimics the foraging behavior of a honey bee swarm. An enhanced version of the artificial bee colony heuristic is also...... proposed to improve the solution quality of the original version. The performance of the enhanced heuristic is evaluated on two sets of standard benchmark instances, and compared with the original artificial bee colony heuristic. The computational results show that the enhanced heuristic outperforms...

  1. The role of colony size on tunnel branching morphogenesis in ant nests.

    Directory of Open Access Journals (Sweden)

    Jacques Gautrais

    Full Text Available Many ant species excavate nests that are made up of chambers and interconnecting tunnels. There is a general trend of an increase in nest complexity with increasing population size. This complexity reflects a higher ramification and anastomosis of tunnels that can be estimated by the meshedness coefficient of the tunnelling networks. It has long been observed that meshedness increases with colony size within and across species, but no explanation has been provided so far. Since colony size is a strong factor controlling collective digging, a high value of the meshedness could simply be a side effect of a larger number of workers. To test this hypothesis, we study the digging dynamics in different group size of ants Messor sancta. We build a model of collective digging that is calibrated from the experimental data. Model's predictions successfully reproduce the topological properties of tunnelling networks observed in experiments, including the increase of the meshedness with group size. We then use the model to investigate situations in which collective digging progresses outward from a centre corresponding to the way tunnelling behaviour occurs in field conditions. Our model predicts that, when all other parameters are kept constant, an increase of the number of workers leads to a higher value of the meshedness and a transition from tree-like structures to highly meshed networks. Therefore we conclude that colony size is a key factor determining tunnelling network complexity in ant colonies.

  2. The role of colony size on tunnel branching morphogenesis in ant nests.

    Science.gov (United States)

    Gautrais, Jacques; Buhl, Jérôme; Valverde, Sergi; Kuntz, Pascale; Theraulaz, Guy

    2014-01-01

    Many ant species excavate nests that are made up of chambers and interconnecting tunnels. There is a general trend of an increase in nest complexity with increasing population size. This complexity reflects a higher ramification and anastomosis of tunnels that can be estimated by the meshedness coefficient of the tunnelling networks. It has long been observed that meshedness increases with colony size within and across species, but no explanation has been provided so far. Since colony size is a strong factor controlling collective digging, a high value of the meshedness could simply be a side effect of a larger number of workers. To test this hypothesis, we study the digging dynamics in different group size of ants Messor sancta. We build a model of collective digging that is calibrated from the experimental data. Model's predictions successfully reproduce the topological properties of tunnelling networks observed in experiments, including the increase of the meshedness with group size. We then use the model to investigate situations in which collective digging progresses outward from a centre corresponding to the way tunnelling behaviour occurs in field conditions. Our model predicts that, when all other parameters are kept constant, an increase of the number of workers leads to a higher value of the meshedness and a transition from tree-like structures to highly meshed networks. Therefore we conclude that colony size is a key factor determining tunnelling network complexity in ant colonies. PMID:25330080

  3. An ant colony optimization based algorithm for identifying gene regulatory elements.

    Science.gov (United States)

    Liu, Wei; Chen, Hanwu; Chen, Ling

    2013-08-01

    It is one of the most important tasks in bioinformatics to identify the regulatory elements in gene sequences. Most of the existing algorithms for identifying regulatory elements are inclined to converge into a local optimum, and have high time complexity. Ant Colony Optimization (ACO) is a meta-heuristic method based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of real ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper designs and implements an ACO based algorithm named ACRI (ant-colony-regulatory-identification) for identifying all possible binding sites of transcription factor from the upstream of co-expressed genes. To accelerate the ants' searching process, a strategy of local optimization is presented to adjust the ants' start positions on the searched sequences. By exploiting the powerful optimization ability of ACO, the algorithm ACRI can not only improve precision of the results, but also achieve a very high speed. Experimental results on real world datasets show that ACRI can outperform other traditional algorithms in the respects of speed and quality of solutions. PMID:23746735

  4. A Novel Approach to the Convergence Proof of Ant Colony Algorithm and Its MATLAB GUI-Based Realization

    Institute of Scientific and Technical Information of China (English)

    DUAN Hai-bin; WANG Dao-bo; YU Xiu-fen

    2006-01-01

    Although ant colony algorithm for the heuristic solution of hard combinational optimization problems enjoy a rapidly growing popularity, but little is known about its convergence properties. Based on the introduction of the basic principle and mathematical model, a novel approach to the convergence proof that applies directly to the ant colony algorithm is proposed in this paper. Then, a MATLAB GUI- based ant colony algorithm simulation platform is developed, and the interface of this simulation platform is very friendly, easy to use and to modify.

  5. Novel Approach to Nonlinear PID Parameter Optimization Using Ant Colony Optimization Algorithm

    Institute of Scientific and Technical Information of China (English)

    Duan Hai-bin; Wang Dao-bo; Yu Xiu-fen

    2006-01-01

    This paper presents an application of an Ant Colony Optimization (ACO) algorithm to optimize the parameters in the design of a type of nonlinear PID controller. The ACO algorithm is a novel heuristic bionic algorithm, which is based on the behaviour of real ants in nature searching for food. In order to optimize the parameters of the nonlinear PID controller using ACO algorithm,an objective function based on position tracing error was constructed, and elitist strategy was adopted in the improved ACO algorithm. Detailed simulation steps are presented. This nonlinear PID controller using the ACO algorithm has high precision of control and quick response.

  6. Gis-Based Route Finding Using ANT Colony Optimization and Urban Traffic Data from Different Sources

    Science.gov (United States)

    Davoodi, M.; Mesgari, M. S.

    2015-12-01

    Nowadays traffic data is obtained from multiple sources including GPS, Video Vehicle Detectors (VVD), Automatic Number Plate Recognition (ANPR), Floating Car Data (FCD), VANETs, etc. All such data can be used for route finding. This paper proposes a model for finding the optimum route based on the integration of traffic data from different sources. Ant Colony Optimization is applied in this paper because the concept of this method (movement of ants in a network) is similar to urban road network and movements of cars. The results indicate that this model is capable of incorporating data from different sources, which may even be inconsistent.

  7. Research on Passive Optical Network Based on Ant Colony Algorithms for Bandwidth Distribution in Uplink Direction

    Science.gov (United States)

    Zhu, Yanping; Ma, Yongsheng; Zheng, Dezhong; Zhao, Lulu; Han, Xu

    This article design PON with working vacation mechanism about bandwidth distribution in uplink direction, and optimize the serving rate of vacation and roving by ant colony algorithms (ACA), giving the objective function. The convergence speed can be improved by setting the threshold of objectives. More and more ants concentrate towards the optimal solution space in the result of the change of hormones with the objective function about the cost of system, and the optimal solution is found. The numerical experiments show that this method can allocate rational severing rate for every ONU with high speed of convergence.

  8. A colony-level response to disease control in a leaf-cutting ant

    Science.gov (United States)

    Hart, Adam; Bot, A. N. M.; Brown, Mark

    2002-03-01

    Parasites and pathogens often impose significant costs on their hosts. This is particularly true for social organisms, where the genetic structure of groups and the accumulation of contaminated waste facilitate disease transmission. In response, hosts have evolved many mechanisms of defence against parasites. Here we present evidence that Atta colombica, a leaf-cutting ant, may combat Escovopsis, a dangerous parasite of Atta's garden fungus, through a colony-level behavioural response. In A. colombica, garden waste is removed from within the colony and transported to the midden - an external waste dump - where it is processed by a group of midden workers. We found that colonies infected with Escovopsis have higher numbers of workers on the midden, where Escovopsis is deposited. Further, midden workers are highly effective in dispersing newly deposited waste away from the dumping site. Thus, the colony-level task allocation strategies of the Atta superorganism may change in response to the threat of disease to a third, essential party.

  9. A Multiuser Detector Based on Artificial Bee Colony Algorithm for DS-UWB Systems

    OpenAIRE

    Zhendong Yin; Xiaohui Liu(High Energy Division, Argonne National Laboratory, Argonne, IL 60439, U.S.A.); Zhilu Wu

    2013-01-01

    Artificial Bee Colony (ABC) algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm. The ABC algorithm was developed to solve optimizing numerical problems and revealed premising results in processing time and solution quality. In ABC, a colony of artificial bees search for rich artificial food sources; the optimizing numerical problems are converted to the problem of finding the best parameter which minimizes an objective function. Then, the artificial bee...

  10. Ant Colony Optimization detects anomalous aerosol variations associated with the Chile earthquake of 27 February 2010

    Science.gov (United States)

    Akhoondzadeh, M.

    2015-04-01

    This study attempts to acknowledge AOD (Aerosol Optical Depth) seismo-atmospheric anomalies around the time of the Chile earthquake of 27 February 2010. Since AOD precursor alone might not be useful as an accurate and stand alone criteria for the earthquake anomalies detection, therefore it would be more appropriate to use and integrate a variety of other precursors to reduce the uncertainty of potential detected seismic anomalies. To achieve this aim, eight other precursors including GPS-TEC (Total Electron Content), H+, He+, O+ densities (cm-3) and total ion density (cm-3) from IAP experiment, electron density (cm-3) and electron temperature (K) from ISL experiment and VLF electric field from ICE experiment have been surveyed to detect unusual variations around the time and location of the Chile earthquake. Moreover, three methods including Interquartile, ANN (Artificial Neural Network) and ACO (Ant Colony Optimization) have been implemented to observe the discord patterns in time series of the AOD precursor. All of the methods indicate a clear abnormal increase in time series of AOD data, 2 days prior to event. Also a striking anomaly is observed in time series of TEC data, 6 days preceding the earthquake. Using the analysis of ICE data, a prominent anomaly is detected in the VLF electric field measurement, 1 day before the earthquake. The time series of H+, He+, O+ densities (cm-3) and total ion density (cm-3) from IAP and also electron density (cm-3) and electron temperature (K) from ISL, illustrate the abnormal behaviors, 3 days before the event. It should be noted that the acknowledgment of the different lead times in outcomes of the implemented precursors strictly depend on the proper understanding of Lithosphere-Atmosphere-Ionosphere (LAI) coupling mechanism during seismic activities. It means that these different anomalies dates between LAI precursors can be a hint of truthfulness of multi-precursors analysis.

  11. Warring arthropod societies: Social spider colonies can delay annihilation by predatory ants via reduced apparency and increased group size.

    Science.gov (United States)

    Keiser, Carl N; Wright, Colin M; Pruitt, Jonathan N

    2015-10-01

    Sociality provides individuals with benefits via collective foraging and anti-predator defense. One of the costs of living in large groups, however, is increased apparency to natural enemies. Here, we test how the individual-level and collective traits of spider societies can increase the risk of discovery and death by predatory ants. We transplanted colonies of the social spider Stegodyphus dumicola into a habitat dense with one of their top predators, the pugnacious ant Anoplolepis custodiens. With three different experiments, we test how colony-wide survivorship in a predator-dense habitat can be altered by colony apparency (i.e., the presence of a capture web), group size, and group composition (i.e., the proportion of bold and shy personality types present). We also test how spiders' social context (i.e., living solitarily vs. among conspecifics) modifies their behaviour toward ants in their capture web. Colonies with capture webs intact were discovered by predatory ants on average 25% faster than colonies with the capture web removed, and all discovered colonies eventually collapsed and succumbed to predation. However, the lag time from discovery by ants to colony collapse was greater for colonies containing more individuals. The composition of individual personality types in the group had no influence on survivorship. Spiders in a social group were more likely to approach ants caught in their web than were isolated spiders. Isolated spiders were more likely to attack a safe prey item (a moth) than they were to attack ants and were more likely to retreat from ants after contact than they were after contact with moths. Together, our data suggest that the physical structures produced by large animal societies can increase their apparency to natural enemies, though larger groups can facilitate a longer lag time between discovery and demise. Lastly, the interaction between spiders and predatory ants seems to depend on the social context in which spiders reside

  12. A multistrategy optimization improved artificial bee colony algorithm.

    Science.gov (United States)

    Liu, Wen

    2014-01-01

    Being prone to the shortcomings of premature and slow convergence rate of artificial bee colony algorithm, an improved algorithm was proposed. Chaotic reverse learning strategies were used to initialize swarm in order to improve the global search ability of the algorithm and keep the diversity of the algorithm; the similarity degree of individuals of the population was used to characterize the diversity of population; population diversity measure was set as an indicator to dynamically and adaptively adjust the nectar position; the premature and local convergence were avoided effectively; dual population search mechanism was introduced to the search stage of algorithm; the parallel search of dual population considerably improved the convergence rate. Through simulation experiments of 10 standard testing functions and compared with other algorithms, the results showed that the improved algorithm had faster convergence rate and the capacity of jumping out of local optimum faster. PMID:24982924

  13. Artificial Bee Colony and Its Application for Image Fusion

    Directory of Open Access Journals (Sweden)

    Prabhat Kumar Sharma

    2012-10-01

    Full Text Available Artificial Bee Colony (ABC is one of the latest swarm algorithm based on the intelligent foraging behavior of honey bees introduced in the year 2005 by Karaboga since then it has been used for optimization of various solutions. And it is recently introduced for processing and analysis of images such as segmentation, object recognition and image retrieval. Fusing images from a vast collection of different images has become one of the interesting challenges and has drawn the attention of researchers towards the development of fusion techniques. In this paper, we have proposed the usage of ABC for optimal fusion of multi-temporal images and studied the effect of variation in the source area.

  14. Automatic image enhancement by artificial bee colony algorithm

    Science.gov (United States)

    Yimit, Adiljan; Hagihara, Yoshihiro; Miyoshi, Tasuku; Hagihara, Yukari

    2013-03-01

    With regard to the improvement of image quality, image enhancement is an important process to assist human with better perception. This paper presents an automatic image enhancement method based on Artificial Bee Colony (ABC) algorithm. In this method, ABC algorithm is applied to find the optimum parameters of a transformation function, which is used in the enhancement by utilizing the local and global information of the image. In order to solve the optimization problem by ABC algorithm, an objective criterion in terms of the entropy and edge information is introduced to measure the image quality to make the enhancement as an automatic process. Several images are utilized in experiments to make a comparison with other enhancement methods, which are genetic algorithm-based and particle swarm optimization algorithm-based image enhancement methods.

  15. Modified artificial bee colony optimization with block perturbation strategy

    Science.gov (United States)

    Jia, Dongli; Duan, Xintao; Khurram Khan, Muhammad

    2015-05-01

    As a newly emerged swarm intelligence-based optimizer, the artificial bee colony (ABC) algorithm has attracted the interest of researchers in recent years owing to its ease of use and efficiency. In this article, a modified ABC algorithm with block perturbation strategy (BABC) is proposed. Unlike basic ABC, in the BABC algorithm, not one element but a block of elements from the parent solutions is changed while producing a new solution. The performance of the BABC algorithm is investigated and compared with that of the basic ABC, modified ABC, Brest's differential evolution, self-adaptive differential evolution and restart covariance matrix adaptation evolution strategy (IPOP-CMA-ES) over a set of widely used benchmark functions. The obtained results show that the performance of BABC is better than, or at least comparable to, that of the basic ABC, improved differential evolution variants and IPOP-CMA-ES in terms of convergence speed and final solution accuracy.

  16. A Multistrategy Optimization Improved Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Wen Liu

    2014-01-01

    Full Text Available Being prone to the shortcomings of premature and slow convergence rate of artificial bee colony algorithm, an improved algorithm was proposed. Chaotic reverse learning strategies were used to initialize swarm in order to improve the global search ability of the algorithm and keep the diversity of the algorithm; the similarity degree of individuals of the population was used to characterize the diversity of population; population diversity measure was set as an indicator to dynamically and adaptively adjust the nectar position; the premature and local convergence were avoided effectively; dual population search mechanism was introduced to the search stage of algorithm; the parallel search of dual population considerably improved the convergence rate. Through simulation experiments of 10 standard testing functions and compared with other algorithms, the results showed that the improved algorithm had faster convergence rate and the capacity of jumping out of local optimum faster.

  17. An artificial bee colony algorithm for uncertain portfolio selection.

    Science.gov (United States)

    Chen, Wei

    2014-01-01

    Portfolio selection is an important issue for researchers and practitioners. In this paper, under the assumption that security returns are given by experts' evaluations rather than historical data, we discuss the portfolio adjusting problem which takes transaction costs and diversification degree of portfolio into consideration. Uncertain variables are employed to describe the security returns. In the proposed mean-variance-entropy model, the uncertain mean value of the return is used to measure investment return, the uncertain variance of the return is used to measure investment risk, and the entropy is used to measure diversification degree of portfolio. In order to solve the proposed model, a modified artificial bee colony (ABC) algorithm is designed. Finally, a numerical example is given to illustrate the modelling idea and the effectiveness of the proposed algorithm.

  18. Improvised Scout Bee Movements in Artificial Bee Colony

    Directory of Open Access Journals (Sweden)

    Tarun Kumar Sharma

    2014-01-01

    Full Text Available In the basic Artificial Bee Colony (ABC algorithm, if the fitness value associated with a food source is not improved for a certain number of specified trials then the corresponding bee becomes a scout to which a random value is assigned for finding the new food source. Basically, it is a mechanism of pulling out the candidate solution which may be entrapped in some local optimizer due to which its value is not improving. In the present study, we propose two new mechanisms for the movements of scout bees. In the first method, the scout bee follows a non-linear interpolated path while in the second one, scout bee follows Gaussian movement. Numerical results and statistical analysis of benchmark unconstrained, constrained and real life engineering design problems indicate that the proposed modifications enhance the performance of ABC.

  19. Ant colony optimization approach for test scheduling of system on chip

    Institute of Scientific and Technical Information of China (English)

    CHEN Ling; PAN Zhong-liang

    2009-01-01

    It is necessary to perform the test of system on chip, the test scheduling determines the test start and finishing time of every core in the system on chip such that the overall test time is minimized. A new test scheduling approach based on chaotic ant colony algorithm is presented in this paper. The optimization model of test scheduling was studied, the model uses the information such as the scale of test sets of both cores and user defined logic. An approach based on chaotic ant colony algorithm was proposed to solve the optimization model of test scheduling. The test of signal integrity faults such as crosstalk were also investigated when performing the test scheduling. Experimental results on many circuits show that the proposed approach can be used to solve test scheduling problems.

  20. Detection Of Ventricular Late Potentials Using Wavelet Transform And ANT Colony Optimization

    Science.gov (United States)

    Subramanian, A. Sankara; Gurusamy, G.; Selvakumar, G.

    2010-10-01

    Ventricular late Potentials (VLPs) are low-level high frequency signals that are usually found with in the terminal part of the QRS complex from patients after Myocardial Infraction. Patients with VLPs are at risk of developing Ventricular Tachycardia, which is the major cause of death if patients suffering from heart disease. In this paper the Discrete Wavelet Transform was used to detect VLPs and then ANT colony optimization (ACO) was applied to classify subjects with and without VLPs. A set of Discrete Wavelet Transform (DWT) coefficients is selected from the wavelet decomposition. Three standard parameters of VLPs such as QRST, D40 and V40 are also established. After that a novel clustering algorithm based on Ant Colony Optimization is developed for classifying arrhythmia types. The wavelet decomposition enabled us to perform the task efficiently and produced reliable results.

  1. Routing in Wireless Sensor Networks Using an Ant Colony Optimization (ACO) Router Chip.

    Science.gov (United States)

    Okdem, Selcuk; Karaboga, Dervis

    2009-01-01

    Wireless Sensor Networks consisting of nodes with limited power are deployed to gather useful information from the field. In WSNs it is critical to collect the information in an energy efficient manner. Ant Colony Optimization, a swarm intelligence based optimization technique, is widely used in network routing. A novel routing approach using an Ant Colony Optimization algorithm is proposed for Wireless Sensor Networks consisting of stable nodes. Illustrative examples, detailed descriptions and comparative performance test results of the proposed approach are included. The approach is also implemented to a small sized hardware component as a router chip. Simulation results show that proposed algorithm provides promising solutions allowing node designers to efficiently operate routing tasks. PMID:22399947

  2. Novel Voltage Scaling Algorithm Through Ant Colony Optimization for Embedded Distributed Systems

    Institute of Scientific and Technical Information of China (English)

    ZHANG Li-sheng; DING Dan

    2007-01-01

    Dynamic voltage scaling (DVS), supported by many DVS-enabled processors, is an efficient technique for energy-efficient embedded systems. Many researchers work on DVS and have presented various DVS algorithms, some with quite good results . However, the previous algorithms either have a large time complexity or obtain results sensitive to the count of the voltage modes. Fine-grained voltage modes lead to optimal results, but coarse-grained voltage modes cause less optimal one. A new algorithm is presented, which is based on ant colony optimization, called ant colony optimization voltage and task scheduling (ACO-VTS) with a low time complexity implemented by parallelizing and its linear time approximation algo rithm. Both of them generate quite good results, saving up to 30% more energy than that of the previous ones under coarse-grained modes, and their results don't depend on the number of modes available.

  3. A graph-based ant colony optimization approach for process planning.

    Science.gov (United States)

    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

  4. Clarifying Cutting and Sewing Processes with Due Windows Using an Effective Ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    Rong-Hwa Huang

    2013-01-01

    Full Text Available The cutting and sewing process is a traditional flow shop scheduling problem in the real world. This two-stage flexible flow shop is often commonly associated with manufacturing in the fashion and textiles industry. Many investigations have demonstrated that the ant colony optimization (ACO algorithm is effective and efficient for solving scheduling problems. This work applies a novel effective ant colony optimization (EACO algorithm to solve two-stage flexible flow shop scheduling problems and thereby minimize earliness, tardiness, and makespan. Computational results reveal that for both small and large problems, EACO is more effective and robust than both the particle swarm optimization (PSO algorithm and the ACO algorithm. Importantly, this work demonstrates that EACO can solve complex scheduling problems in an acceptable period of time.

  5. Automated Software Testing Using Metahurestic Technique Based on An Ant Colony Optimization

    CERN Document Server

    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

  6. Image Watermarking Algorithm Based on Multiobjective Ant Colony Optimization and Singular Value Decomposition in Wavelet Domain

    Directory of Open Access Journals (Sweden)

    Khaled Loukhaoukha

    2013-01-01

    Full Text Available We present a new optimal watermarking scheme based on discrete wavelet transform (DWT and singular value decomposition (SVD using multiobjective ant colony optimization (MOACO. A binary watermark is decomposed using a singular value decomposition. Then, the singular values are embedded in a detailed subband of host image. The trade-off between watermark transparency and robustness is controlled by multiple scaling factors (MSFs instead of a single scaling factor (SSF. Determining the optimal values of the multiple scaling factors (MSFs is a difficult problem. However, a multiobjective ant colony optimization is used to determine these values. Experimental results show much improved performances of the proposed scheme in terms of transparency and robustness compared to other watermarking schemes. Furthermore, it does not suffer from the problem of high probability of false positive detection of the watermarks.

  7. Structural link prediction based on ant colony approach in social networks

    Science.gov (United States)

    Sherkat, Ehsan; Rahgozar, Maseud; Asadpour, Masoud

    2015-02-01

    As the size and number of online social networks are increasing day by day, social network analysis has become a popular issue in many branches of science. The link prediction is one of the key rolling issues in the analysis of social network's evolution. As the size of social networks is increasing, the necessity for scalable link prediction algorithms is being felt more. The aim of this paper is to introduce a new unsupervised structural link prediction algorithm based on the ant colony approach. Recently, ant colony approach has been used for solving some graph problems. Different kinds of networks are used for testing the proposed approach. In some networks, the proposed scalable algorithm has the best result in comparison to other structural unsupervised link prediction algorithms. In order to evaluate the algorithm results, methods like the top- n precision, area under the Receiver Operating Characteristic (ROC) and Precision-Recall curves are carried out on real-world networks.

  8. Colony insularity through queen control on worker social motivation in ants.

    Science.gov (United States)

    Boulay, Raphaël; Katzav-Gozansky, Tamar; Vander Meer, Robert K; Hefetz, Abraham

    2003-05-01

    We investigated the relative contribution of the queen and workers to colony nestmate recognition cues and on colony insularity in the Carpenter ant Camponotus fellah. Workers were either individually isolated, preventing contact with both queen and workers (colonial deprived, CD), kept in queenless groups, allowing only worker-worker interactions (queen deprived, QD) or in queenright (QR) groups. Two weeks post-separation QD and QR workers were amicable towards each other but both rejected their CD nestmates, which suggests that the queen does not measurably influence the colony recognition cues. By contrast, aggression between QD and QR workers from the same original colony was apparent only after six months of separation. This clearly demonstrates the power of the Gestalt and indicates that the queen is not a dominant contributor to the nestmate recognition cues in this species. Aggression between nestmates was correlated with a greater hydrocarbon (HC) profile divergence for CD than for QD and QR workers, supporting the importance of worker-worker interactions in maintaining the colony Gestalt odour. While the queen does not significantly influence nestmate recognition cues, she does influence colony insularity since within 3 days QD (queenless for six months) workers from different colony origins merged to form a single queenless colony. By contrast, the corresponding QR colonies maintained their territoriality and did not merge. The originally divergent cuticular and postpharyngeal gland HC profiles became congruent following the merger. Therefore, while workers supply and blend the recognition signal, the queen affects worker-worker interaction by reducing social motivation and tolerance of alien conspecifics. PMID:12803913

  9. Colony insularity through queen control on worker social motivation in ants.

    Science.gov (United States)

    Boulay, Raphaël; Katzav-Gozansky, Tamar; Vander Meer, Robert K; Hefetz, Abraham

    2003-05-01

    We investigated the relative contribution of the queen and workers to colony nestmate recognition cues and on colony insularity in the Carpenter ant Camponotus fellah. Workers were either individually isolated, preventing contact with both queen and workers (colonial deprived, CD), kept in queenless groups, allowing only worker-worker interactions (queen deprived, QD) or in queenright (QR) groups. Two weeks post-separation QD and QR workers were amicable towards each other but both rejected their CD nestmates, which suggests that the queen does not measurably influence the colony recognition cues. By contrast, aggression between QD and QR workers from the same original colony was apparent only after six months of separation. This clearly demonstrates the power of the Gestalt and indicates that the queen is not a dominant contributor to the nestmate recognition cues in this species. Aggression between nestmates was correlated with a greater hydrocarbon (HC) profile divergence for CD than for QD and QR workers, supporting the importance of worker-worker interactions in maintaining the colony Gestalt odour. While the queen does not significantly influence nestmate recognition cues, she does influence colony insularity since within 3 days QD (queenless for six months) workers from different colony origins merged to form a single queenless colony. By contrast, the corresponding QR colonies maintained their territoriality and did not merge. The originally divergent cuticular and postpharyngeal gland HC profiles became congruent following the merger. Therefore, while workers supply and blend the recognition signal, the queen affects worker-worker interaction by reducing social motivation and tolerance of alien conspecifics.

  10. ABCluster: the artificial bee colony algorithm for cluster global optimization.

    Science.gov (United States)

    Zhang, Jun; Dolg, Michael

    2015-10-01

    Global optimization of cluster geometries is of fundamental importance in chemistry and an interesting problem in applied mathematics. In this work, we introduce a relatively new swarm intelligence algorithm, i.e. the artificial bee colony (ABC) algorithm proposed in 2005, to this field. It is inspired by the foraging behavior of a bee colony, and only three parameters are needed to control it. We applied it to several potential functions of quite different nature, i.e., the Coulomb-Born-Mayer, Lennard-Jones, Morse, Z and Gupta potentials. The benchmarks reveal that for long-ranged potentials the ABC algorithm is very efficient in locating the global minimum, while for short-ranged ones it is sometimes trapped into a local minimum funnel on a potential energy surface of large clusters. We have released an efficient, user-friendly, and free program "ABCluster" to realize the ABC algorithm. It is a black-box program for non-experts as well as experts and might become a useful tool for chemists to study clusters. PMID:26327507

  11. Intercluster Ant Colony Optimization Algorithm for Wireless Sensor Network in Dense Environment

    OpenAIRE

    Jung-Yoon Kim; Tripti Sharma; Brijesh Kumar; Tomar, G. S.; Karan Berry; Won-Hyung Lee

    2014-01-01

    Wireless sensor networks have grown rapidly with the innovation in Information Technology. Sensor nodes are distributed and deployed over the area for gathering requisite information. Sensor nodes possess a negative characteristic of limited energy which pulls back the network from exploiting its peak capabilities. Hence, it is necessary to gather and transfer the information in an optimized way which reduces the energy dissipation. Ant Colony Optimization (ACO) is being widely used in optimi...

  12. Statistical Performance Analysis of an Ant-Colony Optimisation Application in S-Net

    OpenAIRE

    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.

  13. An ant colony algorithm for the sequential testing problem under precedence constraints

    OpenAIRE

    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...

  14. A Schedule Optimization Model on Multirunway Based on Ant Colony Algorithm

    OpenAIRE

    Yu Jiang; Zhaolong Xu; Xinxing Xu; Zhihua Liao; Yuxiao Luo

    2014-01-01

    In order to make full use of the slot of runway, reduce flight delay, and ensure fairness among airlines, a schedule optimization model for arrival-departure flights is established in the paper. The total delay cost and fairness among airlines are two objective functions. The ant colony algorithm is adopted to solve this problem and the result is more efficient and reasonable when compared with FCFS (first come first served) strategy. Optimization results show that the flight delay and fair d...

  15. A Graph-Based Ant Colony Optimization Approach for Process Planning

    OpenAIRE

    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 operatio...

  16. An Improved Ant Colony Optimization Approach for Optimization of Process Planning

    OpenAIRE

    JinFeng Wang; XiaoLiang Fan; Haimin Ding

    2014-01-01

    Computer-aided process planning (CAPP) is an important interface between computer-aided design (CAD) and computer-aided manufacturing (CAM) in computer-integrated manufacturing environments (CIMs). In this paper, process planning problem is described based on a weighted graph, and an ant colony optimization (ACO) approach is improved to deal with it effectively. The weighted graph consists of nodes, directed arcs, and undirected arcs, which denote operations, precedence constraints among oper...

  17. Scalable Clustering of High-Dimensional Data Technique Using SPCM with Ant Colony Optimization Intelligence

    OpenAIRE

    Thenmozhi Srinivasan; Balasubramanie Palanisamy

    2015-01-01

    Clusters of high-dimensional data techniques are emerging, according to data noisy and poor quality challenges. This paper has been developed to cluster data using high-dimensional similarity based PCM (SPCM), with ant colony optimization intelligence which is effective in clustering nonspatial data without getting knowledge about cluster number from the user. The PCM becomes similarity based by using mountain method with it. Though this is efficient clustering, it is checked for optimizatio...

  18. Finding a Maximum Clique using Ant Colony Optimization and Particle Swarm Optimization in Social Networks

    OpenAIRE

    Soleimani-Pouri, Mohammad; Rezvanian, Alireza; Meybodi, Mohammad Reza

    2013-01-01

    Interaction between users in online social networks plays a key role in social network analysis. One on important types of social group is full connected relation between some users, which known as clique structure. Therefore finding a maximum clique is essential for some analysis. In this paper, we proposed a new method using ant colony optimization algorithm and particle swarm optimization algorithm. In the proposed method, in order to attain better results, it is improved process of pherom...

  19. Ant colony optimization applied to route planning using link travel time predictions

    OpenAIRE

    Claes, Rutger; Holvoet, Tom

    2011-01-01

    Finding the shortest path in a road network is a well known problem. Various proven static algorithms such as Dijkstra and A* are extensively evaluated and implemented. When confronted with dynamic costs, such as link travel time predictions, alternative route planning algorithms have to be applied. This paper applies Ant Colony Optimization combined with link travel time predictions to find routes that reduce the time spend by travels by taking into account link travel time predictions. The ...

  20. Structural Damage Detection Based on Modal Parameters Using Continuous Ant Colony Optimization

    OpenAIRE

    Aditi Majumdar; Bharadwaj Nanda; Dipak Kumar Maiti; Damodar Maity

    2014-01-01

    A method is presented to detect and quantify structural damages from changes in modal parameters (such as natural frequencies and mode shapes). An inverse problem is formulated to minimize the objective function, defined in terms of discrepancy between the vibration data identified by modal testing and those computed from analytical model, which then solved to locate and assess the structural damage using continuous ant colony optimization algorithm. The damage is formulated as stiffness redu...

  1. Improvement to the cooperative rules methodology by using the ant colony system algorithm

    OpenAIRE

    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. ...

  2. A colony-level response to disease control in a leaf-cutting ant

    OpenAIRE

    Brown, Mark

    2002-01-01

    PUBLISHED Parasites and pathogens often impose significant costs on their hosts. This is particularly true for social organisms, where the genetic structure of groups and the accumulation of contaminated waste facilitate disease transmission. In response, hosts have evolved many mechanisms of defence against parasites. Here we present evidence that Atta colombica, a leaf-cutting ant, may combat Escovopsis, a dangerous parasite of Atta?s garden fungus, through a colony-level behavioural res...

  3. Applying Ant Colony Optimization Algorithms for High-Level Behavior Learning and Reproduction from Demonstrations

    OpenAIRE

    Fonooni, Benjamin; Jevtić, Aleksandar; Hellström, Thomas; Janlert, Lars-Erik

    2015-01-01

    International audience In domains where robots carry out human’s tasks, the ability to learn new behaviors easily and quickly plays an important role. Two major challenges with Learning from Demonstration (LfD) are to identify what information in a demonstrated behavior requires attention by the robot, and to generalize the learned behavior such that the robot is able to perform the same behavior in novel situations.The main goal of this paper is to incorporate Ant Colony Optimization (ACO...

  4. A hierarchical classification ant colony algorithm for predicting gene ontology terms

    OpenAIRE

    Otero, Fernando E. B.; Freitas, Alex. A.; Johnson, Colin G.

    2009-01-01

    This paper proposes a novel Ant Colony Optimisation algorithm for the hierarchical problem of predicting protein functions using the Gene Ontology (GO). The GO structure represents a challenging case of hierarchical classification, since its terms are organised in a direct acyclic graph fashion where a term can have more than one parent in contrast to only one parent in tree structures. The proposed method discovers an ordered list of classification rules which is able to predict all GO terms...

  5. Ant Colony System for a Fuzzy Adjacent Multiple-Level Warehouse Layout Problem

    Institute of Scientific and Technical Information of China (English)

    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.

  6. Ant colony optimisation inversion of surface and borehole magnetic data under lithological constraints

    Science.gov (United States)

    Liu, Shuang; Hu, Xiangyun; Liu, Tianyou; Xi, Yufei; Cai, Jianchao; Zhang, Henglei

    2015-01-01

    The ant colony optimisation algorithm has successfully been used to invert for surface magnetic data. However, the resolution of the distributions of the recovered physical property for deeply buried magnetic sources is not generally very high because of geophysical ambiguities. We use three approaches to deal with this problem. First, the observed surface magnetic data are taken together with the three-component borehole magnetic anomalies to recover the distributions of the physical properties. This cooperative inversion strategy improves the resolution of the inversion results in the vertical direction. Additionally, as the ant colony tours the discrete nodes, we force it to visit the nodes with physical properties that agree with the drilled lithologies. These lithological constraints reduce the non-uniqueness of the inversion problem. Finally, we also implement a K-means cluster analysis for the distributions of the magnetic cells after each iteration, in order to separate the distributions of magnetisation intensity instead of concentrating the distribution in a single area. We tested our method using synthetic data and found that all tests returned favourable results. In the case study of the Mengku iron-ore deposit in northwest China, the recovered distributions of magnetisation are in good agreement with the locations and shapes of the magnetite orebodies as inferred by drillholes. Uncertainty analysis shows that the ant colony algorithm is robust in the presence of noise and that the proposed approaches significantly improve the quality of the inversion results.

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

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

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

  8. Advancement on techniques for the separation and maintenance of the red imported fire ant colonies

    Institute of Scientific and Technical Information of China (English)

    JIAN CHEN

    2007-01-01

    Advancement has recently been made on the techniques for separating andmaintaining colonies of red imported fire ants, Solenopsis invicta Buren. A new brood rescuemethod significantly improved the efficiency in separating colony from mound soil.Furthermore, a new method was developed to separate brood from the colony using fire antrepellants. Finally, a cost-effective method was developed to coat containers with dilutedFluon(R) (AGC Chemicals America, Inc, Moorestown, NJ, USA), an aqueouspolytetrafluoroethylene, to prevent housed ants from escaping a container. Usually theoriginal Fluon(R) solution is directly applied to the wall of the containers. Reduced concentrations of Fluon(R) were found to be equally effective in preventing ant escape. The use ofdiluted Fluon(R) solutions to coat the containers was recommended because of environmentaland cost-saving benefits. Application of these new techniques can significantly reduce labor,cost and environmental contamination. This review paper collates all the new techniques inone reference which readers can use as a manual.

  9. Selective Marketing for Retailers to promote Stock using improved Ant Colony Algorithm

    Directory of Open Access Journals (Sweden)

    S.SURIYA

    2013-10-01

    Full Text Available Data mining is a knowledge discovery process which deals with analysing large storage of data in order to identify the relevant data. It is a powerful tool to uncover relationships within the data.Association rule mining is an important data mining model to mine frequent items in huge repository of data. It frames out association rules with the help of minimum support and confidence value which inturns paves way to identify the occurrence of frequent item sets. Frequent pattern mining starts from analysis of customers buying habits. From which various associations between the different items that the customers purchase are identified. With the help of such associations retailers perform selective marketing to promote their business. Biologically inspired algorithms have their process observed in nature as their origin. The best feature of Ant colony algorithm, which is a bio inspired algorithm based on the behaviour of natural ant colonies, is its parallel search over the problem data and previously obtained results from it. Dynamic memory management is done by pheromone updating operation. During each cycle, solutions are constructed by evaluation of the transition probability throughpheromone level modification. An improved pheromone updating rule is used to find out all the frequent items. The proposed approach was tested using MATLAB along with WEKA toolkit. The experimental results prove that the stigmeric communication of improved ant colony algorithm helps in mining the frequent items faster and effectively than the existing algorithms.

  10. TABU SEARCH SEBAGAI LOCAL SEARCH PADA ALGORITMA ANT COLONY UNTUK PENJADWALAN FLOWSHOP

    Directory of Open Access Journals (Sweden)

    Iwan Halim Sahputra

    2009-01-01

    Full Text Available Ant colony optimization (ACO is one of the meta-heuristic methods developed for finding solutions to optimization problems such as scheduling. Local search method is one part of the ACO which determines the quality of the resulting solution. In this paper, Tabu Search was proposed as a method of local search in ACO to solve the problem of flowshop scheduling. The purpose of this scheduling was to minimize the makespan. Makespan and computation time of the proposed method were compared to the ACO that implemented Job-Index as local search method. Using proposed algorithm, makespan values obtained were not significantly different than solutions of ACO using Job-Index method, and had computation time shorter. Abstract in Bahasa Indonesia: Ant colony optimization (ACO adalah salah satu metode meta-heuristic yang dikembangkan untuk mencari solusi bagi permasalahan optimasi seperti penjadwalan. Metode local search merupakan salah satu bagian dari ACO yang menentukan kualitas solusi yang dihasilkan. Dalam makalah ini Tabu Search diusulkan sebagai metode local search dalam algoritma ACO untuk menyelesaikan masalah penjadwalan flowshop. Tujuan dari penjadwalan ini adalah untuk meminimalkan makespan. Hasil makespan dan computation time dari metode usulan ini akan dibandingkan dengan algoritma ACO yang menggunakan Job-Index sebagai metode local search. Dengan menggunakan algoritma Tabu Search sebagai local search didapat nilai makespan yang tidak berbeda secara signifikan dibandingkan yang menggunakan metode Job-Index, dengan kelebihan computation time yang lebih singkat. Kata kunci: Tabu Search, Ant Colony Algorithm, Local Search, Penjadwalan Flowshop.

  11. An Artificial Bee Colony Algorithm for the Capacitated Vehicle Routing Problem

    DEFF Research Database (Denmark)

    Szeto, W.Y.; Wu, Yongzhong; Ho, Sin C.

    This paper introduces an artificial bee colony heuristic for the capacitated vehicle routing problem. The artificial bee colony heuristic is a swarm-based heuristic, which mimics the foraging behavior of a honey bee swarm. The performance of the heuristic is evaluated on two sets of benchmark...

  12. Queen-worker caste ratio depends on colony size in the pharaoh ant (Monomorium pharaonis)

    DEFF Research Database (Denmark)

    Schmidt, Anna Mosegaard; Linksvayer, Timothy Arnold; Boomsma, Jacobus Jan;

    2011-01-01

    and body size of eclosing workers, gynes and males. We found that smaller colonies produced more new queens relative to workers, and that these queens and workers both tended to be larger. However, colony size had no effect on the size of males or on the sex ratio of the individuals reared......The success of an ant colony depends on the simultaneous presence of reproducing queens and nonreproducing workers in a ratio that will maximize colony growth and reproduction. Despite its presumably crucial role, queen–worker caste ratios (the ratio of adult queens to workers) and the factors...... affecting this variable remain scarcely studied. Maintaining polygynous pharaoh ant (Monomorium pharaonis) colonies in the laboratory has provided us with the opportunity to experimentally manipulate colony size, one of the key factors that can be expected to affect colony level queen–worker caste ratios...

  13. Ant Ballet: Phase I

    OpenAIRE

    Ollie Palmer

    2014-01-01

    The Ant Ballet project aims to create a precisely choreographed movement from a colony of ants through the use of artificial pheromones. This article presents an annotated storyboard of the film that documents the first set of experiments within the project. The full film can be viewed online at href="http://www.antballet.org"www.antballet.org.

  14. Search tree-based approach for the p-median problem using the ant colony optimization algorithm

    OpenAIRE

    Gabriel Bodnariuc; Sergiu Cataranciuc

    2014-01-01

    In this paper we present an approximation algorithm for the $p$-median problem that uses the principles of ant colony optimization technique. We introduce a search tree that keeps the partial solutions during the solution process of the $p$-median problem. An adaptation is proposed that allows ant colony optimization algorithm to perform on this tree and obtain good results in short time.

  15. A new ant colony-based routing algorithm with unidirectional link in UV mesh communication wireless network

    Institute of Scientific and Technical Information of China (English)

    KE Xi-zheng; HE Hua; WU Chang-li

    2011-01-01

    Aiming at the unidirectional links coming from nodes with different transmitting power and the obstacle blocking in UV mesh wireless communication network and the traditional ant colony algorithm only supporting bidirectional links, a new ant colony based routing algorithm with unidirectional link in UV mesh communication wireless network is proposed. The simulation results show that the proposed algorithm can improve the overall network connectivity and the survivability by supporting the combination of unidirectional link and bidirectional link.

  16. Optimization of Straight Cylindrical Turning Using Artificial Bee Colony (ABC) Algorithm

    Science.gov (United States)

    Prasanth, Rajanampalli Seshasai Srinivasa; Hans Raj, Kandikonda

    2016-06-01

    Artificial bee colony (ABC) algorithm, that mimics the intelligent foraging behavior of honey bees, is increasingly gaining acceptance in the field of process optimization, as it is capable of handling nonlinearity, complexity and uncertainty. Straight cylindrical turning is a complex and nonlinear machining process which involves the selection of appropriate cutting parameters that affect the quality of the workpiece. This paper presents the estimation of optimal cutting parameters of the straight cylindrical turning process using the ABC algorithm. The ABC algorithm is first tested on four benchmark problems of numerical optimization and its performance is compared with genetic algorithm (GA) and ant colony optimization (ACO) algorithm. Results indicate that, the rate of convergence of ABC algorithm is better than GA and ACO. Then, the ABC algorithm is used to predict optimal cutting parameters such as cutting speed, feed rate, depth of cut and tool nose radius to achieve good surface finish. Results indicate that, the ABC algorithm estimated a comparable surface finish when compared with real coded genetic algorithm and differential evolution algorithm.

  17. Parametric Optimization of Nd:YAG Laser Beam Machining Process Using Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Rajarshi Mukherjee

    2013-01-01

    Full Text Available Nd:YAG laser beam machining (LBM process has a great potential to manufacture intricate shaped microproducts with its unique characteristics. In practical applications, such as drilling, grooving, cutting, or scribing, the optimal combination of Nd:YAG LBM process parameters needs to be sought out to provide the desired machining performance. Several mathematical techniques, like Taguchi method, desirability function, grey relational analysis, and genetic algorithm, have already been applied for parametric optimization of Nd:YAG LBM processes, but in most of the cases, suboptimal or near optimal solutions have been reached. This paper focuses on the application of artificial bee colony (ABC algorithm to determine the optimal Nd:YAG LBM process parameters while considering both single and multiobjective optimization of the responses. A comparative study with other population-based algorithms, like genetic algorithm, particle swarm optimization, and ant colony optimization algorithm, proves the global applicability and acceptability of ABC algorithm for parametric optimization. In this algorithm, exchange of information amongst the onlooker bees minimizes the search iteration for the global optimal and avoids generation of suboptimal solutions. The results of two sample paired t-tests also demonstrate its superiority over the other optimization algorithms.

  18. Discrete artificial bee colony algorithm for lot-streaming flowshop with total flowtime minimization

    Science.gov (United States)

    Sang, Hongyan; Gao, Liang; Pan, Quanke

    2012-09-01

    Unlike a traditional flowshop problem where a job is assumed to be indivisible, in the lot-streaming flowshop problem, a job is allowed to overlap its operations between successive machines by splitting it into a number of smaller sub-lots and moving the completed portion of the sub-lots to downstream machine. In this way, the production is accelerated. This paper presents a discrete artificial bee colony (DABC) algorithm for a lot-streaming flowshop scheduling problem with total flowtime criterion. Unlike the basic ABC algorithm, the proposed DABC algorithm represents a solution as a discrete job permutation. An efficient initialization scheme based on the extended Nawaz-Enscore-Ham heuristic is utilized to produce an initial population with a certain level of quality and diversity. Employed and onlooker bees generate new solutions in their neighborhood, whereas scout bees generate new solutions by performing insert operator and swap operator to the best solution found so far. Moreover, a simple but effective local search is embedded in the algorithm to enhance local exploitation capability. A comparative experiment is carried out with the existing discrete particle swarm optimization, hybrid genetic algorithm, threshold accepting, simulated annealing and ant colony optimization algorithms based on a total of 160 randomly generated instances. The experimental results show that the proposed DABC algorithm is quite effective for the lot-streaming flowshop with total flowtime criterion in terms of searching quality, robustness and effectiveness. This research provides the references to the optimization research on lot-streaming flowshop.

  19. An Improved Artificial Bee Colony-Based Approach for Zoning Protected Ecological Areas.

    Directory of Open Access Journals (Sweden)

    Jing Shao

    Full Text Available China is facing ecological and environmental challenges as its urban growth rate continues to rise, and zoning protected ecological areas is recognized as an effective response measure. Zoning inherently involves both site attributes and aggregation attributes, and the combination of mathematical models and heuristic algorithms have proven advantageous. In this article, an improved artificial bee colony (IABC-based approach is proposed for zoning protected ecological areas at a regional scale. Three main improvements were made: the first is the use of multiple strategies to generate the initial bee population of a specific quality and diversity, the second is an exploitation search procedure to generate neighbor solutions combining "replace" and "alter" operations, and the third is a "swap" strategy to enable a local search for the iterative optimal solution. The IABC algorithm was verified using simulated data. Then it was applied to define an optimum scheme of protected ecological areas of Sanya (in the Hainan province of China, and a reasonable solution was obtained. Finally, a comparison experiment with other methods (agent-based land allocation model, ant colony optimization, and density slicing was conducted and demonstrated that the IABC algorithm was more effective and efficient than the other methods. Through this study, we aimed to provide a scientifically sound, practical approach for zoning procedures.

  20. Blending of heritable recognition cues among ant nestmates creates distinct colony gestalt odours but prevents within-colony nepotism.

    Science.gov (United States)

    van Zweden, J S; Brask, J B; Christensen, J H; Boomsma, J J; Linksvayer, T A; d'Ettorre, P

    2010-07-01

    The evolution of sociality is facilitated by the recognition of close kin, but if kin recognition is too accurate, nepotistic behaviour within societies can dissolve social cohesion. In social insects, cuticular hydrocarbons act as nestmate recognition cues and are usually mixed among colony members to create a Gestalt odour. Although earlier studies have established that hydrocarbon profiles are influenced by heritable factors, transfer among nestmates and additional environmental factors, no studies have quantified these relative contributions for separate compounds. Here, we use the ant Formica rufibarbis in a cross-fostering design to test the degree to which hydrocarbons are heritably synthesized by young workers and transferred by their foster workers. Bioassays show that nestmate recognition has a significant heritable component. Multivariate quantitative analyses based on 38 hydrocarbons reveal that a subset of branched alkanes are heritably synthesized, but that these are also extensively transferred among nestmates. In contrast, especially linear alkanes are less heritable and little transferred; these are therefore unlikely to act as cues that allow within-colony nepotistic discrimination or as nestmate recognition cues. These results indicate that heritable compounds are suitable for establishing a genetic Gestalt for efficient nestmate recognition, but that recognition cues within colonies are insufficiently distinct to allow nepotistic kin discrimination. PMID:20492083

  1. Breeding system, colony structure, and genetic differentiation in the Camponotus festinatus species complex of carpenter ants.

    Science.gov (United States)

    Goodisman, Michael A D; Hahn, Daniel A

    2005-10-01

    All social insects live in highly organized societies. However, different social insect species display striking variation in social structure. This variation can significantly affect the genetic structure within populations and, consequently, the divergence between species. The purpose of this study was to determine if variation in social structure was associated with species diversification in the Camponotus festinatus desert carpenter ant species complex. We used polymorphic DNA microsatellite markers to dissect the breeding system of these ants and to determine if distinct C. festinatus forms hybridized in their natural range. Our analysis of single-queen colonies established in the laboratory revealed that queens typically mated with only a single male. The genotypes of workers sampled from a field population suggested that multiple, related queens occasionally reproduced within colonies and that colonies inhabited multiple nests. Camponotus festinatus workers derived from colonies of the same form originating at different locales were strongly differentiated, suggesting that gene flow was geographically restricted. Overall, our data indicate that C. festinatus populations are highly structured. Distinct C. festinatus forms possess similar social systems but are genetically isolated. Consequently, our data suggest that diversification in the C. festinatus species complex is not necessarily associated with a shift in social structure. PMID:16405162

  2. Breeding system, colony structure, and genetic differentiation in the Camponotus festinatus species complex of carpenter ants.

    Science.gov (United States)

    Goodisman, Michael A D; Hahn, Daniel A

    2005-10-01

    All social insects live in highly organized societies. However, different social insect species display striking variation in social structure. This variation can significantly affect the genetic structure within populations and, consequently, the divergence between species. The purpose of this study was to determine if variation in social structure was associated with species diversification in the Camponotus festinatus desert carpenter ant species complex. We used polymorphic DNA microsatellite markers to dissect the breeding system of these ants and to determine if distinct C. festinatus forms hybridized in their natural range. Our analysis of single-queen colonies established in the laboratory revealed that queens typically mated with only a single male. The genotypes of workers sampled from a field population suggested that multiple, related queens occasionally reproduced within colonies and that colonies inhabited multiple nests. Camponotus festinatus workers derived from colonies of the same form originating at different locales were strongly differentiated, suggesting that gene flow was geographically restricted. Overall, our data indicate that C. festinatus populations are highly structured. Distinct C. festinatus forms possess similar social systems but are genetically isolated. Consequently, our data suggest that diversification in the C. festinatus species complex is not necessarily associated with a shift in social structure.

  3. Multiple ant-bee colony optimization for load balancing in packet-switched networks

    CERN Document Server

    Kashefikia, Mehdi; Moghadam, Reza Askari

    2011-01-01

    One of the important issues in computer networks is "Load Balancing" which leads to efficient use of the network resources. To achieve a balanced network it is necessary to find different routes between the source and destination. In the current paper we propose a new approach to find different routes using swarm intelligence techniques and multi colony algorithms. In the proposed algorithm that is an improved version of MACO algorithm, we use different colonies of ants and bees and appoint these colony members as intelligent agents to monitor the network and update the routing information. The survey includes comparison and critiques of MACO. The simulation results show a tangible improvement in the aforementioned approach.

  4. MULTIPLE ANT-BEE COLONY OPTIMIZATION FOR LOAD BALANCING IN PACKET-SWITCHED NETWORKS

    Directory of Open Access Journals (Sweden)

    Mehdi Kashefi Kia

    2011-10-01

    Full Text Available One of the important issues in computer networks is “Load Balancing” which leads to efficient use of the network resources. To achieve a balanced network it is necessary to find different routes between the source and destination. In the current paper we propose a new approach to find different routes using swarm intelligence techniques and multi colony algorithms. In the proposed algorithm that is an improved version of MACO algorithm, we use different colonies of ants and bees and appoint these colony members as intelligent agents to monitor the network and update the routing information. The survey includes comparison and critiques of MACO. The simulation results show a tangible improvement in the aforementioned approach.

  5. Arboreal ant colonies as 'hot-points' of cryptic diversity for myrmecophiles: the weaver ant Camponotus sp. aff. textor and its interaction network with its associates.

    Directory of Open Access Journals (Sweden)

    Gabriela Pérez-Lachaud

    Full Text Available INTRODUCTION: Systematic surveys of macrofaunal diversity within ant colonies are lacking, particularly for ants nesting in microhabitats that are difficult to sample. Species associated with ants are generally small and rarely collected organisms, which makes them more likely to be unnoticed. We assumed that this tendency is greater for arthropod communities in microhabitats with low accessibility, such as those found in the nests of arboreal ants that may constitute a source of cryptic biodiversity. MATERIALS AND METHODS: We investigated the invertebrate diversity associated with an undescribed, but already threatened, Neotropical Camponotus weaver ant. As most of the common sampling methods used in studies of ant diversity are not suited for evaluating myrmecophile diversity within ant nests, we evaluated the macrofauna within ant nests through exhaustive colony sampling of three nests and examination of more than 80,000 individuals. RESULTS: We identified invertebrates from three classes belonging to 18 taxa, some of which were new to science, and recorded the first instance of the co-occurrence of two brood parasitoid wasp families attacking the same ant host colony. This diversity of ant associates corresponded to a highly complex interaction network. Agonistic interactions prevailed, but the prevalence of myrmecophiles was remarkably low. CONCLUSIONS: Our data support the hypothesis of the evolution of low virulence in a variety of symbionts associated with large insect societies. Because most myrmecophiles found in this work are rare, strictly specific, and exhibit highly specialized biology, the risk of extinction for these hitherto unknown invertebrates and their natural enemies is high. The cryptic, far unappreciated diversity within arboreal ant nests in areas at high risk of habitat loss qualifies these nests as 'hot-points' of biodiversity that urgently require special attention as a component of conservation and management

  6. A Stochastic Inversion Method for Potential Field Data: Ant Colony Optimization

    Science.gov (United States)

    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.

  7. Sociogenomics of cooperation and conflict during colony founding in the fire ant Solenopsis invicta.

    Directory of Open Access Journals (Sweden)

    Fabio Manfredini

    Full Text Available One of the fundamental questions in biology is how cooperative and altruistic behaviors evolved. The majority of studies seeking to identify the genes regulating these behaviors have been performed in systems where behavioral and physiological differences are relatively fixed, such as in the honey bee. During colony founding in the monogyne (one queen per colony social form of the fire ant Solenopsis invicta, newly-mated queens may start new colonies either individually (haplometrosis or in groups (pleometrosis. However, only one queen (the "winner" in pleometrotic associations survives and takes the lead of the young colony while the others (the "losers" are executed. Thus, colony founding in fire ants provides an excellent system in which to examine the genes underpinning cooperative behavior and how the social environment shapes the expression of these genes. We developed a new whole genome microarray platform for S. invicta to characterize the gene expression patterns associated with colony founding behavior. First, we compared haplometrotic queens, pleometrotic winners and pleometrotic losers. Second, we manipulated pleometrotic couples in order to switch or maintain the social ranks of the two cofoundresses. Haplometrotic and pleometrotic queens differed in the expression of genes involved in stress response, aging, immunity, reproduction and lipid biosynthesis. Smaller sets of genes were differentially expressed between winners and losers. In the second experiment, switching social rank had a much greater impact on gene expression patterns than the initial/final rank. Expression differences for several candidate genes involved in key biological processes were confirmed using qRT-PCR. Our findings indicate that, in S. invicta, social environment plays a major role in the determination of the patterns of gene expression, while the queen's physiological state is secondary. These results highlight the powerful influence of social environment

  8. Automatic pressurized water reactor loading pattern design using ant colony algorithms

    International Nuclear Information System (INIS)

    Highlights: ► An automatic core reload design tool was developed for a pressurized water reactor. ► Three different algorithms, i.e., the rank-based ant system, max–min ant system, and Ant-Q are adopted. ► Safety requirements are formulated as penalty terms of the quality function. ► Firstly, fuel assemblies are permutated to some degree and then fuel assemblies are rotated. - Abstract: An automatic core reload design tool was developed for a pressurized water reactor (PWR). A loading pattern (LP) was searched for using three different algorithms: the rank-based ant system (RAS), max–min ant system (MMAS), and Ant-Q which are variants of the ant colony algorithm. The fuel assemblies (FAs) were permuted in a one eighth core position and then the LP was copied to the other one eighth core with mirror symmetry, to form a quarter core LP, which was extended to a full core LP with rotational symmetry. Heuristic information was implemented to reduce search space and thus computation time. Safety requirements, such as the hot channel factor FΔH and moderator temperature coefficient (MTC), which must be satisfied, were formulated as penalty terms of the quality function. The search procedure contained two steps. The first step was to place the FA so that FΔH and MTC might be slightly violated, and the second step was to rotate the FA, which would improve the FΔH and MTC and the fuel cycle length. When the LP was designed, the SIMULATE-3 code calculated the FΔH, MTC, and cycle length, which were used to update the pheromone. The results demonstrated that the developed tool can obtain a LP which possesses the desired cycle length and also satisfies safety requirements.

  9. Enhancing Artificial Bee Colony Algorithm with Self-Adaptive Searching Strategy and Artificial Immune Network Operators for Global Optimization

    OpenAIRE

    Tinggui Chen; Renbin Xiao

    2014-01-01

    Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric e...

  10. Adaptive job Scheduling for Computational Grid based on Ant Colony Optimization with Genetic Parameter Selection

    Directory of Open Access Journals (Sweden)

    Saurabh Mandloi

    2013-03-01

    Full Text Available Demand of new generation of internet technology applied a distributed process of computing for service providing and resource allocation. The service and resource allocation needed a computational grid for task processing. Computational grid manages a process of resource and task allocation process. The allocation of resource and task effect the performance of grid mechanism. For the scheduling of task and resource for computational grid used a queuing process model such as first come first served process. But the performance of this model is very impartial, now various authors and researchers used a process of efficient searching technique for job allocation such as heuristic and meta-heuristic improved the performance of computational grid. But the uncontrolled nature of meta-heuristic such as ant colony optimization degraded the performance of grid allocation. In this paper we proposed a controlled mechanism of ant colony optimization with genetic algorithm for task scheduling in computational grid. For the performance evaluation of computational grid we used 6 *6, 10*10 and 20 *5 grid parameter and the measurement of performance in terms of job completion and failure of job. Our empirical evaluation shows that better performance instead of ANT and PSO scheduling technique.

  11. Colony-Level Differences in the Scaling Rules Governing Wood Ant Compound Eye Structure.

    Science.gov (United States)

    Perl, Craig D; Niven, Jeremy E

    2016-01-01

    Differential organ growth during development is essential for adults to maintain the correct proportions and achieve their characteristic shape. Organs scale with body size, a process known as allometry that has been studied extensively in a range of organisms. Such scaling rules, typically studied from a limited sample, are assumed to apply to all members of a population and/or species. Here we study scaling in the compound eyes of workers of the wood ant, Formica rufa, from different colonies within a single population. Workers' eye area increased with body size in all the colonies showing a negative allometry. However, both the slope and intercept of some allometric scaling relationships differed significantly among colonies. Moreover, though mean facet diameter and facet number increased with body size, some colonies primarily increased facet number whereas others increased facet diameter, showing that the cellular level processes underlying organ scaling differed among colonies. Thus, the rules that govern scaling at the organ and cellular levels can differ even within a single population. PMID:27068571

  12. Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization

    Directory of Open Access Journals (Sweden)

    Lianbo Ma

    2014-01-01

    Full Text Available This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness.

  13. A hybrid artificial bee colony algorithm for numerical function optimization

    Science.gov (United States)

    Alqattan, Zakaria N.; Abdullah, Rosni

    2015-02-01

    Artificial Bee Colony (ABC) algorithm is one of the swarm intelligence algorithms; it has been introduced by Karaboga in 2005. It is a meta-heuristic optimization search algorithm inspired from the intelligent foraging behavior of the honey bees in nature. Its unique search process made it as one of the most competitive algorithm with some other search algorithms in the area of optimization, such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO). However, the ABC performance of the local search process and the bee movement or the solution improvement equation still has some weaknesses. The ABC is good in avoiding trapping at the local optimum but it spends its time searching around unpromising random selected solutions. Inspired by the PSO, we propose a Hybrid Particle-movement ABC algorithm called HPABC, which adapts the particle movement process to improve the exploration of the original ABC algorithm. Numerical benchmark functions were used in order to experimentally test the HPABC algorithm. The results illustrate that the HPABC algorithm can outperform the ABC algorithm in most of the experiments (75% better in accuracy and over 3 times faster).

  14. Loading pattern optimization of PWR reactors using Artificial Bee Colony

    International Nuclear Information System (INIS)

    Highlights: → ABC algorithm is comparable to the canonical GA algorithm and PSO. → The performance of ABC shows that the algorithm is quiet promising. → The final band width of search fitness values by ABC is narrow. → The ABC algorithm is relatively easy to implement. - Abstract: In this paper a core reloading technique using Artificial Bee Colony algorithm, ABC, is presented in the context of finding an optimal configuration of fuel assemblies. The proposed method can be used for in-core fuel management optimization problems in pressurized water reactors. To evaluate the proposed technique, the power flattening of a VVER-1000 core is considered as an objective function although other variables such as Keff, power peaking factor, burn up and cycle length can also be taken into account. The proposed optimization method is applied to a core design optimization problem previously solved with Genetic and Particle Swarm Intelligence Algorithm. The results, convergence rate and reliability of the new method are quite promising and show that the ABC algorithm performs very well and is comparable to the canonical Genetic Algorithm and Particle Swarm Intelligence, hence demonstrating its potential for other optimization applications in nuclear engineering field as, for instance, the cascade problems.

  15. Hierarchical artificial bee colony algorithm for RFID network planning optimization.

    Science.gov (United States)

    Ma, Lianbo; Chen, Hanning; Hu, Kunyuan; Zhu, Yunlong

    2014-01-01

    This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness. PMID:24592200

  16. Lévy flight artificial bee colony algorithm

    Science.gov (United States)

    Sharma, Harish; Bansal, Jagdish Chand; Arya, K. V.; Yang, Xin-She

    2016-08-01

    Artificial bee colony (ABC) optimisation algorithm is a relatively simple and recent population-based probabilistic approach for global optimisation. The solution search equation of ABC is significantly influenced by a random quantity which helps in exploration at the cost of exploitation of the search space. In the ABC, there is a high chance to skip the true solution due to its large step sizes. In order to balance between diversity and convergence in the ABC, a Lévy flight inspired search strategy is proposed and integrated with ABC. The proposed strategy is named as Lévy Flight ABC (LFABC) has both the local and global search capability simultaneously and can be achieved by tuning the Lévy flight parameters and thus automatically tuning the step sizes. In the LFABC, new solutions are generated around the best solution and it helps to enhance the exploitation capability of ABC. Furthermore, to improve the exploration capability, the numbers of scout bees are increased. The experiments on 20 test problems of different complexities and five real-world engineering optimisation problems show that the proposed strategy outperforms the basic ABC and recent variants of ABC, namely, Gbest-guided ABC, best-so-far ABC and modified ABC in most of the experiments.

  17. Ant-Based Phylogenetic Reconstruction (ABPR: A new distance algorithm for phylogenetic estimation based on ant colony optimization

    Directory of Open Access Journals (Sweden)

    Karla Vittori

    2008-12-01

    Full Text Available We propose a new distance algorithm for phylogenetic estimation based on Ant Colony Optimization (ACO, named Ant-Based Phylogenetic Reconstruction (ABPR. ABPR joins two taxa iteratively based on evolutionary distance among sequences, while also accounting for the quality of the phylogenetic tree built according to the total length of the tree. Similar to optimization algorithms for phylogenetic estimation, the algorithm allows exploration of a larger set of nearly optimal solutions. We applied the algorithm to four empirical data sets of mitochondrial DNA ranging from 12 to 186 sequences, and from 898 to 16,608 base pairs, and covering taxonomic levels from populations to orders. We show that ABPR performs better than the commonly used Neighbor-Joining algorithm, except when sequences are too closely related (e.g., population-level sequences. The phylogenetic relationships recovered at and above species level by ABPR agree with conventional views. However, like other algorithms of phylogenetic estimation, the proposed algorithm failed to recover expected relationships when distances are too similar or when rates of evolution are very variable, leading to the problem of long-branch attraction. ABPR, as well as other ACO-based algorithms, is emerging as a fast and accurate alternative method of phylogenetic estimation for large data sets.

  18. A multi-objective ant colony optimization method applied to switch engine scheduling in railroad yards

    Directory of Open Access Journals (Sweden)

    Jodelson A. Sabino

    2010-08-01

    Full Text Available This paper proposes an ant colony optimization algorithm to assist railroad yard operational planning staff in their daily tasks. The proposed algorithm tries to minimize a multi-objective function that considers both fixed and variable transportation costs involved in moving railroad cars within the railroad yard area. This is accomplished by searching the best switch engine schedule for a given time horizon. As the algorithm was designed for real life application, the solution must be delivered in a predefined processing time and it must be in accordance with railroad yard operational policies. A railroad yard operations simulator was built to produce artificial instances in order to tune the parameters of the algorithm. The project is being developed together with industrial professionals from the Tubarão Railroad Terminal, which is the largest railroad yard in Latin America.Este trabalho propõe um algoritmo de otimização com colônia de formigas para auxiliar a equipe de planejamento de operações de pátios ferroviários em suas tarefas diárias. O algoritmo proposto é baseado em uma função multi-objetivo que busca a redução dos custos fixo e variável de movimentação de vagões no pátio. Isto é feito através da busca da melhor programação para as locomotivas de manobra, considerando um dado horizonte de planejamento. Como o algoritmo foi desenvolvido para aplicação na vida real, a solução deve ser entregue em um tempo de processamento definido previamente e deve obedecer as políticas operacionais do pátio. Foi desenvolvido um simulador de operações de pátio que gera instâncias artificiais utilizadas para ajuste dos parâmetros do algoritmo. O projeto está sendo desenvolvido em conjunto com profissionais envolvidos na operação do Terminal Ferroviário de Tubarão, o qual é o maior pátio de manobras da América Latina.

  19. Application of the artificial bee colony algorithm for solving the set covering problem.

    Science.gov (United States)

    Crawford, Broderick; Soto, Ricardo; Cuesta, Rodrigo; Paredes, Fernando

    2014-01-01

    The set covering problem is a formal model for many practical optimization problems. In the set covering problem the goal is to choose a subset of the columns of minimal cost that covers every row. Here, we present a novel application of the artificial bee colony algorithm to solve the non-unicost set covering problem. The artificial bee colony algorithm is a recent swarm metaheuristic technique based on the intelligent foraging behavior of honey bees. Experimental results show that our artificial bee colony algorithm is competitive in terms of solution quality with other recent metaheuristic approaches for the set covering problem. PMID:24883356

  20. Application of Artificial Bee Colony in Model Parameter Identification of Solar Cells

    Directory of Open Access Journals (Sweden)

    Rongjie Wang

    2015-07-01

    Full Text Available The identification of values of solar cell parameters is of great interest for evaluating solar cell performances. The algorithm of an artificial bee colony was used to extract model parameters of solar cells from current-voltage characteristics. Firstly, the best-so-for mechanism was introduced to the original artificial bee colony. Then, a method was proposed to identify parameters for a single diode model and double diode model using this improved artificial bee colony. Experimental results clearly demonstrate the effectiveness of the proposed method and its superior performance compared to other competing methods.

  1. A Multi-pipe Path Planning by Modified Ant Colony Optimization

    Institute of Scientific and Technical Information of China (English)

    QU Yan-feng; JIANG Dan; LIU Bin

    2011-01-01

    Path planning in 3D geometry space is used to find an optimal path in the restricted environment, according to a certain evaluation criteria. To solve the problem of long searching time and slow solving speed in 3D path planning, a modified ant colony optimization is proposed in this paper. Firstly, the grid method for environment modeling is adopted. Heuristic information is connected with the planning space. A semi-iterative global pheromone update mechanism is proposed. Secondly, the optimal ants mutate the paths to improve the diversity of the algorithm after a defined iterative number. Thirdly, co-evolutionary algorithm is used. Finally, the simulation result shows the effectiveness of the proposed algorithm in solving the problem of 3D pipe path planning.

  2. Ant Colony Optimization Algorithm For PAPR Reduction In Multicarrier Code Division Multiple Access System

    Directory of Open Access Journals (Sweden)

    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.

  3. Toward E-Content Adaptation: Units’ Sequence and Adapted Ant Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Naoual Chaouni Benabdellah

    2015-09-01

    Full Text Available An adapted ant colony algorithm is proposed to adapt e-content to learner’s profile. The pertinence of proposed units keeps learners motivated. A model of categorization of course’s units is presented. Two learning paths are discussed based on a predefined graph. In addition, the ant algorithm is simulated on the proposed model. The adapted algorithm requires a definition of a new pheromone which is a parameter responsible for defining whether the unit is in the right pedagogical sequence or in the wrong one. Moreover, it influences the calculation of quantity of pheromone deposited on each arc. Accordingly, results show that there are positive differences in learner’s passages to propose the suitable units depending on the sequence and the number of successes. The proposed units do not depend on the change of number of units around 10 to 30 units in the algorithm process.

  4. ON THE SUITABILITY OF USING ANT COLONY OPTIMIZATION FOR ROUTING MULTIMEDIA CONTENT OVER WIRELESS SENSOR NETWORKS

    Directory of Open Access Journals (Sweden)

    Hiba Al-Zurba

    2011-07-01

    Full Text Available This paper studies the suitability of using a meta-heuristic ant colony technique in routing multimediacontent over wireless sensor networks. The presented technique is both energy and QoS-aware. Ant colonyalgorithm is used to find the optimal routing path. Optimality is in the sense of minimizing energyconsumption and increasing link quality and reliability. The proposed approach results in minimizingenergy consumption and prolonging the lifetime of the network. Moreover, the optimal path has a high linkquality and reliability which enhances video frame quality and ensures high probability of successfuldelivery of video frames. The importance given to energy consumption, link quality, and link reliabilitymetrics can be varied depending on the multimedia application requirements.

  5. Novel method based on ant colony optimization for solving ill-conditioned linear systems of equations

    Institute of Scientific and Technical Information of China (English)

    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.

  6. Social transfer of pathogenic fungus promotes active immunisation in ant colonies.

    Directory of Open Access Journals (Sweden)

    Matthias Konrad

    Full Text Available Due to the omnipresent risk of epidemics, insect societies have evolved sophisticated disease defences at the individual and colony level. An intriguing yet little understood phenomenon is that social contact to pathogen-exposed individuals reduces susceptibility of previously naive nestmates to this pathogen. We tested whether such social immunisation in Lasius ants against the entomopathogenic fungus Metarhizium anisopliae is based on active upregulation of the immune system of nestmates following contact to an infectious individual or passive protection via transfer of immune effectors among group members--that is, active versus passive immunisation. We found no evidence for involvement of passive immunisation via transfer of antimicrobials among colony members. Instead, intensive allogrooming behaviour between naive and pathogen-exposed ants before fungal conidia firmly attached to their cuticle suggested passage of the pathogen from the exposed individuals to their nestmates. By tracing fluorescence-labelled conidia we indeed detected frequent pathogen transfer to the nestmates, where they caused low-level infections as revealed by growth of small numbers of fungal colony forming units from their dissected body content. These infections rarely led to death, but instead promoted an enhanced ability to inhibit fungal growth and an active upregulation of immune genes involved in antifungal defences (defensin and prophenoloxidase, PPO. Contrarily, there was no upregulation of the gene cathepsin L, which is associated with antibacterial and antiviral defences, and we found no increased antibacterial activity of nestmates of fungus-exposed ants. This indicates that social immunisation after fungal exposure is specific, similar to recent findings for individual-level immune priming in invertebrates. Epidemiological modeling further suggests that active social immunisation is adaptive, as it leads to faster elimination of the disease and lower

  7. Ant Colony Optimization In Multi-Agent Systems With NetLogo

    Directory of Open Access Journals (Sweden)

    Mustafa Tüker

    2013-02-01

    Full Text Available Multi-agent systems (MAS offer an effective way to model and solve complex optimization problems. In this study, MAS and ant colonies have been used together to solve the Travelling Salesmen Problem (TSP. System simulation has been realized with NetLogo which is an agent-based programming environment. It has been explained in detail with code examples that how to use NetLogo for modeling and simulation of the problem. Algorithm has been tested for different numbers of nodes and obtained results have been discussed.

  8. CACER:A Novel E-commerce Recommendation Model Based on Crazy Ant Colony Algorithms

    Institute of Scientific and Technical Information of China (English)

    王征; 刘庆强

    2013-01-01

    In order to deal with the problems of E-commerce online marketing, a novel E-commerce recommendation system model was given to lead consumers to efficient retrieval and consumption. And the system model was built with a crazy ant colony algorithm. Then its model, message structures and working flows were presented as following. At last, an application example and compared results were given to be analyzed. Simulation results show the model can perform better in real-time and customer satisfaction than the olds do.

  9. An Energy Aware Ant Colony Algorithm for the Routing of Wireless Sensor Networks

    Science.gov (United States)

    Cheng, Deqiang; Xun, Yangyang; Zhou, Ting; Li, Wenjie

    Based on the characteristics of routing protocol for wireless sensor networks, an energy aware ant colony algorithm (EAACA) for the routing of wireless sensor networks is proposed in this paper. When EAACA routing protocol chooses the next neighbor node, not only the distance of sink node, but also the residual energy of the next node and the path of the average energy are taken into account. Theoretical analysis and simulation results show that compared with the traditional ACA algorithm for the routing of wireless sensor network, EAACA routing protocol balances the energy consumption of nodes in the network and extends the network lifetime.

  10. Optimization of China Crude Oil Transportation Network with Genetic Ant Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Yao Wang

    2015-08-01

    Full Text Available Taking into consideration both shipping and pipeline transport, this paper first analysed the risk factors for different modes of crude oil import transportation. Then, based on the minimum of both transportation cost and overall risk, a multi-objective programming model was established to optimize the transportation network of crude oil import, and the genetic algorithm and ant colony algorithm were employed to solve the problem. The optimized result shows that VLCC (Very Large Crude Carrier is superior in long distance sea transportation, whereas pipeline transport is more secure than sea transport. Finally, this paper provides related safeguard suggestions on crude oil import transportation.

  11. Ant colony system algorithm for the optimization of beer fermentation control

    Institute of Scientific and Technical Information of China (English)

    肖杰; 周泽魁; 张光新

    2004-01-01

    Beer fermentation is a dynamic process that must be guided along a temperature profile to obtain the desired results. Ant colony system algorithm was applied to optimize the kinetic model of this process. During a fixed period of fermentation time, a series of different temperature profiles of the mixture were constructed. An optimal one was chosen at last. Optimal temperature profile maximized the final ethanol production and minimized the byproducts concentration and spoilage risk. The satisfactory results obtained did not require much computation effort.

  12. Ant colony optimization for bearings-only maneuvering target tracking in sensors network

    Institute of Scientific and Technical Information of China (English)

    Benlian XU; Zhiquan WANG; Zhengyi WU

    2007-01-01

    In this paper, the problem of bearings-only maneuvering target tracking in sensors network is investigated.Two objectives are proposed and optimized by the ant colony optimization (ACO), then two kinds of node searching strategies of the ACO algorithm are presented. On the basis of the nodes determined by the ACO algorithm, the interacting multiple models extended Kalman filter (IMMEKF) for the multi-sensor bearings-only maneuvering target tracking is introduced. Simulation results indicate that the proposed ACO algorithm performs better than the Closest Nodes method.Furthermore, the Strategy 2 of the two given strategies is preferred in terms of the requirement of real time.

  13. Integration of GPS and DinSAR for Deformation Monitoring Based on Ant Colony Optimization

    Science.gov (United States)

    Shi, Guoqiang; He, Xiufeng; Xiao, Ruya

    2014-11-01

    To acquire three-dimensional earth surface deformation, a measurement method based on ant colony optimization (ACO) is proposed. It highly integrates high-accuracy GPS observations from sparse ground points with InSAR line-of-sight (LOS) direction information. Two constraints, GPS and DInSAR observations, are employed in constructing the energy function whose minimum value will be searched by the ACO operated in continuous space. Compared with conventional interpolation algorithms, the proposed method increases the three-dimensional deformation observation accuracy, especially showing the improvement in the up direction.

  14. Ant Colony Algorithm and Optimization of Test Conditions in Analytical Chemistry

    Institute of Scientific and Technical Information of China (English)

    丁亚平; 吴庆生; 苏庆德

    2003-01-01

    The research for the new algorithm is in the forward position and an issue of general interest in chemometrics all along.A novel chemometrics method,Chemical Ant Colony Algorithm,has first been developed.In this paper,the basic principle,theevaluation function,and the parameter choice were discussed.This method has been successfully applied to the fitting of nonlinear multivariate function and the optimization of test conditions in chrome-azure-S-Al spctrophotometric system.The sum of residual square of the results is 0.0009,which has reached a good convergence result.

  15. DANTE - The combination between an ant colony optimization algorithm and a depth search method

    OpenAIRE

    Cardoso, Pedro J. S.; Jesus, Mário Carlos Machado; Marquez, A.

    2008-01-01

    The ε-DANTE method is an hybrid meta-heuristic. In combines the evolutionary Ant Colony Optimization (ACO) algorithms with a limited Depth Search. This Depth Search is based in the pheromone trails used by the ACO, which allows it to be oriented to the more promising areas of the search space. Some results are presented for the multiple objective k-Degree Spanning Trees problem, proving the effectiveness of the method when compared with other already tested evolutionary methods. © 2008 IEEE.

  16. Optimization of travel salesman problem using the ant colony system and Greedy search

    International Nuclear Information System (INIS)

    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)

  17. Ant colony optimization image registration algorithm based on wavelet transform and mutual information

    Science.gov (United States)

    Zhang, Hong; Sun, Yanfeng; Zhai, Bing; Wang, Yiding

    2013-07-01

    This paper studies on the image registration of the medical images. Wavelet transform is adopted to decompose the medical images because the resolution of the medical image is high and the computational amount of the registration is large. Firstly, the low frequency sub-images are matched. Then source images are matched. The image registration was fulfilled by the ant colony optimization algorithm to search the extremum of the mutual information. The experiment result demonstrates the proposed approach can not only reduce calculation amount, but also skip from the local extremum during optimization process, and search the optimization value.

  18. ENHANCEMENT AND COMPARISON OF ANT COLONY OPTIMIZATION FOR SOFTWARE RELIABILITY MODELS

    Directory of Open Access Journals (Sweden)

    Latha Shanmugam

    2013-01-01

    Full Text Available In Common parlance, the traditional software reliability estimation methods often rely on assumptions like statistical distributions that are often dubious and unrealistic. The ability to predict the number of faults during development phase and a proper testing process helps in specifying timely release of software and efficient management of project resources. In the Present Study Enhancement and Comparison of Ant Colony Optimization Methods for Software Reliability Models are studied and the estimation accuracy was calculated. The Enhanced method shows significant advantages in finding the goodness of fit for software reliability model such as finite and infinite failure Poisson model and binomial models.

  19. Optimal Path Identification Using ANT Colony Optimisation in Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Aniket. A. Gurav

    2013-05-01

    Full Text Available Wireless Sensor Network WSN is tightly constrained for energy, computational power and memory. All applications of WSN require to forward data from remote sensor node SN to base station BS. The path length and numbers of nodes in path by which data is forwarded affect the basic performance of WSN. In this paper we present bio-Inspired Ant Colony Optimisation ACO algorithm for Optimal path Identification OPI for p acket transmission to communicate between SN to BS. Our modified algorithm OPI using ACO cons iders the path length and the number of hops in path for data packet transmission, with an aim to reduce communication overheads .

  20. Optimum Distribution Generator Placement in Power Distribution System Using Ant Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Mehdi Mahdavi

    2009-03-01

    Full Text Available The recent development in renewable energy systems and the high demand for having clean and low cost energy sources encourage people to use distributed generator (DG systems. Proper addition and placement of DG units can increase reliability and reduce the loss and production cost. In this paper using Ant Colony method, we developed an optimum placing scheme for DGs. The proposed method is tested on an IEEE 34-shinhe system. Results show that if DGs are able to generate active power, their effectiveness will increase.

  1. Structural Damage Detection Based on Modal Parameters Using Continuous Ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    Aditi Majumdar

    2014-01-01

    Full Text Available A method is presented to detect and quantify structural damages from changes in modal parameters (such as natural frequencies and mode shapes. An inverse problem is formulated to minimize the objective function, defined in terms of discrepancy between the vibration data identified by modal testing and those computed from analytical model, which then solved to locate and assess the structural damage using continuous ant colony optimization algorithm. The damage is formulated as stiffness reduction factor. The study indicates potentiality of the developed code to solve a wide range of inverse identification problems.

  2. Parallel Machine Scheduling (PMS) in Manufacturing Systems Using the Ant Colonies Optimization Algorithmic Rule

    Science.gov (United States)

    Senthiil, P. V.; Selladurai, V.; Rajesh, R.

    This study introduces a new approach for decentralized scheduling in a parallel machine environment based on the ant colonies optimization algorithm. The algorithm extends the use of the traveling salesman problem for scheduling in one single machine, to a multiple machine problem. The results are presented using simple and illustrative examples and show that the algorithm is able to optimize the different scheduling problems. Using the same parameters, the completion time of the tasks is minimized and the processing time of the parallel machines is balanced.

  3. Ant colony optimisation for resource searching in dynamic peer-to-peer grids

    OpenAIRE

    Krynicki, Kamil; Jaén Martínez, Francisco Javier; Mocholi Agües, Jose Antonio

    2014-01-01

    The applicability of peer-to-peer (p2p) in the domain of grid computing has been an important subject over the past years. Nevertheless, the sole merger between p2p and the concept of grid is not sufficient to guarantee non-trivial efficiency. Some claim that ant colony optimisation (ACO) algorithms might provide a definite answer to this question. However, the use of ACO in grid networks causes several problems. The first and foremost stems out of the fact that ACO algorithms usually perform...

  4. Study on Cloud Computing Resource Scheduling Strategy Based on the Ant Colony Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Lingna He

    2012-09-01

    Full Text Available In order to replace the traditional Internet software usage patterns and enterprise management mode, this paper proposes a new business calculation mode- cloud computing, resources scheduling strategy is the key technology in cloud computing, Based on the study of cloud computing system structure and the mode of operation, The key research for cloud computing the process of the work scheduling and resource allocation problems based on ant colony algorithm , Detailed analysis and design of the specific implementation for cloud resources scheduling . And in CloudSim simulation environment and simulation experiments, the results show that the algorithm has better scheduling performance and load balance than general algorithm.

  5. Strict monandry in the ponerine army ant genus Simopelta suggests that colony size and complexity drive mating system evolution in social insects

    DEFF Research Database (Denmark)

    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 sharp contrast to other army ants. We hypothesize that differences in mature colony size and social complexity may explain these striking discrepancies....

  6. Quantifying the effect of colony size and food distribution on harvester ant foraging.

    Directory of Open Access Journals (Sweden)

    Tatiana P Flanagan

    Full Text Available Desert seed-harvester ants, genus Pogonomyrmex, are central place foragers that search for resources collectively. We quantify how seed harvesters exploit the spatial distribution of seeds to improve their rate of seed collection. We find that foraging rates are significantly influenced by the clumpiness of experimental seed baits. Colonies collected seeds from larger piles faster than randomly distributed seeds. We developed a method to compare foraging rates on clumped versus random seeds across three Pogonomyrmex species that differ substantially in forager population size. The increase in foraging rate when food was clumped in larger piles was indistinguishable across the three species, suggesting that species with larger colonies are no better than species with smaller colonies at collecting clumped seeds. These findings contradict the theoretical expectation that larger groups are more efficient at exploiting clumped resources, thus contributing to our understanding of the importance of the spatial distribution of food sources and colony size for communication and organization in social insects.

  7. Role of relative humidity in colony founding and queen survivorship in two carpenter ant species.

    Science.gov (United States)

    Mankowski, Mark E; Morrell, J J

    2011-06-01

    Conditions necessary for optimal colony foundation in two carpenter ant species, Camponotus modoc Wheeler and Camponotus vicinus Mayr, were studied. Camponotus modoc and C. vicinus queens were placed in Douglas-fir, Pseudotsuga menziesii (Mirb. Franco) and Styrofoam blocks conditioned in sealed chambers at 70, 80, or 100% RH. Nanitic workers produced after 12 wk were used to assess the effects of substrate and moisture content on colony initiation. Queens of C. vicinus in Douglas-fir and Styrofoam produced worker numbers that did not differ significantly with moisture content; however, the number of colonies initiated by C. modoc differed significantly with moisture content. The results indicate that colony founding in C. vicinus is less sensitive to moisture content than C. modoc for Douglas-fir and Styrofoam. In another test, groups of queens of each species were exposed to 20, 50, 70, and 100% RH and the time until 50% mortality occurred was recorded for each species. C. vicinus lived significantly longer at each of the test humidities than C. modoc, suggesting that the former species is adapted to better survive under xeric conditions. PMID:21735888

  8. Role of relative humidity in colony founding and queen survivorship in two carpenter ant species.

    Science.gov (United States)

    Mankowski, Mark E; Morrell, J J

    2011-06-01

    Conditions necessary for optimal colony foundation in two carpenter ant species, Camponotus modoc Wheeler and Camponotus vicinus Mayr, were studied. Camponotus modoc and C. vicinus queens were placed in Douglas-fir, Pseudotsuga menziesii (Mirb. Franco) and Styrofoam blocks conditioned in sealed chambers at 70, 80, or 100% RH. Nanitic workers produced after 12 wk were used to assess the effects of substrate and moisture content on colony initiation. Queens of C. vicinus in Douglas-fir and Styrofoam produced worker numbers that did not differ significantly with moisture content; however, the number of colonies initiated by C. modoc differed significantly with moisture content. The results indicate that colony founding in C. vicinus is less sensitive to moisture content than C. modoc for Douglas-fir and Styrofoam. In another test, groups of queens of each species were exposed to 20, 50, 70, and 100% RH and the time until 50% mortality occurred was recorded for each species. C. vicinus lived significantly longer at each of the test humidities than C. modoc, suggesting that the former species is adapted to better survive under xeric conditions.

  9. Hybrid Artificial Bee Colony Algorithm and Particle Swarm Search for Global Optimization

    OpenAIRE

    Wang Chun-Feng; Liu Kui; Shen Pei-Ping

    2014-01-01

    Artificial bee colony (ABC) algorithm is one of the most recent swarm intelligence based algorithms, which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To overcome this problem, we propose a novel artificial bee colony algorithm based on particle swarm search mechanism. In this algorithm, for improving the convergence speed, t...

  10. Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem

    OpenAIRE

    Baykaso&#;lu, Adil; Özbak&#;r, Lale; Tapkan, P&#;nar

    2007-01-01

    In this study a relatively new member of swarm intelligence family that is named as "artificial bee colony" is explained in detail. Actually, different names were used in the literature for the algorithms inspired from natural honey bees. Here we prefer to use the name "artificial bee colony" to reflect population characteristic of the algorithm. A very detailed literature review along with a categorization is presented in this study. All accessible previous work on bee based optimization alg...

  11. Application of the Artificial Bee Colony Algorithm for Solving the Set Covering Problem

    OpenAIRE

    Broderick Crawford; Ricardo Soto; Rodrigo Cuesta; Fernando Paredes

    2014-01-01

    The set covering problem is a formal model for many practical optimization problems. In the set covering problem the goal is to choose a subset of the columns of minimal cost that covers every row. Here, we present a novel application of the artificial bee colony algorithm to solve the non-unicost set covering problem. The artificial bee colony algorithm is a recent swarm metaheuristic technique based on the intelligent foraging behavior of honey bees. Experimental results show...

  12. Meta-heuristic ant colony optimization technique to forecast the amount of summer monsoon rainfall: skill comparison with Markov chain model

    Science.gov (United States)

    Chaudhuri, Sutapa; Goswami, Sayantika; Das, Debanjana; Middey, Anirban

    2014-05-01

    Forecasting summer monsoon rainfall with precision becomes crucial for the farmers to plan for harvesting in a country like India where the national economy is mostly based on regional agriculture. The forecast of monsoon rainfall based on artificial neural network is a well-researched problem. In the present study, the meta-heuristic ant colony optimization (ACO) technique is implemented to forecast the amount of summer monsoon rainfall for the next day over Kolkata (22.6°N, 88.4°E), India. The ACO technique belongs to swarm intelligence and simulates the decision-making processes of ant colony similar to other adaptive learning techniques. ACO technique takes inspiration from the foraging behaviour of some ant species. The ants deposit pheromone on the ground in order to mark a favourable path that should be followed by other members of the colony. A range of rainfall amount replicating the pheromone concentration is evaluated during the summer monsoon season. The maximum amount of rainfall during summer monsoon season (June—September) is observed to be within the range of 7.5-35 mm during the period from 1998 to 2007, which is in the range 4 category set by the India Meteorological Department (IMD). The result reveals that the accuracy in forecasting the amount of rainfall for the next day during the summer monsoon season using ACO technique is 95 % where as the forecast accuracy is 83 % with Markov chain model (MCM). The forecast through ACO and MCM are compared with other existing models and validated with IMD observations from 2008 to 2012.

  13. An Improved Ant Colony Algorithm for a Single-machine Scheduling Problem with Setup Times

    Institute of Scientific and Technical Information of China (English)

    YE Qiang; LIU Xinbao; LIU Lin; YANG Shanglin

    2006-01-01

    Motivated by industrial applications we study a single-machine scheduling problem in which all the jobs are mutually independent and available at time zero. The machine processes the jobs sequentially and it is not idle if there is any job to be processed. The operation of each job cannot be interrupted. The machine cannot process more than one job at a time. A setup time is needed if the machine switches from one type of job to another. The objective is to find an optimal schedule with the minimal total jobs' completion time. While the sum of jobs' processing time is always a constant, the objective is to minimize the sum of setup times. Ant colony optimization (ACO) is a meta-heuristic that has recently been applied to scheduling problem. In this paper we propose an improved ACO-Branching Ant Colony with Dynamic Perturbation (DPBAC) algorithm for the single-machine scheduling problem. DPBAC improves traditional ACO in following aspects: introducing Branching Method to choose starting points; improving state transition rules; introducing Mutation Method to shorten tours; improving pheromone updating rules and introducing Conditional Dynamic Perturbation Strategy. Computational results show that DPBAC algorithm is superior to the traditional ACO algorithm.

  14. T-QoS-aware based parallel ant colony algorithm for services composition

    Institute of Scientific and Technical Information of China (English)

    Lin Zhang; Kaili Rao; Ruchuan Wang

    2015-01-01

    In order to make cloud users get credible, high-quality composition of services, the trust quality of service aware (T-QoS-aware) based paral el ant colony algorithm is proposed. Our approach takes the service credibility as the weight of the quality of service, then calculates the trust service quality T-QoS for each service, making the service composition situated in a credible environment. Through the establishment on a per-service T-QoS initialization pheromone matrix, we can reduce the colony’s initial search time. By modifying the pheromone updating rules and intro-ducing two ant colonies to search from different angles in paral el, we can avoid fal ing into the local optimal solution, and quickly find the optimal combination of global solutions. Experiments show that our approach can combine high-quality services and the improve-ment of the operational success rate. Also, the convergence rate and the accuracy of optimal combination are improved.

  15. A New Constructive Method for Electric Power System Reconfiguration Using Ant Colony

    Directory of Open Access Journals (Sweden)

    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.

  16. An improved ant colony optimization approach for optimization of process planning.

    Science.gov (United States)

    Wang, JinFeng; Fan, XiaoLiang; Ding, Haimin

    2014-01-01

    Computer-aided process planning (CAPP) is an important interface between computer-aided design (CAD) and computer-aided manufacturing (CAM) in computer-integrated manufacturing environments (CIMs). In this paper, process planning problem is described based on a weighted graph, and an ant colony optimization (ACO) approach is improved to deal with it effectively. The weighted graph consists of nodes, directed arcs, and undirected arcs, which denote operations, precedence constraints among operation, and the possible visited path among operations, respectively. Ant colony goes through the necessary nodes on the graph to achieve the optimal solution with the objective of minimizing total production costs (TPCs). A pheromone updating strategy proposed in this paper is incorporated in the standard ACO, which includes Global Update Rule and Local Update Rule. A simple method by controlling the repeated number of the same process plans is designed to avoid the local convergence. A case has been carried out to study the influence of various parameters of ACO on the system performance. Extensive comparative experiments have been carried out to validate the feasibility and efficiency of the proposed approach. PMID:25097874

  17. Scalable unit commitment by memory-bounded ant colony optimization with A{sup *} local search

    Energy Technology Data Exchange (ETDEWEB)

    Saber, Ahmed Yousuf; Alshareef, Abdulaziz Mohammed [Department of Electrical and Computer Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah 21589 (Saudi Arabia)

    2008-07-15

    Ant colony optimization (ACO) is successfully applied in optimization problems. Performance of the basic ACO for small problems with moderate dimension and searching space is satisfactory. As the searching space grows exponentially in the large-scale unit commitment problem, the basic ACO is not applicable for the vast size of pheromone matrix of ACO in practical time and physical computer-memory limit. However, memory-bounded methods prune the least-promising nodes to fit the system in computer memory. Therefore, the authors propose memory-bounded ant colony optimization (MACO) in this paper for the scalable (no restriction for system size) unit commitment problem. This MACO intelligently solves the limitation of computer memory, and does not permit the system to grow beyond a bound on memory. In the memory-bounded ACO implementation, A{sup *} heuristic is introduced to increase local searching ability and probabilistic nearest neighbor method is applied to estimate pheromone intensity for the forgotten value. Finally, the benchmark data sets and existing methods are used to show the effectiveness of the proposed method. (author)

  18. 双种群改进蚁群算法%Dual population ant colony optimization algorithm

    Institute of Scientific and Technical Information of China (English)

    郏宣耀; 滕少华

    2006-01-01

    基本蚁群优化(Basic Ant Colony Optimization,BACO)算法在进化中容易出现停滞,其根源是蚁群算法中信息的正反馈. 在大量蚂蚁选择相同路径后,该路径上的信息素浓度远高于其他路径,算法很难再搜索到邻域空间中的其他优良解. 对此,提出一种双种群改进蚁群(Dual Population Ant Colony Optimization,DPACO)算法. 借鉴遗传算法中个体多样性特点,将蚁群算法中的蚂蚁分成两个群体分别独立进行进化,并定期进行信息交换. 这一方法缓解了因信息素浓度失衡而造成的局部收敛,有效改进算法的搜索性能,实验结果表明该算法有效可行.

  19. A Rule-Based Model for Bankruptcy Prediction Based on an Improved Genetic Ant Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Yudong Zhang

    2013-01-01

    Full Text Available In this paper, we proposed a hybrid system to predict corporate bankruptcy. The whole procedure consists of the following four stages: first, sequential forward selection was used to extract the most important features; second, a rule-based model was chosen to fit the given dataset since it can present physical meaning; third, a genetic ant colony algorithm (GACA was introduced; the fitness scaling strategy and the chaotic operator were incorporated with GACA, forming a new algorithm—fitness-scaling chaotic GACA (FSCGACA, which was used to seek the optimal parameters of the rule-based model; and finally, the stratified K-fold cross-validation technique was used to enhance the generalization of the model. Simulation experiments of 1000 corporations’ data collected from 2006 to 2009 demonstrated that the proposed model was effective. It selected the 5 most important factors as “net income to stock broker’s equality,” “quick ratio,” “retained earnings to total assets,” “stockholders’ equity to total assets,” and “financial expenses to sales.” The total misclassification error of the proposed FSCGACA was only 7.9%, exceeding the results of genetic algorithm (GA, ant colony algorithm (ACA, and GACA. The average computation time of the model is 2.02 s.

  20. Ants Colony Optimisation of a Measuring Path of Prismatic Parts on a CMM

    Directory of Open Access Journals (Sweden)

    Stojadinovic Slavenko M.

    2016-03-01

    Full Text Available This paper presents optimisation of a measuring probe path in inspecting the prismatic parts on a CMM. The optimisation model is based on: (i the mathematical model that establishes an initial collision-free path presented by a set of points, and (ii the solution of Travelling Salesman Problem (TSP obtained with Ant Colony Optimisation (ACO. In order to solve TSP, an ACO algorithm that aims to find the shortest path of ant colony movement (i.e. the optimised path is applied. Then, the optimised path is compared with the measuring path obtained with online programming on CMM ZEISS UMM500 and with the measuring path obtained in the CMM inspection module of Pro/ENGINEER® software. The results of comparing the optimised path with the other two generated paths show that the optimised path is at least 20% shorter than the path obtained by on-line programming on CMM ZEISS UMM500, and at least 10% shorter than the path obtained by using the CMM module in Pro/ENGINEER®.

  1. Stochastic time-dependent vehicle routing problem: Mathematical models and ant colony algorithm

    Directory of Open Access Journals (Sweden)

    Zhengyu Duan

    2015-11-01

    Full Text Available This article addresses the stochastic time-dependent vehicle routing problem. Two mathematical models named robust optimal schedule time model and minimum expected schedule time model are proposed for stochastic time-dependent vehicle routing problem, which can guarantee delivery within the time windows of customers. The robust optimal schedule time model only requires the variation range of link travel time, which can be conveniently derived from historical traffic data. In addition, the robust optimal schedule time model based on robust optimization method can be converted into a time-dependent vehicle routing problem. Moreover, an ant colony optimization algorithm is designed to solve stochastic time-dependent vehicle routing problem. As the improvements in initial solution and transition probability, ant colony optimization algorithm has a good performance in convergence. Through computational instances and Monte Carlo simulation tests, robust optimal schedule time model is proved to be better than minimum expected schedule time model in computational efficiency and coping with the travel time fluctuations. Therefore, robust optimal schedule time model is applicable in real road network.

  2. Improving Regression Testing through Modified Ant Colony Algorithm on a Dependency Injected Test Pattern

    Directory of Open Access Journals (Sweden)

    G.Keerthi Lakshmi

    2012-03-01

    Full Text Available Performing regression testing on a pre production environment is often viewed by software practitioners as a daunting task since often the test execution shall by-pass the stipulated downtime or the test coverage would be non linear. Choosing the exact test cases to match this type of complexity not only needs prior knowledge of the system, but also a right use of calculations to set the goals right. On systems that are just entering the production environment after getting promoted from the staging phase, trade-offs are often needed to between time and the test coverage to ensure the maximum test cases are covered within the stipulated time. There arises a need to refine the test cases to accommodate the maximum test coverage it makes within the stipulated period of time since at most of the times, the most important test cases are often not deemed to qualify under the sanity test suite and any bugs that creped in them would go undetected until it is found out by the actual user at firsthand. Hence An attempt has been made in the paper to layout a testing framework to address the process of improving the regression suite by adopting a modified version of the Ant Colony Algorithm over and thus dynamically injecting dependency over the best route encompassed by the ant colony.

  3. An approach using quantum ant colony optimization applied to the problem of identification of nuclear power plant transients

    International Nuclear Information System (INIS)

    Using concepts and principles of the quantum computation, as the quantum bit and superposition of states, coupled with the biological metaphor of a colony of ants, used in the Ant Colony Optimization algorithm (ACO), Wang et al developed the Quantum Ant Colony Optimization (QACO). In this paper we present a modification of the algorithm proposed by Wang et al. While the original QACO was used just for simple benchmarks functions with, at the most, two dimensions, QACOAlfa was developed for application where the original QACO, due to its tendency to converge prematurely, does not obtain good results, as in complex multidimensional functions. Furthermore, to evaluate its behavior, both algorithms are applied to the real problem of identification of accidents in PWR nuclear power plants. (author)

  4. An approach using quantum ant colony optimization applied to the problem of identification of nuclear power plant transients

    Energy Technology Data Exchange (ETDEWEB)

    Silva, Marcio H.; Schirru, Roberto; Medeiros, Jose A.C.C., E-mail: marciohenrique@lmp.ufrj.b, E-mail: schirru@lmp.ufrj.b, E-mail: canedo@lmp.ufrj.b [Coordenacao dos Programas de Pos-Graduacao de Engenharia (COPPE/UFRJ), RJ (Brazil). Programa de Engenharia Nuclear. Lab. de Monitoramento de Processos

    2009-07-01

    Using concepts and principles of the quantum computation, as the quantum bit and superposition of states, coupled with the biological metaphor of a colony of ants, used in the Ant Colony Optimization algorithm (ACO), Wang et al developed the Quantum Ant Colony Optimization (QACO). In this paper we present a modification of the algorithm proposed by Wang et al. While the original QACO was used just for simple benchmarks functions with, at the most, two dimensions, QACO{sub A}lfa was developed for application where the original QACO, due to its tendency to converge prematurely, does not obtain good results, as in complex multidimensional functions. Furthermore, to evaluate its behavior, both algorithms are applied to the real problem of identification of accidents in PWR nuclear power plants. (author)

  5. Estimating the net electricity energy generation and demand using the ant colony optimization approach: Case of Turkey

    International Nuclear Information System (INIS)

    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

  6. Estimating the net electricity energy generation and demand using the ant colony optimization approach. Case of Turkey

    Energy Technology Data Exchange (ETDEWEB)

    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)

  7. Estimating the net electricity energy generation and demand using the ant colony optimization approach: Case of Turkey

    Energy Technology Data Exchange (ETDEWEB)

    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.

  8. Effects of a juvenile hormone analogue pyriproxyfen on monogynous and polygynous colonies of the Pharaoh ant Monomorium pharaonis (Hymenoptera: Formicidae).

    Science.gov (United States)

    Tay, J W; Lee, C Y

    2015-09-01

    To evaluate the effects of the juvenile hormone analogue pyriproxyfen on colonies of the Pharaoh ant Monomorium pharaonis (L.), peanut oil containing different concentrations (0.3, 0.6, or 0.9%) of pyriproxyfen was fed to monogynous (1 queen, 500 workers, and 0.1 g of brood) and polygynous (8 queens, 50 workers, and 0.1 g of brood) laboratory colonies of M. pharaonis. Due to its delayed activity, pyriproxyfen at all concentrations resulted in colony elimination. Significant reductions in brood volume were recorded at weeks 3 - 6, and complete brood mortality was observed at week 8 in all treated colonies. Brood mortality was attributed to the disruption of brood development and cessation of egg production by queens. All polygynous colonies exhibited significant reduction in the number of queens present at week 10 compared to week 1. Number of workers was significantly lower in all treated colonies compared to control colonies at week 8 due to old-age attrition of the workers without replacement. At least 98.67 ± 1.33% of workers were dead at week 10 in all treated colonies. Thus, treatment with slow acting pyriproxyfen at concentrations of 0.3 - 0.9% is an effective strategy for eliminating Pharaoh ant colonies. PMID:26695205

  9. Improved multi-objective ant colony optimization algorithm and its application in complex reasoning

    Science.gov (United States)

    Wang, Xinqing; Zhao, Yang; Wang, Dong; Zhu, Huijie; Zhang, Qing

    2013-09-01

    The problem of fault reasoning has aroused great concern in scientific and engineering fields. However, fault investigation and reasoning of complex system is not a simple reasoning decision-making problem. It has become a typical multi-constraint and multi-objective reticulate optimization decision-making problem under many influencing factors and constraints. So far, little research has been carried out in this field. This paper transforms the fault reasoning problem of complex system into a paths-searching problem starting from known symptoms to fault causes. Three optimization objectives are considered simultaneously: maximum probability of average fault, maximum average importance, and minimum average complexity of test. Under the constraints of both known symptoms and the causal relationship among different components, a multi-objective optimization mathematical model is set up, taking minimizing cost of fault reasoning as the target function. Since the problem is non-deterministic polynomial-hard(NP-hard), a modified multi-objective ant colony algorithm is proposed, in which a reachability matrix is set up to constrain the feasible search nodes of the ants and a new pseudo-random-proportional rule and a pheromone adjustment mechinism are constructed to balance conflicts between the optimization objectives. At last, a Pareto optimal set is acquired. Evaluation functions based on validity and tendency of reasoning paths are defined to optimize noninferior set, through which the final fault causes can be identified according to decision-making demands, thus realize fault reasoning of the multi-constraint and multi-objective complex system. Reasoning results demonstrate that the improved multi-objective ant colony optimization(IMACO) can realize reasoning and locating fault positions precisely by solving the multi-objective fault diagnosis model, which provides a new method to solve the problem of multi-constraint and multi-objective fault diagnosis and

  10. Multipath Data Transmission with minimization of Congestion Using Ant Colony Optimization for MTSP and Total Queue Length

    OpenAIRE

    Dhriti Sundar Maity; Subhrananda Goswami

    2015-01-01

    This paper represents The Ant Colony Optimization for MTSP and Swarm Inspired Multipath Data Transmission with Congestion Control in MANET using Total Queue Length based on the behavioral nature in the biological ants. We consider the problem of congestion control for multicast traffic in wireless networks. MANET is multi hop wireless network in which the network components such as PC, mobile phones are mobile in nature. The components can communicate with each other without going through its...

  11. High recombination frequency creates genotypic diversity in colonies of the leaf-cutting ant Acromyrmex echinatior

    DEFF Research Database (Denmark)

    Sirviö, A.; Gadau, J.; Rueppell, O.;

    2006-01-01

    Honeybees are known to have genetically diverse colonies because queens mate with many males and the recombination rate is extremely high. Genetic diversity among social insect workers has been hypothesized to improve general performance of large and complex colonies, but this idea has not been...... tested in other social insects. Here, we present a linkage map and an estimate of the recombination rate for Acromyrmex echinatior, a leaf-cutting ant that resembles the honeybee in having multiple mating of queens and colonies of approximately the same size. A map of 145 AFLP markers in 22 linkage...... demonstrated that variation in division of labour and pathogen resistance has a genetic component and that genotypic diversity among workers may thus give colonies of this leaf-cutting ant a functional advantage. The present result is therefore consistent with the hypothesis that complex social life can select...

  12. A modified scout bee for artificial bee colony algorithm and its performance on optimization problems

    Directory of Open Access Journals (Sweden)

    Syahid Anuar

    2016-10-01

    Full Text Available The artificial bee colony (ABC is one of the swarm intelligence algorithms used to solve optimization problems which is inspired by the foraging behaviour of the honey bees. In this paper, artificial bee colony with the rate of change technique which models the behaviour of scout bee to improve the performance of the standard ABC in terms of exploration is introduced. The technique is called artificial bee colony rate of change (ABC-ROC because the scout bee process depends on the rate of change on the performance graph, replace the parameter limit. The performance of ABC-ROC is analysed on a set of benchmark problems and also on the effect of the parameter colony size. Furthermore, the performance of ABC-ROC is compared with the state of the art algorithms.

  13. Energy Aware Reliable Routing Protocol (EARRP) for Mobile AdHoc Networks Using Bee Foraging Behavior and Ant Colony Optimization

    OpenAIRE

    K. G. Santhiya; Arumugam, N.

    2012-01-01

    Energy aware reliable routing in mobile ad hoc networks is an astonishing task and in this paper we propose to design, develop such protocol which will be a good solution. For developing such protocol EARRP, two swarm intelligence techniques are involved namely ant colony optimization and bee colony foraging behavior. For optimization, we proposed adaptive solutions in order to estimate MAC overhead, link eminence and residual energy. After estimating the above said metrics, the fitness funct...

  14. Combining support vector regression and ant colony optimization to reduce NOx emissions in coal-fired utility boilers

    Energy Technology Data Exchange (ETDEWEB)

    Ligang Zheng; Hao Zhou; Chunlin Wang; Kefa Cen [Zhejiang University, Hangzhou (China). State Key Laboratory of Clean Energy Utilization

    2008-03-15

    Combustion optimization has recently demonstrated its potential to reduce NOx emissions in high capacity coal-fired utility boilers. In the present study, support vector regression (SVR), as well as artificial neural networks (ANN), was proposed to model the relationship between NOx emissions and operating parameters of a 300 MW coal-fired utility boiler. The predicted NOx emissions from the SVR model, by comparing with that of the ANN-based model, showed better agreement with the values obtained in the experimental tests on this boiler operated at different loads and various other operating parameters. The mean modeling error and the correlation factor were 1.58% and 0.94, respectively. Then, the combination of the SVR model with ant colony optimization (ACO) to reduce NOx emissions was presented in detail. The experimental results showed that the proposed approach can effectively reduce NOx emissions from the coal-fired utility boiler by about 18.69% (65 ppm). A time period of less than 6 min was required for NOx emissions modeling, and 2 min was required for a run of optimization under a PC system. The computing times are suitable for the online application of the proposed method to actual power plants. 37 refs., 8 figs., 3 tabs.

  15. Application of the ant colony search algorithm to reactive power pricing in an open electricity market

    International Nuclear Information System (INIS)

    Reactive power management is essential to transfer real energy and support power system security. Developing an accurate and feasible method for reactive power pricing is important in the electricity market. In conventional optimal power flow models the production cost of reactive power was ignored. In this paper, the production cost of reactive power and investment cost of capacitor banks were included into the objective function of the OPF problem. Then, using ant colony search algorithm, the optimal problem was solved. Marginal price theory was used for calculation of the cost of active and reactive power at each bus in competitive electric markets. Application of the proposed method on IEEE 14-bus system confirms its validity and effectiveness. Results from several case studies show clearly the effects of various factors on reactive power price. (author)

  16. The Lobe Fissure Tracking by the Modified Ant Colony Optimization Framework in CT Images

    Directory of Open Access Journals (Sweden)

    Chii-Jen Chen

    2014-11-01

    Full Text Available Chest computed tomography (CT is the most commonly used technique for the inspection of lung lesions. However, the lobe fissures in lung CT is still difficult to observe owing to its imaging structure. Therefore, in this paper, we aimed to develop an efficient tracking framework to extract the lobe fissures by the proposed modified ant colony optimization (ACO algorithm. We used the method of increasing the consistency of pheromone on lobe fissure to improve the accuracy of path tracking. In order to validate the proposed system, we had tested our method in a database from 15 lung patients. In the experiment, the quantitative assessment shows that the proposed ACO method achieved the average F-measures of 80.9% and 82.84% in left and right lungs, respectively. The experiments indicate our method results more satisfied performance, and can help investigators detect lung lesion for further examination.

  17. Hybrid real-code ant colony optimisation for constrained mechanical design

    Science.gov (United States)

    Pholdee, Nantiwat; Bureerat, Sujin

    2016-01-01

    This paper proposes a hybrid meta-heuristic based on integrating a local search simplex downhill (SDH) method into the search procedure of real-code ant colony optimisation (ACOR). This hybridisation leads to five hybrid algorithms where a Monte Carlo technique, a Latin hypercube sampling technique (LHS) and a translational propagation Latin hypercube design (TPLHD) algorithm are used to generate an initial population. Also, two numerical schemes for selecting an initial simplex are investigated. The original ACOR and its hybrid versions along with a variety of established meta-heuristics are implemented to solve 17 constrained test problems where a fuzzy set theory penalty function technique is used to handle design constraints. The comparative results show that the hybrid algorithms are the top performers. Using the TPLHD technique gives better results than the other sampling techniques. The hybrid optimisers are a powerful design tool for constrained mechanical design problems.

  18. Efficiency improvement of ant colony optimization in solving the moderate LTSP

    Institute of Scientific and Technical Information of China (English)

    Munan Li

    2015-01-01

    In solving smal- to medium-scale travel ing salesman problems (TSPs) of both symmetric and asymmetric types, the traditional ant colony optimization (ACO) algorithm could work wel , providing high accuracy and satisfactory efficiency. However, when the scale of the TSP increases, ACO, a heuristic algorithm, is greatly chal enged with respect to accuracy and efficiency. A novel pheromone-trail updating strategy that moderately reduces the iteration time required in real optimization problem-solving is proposed. In comparison with the traditional strategy of the ACO in several experiments, the proposed strategy shows advan-tages in performance. Therefore, this strategy of pheromone-trail updating is proposed as a valuable approach that reduces the time-complexity and increases its efficiency with less iteration time in real optimization applications. Moreover, this strategy is espe-cial y applicable in solving the moderate large-scale TSPs based on ACO.

  19. An ant colony optimization heuristic for an integrated production and distribution scheduling problem

    Science.gov (United States)

    Chang, Yung-Chia; Li, Vincent C.; Chiang, Chia-Ju

    2014-04-01

    Make-to-order or direct-order business models that require close interaction between production and distribution activities have been adopted by many enterprises in order to be competitive in demanding markets. This article considers an integrated production and distribution scheduling problem in which jobs are first processed by one of the unrelated parallel machines and then distributed to corresponding customers by capacitated vehicles without intermediate inventory. The objective is to find a joint production and distribution schedule so that the weighted sum of total weighted job delivery time and the total distribution cost is minimized. This article presents a mathematical model for describing the problem and designs an algorithm using ant colony optimization. Computational experiments illustrate that the algorithm developed is capable of generating near-optimal solutions. The computational results also demonstrate the value of integrating production and distribution in the model for the studied problem.

  20. Reliability worth applied to transmission expansion planning based on ant colony system

    Energy Technology Data Exchange (ETDEWEB)

    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)

  1. A convenient and robust edge detection method based on ant colony optimization

    Science.gov (United States)

    Liu, Xiaochen; Fang, Suping

    2015-10-01

    Edge detection is usually used as a preprocessing operation in many machine vision industrial applications. Recently, ant colony optimization (ACO) as a relatively new meta-heuristic approach has been used to tackle the edge detection problem. In this work, a convenient and robust method for edge detection based on ACO is proposed, which employs a new heuristic function, adopts a user-defined threshold in pheromone update process and provides a group of suitable parameter values. Experimental results clearly demonstrated the effectiveness of the proposed method, and at the same time, in the presence of noise, the proposed approach outperforms other two ACO-based edge detection techniques and four conventional edge detectors.

  2. Ant colony optimisation for economic dispatch problem with non-smooth cost functions

    Energy Technology Data Exchange (ETDEWEB)

    Pothiya, Saravuth; Kongprawechnon, Waree [School of Communication, Instrumentation and Control, Sirindhorn International Institute of Technology, Thammasat University, P.O. Box 22, Pathumthani (Thailand); Ngamroo, Issarachai [Center of Excellence for Innovative Energy Systems, Faculty of Engineering, King Mongkut' s Institute of Technology Ladkrabang, Bangkok 10520 (Thailand)

    2010-06-15

    This paper presents a novel and efficient optimisation approach based on the ant colony optimisation (ACO) for solving the economic dispatch (ED) problem with non-smooth cost functions. In order to improve the performance of ACO algorithm, three additional techniques, i.e. priority list, variable reduction, and zoom feature are presented. To show its efficiency and effectiveness, the proposed ACO is applied to two types of ED problems with non-smooth cost functions. Firstly, the ED problem with valve-point loading effects consists of 13 and 40 generating units. Secondly, the ED problem considering the multiple fuels consists of 10 units. Additionally, the results of the proposed ACO are compared with those of the conventional heuristic approaches. The experimental results show that the proposed ACO approach is comparatively capable of obtaining higher quality solution and faster computational time. (author)

  3. Application of the ant colony search algorithm to reactive power pricing in an open electricity market

    Energy Technology Data Exchange (ETDEWEB)

    Ketabi, Abbas; Alibabaee, Ahmad [Department of Electrical Engineering, University of Kashan, Kashan (Iran); Feuillet, R. [Laboratoire d' Electrotechnique de Grenoble, INPG/ENSIEG, 38402 Saint Martin d' Heres, Cedex (France)

    2010-07-15

    Reactive power management is essential to transfer real energy and support power system security. Developing an accurate and feasible method for reactive power pricing is important in the electricity market. In conventional optimal power flow models the production cost of reactive power was ignored. In this paper, the production cost of reactive power and investment cost of capacitor banks were included into the objective function of the OPF problem. Then, using ant colony search algorithm, the optimal problem was solved. Marginal price theory was used for calculation of the cost of active and reactive power at each bus in competitive electric markets. Application of the proposed method on IEEE 14-bus system confirms its validity and effectiveness. Results from several case studies show clearly the effects of various factors on reactive power price. (author)

  4. Optimization of fuel reloads for a BWR using the ant colony system

    International Nuclear Information System (INIS)

    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)

  5. Designing Daily Patrol Routes for Policing Based on ANT Colony Algorithm

    Science.gov (United States)

    Chen, H.; Cheng, T.; Wise, S.

    2015-07-01

    In this paper, we address the problem of planning police patrol routes to regularly cover street segments of high crime density (hotspots) with limited police forces. A good patrolling strategy is required to minimise the average time lag between two consecutive visits to hotspots, as well as coordinating multiple patrollers and imparting unpredictability in patrol routes. Previous studies have designed different police patrol strategies for routing police patrol, but these strategies have difficulty in generalising to real patrolling and meeting various requirements. In this research we develop a new police patrolling strategy based on Bayesian method and ant colony algorithm. In this strategy, virtual marker (pheromone) is laid to mark the visiting history of each crime hotspot, and patrollers continuously decide which hotspot to patrol next based on pheromone level and other variables. Simulation results using real data testifies the effective, scalable, unpredictable and extensible nature of this strategy.

  6. An approach using quantum ant colony optimization applied to the problem of nuclear reactors reload

    International Nuclear Information System (INIS)

    The basic concept behind the nuclear reactor fuel reloading problem is to find a configuration of new and used fuel elements, to keep the plant working at full power by the largest possible duration, within the safety restrictions. The main restriction is the power peaking factor, which is the limit value for the preservation of the fuel assembly. The QACOAlfa algorithm is a modified version of Quantum Ant Colony Optimization (QACO) proposed by Wang et al, which uses a new actualization method and a pseudo evaporation step. We examined the QACOAlfa behavior associated to physics of reactors code RECNOD when applied to this problem. Although the QACO have been developed for continuous functions, the binary model used in this work allows applying it to discrete problems, such as the mentioned above. (author)

  7. A collaborative ant colony metaheuristic for distributed multi-level lot-sizing

    CERN Document Server

    Buer, Tobias; Gehring, Hermann

    2012-01-01

    The paper presents an ant colony optimization metaheuristic for collaborative planning. Collaborative planning is used to coordinate individual plans of self-interested decision makers with private information in order to increase the overall benefit of the coalition. The method consists of a new search graph based on encoded solutions. Distributed and private information is integrated via voting mechanisms and via a simple but effective collaborative local search procedure. The approach is applied to a distributed variant of the multi-level lot-sizing problem and evaluated by means of 352 benchmark instances from the literature. The proposed approach clearly outperforms existing approaches on the sets of medium and large sized instances. While the best method in the literature so far achieves an average deviation from the best known non-distributed solutions of 46 percent for the set of the largest instances, for example, the presented approach reduces the average deviation to only 5 percent.

  8. Using Data Mining to Find Patterns in Ant Colony Algorithm Solutions to the Travelling Salesman Problem

    Institute of Scientific and Technical Information of China (English)

    YAN Shiliang; WANG Yinling

    2007-01-01

    Travelling Salesman Problem (TSP) is a classical optimization problem and it is one of a class of NP-Problem. The purposes of this work is to apply data mining methodologies to explore the patterns in data generated by an Ant Colony Algorithm (ACA) performing a searching operation and to develop a rule set searcher which approximates the ACA'S searcher. An attribute-oriented induction methodology was used to explore the relationship between an operations' sequence and its attributes and a set of rules has been developed. At the end of this paper, the experimental results have shown that the proposed approach has good performance with respect to the quality of solution and the speed of computation.

  9. Multiple Optimal Path Identification using Ant Colony Optimisation in Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Aniket. A. Gurav

    2013-10-01

    Full Text Available Wireless Sensor Network WSN is tightly constrained for resources like energy, computational power andmemory. Many applications of WSN require to communicate sensitive information at sensor nodes SN toBase station BS. The basic performance of WSN depends upon the path length and numbers of nodes in thepath by which data is forwarded to BS. In this paper we present bio-inspired Ant Colony Optimisation ACOalgorithm for Optimal Path Identification OPI for packet transmission to communicate between SN to BS.Our modified algorithm OPI using ACO is base-station driven which considers the path length and thenumber of hops in path for data packet transmission. This modified algorithm finds optimal path OP aswell as several suboptimal paths between SN & BS which are useful for effective communication.

  10. An approach using quantum ant colony optimization applied to the problem of nuclear reactors reload

    Energy Technology Data Exchange (ETDEWEB)

    Silva, Marcio H.; Lima, Alan M.M. de; Schirru, Roberto; Medeiros, J.A.C.C., E-mail: marciohenrique@lmp.ufrj.b, E-mail: schirru@lmp.ufrj.b, E-mail: canedo@lmp.ufrj.b [Coordenacao dos Programas de Pos-Graduacao de Engenharia (COPPE/UFRJ), RJ (Brazil). Programa de Engenharia Nuclear. Lab. de Monitoramento de Processos

    2009-07-01

    The basic concept behind the nuclear reactor fuel reloading problem is to find a configuration of new and used fuel elements, to keep the plant working at full power by the largest possible duration, within the safety restrictions. The main restriction is the power peaking factor, which is the limit value for the preservation of the fuel assembly. The QACO{sub A}lfa algorithm is a modified version of Quantum Ant Colony Optimization (QACO) proposed by Wang et al, which uses a new actualization method and a pseudo evaporation step. We examined the QACO{sub A}lfa behavior associated to physics of reactors code RECNOD when applied to this problem. Although the QACO have been developed for continuous functions, the binary model used in this work allows applying it to discrete problems, such as the mentioned above. (author)

  11. Ant colony optimization algorithm and its application to Neuro-Fuzzy controller design

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    An adaptive ant colony algorithm is proposed based on dynamically adjusting the strategy of updating trail information.The algorithm can keep good balance between accelerating convergence and averting precocity and stagnation.The results of function optimization show that the algorithm has good searching ability and high convergence speed.The algorithm is employed to design a neuro-fuzzy controller for real-time control of an inverted pendulum.In order to avoid the combinatorial explosion of fuzzy.rules due to multivariable inputs,a state variable synthesis scheme is emploved to reduce the number of fuzzy rules greatly.The simulation results show that the designed controller can control the inverted pendulum successfully.

  12. Multi-Constrained Dynamic QoS Multicast Routing Design Using Ant Colony System

    Institute of Scientific and Technical Information of China (English)

    GUI Zhi-bo; WU Xiao-quan

    2005-01-01

    In this paper, an Ant Colony System (AC) based heuristic algorithm is presented to find the multi-constrained dynamic Quality of Service (QoS) multicast routing. We also explore the scalability of the AC algorithm and multicast tree by using "Pull" mode instead of "Push" mode, and the improvement on the time complexity of AC algorithm by using a new data structure, I.e., a pointer array instead of the previous "matrix" structure. Our extensive tests show that the presented algorithm can find the global optimum or suboptimum, and has a good scalability with dynamic adaptation to the change of multicast group, and gives better performance in terms of the total cost than other two algorithms.

  13. Energy Efficient Ant Colony Algorithms for Data Aggregation in Wireless Sensor Networks

    CERN Document Server

    Lin, Chi; Xia, Feng; Li, Mingchu; Yao, Lin; Pei, Zhongyi

    2012-01-01

    In this paper, a family of ant colony algorithms called DAACA for data aggregation has been presented which contains three phases: the initialization, packet transmission and operations on pheromones. After initialization, each node estimates the remaining energy and the amount of pheromones to compute the probabilities used for dynamically selecting the next hop. After certain rounds of transmissions, the pheromones adjustment is performed periodically, which combines the advantages of both global and local pheromones adjustment for evaporating or depositing pheromones. Four different pheromones adjustment strategies are designed to achieve the global optimal network lifetime, namely Basic-DAACA, ES-DAACA, MM-DAACA and ACS-DAACA. Compared with some other data aggregation algorithms, DAACA shows higher superiority on average degree of nodes, energy efficiency, prolonging the network lifetime, computation complexity and success ratio of one hop transmission. At last we analyze the characteristic of DAACA in th...

  14. Gene order computation using Alzheimer's DNA microarray gene expression data and the Ant Colony Optimisation algorithm.

    Science.gov (United States)

    Pang, Chaoyang; Jiang, Gang; Wang, Shipeng; Hu, Benqiong; Liu, Qingzhong; Deng, Youping; Huang, Xudong

    2012-01-01

    As Alzheimer's Disease (AD) is the most common form of dementia, the study of AD-related genes via biocomputation is an important research topic. One method of studying AD-related gene is to cluster similar genes together into a gene order. Gene order is a good clustering method as the results can be optimal globally while other clustering methods are only optimal locally. Herein we use the Ant Colony Optimisation (ACO)-based algorithm to calculate the gene order from an Alzheimer's DNA microarray dataset. We test it with four distance measurements: Pearson distance, Spearmen distance, Euclidean distance, and squared Euclidean distance. Our computing results indicate: a different distance formula generated a different quality of gene order, the squared Euclidean distance approach produced the optimal AD-related gene order.

  15. DESIGNING DAILY PATROL ROUTES FOR POLICING BASED ON ANT COLONY ALGORITHM

    Directory of Open Access Journals (Sweden)

    H. Chen

    2015-07-01

    Full Text Available In this paper, we address the problem of planning police patrol routes to regularly cover street segments of high crime density (hotspots with limited police forces. A good patrolling strategy is required to minimise the average time lag between two consecutive visits to hotspots, as well as coordinating multiple patrollers and imparting unpredictability in patrol routes. Previous studies have designed different police patrol strategies for routing police patrol, but these strategies have difficulty in generalising to real patrolling and meeting various requirements. In this research we develop a new police patrolling strategy based on Bayesian method and ant colony algorithm. In this strategy, virtual marker (pheromone is laid to mark the visiting history of each crime hotspot, and patrollers continuously decide which hotspot to patrol next based on pheromone level and other variables. Simulation results using real data testifies the effective, scalable, unpredictable and extensible nature of this strategy.

  16. Rescheduling of observing spacecraft using fuzzy neural network and ant colony algorithm

    Institute of Scientific and Technical Information of China (English)

    Li Yuqing; Wang Rixin; Xu Minqiang

    2014-01-01

    This paper aims at rescheduling of observing spacecraft imaging plans under uncertain-ties. Firstly, uncertainties in spacecraft observation scheduling are analyzed. Then, considering the uncertainties with fuzzy features, this paper proposes a fuzzy neural network and a hybrid resched-uling policy to deal with them. It then establishes a mathematical model and manages to solve the rescheduling problem by proposing an ant colony algorithm, which introduces an adaptive control mechanism and takes advantage of the information in an existing schedule. Finally, the above method is applied to solve the rescheduling problem of a certain type of earth-observing satellite. The computation of the example shows that the approach is feasible and effective in dealing with uncertainties in spacecraft observation scheduling. The approach designed here can be useful in solving the problem that the original schedule is contaminated by disturbances.

  17. An Energy Consumption Optimized Clustering Algorithm for Radar Sensor Networks Based on an Ant Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Jiang Ting

    2010-01-01

    Full Text Available We optimize the cluster structure to solve problems such as the uneven energy consumption of the radar sensor nodes and random cluster head selection in the traditional clustering routing algorithm. According to the defined cost function for clusters, we present the clustering algorithm which is based on radio-free space path loss. In addition, we propose the energy and distance pheromones based on the residual energy and aggregation of the radar sensor nodes. According to bionic heuristic algorithm, a new ant colony-based clustering algorithm for radar sensor networks is also proposed. Simulation results show that this algorithm can get a better balance of the energy consumption and then remarkably prolong the lifetime of the radar sensor network.

  18. 3D sensor placement strategy using the full-range pheromone ant colony system

    Science.gov (United States)

    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.

  19. Quaternion-Based Improved Artificial Bee Colony Algorithm for Color Remote Sensing Image Edge Detection

    Directory of Open Access Journals (Sweden)

    Dujin Liu

    2015-01-01

    Full Text Available As the color remote sensing image has the most notable features such as huge amount of data, rich image details, and the containing of too much noise, the edge detection becomes a grave challenge in processing of remote sensing image data. To explore a possible solution to the urgent problem, in this paper, we first introduced the quaternion into the representation of color image. In this way, a color can be represented and analyzed as a single entity. Then a novel artificial bee colony method named improved artificial bee colony which can improve the performance of conventional artificial bee colony was proposed. In this method, in order to balance the exploration and the exploitation, two new search equations were presented to generate candidate solutions in the employed bee phase and the onlookers phase, respectively. Additionally, some more reasonable artificial bee colony parameters were proposed to improve the performance of the artificial bee colony. Then we applied the proposed method to the quaternion vectors to perform the edge detection of color remote sensing image. Experimental results show that our method can get a better edge detection effect than other methods.

  20. Performance Evaluation of Different Network Topologies Based On Ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    Joydip Dhar

    2010-11-01

    Full Text Available All networks tend to become more and more complicated. They can be wired, with lots of routers, orwireless, with lots of mobile node. The problem remains the same, in order to get the best from thenetwork; there is a need to find the shortest path. The more complicated the network is, the more difficultit is to manage the routes and indicate which one is the best. The Nature gives us a solution to find theshortest path. The ants, in their necessity to find food and brings it back to the nest, manage not only toexplore a vast area, but also to indicate to their peers the location of the food while bringing it back tothe nest. Most of the time, they will find the shortest path and adapt to ground changes, hence provingtheir great efficiency toward this difficult task. The purpose of this paper is to evaluate the performanceof different network topologies based on Ant Colony Optimization Algorithm. Simulation is done in NS-2.

  1. Automatic boiling water reactor loading pattern design using ant colony optimization algorithm

    International Nuclear Information System (INIS)

    An automatic boiling water reactor (BWR) loading pattern (LP) design methodology was developed using the rank-based ant system (RAS), which is a variant of the ant colony optimization (ACO) algorithm. To reduce design complexity, only the fuel assemblies (FAs) of one eight-core positions were determined using the RAS algorithm, and then the corresponding FAs were loaded into the other parts of the core. Heuristic information was adopted to exclude the selection of the inappropriate FAs which will reduce search space, and thus, the computation time. When the LP was determined, Haling cycle length, beginning of cycle (BOC) shutdown margin (SDM), and Haling end of cycle (EOC) maximum fraction of limit for critical power ratio (MFLCPR) were calculated using SIMULATE-3 code, which were used to evaluate the LP for updating pheromone of RAS. The developed design methodology was demonstrated using FAs of a reference cycle of the BWR6 nuclear power plant. The results show that, the designed LP can be obtained within reasonable computation time, and has a longer cycle length than that of the original design.

  2. Automatic boiling water reactor loading pattern design using ant colony optimization algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Wang, C.-D. [Department of Engineering and System Science, National Tsing Hua University, 101, Section 2 Kuang Fu Road, Hsinchu 30013, Taiwan (China); Nuclear Engineering Division, Institute of Nuclear Energy Research, No. 1000, Wenhua Rd., Jiaan Village, Longtan Township, Taoyuan County 32546, Taiwan (China)], E-mail: jdwang@iner.gov.tw; Lin Chaung [Department of Engineering and System Science, National Tsing Hua University, 101, Section 2 Kuang Fu Road, Hsinchu 30013, Taiwan (China)

    2009-08-15

    An automatic boiling water reactor (BWR) loading pattern (LP) design methodology was developed using the rank-based ant system (RAS), which is a variant of the ant colony optimization (ACO) algorithm. To reduce design complexity, only the fuel assemblies (FAs) of one eight-core positions were determined using the RAS algorithm, and then the corresponding FAs were loaded into the other parts of the core. Heuristic information was adopted to exclude the selection of the inappropriate FAs which will reduce search space, and thus, the computation time. When the LP was determined, Haling cycle length, beginning of cycle (BOC) shutdown margin (SDM), and Haling end of cycle (EOC) maximum fraction of limit for critical power ratio (MFLCPR) were calculated using SIMULATE-3 code, which were used to evaluate the LP for updating pheromone of RAS. The developed design methodology was demonstrated using FAs of a reference cycle of the BWR6 nuclear power plant. The results show that, the designed LP can be obtained within reasonable computation time, and has a longer cycle length than that of the original design.

  3. Remote Sensing Classification based on Improved Ant Colony Rules Mining Algorithm

    Directory of Open Access Journals (Sweden)

    Shuying Liu

    2014-09-01

    Full Text Available Data mining can uncover previously undetected relationships among data items using automated data analysis techniques. In data mining, association rule mining is a prevalent and well researched method for discovering useful relations between variables in large databases. This paper investigates the principle of traditional rule mining, which will produce more non-essential candidate sets when it reads data into candidate items. Particularly when it deals with massive data, if the minimum support and minimum confidence are relatively small, combinatorial explosion of frequent item sets will occur and computing power and storage space required are likely to exceed the limits of machine. A new ant colony algorithm based on conventional Ant-Miner algorithm is proposed and is used in rules mining. Measurement formula of effectiveness of the rules is improved and pheromone concentration update strategy is also carried out. The experiment results show that execution time of proposed algorithm is lower than traditional algorithm and has better execution time and accuracy

  4. Optic disc detection in color fundus images using ant colony optimization.

    Science.gov (United States)

    Pereira, Carla; Gonçalves, Luís; Ferreira, Manuel

    2013-03-01

    Diabetic retinopathy has been revealed as the most common cause of blindness among people of working age in developed countries. However, loss of vision could be prevented by an early detection of the disease and, therefore, by a regular screening program to detect retinopathy. Due to its characteristics, the digital color fundus photographs have been the easiest way to analyze the eye fundus. An important prerequisite for automation is the segmentation of the main anatomical features in the image, particularly the optic disc. Currently, there are many works reported in the literature with the purpose of detecting and segmenting this anatomical structure. Though, none of them performs as needed, especially when dealing with images presenting pathologies and a great variability. Ant colony optimization (ACO) is an optimization algorithm inspired by the foraging behavior of some ant species that has been applied in image processing with different purposes. In this paper, this algorithm preceded by anisotropic diffusion is used for optic disc detection in color fundus images. Experimental results demonstrate the good performance of the proposed approach as the optic disc was detected in most of all the images used, even in the images with great variability. PMID:23160896

  5. Optimal Selection of Parameters for Nonuniform Embedding of Chaotic Time Series Using Ant Colony Optimization.

    Science.gov (United States)

    Shen, Meie; Chen, Wei-Neng; Zhang, Jun; Chung, Henry Shu-Hung; Kaynak, Okyay

    2013-04-01

    The optimal selection of parameters for time-delay embedding is crucial to the analysis and the forecasting of chaotic time series. Although various parameter selection techniques have been developed for conventional uniform embedding methods, the study of parameter selection for nonuniform embedding is progressed at a slow pace. In nonuniform embedding, which enables different dimensions to have different time delays, the selection of time delays for different dimensions presents a difficult optimization problem with combinatorial explosion. To solve this problem efficiently, this paper proposes an ant colony optimization (ACO) approach. Taking advantage of the characteristic of incremental solution construction of the ACO, the proposed ACO for nonuniform embedding (ACO-NE) divides the solution construction procedure into two phases, i.e., selection of embedding dimension and selection of time delays. In this way, both the embedding dimension and the time delays can be optimized, along with the search process of the algorithm. To accelerate search speed, we extract useful information from the original time series to define heuristics to guide the search direction of ants. Three geometry- or model-based criteria are used to test the performance of the algorithm. The optimal embeddings found by the algorithm are also applied in time-series forecasting. Experimental results show that the ACO-NE is able to yield good embedding solutions from both the viewpoints of optimization performance and prediction accuracy. PMID:23144038

  6. Apply Local Clustering Method to Improve the Running Speed of Ant Colony Optimization

    CERN Document Server

    Pang, Chao-Yang; Li, Xia; Hu, Be-Qiong

    2009-01-01

    Ant Colony Optimization (ACO) has time complexity O(t*m*N*N), and its typical application is to solve Traveling Salesman Problem (TSP), where t, m, and N denotes the iteration number, number of ants, number of cities respectively. Cutting down running time is one of study focuses, and one way is to decrease parameter t and N, especially N. For this focus, the following method is presented in this paper. Firstly, design a novel clustering algorithm named Special Local Clustering algorithm (SLC), then apply it to classify all cities into compact classes, where compact class is the class that all cities in this class cluster tightly in a small region. Secondly, let ACO act on every class to get a local TSP route. Thirdly, all local TSP routes are jointed to form solution. Fourthly, the inaccuracy of solution caused by clustering is eliminated. Simulation shows that the presented method improves the running speed of ACO by 200 factors at least. And this high speed is benefit from two factors. One is that class ha...

  7. A New Tool Wear Monitoring Method Based on Ant Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Qianjian Guo

    2013-06-01

    Full Text Available Tool wear prediction is a major contributor to the dimensional errors of a work piece in precision machining, which plays an important role in industry for higher productivity and product quality. Tool wear monitoring is an effective way to predict the tool wear loss in milling process. In this paper, a new bionic prediction model is presented based on the generation mechanism of tool wear loss. Different milling conditions are estimated as the input variables, tool wear loss is estimated as the output variable, neural network method is proposed to establish the mapping relation and ant algorithm is used to train the weights of BP neural networks during tool wear modeling. Finally, a real-time tool wear loss estimator is developed based on ant colony alogrithm and experiments have been conducted for measuring tool wear based on the estimator in a milling machine. The experimental and estimated results are found to be in satisfactory agreement with average error lower than 6%.

  8. Azcaxalli: A system based on Ant Colony Optimization algorithms, applied to fuel reloads design in a Boiling Water Reactor

    International Nuclear Information System (INIS)

    This paper presents some results of the implementation of several optimization algorithms based on ant colonies, applied to the fuel reload design in a Boiling Water Reactor. The system called Azcaxalli is constructed with the following algorithms: Ant Colony System, Ant System, Best-Worst Ant System and MAX-MIN Ant System. Azcaxalli starts with a random fuel reload. Ants move into reactor core channels according to the State Transition Rule in order to select two fuel assemblies into a 1/8 part of the reactor core and change positions between them. This rule takes into account pheromone trails and acquired knowledge. Acquired knowledge is obtained from load cycle values of fuel assemblies. Azcaxalli claim is to work in order to maximize the cycle length taking into account several safety parameters. Azcaxalli's objective function involves thermal limits at the end of the cycle, cold shutdown margin at the beginning of the cycle and the neutron effective multiplication factor for a given cycle exposure. Those parameters are calculated by CM-PRESTO code. Through the Haling Principle is possible to calculate the end of the cycle. This system was applied to an equilibrium cycle of 18 months of Laguna Verde Nuclear Power Plant in Mexico. The results show that the system obtains fuel reloads with higher cycle lengths than the original fuel reload. Azcaxalli results are compared with genetic algorithms, tabu search and neural networks results.

  9. Azcaxalli: A system based on Ant Colony Optimization algorithms, applied to fuel reloads design in a Boiling Water Reactor

    Energy Technology Data Exchange (ETDEWEB)

    Esquivel-Estrada, Jaime, E-mail: jaime.esquivel@fi.uaemex.m [Facultad de Ingenieria, Universidad Autonoma del Estado de Mexico, Cerro de Coatepec S/N, Toluca de Lerdo, Estado de Mexico 50000 (Mexico); Instituto Nacional de Investigaciones Nucleares, Carr. Mexico Toluca S/N, Ocoyoacac, Estado de Mexico 52750 (Mexico); Ortiz-Servin, Juan Jose, E-mail: juanjose.ortiz@inin.gob.m [Instituto Nacional de Investigaciones Nucleares, Carr. Mexico Toluca S/N, Ocoyoacac, Estado de Mexico 52750 (Mexico); Castillo, Jose Alejandro; Perusquia, Raul [Instituto Nacional de Investigaciones Nucleares, Carr. Mexico Toluca S/N, Ocoyoacac, Estado de Mexico 52750 (Mexico)

    2011-01-15

    This paper presents some results of the implementation of several optimization algorithms based on ant colonies, applied to the fuel reload design in a Boiling Water Reactor. The system called Azcaxalli is constructed with the following algorithms: Ant Colony System, Ant System, Best-Worst Ant System and MAX-MIN Ant System. Azcaxalli starts with a random fuel reload. Ants move into reactor core channels according to the State Transition Rule in order to select two fuel assemblies into a 1/8 part of the reactor core and change positions between them. This rule takes into account pheromone trails and acquired knowledge. Acquired knowledge is obtained from load cycle values of fuel assemblies. Azcaxalli claim is to work in order to maximize the cycle length taking into account several safety parameters. Azcaxalli's objective function involves thermal limits at the end of the cycle, cold shutdown margin at the beginning of the cycle and the neutron effective multiplication factor for a given cycle exposure. Those parameters are calculated by CM-PRESTO code. Through the Haling Principle is possible to calculate the end of the cycle. This system was applied to an equilibrium cycle of 18 months of Laguna Verde Nuclear Power Plant in Mexico. The results show that the system obtains fuel reloads with higher cycle lengths than the original fuel reload. Azcaxalli results are compared with genetic algorithms, tabu search and neural networks results.

  10. 蚁群算法参数分析%Parametric Study of Ant Colony Optimization

    Institute of Scientific and Technical Information of China (English)

    陈一昭; 姜麟

    2011-01-01

    The basic principle of Ant Colony algorithm and main parameters about this algorithm are determined are described. These parameters which greatly influence the ant colony algorithm' capacity of searching optimal so-lution comprise the inspired factor (e), the expectation inspired factor β, ant population m, information strength Q and the pheromone volatilization factor ρ. Employing the Travelling Salesman problem (TSP) as an example, dif-ferent combined conditions about these parameters are studied. Firstly, according to the result of numerical exam-ples, selects [(e),β,m,Q,ρ] =[1.5, 4.2, 30,200,0.5], Secondly, 4 parameters of them, Conducts numerical experiments by changing the remaining parameter value are fixed. Through getting (e) ∈ [0. 7,1.1],β ∈ [3.8, 4. 5] ,Q ∈ [400,950] and ρ ∈ [0. 7,0. 9] , the stable global optimal solution could be achieved.%介绍了蚁群算法的基本原理.确定了蚁群算法中的主要参数,这些参数对蚁群算法的寻优能力的影响非常之大,有启发因子δ,期望启发因子β,蚁群数量m,信息强度Q和信息素会发因子ρ等参数,以旅行商问题为例优化以上参数,研究这些参数的组合情况.首先根据数值试验选定[δ,β,m,Q,p]=[1.5,4.2,30,200,0.5].固定四个参数,改变一个参数进行数值试验.得到δ∈ [0.7,1.1],β∈[3.8,4.5],Q∈ [400,950]和p∈[0.7,0.9]能得到稳定的全局最优解.

  11. Enhancing Artificial Bee Colony Algorithm with Self-Adaptive Searching Strategy and Artificial Immune Network Operators for Global Optimization

    Directory of Open Access Journals (Sweden)

    Tinggui Chen

    2014-01-01

    Full Text Available Artificial bee colony (ABC algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA, artificial colony optimization (ACO, and particle swarm optimization (PSO. However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments.

  12. Enhancing artificial bee colony algorithm with self-adaptive searching strategy and artificial immune network operators for global optimization.

    Science.gov (United States)

    Chen, Tinggui; Xiao, Renbin

    2014-01-01

    Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments. PMID:24772023

  13. Fracture enhancement based on artificial ants and fuzzy c-means clustering (FCMC) in Dezful Embayment of Iran

    Science.gov (United States)

    Nasseri, Aynur; Jafar Mohammadzadeh, Mohammad; Hashem Tabatabaei Raeisi, S.

    2015-04-01

    This paper deals with the application of the ant colony algorithm (AC) to a seismic dataset from Dezful Embayment in the southwest region of Iran. The objective of the approach is to generate an accurate representation of faults and discontinuities to assist in pertinent matters such as well planning and field optimization. The AC analyzed all spatial discontinuities in the seismic attributes from which features were extracted. True fault information from the attributes was detected by many artificial ants, whereas noise and the remains of the reflectors were eliminated. Furthermore, the fracture enhancement procedure was conducted by three steps on seismic data of the area. In the first step several attributes such as chaos, variance/coherence and dip deviation were taken into account; the resulting maps indicate high-resolution contrast for the variance attribute. Subsequently, the enhancement of spatial discontinuities was performed and finally elimination of the noise and remains of non-faulting events was carried out by simulating the behavior of ant colonies. After considering stepwise attribute optimization, focusing on chaos and variance in particular, an attribute fusion was generated and used in the ant colony algorithm. The resulting map displayed the highest performance in feature detection along the main structural feature trend, confined to a NW-SE direction. Thus, the optimized attribute fusion might be used with greater confidence to map the structural feature network with more accuracy and resolution. In order to assess the performance of the AC in feature detection, and cross validate the reliability of the method used, fuzzy c-means clustering (FCMC) was employed for the same dataset. Comparing the maps illustrates the effectiveness and preference of the AC approach due to its high resolution contrast for structural feature detection compared to the FCMC method. Accordingly, 3D planes of discontinuity determined spatial distribution of fractures

  14. 新型的双种群蚁群算法%Novel dual population ant colony algorithm

    Institute of Scientific and Technical Information of China (English)

    张晓伟; 李笑雪

    2011-01-01

    A novel ant colony algorithm is proposed based on the bionics of cooperation relation between soldier ant and worker ant in the foraging process. Soldier ant population and worker ant population are designed to search problem solution by parallel way in proposed algorithm.The dynamic equilibrium between solution diversity and convergence speed is achieved by using the effect of the soldier ant's distribution to worker ants' movement choice. Experimental results on traveling salesman problem show that proposed algorithm has a good global searching ability and high convergence speed.%基于对蚂蚁种群中兵蚁和工蚁在觅食过程中合作关系的仿生,提出了一种改进型蚁群算法.在该算法中同时存在着兵蚁子种群与工蚁子种群两个种群,两个子种群并行搜索,通过兵蚁的分布来影响到工蚁的移动选择,以取得各蚂蚁子群体中解的多样性和收敛性之间的动态平衡.基于旅行商问题的实验证明,算法具有较好的全局搜索能力和收敛速度.

  15. Forced Evolution in Silico by Artificial Transposons and their Genetic Operators: The John Muir Ant Problem

    CERN Document Server

    Spirov, Alexander V; Zamdborg, Leonid; Merelo, Juan J; Levchenko, Vladimir F

    2009-01-01

    Modern evolutionary computation utilizes heuristic optimizations based upon concepts borrowed from the Darwinian theory of natural selection. We believe that a vital direction in this field must be algorithms that model the activity of genomic parasites, such as transposons, in biological evolution. This publication is our first step in the direction of developing a minimal assortment of algorithms that simulate the role of genomic parasites. Specifically, we started in the domain of genetic algorithms (GA) and selected the Artificial Ant Problem as a test case. We define these artificial transposons as a fragment of an ant's code that possesses properties that cause it to stand apart from the rest. We concluded that artificial transposons, analogous to real transposons, are truly capable of acting as intelligent mutators that adapt in response to an evolutionary problem in the course of co-evolution with their hosts.

  16. Fuel lattice design in a boiling water reactor using an ant-colony-based system

    International Nuclear Information System (INIS)

    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

  17. Fuel lattice design in a boiling water reactor using an ant-colony-based system

    Energy Technology Data Exchange (ETDEWEB)

    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

  18. A new quantum inspired chaotic artificial bee colony algorithm for optimal power flow problem

    International Nuclear Information System (INIS)

    Highlights: • Quantum theory is introduced to artificial bee colony algorithm (ABC) to increase population diversity. • A chaotic local search operator is used to enhance local search ability of ABC. • Quantum inspired chaotic ABC method (QCABC) is proposed to solve optimal power flow. • The feasibility and effectiveness of the proposed QCABC is verified by examples. - Abstract: This paper proposes a new artificial bee colony algorithm with quantum theory and the chaotic local search strategy (QCABC), and uses it to solve the optimal power flow (OPF) problem. Under the quantum computing theory, the QCABC algorithm encodes each individual with quantum bits to form a corresponding quantum bit string. By determining each quantum bits value, we can get the value of the individual. After the scout bee stage of the artificial bee colony algorithm, we begin the chaotic local search in the vicinity of the best individual found so far. Finally, the quantum rotation gate is used to process each quantum bit so that all individuals can update toward the direction of the best individual. The QCABC algorithm is carried out to deal with the OPF problem in the IEEE 30-bus and IEEE 118-bus standard test systems. The results of the QCABC algorithm are compared with other algorithms (artificial bee colony algorithm, genetic algorithm, particle swarm optimization algorithm). The comparison shows that the QCABC algorithm can effectively solve the OPF problem and it can get the better optimal results than those of other algorithms

  19. Training a Feed-Forward Neural Network with Artificial Bee Colony based Backpropagation Method

    Directory of Open Access Journals (Sweden)

    Sudarshan Nandy

    2012-09-01

    Full Text Available Back-propagation algorithm is one of the most widely used and popular techniques to optimize the feedforward neural network training. Nature inspired meta-heuristic algorithms also provide derivative-freesolution to optimize complex problem. Artificial bee colony algorithm is a nature inspired meta-heuristicalgorithm, mimicking the foraging or food source searching behaviour of bees in a bee colony and thisalgorithm is implemented in several applications for an improved optimized outcome. The proposedmethod in this paper includes an improved artificial bee colony algorithm based back-propagation neuralnetwork training method for fast and improved convergence rate of the hybrid neural network learningmethod. The result is analysed with the genetic algorithm based back-propagation method, and it isanother hybridized procedure of its kind. Analysis is performed over standard data sets, reflecting the lightof efficiency of proposed method in terms of convergence speed and rate.

  20. Comparative Analysis of Improved Cuckoo Search(ICS) Algorithm and Artificial Bee Colony (ABC) Algorithm on Continuous Optimization Problems

    OpenAIRE

    Shariba Islam Tusiy; Nasif Shawkat; Md. Arman Ahmed; Biswajit Panday; Nazmus Sakib

    2015-01-01

    This work is related on two well-known algorithm, Improved Cuckoo Search and Artificial Bee Colony Algorithm which are inspired from nature. Improved Cuckoo Search (ICS) algorithm is based on Lévy flight and behavior of some birds and fruit flies and they have some assumptions and each assumption is highly observed to maintain their characteristics. Besides Artificial Bee Colony (ABC) algorithm is based on swarm intelligence, which is based on bee colony with the way the bees maintain their l...