Rule Extraction Based on Extreme Learning Machine and an Improved Ant-Miner Algorithm for Transient Stability Assessment.

Science.gov (United States)

Li, Yang; Li, Guoqing; Wang, Zhenhao

2015-01-01

In order to overcome the problems of poor understandability of the pattern recognition-based transient stability assessment (PRTSA) methods, a new rule extraction method based on extreme learning machine (ELM) and an improved Ant-miner (IAM) algorithm is presented in this paper. First, the basic principles of ELM and Ant-miner algorithm are respectively introduced. Then, based on the selected optimal feature subset, an example sample set is generated by the trained ELM-based PRTSA model. And finally, a set of classification rules are obtained by IAM algorithm to replace the original ELM network. The novelty of this proposal is that transient stability rules are extracted from an example sample set generated by the trained ELM-based transient stability assessment model by using IAM algorithm. The effectiveness of the proposed method is shown by the application results on the New England 39-bus power system and a practical power system--the southern power system of Hebei province.

Single Allocation Hub-and-spoke Networks Design Based on Ant Colony Optimization Algorithm

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Yang Pingle

2014-10-01

Full Text Available Capacitated single allocation hub-and-spoke networks can be abstracted as a mixed integer linear programming model equation with three variables. Introducing an improved ant colony algorithm, which has six local search operators. Meanwhile, introducing the "Solution Pair" concept to decompose and optimize the composition of the problem, the problem can become more specific and effectively meet the premise and advantages of using ant colony algorithm. Finally, location simulation experiment is made according to Australia Post data to demonstrate this algorithm has good efficiency and stability for solving this problem.

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

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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.

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

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Rafid Sagban

2015-01-01

Full Text Available 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.

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.

Hybrid Bee Ant Colony Algorithm for Effective Load Balancing And ...

African Journals Online (AJOL)

PROF. OLIVER OSUAGWA

Ant Colony algorithm is used in this hybrid Bee Ant Colony algorithm to solve load balancing issues ... Genetic Algorithm (MO-GA) for dynamic job scheduling that .... Information Networking and Applications Workshops. [7]. M. Dorigo & T.

Ant colony search algorithm for optimal reactive power optimization

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Lenin K.

2006-01-01

Full Text Available The paper presents an (ACSA Ant colony search Algorithm for Optimal Reactive Power Optimization and voltage control of power systems. ACSA is a new co-operative agents’ approach, which is inspired by the observation of the behavior of real ant colonies on the topic of ant trial formation and foraging methods. Hence, in the ACSA a set of co-operative agents called "Ants" co-operates to find good solution for Reactive Power Optimization problem. The ACSA is applied for optimal reactive power optimization is evaluated on standard IEEE, 30, 57, 191 (practical test bus system. The proposed approach is tested and compared to genetic algorithm (GA, Adaptive Genetic Algorithm (AGA.

Automatic optimization of a nuclear reactor reload using the algorithm Ant-Q

International Nuclear Information System (INIS)

Machado, Liana; Schirru, Roberto

2002-01-01

The nuclear fuel reload optimization is a NP-Complete combinatorial optimization problem. For decades this problem was solved using an expert's knowledge. From the eighties, however there have been efforts to automatic fuel reload and the more recent ones show the Genetic Algorithm's (GA) efficiency on this problem. Following this trend, our aim is to optimization nuclear fuel reload using Ant-Q, artificial theory based algorithms. Ant-Q's results on the Traveling salesman Problem, which is conceptuality similar to fuel reload, are better than GA's. Ant-Q was tested in real application on the cycle 7 reload of Angra I. Comparing Ant-Q result with the GA's, it can be verified that, even without a local heuristics, the former algorithm, as it superiority comparing the GA in Angra I show. Is a valid technique to solve the nuclear fuel reload problem. (author)

Improving the Interpretability of Classification Rules Discovered by an Ant Colony Algorithm: Extended Results

OpenAIRE

Otero, Fernando E.B.; Freitas, Alex A.

2016-01-01

The vast majority of Ant Colony Optimization (ACO) algorithms for inducing classification rules use an ACO-based procedure to create a rule in an one-at-a-time fashion. An improved search strategy has been proposed in the cAnt-MinerPB algorithm, where an ACO-based procedure is used to create a complete list of rules (ordered rules)-i.e., the ACO search is guided by the quality of a list of rules, instead of an individual rule. In this paper we propose an extension of the cAnt-MinerPB algorith...

A Generalized Ant Colony Algorithm for Job一shop Scheduling Problem

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ZHANG Hong-Guo

2017-02-01

Full Text Available Aiming at the problem of ant colony algorithm for solving Job一shop scheduling problem. Considering the complexity of the algorithm that uses disjunctive graph to describe the relationship between workpiece processing. To solve the problem of optimal solution，a generalized ant colony algorithm is proposed. Under the premise of considering constrained relationship between equipment and process，the pheromone update mechanism is applied to solve Job-shop scheduling problem，so as to improve the quality of the solution. In order to improve the search efficiency，according to the state transition rules of ant colony algorithm，this paper makes a detailed study on the selection and improvement of the parameters in the algorithm，and designs the pheromone update strategy. Experimental results show that a generalized ant colony algorithm is more feasible and more effective. Compared with other algorithms in the literature，the results prove that the algorithm improves in computing the optimal solution and convergence speed.

Improving the Interpretability of Classification Rules Discovered by an Ant Colony Algorithm: Extended Results.

Science.gov (United States)

Otero, Fernando E B; Freitas, Alex A

2016-01-01

Most ant colony optimization (ACO) algorithms for inducing classification rules use a ACO-based procedure to create a rule in a one-at-a-time fashion. An improved search strategy has been proposed in the cAnt-Miner[Formula: see text] algorithm, where an ACO-based procedure is used to create a complete list of rules (ordered rules), i.e., the ACO search is guided by the quality of a list of rules instead of an individual rule. In this paper we propose an extension of the cAnt-Miner[Formula: see text] algorithm to discover a set of rules (unordered rules). The main motivations for this work are to improve the interpretation of individual rules by discovering a set of rules and to evaluate the impact on the predictive accuracy of the algorithm. We also propose a new measure to evaluate the interpretability of the discovered rules to mitigate the fact that the commonly used model size measure ignores how the rules are used to make a class prediction. Comparisons with state-of-the-art rule induction algorithms, support vector machines, and the cAnt-Miner[Formula: see text] producing ordered rules are also presented.

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

OpenAIRE

Lingna He; Qingshui Li; Linan Zhu

2012-01-01

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...

Analysis of Ant Colony Optimization and Population-Based Evolutionary Algorithms on Dynamic Problems

DEFF Research Database (Denmark)

Lissovoi, Andrei

the dynamic optimum for finite alphabets up to size μ, while MMAS is able to do so for any finite alphabet size. Parallel Evolutionary Algorithms on Maze. We prove that while a (1 + λ) EA is unable to track the optimum of the dynamic fitness function Maze for offspring population size up to λ = O(n1-ε......This thesis presents new running time analyses of nature-inspired algorithms on various dynamic problems. It aims to identify and analyse the features of algorithms and problem classes which allow efficient optimization to occur in the presence of dynamic behaviour. We consider the following...... settings: λ-MMAS on Dynamic Shortest Path Problems. We investigate how in-creasing the number of ants simulated per iteration may help an ACO algorithm to track optimum in a dynamic problem. It is shown that while a constant number of ants per-vertex is sufficient to track some oscillations, there also...

An experimental analysis of design choices of multi-objective ant colony optimization algorithms

OpenAIRE

Lopez-Ibanez, Manuel; Stutzle, Thomas

2012-01-01

There have been several proposals on how to apply the ant colony optimization (ACO) metaheuristic to multi-objective combinatorial optimization problems (MOCOPs). This paper proposes a new formulation of these multi-objective ant colony optimization (MOACO) algorithms. This formulation is based on adding specific algorithm components for tackling multiple objectives to the basic ACO metaheuristic. Examples of these components are how to represent multiple objectives using pheromone and heuris...

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.

AUTOMATA PROGRAMS CONSTRUCTION FROM SPECIFICATION WITH AN ANT COLONY OPTIMIZATION ALGORITHM BASED ON MUTATION GRAPH

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Daniil S. Chivilikhin

2014-11-01

Full Text Available The procedure of testing traditionally used in software engineering cannot guarantee program correctness; therefore verification is used at the excess requirements to programs reliability. Verification makes it possible to check certain properties of programs in all possible computational states; however, this process is very complex. In the model checking method a model of the program is built (often, manually and requirements in terms of temporal logic are formulated. Such temporal properties of the model can be checked automatically. The main issue in this framework is the gap between the program and its model. Automata-based programming paradigm gives the possibility to overcome this limitation. In this paradigm, program logic is represented using finite-state machines. The advantage of finite-state machines is that their models can be constructed automatically. The paper deals with the application of mutation-based ant colony optimization algorithm to the problem of finite-state machine construction from their specification, defined by test scenarios and temporal properties. The presented approach has been tested on the elevator doors control problem as well as on randomly generated data. Obtained results show the ant colony algorithm is two-three times faster than the previously used genetic algorithm. The proposed approach can be recommended for inferring control programs for critical systems.

Revolutionizing Remote Exploration with ANTS

Science.gov (United States)

Clark, P. E.; Rilee, M. L.; Curtis, S.; Truszkowski, W.

2002-05-01

We are developing the Autonomous Nano-Technology Swarm (ANTS) architecture based on an insect colony analogue for the cost-effective, efficient, systematic survey of remote or inaccessible areas with multiple object targets, including planetary surface, marine, airborne, and space environments. The mission context is the exploration in the 2020s of the most compelling remaining targets in the solar system: main belt asteroids. Main belt asteroids harbor important clues to Solar System origins and evolution which are central to NASA's goals in Space Science. Asteroids are smaller than planets, but their number is far greater, and their combined surface area likely dwarfs the Earth's. An asteroid survey will dramatically increase our understanding of the local resources available for the Human Exploration and Development of Space. During the mission composition, shape, gravity, and orbit parameters could be returned to Earth for perhaps several thousand asteroids. A survey of this area will rival the great explorations that encircled this globe, opened up the New World, and laid the groundwork for the progress and challenges of the last centuries. The ANTS architecture for a main belt survey consists of a swarm of as many as a thousand or more highly specialized pico-spacecraft that form teams to survey as many as one hundred asteroids a month. Multi-level autonomy is critical for ANTS and the objective of the proposed study is to work through the implications and constraints this entails. ANTS couples biologically inspired autonomic control for basic functions to higher level artificial intelligence that together enable individual spacecraft to operate as specialized, cooperative, social agents. This revolutionary approach postulates highly advanced, but familiar, components integrated and operated in a way that uniquely transcends any evolutionary extrapolation of existing trends and enables thousand-spacecraft missions.

Automatic optimization of a nuclear reactor reload using the algorithm Ant-Q; A otimizacao automatica da recarga nuclear utilizando o algoritmo Ant-Q

Energy Technology Data Exchange (ETDEWEB)

Machado, Liana; Schirru, Roberto [Universidade Federal, Rio de Janeiro, RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia. Programa de Engenharia Nuclear

2002-07-01

The nuclear fuel reload optimization is a NP-Complete combinatorial optimization problem. For decades this problem was solved using an expert's knowledge. From the eighties, however there have been efforts to automatic fuel reload and the more recent ones show the Genetic Algorithm's (GA) efficiency on this problem. Following this trend, our aim is to optimization nuclear fuel reload using Ant-Q, artificial theory based algorithms. Ant-Q's results on the Traveling salesman Problem, which is conceptuality similar to fuel reload, are better than GA's. Ant-Q was tested in real application on the cycle 7 reload of Angra I. Comparing Ant-Q result with the GA's, it can be verified that, even without a local heuristics, the former algorithm, as it superiority comparing the GA in Angra I show. Is a valid technique to solve the nuclear fuel reload problem. (author)

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.

Simulated Annealing-Based Ant Colony Algorithm for Tugboat Scheduling Optimization

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Qi Xu

2012-01-01

Full Text Available As the “first service station” for ships in the whole port logistics system, the tugboat operation system is one of the most important systems in port logistics. This paper formulated the tugboat scheduling problem as a multiprocessor task scheduling problem (MTSP after analyzing the characteristics of tugboat operation. The model considers factors of multianchorage bases, different operation modes, and three stages of operations (berthing/shifting-berth/unberthing. The objective is to minimize the total operation times for all tugboats in a port. A hybrid simulated annealing-based ant colony algorithm is proposed to solve the addressed problem. By the numerical experiments without the shifting-berth operation, the effectiveness was verified, and the fact that more effective sailing may be possible if tugboats return to the anchorage base timely was pointed out; by the experiments with the shifting-berth operation, one can see that the objective is most sensitive to the proportion of the shifting-berth operation, influenced slightly by the tugboat deployment scheme, and not sensitive to the handling operation times.

Intelligent PID controller based on ant system algorithm and fuzzy inference and its application to bionic artificial leg

Institute of Scientific and Technical Information of China (English)

谭冠政; 曾庆冬; 李文斌

2004-01-01

A designing method of intelligent proportional-integral-derivative(PID) controllers was proposed based on the ant system algorithm and fuzzy inference. This kind of controller is called Fuzzy-ant system PID controller. It consists of an off-line part and an on-line part. In the off-line part, for a given control system with a PID controller,by taking the overshoot, setting time and steady-state error of the system unit step response as the performance indexes and by using the ant system algorithm, a group of optimal PID parameters K*p , Ti* and T*d can be obtained, which are used as the initial values for the on-line tuning of PID parameters. In the on-line part, based on Kp* , Ti*and Td* and according to the current system error e and its time derivative, a specific program is written, which is used to optimize and adjust the PID parameters on-line through a fuzzy inference mechanism to ensure that the system response has optimal transient and steady-state performance. This kind of intelligent PID controller can be used to control the motor of the intelligent bionic artificial leg designed by the authors. The result of computer simulation experiment shows that the controller has less overshoot and shorter setting time.

Reliable Ant Colony Routing Algorithm for Dual-Channel Mobile Ad Hoc Networks

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YongQiang Li

2018-01-01

Full Text Available For the problem of poor link reliability caused by high-speed dynamic changes and congestion owing to low network bandwidth in ad hoc networks, an ant colony routing algorithm, based on reliable path under dual-channel condition (DSAR, is proposed. First, dual-channel communication mode is used to improve network bandwidth, and a hierarchical network model is proposed to optimize the dual-layer network. Thus, we reduce network congestion and communication delay. Second, a comprehensive reliable path selection strategy is designed, and the reliable path is selected ahead of time to reduce the probability of routing restart. Finally, the ant colony algorithm is used to improve the adaptability of the routing algorithm to changes of network topology. Simulation results show that DSAR improves the reliability of routing, packet delivery, and throughput.

A universal optimization strategy for ant colony optimization algorithms based on the Physarum-inspired mathematical model

International Nuclear Information System (INIS)

Zhang, Zili; Gao, Chao; Liu, Yuxin; Qian, Tao

2014-01-01

Ant colony optimization (ACO) algorithms often fall into the local optimal solution and have lower search efficiency for solving the travelling salesman problem (TSP). According to these shortcomings, this paper proposes a universal optimization strategy for updating the pheromone matrix in the ACO algorithms. The new optimization strategy takes advantages of the unique feature of critical paths reserved in the process of evolving adaptive networks of the Physarum-inspired mathematical model (PMM). The optimized algorithms, denoted as PMACO algorithms, can enhance the amount of pheromone in the critical paths and promote the exploitation of the optimal solution. Experimental results in synthetic and real networks show that the PMACO algorithms are more efficient and robust than the traditional ACO algorithms, which are adaptable to solve the TSP with single or multiple objectives. Meanwhile, we further analyse the influence of parameters on the performance of the PMACO algorithms. Based on these analyses, the best values of these parameters are worked out for the TSP. (paper)

Simulation optimization based ant colony algorithm for the uncertain quay crane scheduling problem

Directory of Open Access Journals (Sweden)

Naoufal Rouky

2019-01-01

Full Text Available This work is devoted to the study of the Uncertain Quay Crane Scheduling Problem (QCSP, where the loading /unloading times of containers and travel time of quay cranes are considered uncertain. The problem is solved with a Simulation Optimization approach which takes advantage of the great possibilities offered by the simulation to model the real details of the problem and the capacity of the optimization to find solutions with good quality. An Ant Colony Optimization (ACO meta-heuristic hybridized with a Variable Neighborhood Descent (VND local search is proposed to determine the assignments of tasks to quay cranes and the sequences of executions of tasks on each crane. Simulation is used inside the optimization algorithm to generate scenarios in agreement with the probabilities of the distributions of the uncertain parameters, thus, we carry out stochastic evaluations of the solutions found by each ant. The proposed optimization algorithm is tested first for the deterministic case on several well-known benchmark instances. Then, in the stochastic case, since no other work studied exactly the same problem with the same assumptions, the Simulation Optimization approach is compared with the deterministic version. The experimental results show that the optimization algorithm is competitive as compared to the existing methods and that the solutions found by the Simulation Optimization approach are more robust than those found by the optimization algorithm.

DESIGNING DAILY PATROL ROUTES FOR POLICING BASED ON ANT COLONY ALGORITHM

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H. Chen

2015-07-01

Full Text Available In this paper, we address the problem of planning police patrol routes to regularly cover street segments of high crime density (hotspots with limited police forces. A good patrolling strategy is required to minimise the average time lag between two consecutive visits to hotspots, as well as coordinating multiple patrollers and imparting unpredictability in patrol routes. Previous studies have designed different police patrol strategies for routing police patrol, but these strategies have difficulty in generalising to real patrolling and meeting various requirements. In this research we develop a new police patrolling strategy based on Bayesian method and ant colony algorithm. In this strategy, virtual marker (pheromone is laid to mark the visiting history of each crime hotspot, and patrollers continuously decide which hotspot to patrol next based on pheromone level and other variables. Simulation results using real data testifies the effective, scalable, unpredictable and extensible nature of this strategy.

Study on bi-directional pedestrian movement using ant algorithms

International Nuclear Information System (INIS)

Gokce, Sibel; Kayacan, Ozhan

2016-01-01

A cellular automata model is proposed to simulate bi-directional pedestrian flow. Pedestrian movement is investigated by using ant algorithms. Ants communicate with each other by dropping a chemical, called a pheromone, on the substrate while crawling forward. Similarly, it is considered that oppositely moving pedestrians drop ‘visual pheromones’ on their way and the visual pheromones might cause attractive or repulsive interactions. This pheromenon is introduced into modelling the pedestrians’ walking preference. In this way, the decision-making process of pedestrians will be based on ‘the instinct of following’. At some densities, the relationships of velocity–density and flux–density are analyzed for different evaporation rates of visual pheromones. Lane formation and phase transition are observed for certain evaporation rates of visual pheromones. (paper)

Energy Aware Simple Ant Routing Algorithm for Wireless Sensor Networks

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Sohail Jabbar

2015-01-01

Full Text Available Network lifetime is one of the most prominent barriers in deploying wireless sensor networks for large-scale applications because these networks employ sensors with nonrenewable scarce energy resources. Sensor nodes dissipate most of their energy in complex routing mechanisms. To cope with limited energy problem, we present EASARA, an energy aware simple ant routing algorithm based on ant colony optimization. Unlike most algorithms, EASARA strives to avoid low energy routes and optimizes the routing process through selection of least hop count path with more energy. It consists of three phases, that is, route discovery, forwarding node, and route selection. We have improved the route discovery procedure and mainly concentrate on energy efficient forwarding node and route selection, so that the network lifetime can be prolonged. The four possible cases of forwarding node and route selection are presented. The performance of EASARA is validated through simulation. Simulation results demonstrate the performance supremacy of EASARA over contemporary scheme in terms of various metrics.

Building optimal regression tree by ant colony system-genetic algorithm: Application to modeling of melting points

Energy Technology Data Exchange (ETDEWEB)

Hemmateenejad, Bahram, E-mail: hemmatb@sums.ac.ir [Department of Chemistry, Shiraz University, Shiraz (Iran, Islamic Republic of); Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz (Iran, Islamic Republic of); Shamsipur, Mojtaba [Department of Chemistry, Razi University, Kermanshah (Iran, Islamic Republic of); Zare-Shahabadi, Vali [Young Researchers Club, Mahshahr Branch, Islamic Azad University, Mahshahr (Iran, Islamic Republic of); Akhond, Morteza [Department of Chemistry, Shiraz University, Shiraz (Iran, Islamic Republic of)

2011-10-17

Highlights: {yields} Ant colony systems help to build optimum classification and regression trees. {yields} Using of genetic algorithm operators in ant colony systems resulted in more appropriate models. {yields} Variable selection in each terminal node of the tree gives promising results. {yields} CART-ACS-GA could model the melting point of organic materials with prediction errors lower than previous models. - Abstract: The classification and regression trees (CART) possess the advantage of being able to handle large data sets and yield readily interpretable models. A conventional method of building a regression tree is recursive partitioning, which results in a good but not optimal tree. Ant colony system (ACS), which is a meta-heuristic algorithm and derived from the observation of real ants, can be used to overcome this problem. The purpose of this study was to explore the use of CART and its combination with ACS for modeling of melting points of a large variety of chemical compounds. Genetic algorithm (GA) operators (e.g., cross averring and mutation operators) were combined with ACS algorithm to select the best solution model. In addition, at each terminal node of the resulted tree, variable selection was done by ACS-GA algorithm to build an appropriate partial least squares (PLS) model. To test the ability of the resulted tree, a set of approximately 4173 structures and their melting points were used (3000 compounds as training set and 1173 as validation set). Further, an external test set containing of 277 drugs was used to validate the prediction ability of the tree. Comparison of the results obtained from both trees showed that the tree constructed by ACS-GA algorithm performs better than that produced by recursive partitioning procedure.

A Novel Spectrum Scheduling Scheme with Ant Colony Optimization Algorithm

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Liping Liu

2018-01-01

Full Text Available Cognitive radio is a promising technology for improving spectrum utilization, which allows cognitive users access to the licensed spectrum while primary users are absent. In this paper, we design a resource allocation framework based on graph theory for spectrum assignment in cognitive radio networks. The framework takes into account the constraints that interference for primary users and possible collision among cognitive users. Based on the proposed model, we formulate a system utility function to maximize the system benefit. Based on the proposed model and objective problem, we design an improved ant colony optimization algorithm (IACO from two aspects: first, we introduce differential evolution (DE process to accelerate convergence speed by monitoring mechanism; then we design a variable neighborhood search (VNS process to avoid the algorithm falling into the local optimal. Simulation results demonstrate that the improved algorithm achieves better performance.

An Improved Ant Colony Algorithm for Solving the Path Planning Problem of the Omnidirectional Mobile Vehicle

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Jiang Zhao

2016-01-01

Full Text Available This paper presents an improved ant colony algorithm for the path planning of the omnidirectional mobile vehicle. The purpose of the improved ant colony algorithm is to design an appropriate route to connect the starting point and ending point of the environment with obstacles. Ant colony algorithm, which is used to solve the path planning problem, is improved according to the characteristics of the omnidirectional mobile vehicle. And in the improved algorithm, the nonuniform distribution of the initial pheromone and the selection strategy with direction play a very positive role in the path search. The coverage and updating strategy of pheromone is introduced to avoid repeated search reducing the effect of the number of ants on the performance of the algorithm. In addition, the pheromone evaporation coefficient is segmented and adjusted, which can effectively balance the convergence speed and search ability. Finally, this paper provides a theoretical basis for the improved ant colony algorithm by strict mathematical derivation, and some numerical simulations are also given to illustrate the effectiveness of the theoretical results.

A novel hybrid chaotic ant swarm algorithm for heat exchanger networks synthesis

International Nuclear Information System (INIS)

Zhang, Chunwei; Cui, Guomin; Peng, Fuyu

2016-01-01

Highlights: • The chaotic ant swarm algorithm is proposed to avoid trapping into a local optimum. • The organization variables update strategy makes full use of advantages of the chaotic search. • The structure evolution strategy is developed to handle integer variables optimization. • Overall three cases taken form the literatures are investigated with better optima. - Abstract: The heat exchanger networks synthesis (HENS) still remains an open problem due to its combinatorial nature, which can easily result in suboptimal design and unacceptable calculation effort. In this paper, a novel hybrid chaotic ant swarm algorithm is proposed. The presented algorithm, which consists of a combination of chaotic ant swarm (CAS) algorithm, structure evolution strategy, local optimization strategy and organization variables update strategy, can simultaneously optimize continuous variables and integer variables. The CAS algorithm chaotically searches and generates new solutions in the given space, and subsequently the structure evolution strategy evolves the structures represented by the solutions and limits the search space. Furthermore, the local optimizing strategy and the organization variables update strategy are introduced to enhance the performance of the algorithm. The study of three different cases, found in the literature, revealed special search abilities in both structure space and continuous variable space.

ADAPTIVE ANT COLONY OPTIMIZATION BASED GRADIENT FOR EDGE DETECTION

Directory of Open Access Journals (Sweden)

Febri Liantoni

2014-08-01

Full Text Available Ant Colony Optimization (ACO is a nature-inspired optimization algorithm which is motivated by ants foraging behavior. Due to its favorable advantages, ACO has been widely used to solve several NP-hard problems, including edge detection. Since ACO initially distributes ants at random, it may cause imbalance ant distribution which later affects path discovery process. In this paper an adaptive ACO is proposed to optimize edge detection by adaptively distributing ant according to gradient analysis. Ants are adaptively distributed according to gradient ratio of each image regions. Region which has bigger gradient ratio, will have bigger number of ant distribution. Experiments are conducted using images from various datasets. Precision and recall are used to quantitatively evaluate performance of the proposed algorithm. Precision and recall of adaptive ACO reaches 76.98 % and 96.8 %. Whereas highest precision and recall for standard ACO are 69.74 % and 74.85 %. Experimental results show that the adaptive ACO outperforms standard ACO which randomly distributes ants.

Efficient distribution of toy products using ant colony optimization algorithm

Science.gov (United States)

Hidayat, S.; Nurpraja, C. A.

2017-12-01

CV Atham Toys (CVAT) produces wooden toys and furniture, comprises 13 small and medium industries. CVAT always attempt to deliver customer orders on time but delivery costs are high. This is because of inadequate infrastructure such that delivery routes are long, car maintenance costs are high, while fuel subsidy by the government is still temporary. This study seeks to minimize the cost of product distribution based on the shortest route using one of five Ant Colony Optimization (ACO) algorithms to solve the Vehicle Routing Problem (VRP). This study concludes that the best of the five is the Ant Colony System (ACS) algorithm. The best route in 1st week gave a total distance of 124.11 km at a cost of Rp 66,703.75. The 2nd week route gave a total distance of 132.27 km at a cost of Rp 71,095.13. The 3rd week best route gave a total distance of 122.70 km with a cost of Rp 65,951.25. While the 4th week gave a total distance of 132.27 km at a cost of Rp 74,083.63. Prior to this study there was no effort to calculate these figures.

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.

A Novel Ant Colony Algorithm for the Single-Machine Total Weighted Tardiness Problem with Sequence Dependent Setup Times

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Fardin Ahmadizar

2011-08-01

Full Text Available This paper deals with the NP-hard single-machine total weighted tardiness problem with sequence dependent setup times. Incorporating fuzzy sets and genetic operators, a novel ant colony optimization algorithm is developed for the problem. In the proposed algorithm, artificial ants construct solutions as orders of jobs based on the heuristic information as well as pheromone trails. To calculate the heuristic information, three well-known priority rules are adopted as fuzzy sets and then aggregated. When all artificial ants have terminated their constructions, genetic operators such as crossover and mutation are applied to generate new regions of the solution space. A local search is then performed to improve the performance quality of some of the solutions found. Moreover, at run-time the pheromone trails are locally as well as globally updated, and limited between lower and upper bounds. The proposed algorithm is experimented on a set of benchmark problems from the literature and compared with other metaheuristics.

Application of an Image Tracking Algorithm in Fire Ant Motion Experiment

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Lichuan Gui

2009-04-01

Full Text Available An image tracking algorithm, which was originally used with the particle image velocimetry (PIV to determine velocities of buoyant solid particles in water, is modified and applied in the presented work to detect motion of fire ant on a planar surface. A group of fire ant workers are put to the bottom of a tub and excited with vibration of selected frequency and intensity. The moving fire ants are captured with an image system that successively acquires image frames of high digital resolution. The background noise in the imaging recordings is extracted by averaging hundreds of frames and removed from each frame. The individual fire ant images are identified with a recursive digital filter, and then they are tracked between frames according to the size, brightness, shape, and orientation angle of the ant image. The speed of an individual ant is determined with the displacement of its images and the time interval between frames. The trail of the individual fire ant is determined with the image tracking results, and a statistical analysis is conducted for all the fire ants in the group. The purpose of the experiment is to investigate the response of fire ants to the substrate vibration. Test results indicate that the fire ants move faster after being excited, but the number of active ones are not increased even after a strong excitation.

Analysis of the Multi Strategy Goal Programming for Micro-Grid Based on Dynamic ant Genetic Algorithm

Science.gov (United States)

Qiu, J. P.; Niu, D. X.

Micro-grid is one of the key technologies of the future energy supplies. Take economic planning. reliability, and environmental protection of micro grid as a basis for the analysis of multi-strategy objective programming problems for micro grid which contains wind power, solar power, and battery and micro gas turbine. Establish the mathematical model of each power generation characteristics and energy dissipation. and change micro grid planning multi-objective function under different operating strategies to a single objective model based on AHP method. Example analysis shows that in combination with dynamic ant mixed genetic algorithm can get the optimal power output of this model.

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.

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

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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.

DISTRIBUTION NETWORK RECONFIGURATION FOR POWER LOSS MINIMIZATION AND VOLTAGE PROFILE ENHANCEMENT USING ANT LION ALGORITHM

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Maryam Shokouhi

2017-06-01

Full Text Available Distribution networks are designed as a ring and operated as a radial form. Therefore, the reconfiguration is a simple and cost-effective way to use existing facilities without the need for any new equipment in distribution networks to achieve various objectives such as: power loss reduction, feeder overload reduction, load balancing, voltage profile improvement, reducing the number of switching considering constraints that ultimately result in the power loss reduction. In this paper, a new method based on the Ant Lion algorithm (a modern meta-heuristic algorithm is provided for the reconfiguration of distribution networks. Considering the extension of the distribution networks and complexity of their communications networks, and the various parameters, using smart techniques is inevitable. The proposed approach is tested on the IEEE 33 & 69-bus radial standard distribution networks. The Evaluation of results in MATLAB software shows the effectiveness of the Ant Lion algorithm in the distribution network reconfiguration.

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

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Yao Wang

2015-08-01

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

A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey

International Nuclear Information System (INIS)

Kıran, Mustafa Servet; Özceylan, Eren; Gündüz, Mesut; Paksoy, Turan

2012-01-01

Highlights: ► PSO and ACO algorithms are hybridized for forecasting energy demands of Turkey. ► Linear and quadratic forms are developed to meet the fluctuations of indicators. ► GDP, population, export and import have significant impacts on energy demand. ► Quadratic form provides better fit solution than linear form. ► Proposed approach gives lower estimation error than ACO and PSO, separately. - Abstract: This paper proposes a new hybrid method (HAP) for estimating energy demand of Turkey using Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). Proposed energy demand model (HAPE) is the first model which integrates two mentioned meta-heuristic techniques. While, PSO, developed for solving continuous optimization problems, is a population based stochastic technique; ACO, simulating behaviors between nest and food source of real ants, is generally used for discrete optimizations. Hybrid method based PSO and ACO is developed to estimate energy demand using gross domestic product (GDP), population, import and export. HAPE is developed in two forms which are linear (HAPEL) and quadratic (HAPEQ). The future energy demand is estimated under different scenarios. In order to show the accuracy of the algorithm, a comparison is made with ACO and PSO which are developed for the same problem. According to obtained results, relative estimation errors of the HAPE model are the lowest of them and quadratic form (HAPEQ) provides better-fit solutions due to fluctuations of the socio-economic indicators.

Comparison Performance of Genetic Algorithm and Ant Colony Optimization in Course Scheduling Optimizing

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Imam Ahmad Ashari

2016-11-01

Full Text Available Scheduling problems at the university is a complex type of scheduling problems. The scheduling process should be carried out at every turn of the semester's. The core of the problem of scheduling courses at the university is that the number of components that need to be considered in making the schedule, some of the components was made up of students, lecturers, time and a room with due regard to the limits and certain conditions so that no collision in the schedule such as mashed room, mashed lecturer and others. To resolve a scheduling problem most appropriate technique used is the technique of optimization. Optimization techniques can give the best results desired. Metaheuristic algorithm is an algorithm that has a lot of ways to solve the problems to the very limit the optimal solution. In this paper, we use a genetic algorithm and ant colony optimization algorithm is an algorithm metaheuristic to solve the problem of course scheduling. The two algorithm will be tested and compared to get performance is the best. The algorithm was tested using data schedule courses of the university in Semarang. From the experimental results we conclude that the genetic algorithm has better performance than the ant colony optimization  algorithm in solving the case of course scheduling.

Analysis of parameter estimation and optimization application of ant colony algorithm in vehicle routing problem

Science.gov (United States)

Xu, Quan-Li; Cao, Yu-Wei; Yang, Kun

2018-03-01

Ant Colony Optimization (ACO) is the most widely used artificial intelligence algorithm at present. This study introduced the principle and mathematical model of ACO algorithm in solving Vehicle Routing Problem (VRP), and designed a vehicle routing optimization model based on ACO, then the vehicle routing optimization simulation system was developed by using c ++ programming language, and the sensitivity analyses, estimations and improvements of the three key parameters of ACO were carried out. The results indicated that the ACO algorithm designed in this paper can efficiently solve rational planning and optimization of VRP, and the different values of the key parameters have significant influence on the performance and optimization effects of the algorithm, and the improved algorithm is not easy to locally converge prematurely and has good robustness.

A seismic fault recognition method based on ant colony optimization

Science.gov (United States)

Chen, Lei; Xiao, Chuangbai; Li, Xueliang; Wang, Zhenli; Huo, Shoudong

2018-05-01

Fault recognition is an important section in seismic interpretation and there are many methods for this technology, but no one can recognize fault exactly enough. For this problem, we proposed a new fault recognition method based on ant colony optimization which can locate fault precisely and extract fault from the seismic section. Firstly, seismic horizons are extracted by the connected component labeling algorithm; secondly, the fault location are decided according to the horizontal endpoints of each horizon; thirdly, the whole seismic section is divided into several rectangular blocks and the top and bottom endpoints of each rectangular block are considered as the nest and food respectively for the ant colony optimization algorithm. Besides that, the positive section is taken as an actual three dimensional terrain by using the seismic amplitude as a height. After that, the optimal route from nest to food calculated by the ant colony in each block is judged as a fault. Finally, extensive comparative tests were performed on the real seismic data. Availability and advancement of the proposed method were validated by the experimental results.

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

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Alok Bajpai

2015-08-01

Full Text Available 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 based on the divide and conquer strategy for the traveling salesman problem which is one of the most important combinatorial problem in which the original problem is partitioned into the group of sub problems. And then we apply the ant colony algorithm using candidate list strategy for each smaller sub problems. After that by applying the local optimization and combining the sub problems to find the good solution for the original problem by improving the exploration efficiency of the ants. At the end of this paper we have also be presented the comparison of result with the normal ant colony system for finding the optimal solution to the traveling salesman problem.

CAS algorithm-based optimum design of PID controller in AVR system

International Nuclear Information System (INIS)

Zhu Hui; Li Lixiang; Zhao Ying; Guo Yu; Yang Yixian

2009-01-01

This paper presents a novel design method for determining the optimal PID controller parameters of an automatic voltage regulator (AVR) system using the chaotic ant swarm (CAS) algorithm. In the tuning process of parameters, the CAS algorithm is iterated to give the optimal parameters of the PID controller based on the fitness theory, where the position vector of each ant in the CAS algorithm corresponds to the parameter vector of the PID controller. The proposed CAS-PID controllers can ensure better control system performance with respect to the reference input in comparison with GA-PID controllers. Numerical simulations are provided to verify the effectiveness and feasibility of PID controller based on CAS algorithm.

BR-Explorer: A sound and complete FCA-based retrieval algorithm (Poster)

OpenAIRE

Messai , Nizar; Devignes , Marie-Dominique; Napoli , Amedeo; Smaïl-Tabbone , Malika

2006-01-01

In this paper we present BR-Explorer, a sound and complete biological data sources retrieval algorithm based on Formal Concept Analysis and domain ontologies. BR-Explorer addresses the problem of retrieving the relevant data sources for a given query. Initially, a formal context representing the relation between biological data sources and their metadata is provided and its corresponding concept lattice is built. Then BR-Explorer starts by generating the formal concept for the considered quer...

Ant colony optimisation-direct cover: a hybrid ant colony direct cover technique for multi-level synthesis of multiple-valued logic functions

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Abd-El-Barr, Mostafa

2010-12-01

The use of non-binary (multiple-valued) logic in the synthesis of digital systems can lead to savings in chip area. Advances in very large scale integration (VLSI) technology have enabled the successful implementation of multiple-valued logic (MVL) circuits. A number of heuristic algorithms for the synthesis of (near) minimal sum-of products (two-level) realisation of MVL functions have been reported in the literature. The direct cover (DC) technique is one such algorithm. The ant colony optimisation (ACO) algorithm is a meta-heuristic that uses constructive greediness to explore a large solution space in finding (near) optimal solutions. The ACO algorithm mimics the ant's behaviour in the real world in using the shortest path to reach food sources. We have previously introduced an ACO-based heuristic for the synthesis of two-level MVL functions. In this article, we introduce the ACO-DC hybrid technique for the synthesis of multi-level MVL functions. The basic idea is to use an ant to decompose a given MVL function into a number of levels and then synthesise each sub-function using a DC-based technique. The results obtained using the proposed approach are compared to those obtained using existing techniques reported in the literature. A benchmark set consisting of 50,000 randomly generated 2-variable 4-valued functions is used in the comparison. The results obtained using the proposed ACO-DC technique are shown to produce efficient realisation in terms of the average number of gates (as a measure of chip area) needed for the synthesis of a given MVL function.

Ant colony algorithm for clustering in portfolio optimization

Science.gov (United States)

Subekti, R.; Sari, E. R.; Kusumawati, R.

2018-03-01

This research aims to describe portfolio optimization using clustering methods with ant colony approach. Two stock portfolios of LQ45 Indonesia is proposed based on the cluster results obtained from ant colony optimization (ACO). The first portfolio consists of assets with ant colony displacement opportunities beyond the defined probability limits of the researcher, where the weight of each asset is determined by mean-variance method. The second portfolio consists of two assets with the assumption that each asset is a cluster formed from ACO. The first portfolio has a better performance compared to the second portfolio seen from the Sharpe index.

Collective search by ants in microgravity

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Stefanie M. Countryman

2015-03-01

Full Text Available The problem of collective search is a tradeoff between searching thoroughly and covering as much area as possible. This tradeoff depends on the density of searchers. Solutions to the problem of collective search are currently of much interest in robotics and in the study of distributed algorithms, for example to design ways that without central control robots can use local information to perform search and rescue operations. Ant colonies operate without central control. Because they can perceive only local, mostly chemical and tactile cues, they must search collectively to find resources and to monitor the colony's environment. Examining how ants in diverse environments solve the problem of collective search can elucidate how evolution has led to diverse forms of collective behavior. An experiment on the International Space Station in January 2014 examined how ants (Tetramorium caespitum perform collective search in microgravity. In the ISS experiment, the ants explored a small arena in which a barrier was lowered to increase the area and thus lower ant density. In microgravity, relative to ground controls, ants explored the area less thoroughly and took more convoluted paths. It appears that the difficulty of holding on to the surface interfered with the ants’ ability to search collectively. Ants frequently lost contact with the surface, but showed a remarkable ability to regain contact with the surface.

Impact of Interference Competition on Exploration and Food Exploitation in the Ant Lasius niger

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Vincent Fourcassié

2012-01-01

Full Text Available Competition acts as a major force in shaping spatially and/or temporally the foraging activity of ant colonies. Interference competition between colonies in particular is widespread in ants where it can prevent the physical access of competitors to a resource, either directly by fighting or indirectly, by segregating the colony foraging areas. Although the consequences of interference competition on ant distribution have been well studied in the literature, the behavioral mechanisms underlying interference competition have been less explored. Little is known on how ants modify their exploration patterns or the choice of a feeding place after experiencing aggressive encounters. In this paper, we show that, at the individual level, the aphid-tending ant Lasius niger reacts to the presence of an alien conspecific through direct aggressive behavior and local recruitment in the vicinity of fights. At the colony level, however, no defensive recruitment is triggered and the “risky” area where aggressive encounters occur is not specifically avoided during further exploration or food exploitation. We discuss how between-species differences in sensitivity to interference competition could be related to the spatial and temporal predictability of food resources at stake.

Using rapidly-exploring random tree-based algorithms to find smooth and optimal trajectories

CSIR Research Space (South Africa)

Matebese, B

2012-10-01

Full Text Available -exploring random tree-based algorithms to fi nd smooth and optimal trajectories B MATEBESE1, MK BANDA2 AND S UTETE1 1CSIR Modelling and Digital Science, PO Box 395, Pretoria, South Africa, 0001 2Department of Applied Mathematics, Stellenbosch University... and complex environments. The RRT algorithm is the most popular and has the ability to find a feasible solution faster than other algorithms. The drawback of using RRT is that, as the number of samples increases, the probability that the algorithm converges...

Application of Ant-Colony-Optimization algorithm for improved management of first flush effects in urban wastewater systems.

Science.gov (United States)

Verdaguer, M; Clara, N; Gutiérrez, O; Poch, M

2014-07-01

The first flush effect in combined sewer systems during storm events often causes overflows and overloads of the sewage treatment, which reduces the efficiency of the sewage treatment and decreases the quality of the receiving waters due to the pollutants that are contributed. The use of retention tanks constitutes a widely used way to mitigate this effect. However, the management of the pollutant loads encounters difficulties when the retention tanks are emptied. A new approach is proposed to solve this problem by fulfilling the treatment requirements in real time, focussing on the characteristics of the wastewater. The method is based on the execution of an Ant Colony Optimisation algorithm to obtain a satisfactory sequence for the discharge of the retention tanks. The discharge sequence considers the volume of stormwater and its concentration of pollutants including Suspended Solids, Biological Oxygen Demand and Chemical Oxygen Demand, Total Nitrogen and Total Phosphorus. The Ant Colony Optimisation algorithm was applied successfully to a case study with overall reduction of pollutant loads stored in retention tanks. The algorithm can be adapted in a simple way to the different scenarios, infrastructures and controllers of sewer systems. Copyright © 2014 Elsevier B.V. All rights reserved.

Solving the Traveling Salesman Problem Based on The Genetic Reactive Bone Route Algorithm whit Ant Colony System

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Majid Yousefikhoshbakht

2016-07-01

Full Text Available The TSP is considered one of the most well-known combinatorial optimization tasks and researchers have paid so much attention to the TSP for many years. In this problem, a salesman starts to move from an arbitrary place called depot and after visits all of the nodes, finally comes back to the depot. The objective is to minimize the total distance traveled by the salesman.  Because this problem is a non-deterministic polynomial (NP-hard problem in nature, a hybrid meta-heuristic algorithm called REACSGA is used for solving the TSP. In REACSGA, a reactive bone route algorithm that uses the ant colony system (ACS for generating initial diversified solutions and the genetic algorithm (GA as an improved procedure are applied. Since the performance of the Metaheuristic algorithms is significantly influenced by their parameters, Taguchi Method is used to set the parameters of the proposed algorithm. The proposed algorithm is tested on several standard instances involving 24 to 318 nodes from the literature. The computational result shows that the results of the proposed algorithm are competitive with other metaheuristic algorithms for solving the TSP in terms of better quality of solution and computational time respectively. In addition, the proposed REACSGA is significantly efficient and finds closely the best known solutions for most of the instances in which thirteen best known solutions are also found.

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

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Milinkovitch Michel C

2007-11-01

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

An assembly sequence planning method based on composite algorithm

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Enfu LIU

2016-02-01

Full Text Available To solve the combination explosion problem and the blind searching problem in assembly sequence planning of complex products, an assembly sequence planning method based on composite algorithm is proposed. In the composite algorithm, a sufficient number of feasible assembly sequences are generated using formalization reasoning algorithm as the initial population of genetic algorithm. Then fuzzy knowledge of assembly is integrated into the planning process of genetic algorithm and ant algorithm to get the accurate solution. At last, an example is conducted to verify the feasibility of composite algorithm.

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

International Nuclear Information System (INIS)

Yuan, Y; Liu, C

2012-01-01

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.

An Effective Hybrid Routing Algorithm in WSN: Ant Colony Optimization in combination with Hop Count Minimization

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Ailian Jiang

2018-03-01

Full Text Available Low cost, high reliability and easy maintenance are key criteria in the design of routing protocols for wireless sensor networks (WSNs. This paper investigates the existing ant colony optimization (ACO-based WSN routing algorithms and the minimum hop count WSN routing algorithms by reviewing their strengths and weaknesses. We also consider the critical factors of WSNs, such as energy constraint of sensor nodes, network load balancing and dynamic network topology. Then we propose a hybrid routing algorithm that integrates ACO and a minimum hop count scheme. The proposed algorithm is able to find the optimal routing path with minimal total energy consumption and balanced energy consumption on each node. The algorithm has unique superiority in terms of searching for the optimal path, balancing the network load and the network topology maintenance. The WSN model and the proposed algorithm have been implemented using C++. Extensive simulation experimental results have shown that our algorithm outperforms several other WSN routing algorithms on such aspects that include the rate of convergence, the success rate in searching for global optimal solution, and the network lifetime.

An improved ant colony optimization algorithm with fault tolerance for job scheduling in grid computing systems.

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Hajara Idris

Full Text Available The Grid scheduler, schedules user jobs on the best available resource in terms of resource characteristics by optimizing job execution time. Resource failure in Grid is no longer an exception but a regular occurring event as resources are increasingly being used by the scientific community to solve computationally intensive problems which typically run for days or even months. It is therefore absolutely essential that these long-running applications are able to tolerate failures and avoid re-computations from scratch after resource failure has occurred, to satisfy the user's Quality of Service (QoS requirement. Job Scheduling with Fault Tolerance in Grid Computing using Ant Colony Optimization is proposed to ensure that jobs are executed successfully even when resource failure has occurred. The technique employed in this paper, is the use of resource failure rate, as well as checkpoint-based roll back recovery strategy. Check-pointing aims at reducing the amount of work that is lost upon failure of the system by immediately saving the state of the system. A comparison of the proposed approach with an existing Ant Colony Optimization (ACO algorithm is discussed. The experimental results of the implemented Fault Tolerance scheduling algorithm show that there is an improvement in the user's QoS requirement over the existing ACO algorithm, which has no fault tolerance integrated in it. The performance evaluation of the two algorithms was measured in terms of the three main scheduling performance metrics: makespan, throughput and average turnaround time.

Exploring with PAM: Prospecting ANTS Missions for Solar System Surveys

Science.gov (United States)

Clark, P. E.; Rilee, M. L.; Curtis, S. A.

2003-01-01

ANTS (Autonomous Nano-Technology Swarm), a large (1000 member) swarm of nano to picoclass (10 to 1 kg) totally autonomous spacecraft, are being developed as a NASA advanced mission concept. ANTS, based on a hierarchical insect social order, use an evolvable, self-similar, hierarchical neural system in which individual spacecraft represent the highest level nodes. ANTS uses swarm intelligence attained through collective, cooperative interactions of the nodes at all levels of the system. At the highest levels this can take the form of cooperative, collective behavior among the individual spacecraft in a very large constellation. The ANTS neural architecture is designed for totally autonomous operation of complex systems including spacecraft constellations. The ANTS (Autonomous Nano Technology Swarm) concept has a number of possible applications. A version of ANTS designed for surveying and determining the resource potential of the asteroid belt, called PAM (Prospecting ANTS Mission), is examined here.

Using ant-behavior-based simulation model AntWeb to improve website organization

Science.gov (United States)

Li, Weigang; Pinheiro Dib, Marcos V.; Teles, Wesley M.; Morais de Andrade, Vlaudemir; Alves de Melo, Alba C. M.; Cariolano, Judas T.

2002-03-01

Some web usage mining algorithms showed the potential application to find the difference among the organizations expected by visitors to the website. However, there are still no efficient method and criterion for a web administrator to measure the performance of the modification. In this paper, we developed an AntWeb, a model inspired by ants' behavior to simulate the sequence of visiting the website, in order to measure the efficient of the web structure. We implemented a web usage mining algorithm using backtrack to the intranet website of the Politec Informatic Ltd., Brazil. We defined throughput (the number of visitors to reach their target pages per time unit relates to the total number of visitors) as an index to measure the website's performance. We also used the link in a web page to represent the effect of visitors' pheromone trails. For every modification in the website organization, for example, putting a link from the expected location to the target object, the simulation reported the value of throughput as a quick answer about this modification. The experiment showed the stability of our simulation model, and a positive modification to the intranet website of the Politec.

Multi-criteria ACO-based Algorithm for Ship’s Trajectory Planning

Directory of Open Access Journals (Sweden)

Agnieszka Lazarowska

2017-03-01

Full Text Available The paper presents a new approach for solving a path planning problem for ships in the environment with static and dynamic obstacles. The algorithm utilizes a heuristic method, classified to the group of Swarm Intelligence approaches, called the Ant Colony Optimization. The method is inspired by a collective behaviour of ant colonies. A group of agents - artificial ants searches through the solution space in order to find a safe, optimal trajectory for a ship. The problem is considered as a multi-criteria optimization task. The criteria taken into account during problem solving are: path safety, path length, the International Regulations for Preventing Collisions at Sea (COLREGs compliance and path smoothness. The paper includes the description of the new multi-criteria ACO-based algorithm along with the presentation and discussion of simulation tests results.

Ants show a leftward turning bias when exploring unknown nest sites.

Science.gov (United States)

Hunt, Edmund R; O'Shea-Wheller, Thomas; Albery, Gregory F; Bridger, Tamsyn H; Gumn, Mike; Franks, Nigel R

2014-12-01

Behavioural lateralization in invertebrates is an important field of study because it may provide insights into the early origins of lateralization seen in a diversity of organisms. Here, we present evidence for a leftward turning bias in Temnothorax albipennis ants exploring nest cavities and in branching mazes, where the bias is initially obscured by thigmotaxis (wall-following) behaviour. Forward travel with a consistent turning bias in either direction is an effective nest exploration method, and a simple decision-making heuristic to employ when faced with multiple directional choices. Replication of the same bias at the colony level would also reduce individual predation risk through aggregation effects, and may lead to a faster attainment of a quorum threshold for nest migration. We suggest the turning bias may be the result of an evolutionary interplay between vision, exploration and migration factors, promoted by the ants' eusociality.

Discrete bacteria foraging optimization algorithm for graph based problems - a transition from continuous to discrete

Science.gov (United States)

Sur, Chiranjib; Shukla, Anupam

2018-03-01

Bacteria Foraging Optimisation Algorithm is a collective behaviour-based meta-heuristics searching depending on the social influence of the bacteria co-agents in the search space of the problem. The algorithm faces tremendous hindrance in terms of its application for discrete problems and graph-based problems due to biased mathematical modelling and dynamic structure of the algorithm. This had been the key factor to revive and introduce the discrete form called Discrete Bacteria Foraging Optimisation (DBFO) Algorithm for discrete problems which exceeds the number of continuous domain problems represented by mathematical and numerical equations in real life. In this work, we have mainly simulated a graph-based road multi-objective optimisation problem and have discussed the prospect of its utilisation in other similar optimisation problems and graph-based problems. The various solution representations that can be handled by this DBFO has also been discussed. The implications and dynamics of the various parameters used in the DBFO are illustrated from the point view of the problems and has been a combination of both exploration and exploitation. The result of DBFO has been compared with Ant Colony Optimisation and Intelligent Water Drops Algorithms. Important features of DBFO are that the bacteria agents do not depend on the local heuristic information but estimates new exploration schemes depending upon the previous experience and covered path analysis. This makes the algorithm better in combination generation for graph-based problems and combination generation for NP hard problems.

Edge detection in digital images using Ant Colony Optimization

Directory of Open Access Journals (Sweden)

Marjan Kuchaki Rafsanjani

2015-11-01

Full Text Available Ant Colony Optimization (ACO is an optimization algorithm inspired by the behavior of real ant colonies to approximate the solutions of difficult optimization problems. In this paper, ACO is introduced to tackle the image edge detection problem. The proposed approach is based on the distribution of ants on an image; ants try to find possible edges by using a state transition function. Experimental results show that the proposed method compared to standard edge detectors is less sensitive to Gaussian noise and gives finer details and thinner edges when compared to earlier ant-based approaches.

Ant Colony Clustering Algorithm and Improved Markov Random Fusion Algorithm in Image Segmentation of Brain Images

Directory of Open Access Journals (Sweden)

Guohua Zou

2016-12-01

Full Text Available New medical imaging technology, such as Computed Tomography and Magnetic Resonance Imaging (MRI, has been widely used in all aspects of medical diagnosis. The purpose of these imaging techniques is to obtain various qualitative and quantitative data of the patient comprehensively and accurately, and provide correct digital information for diagnosis, treatment planning and evaluation after surgery. MR has a good imaging diagnostic advantage for brain diseases. However, as the requirements of the brain image definition and quantitative analysis are always increasing, it is necessary to have better segmentation of MR brain images. The FCM (Fuzzy C-means algorithm is widely applied in image segmentation, but it has some shortcomings, such as long computation time and poor anti-noise capability. In this paper, firstly, the Ant Colony algorithm is used to determine the cluster centers and the number of FCM algorithm so as to improve its running speed. Then an improved Markov random field model is used to improve the algorithm, so that its antinoise ability can be improved. Experimental results show that the algorithm put forward in this paper has obvious advantages in image segmentation speed and segmentation effect.

Gas ultrasonic flow rate measurement through genetic-ant colony optimization based on the ultrasonic pulse received signal model

Science.gov (United States)

Hou, Huirang; Zheng, Dandan; Nie, Laixiao

2015-04-01

For gas ultrasonic flowmeters, the signals received by ultrasonic sensors are susceptible to noise interference. If signals are mingled with noise, a large error in flow measurement can be caused by triggering mistakenly using the traditional double-threshold method. To solve this problem, genetic-ant colony optimization (GACO) based on the ultrasonic pulse received signal model is proposed. Furthermore, in consideration of the real-time performance of the flow measurement system, the improvement of processing only the first three cycles of the received signals rather than the whole signal is proposed. Simulation results show that the GACO algorithm has the best estimation accuracy and ant-noise ability compared with the genetic algorithm, ant colony optimization, double-threshold and enveloped zero-crossing. Local convergence doesn’t appear with the GACO algorithm until -10 dB. For the GACO algorithm, the converging accuracy and converging speed and the amount of computation are further improved when using the first three cycles (called GACO-3cycles). Experimental results involving actual received signals show that the accuracy of single-gas ultrasonic flow rate measurement can reach 0.5% with GACO-3 cycles, which is better than with the double-threshold method.

PinaColada: peptide-inhibitor ant colony ad-hoc design algorithm.

Science.gov (United States)

Zaidman, Daniel; Wolfson, Haim J

2016-08-01

Design of protein-protein interaction (PPI) inhibitors is a major challenge in Structural Bioinformatics. Peptides, especially short ones (5-15 amino acid long), are natural candidates for inhibition of protein-protein complexes due to several attractive features such as high structural compatibility with the protein binding site (mimicking the surface of one of the proteins), small size and the ability to form strong hotspot binding connections with the protein surface. Efficient rational peptide design is still a major challenge in computer aided drug design, due to the huge space of possible sequences, which is exponential in the length of the peptide, and the high flexibility of peptide conformations. In this article we present PinaColada, a novel computational method for the design of peptide inhibitors for protein-protein interactions. We employ a version of the ant colony optimization heuristic, which is used to explore the exponential space ([Formula: see text]) of length n peptide sequences, in combination with our fast robotics motivated PepCrawler algorithm, which explores the conformational space for each candidate sequence. PinaColada is being run in parallel, on a DELL PowerEdge 2.8 GHZ computer with 20 cores and 256 GB memory, and takes up to 24 h to design a peptide of 5-15 amino acids length. An online server available at: http://bioinfo3d.cs.tau.ac.il/PinaColada/. danielza@post.tau.ac.il; wolfson@tau.ac.il. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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...... that give us a more detailed impression of the 1-ANT’s performance. Furthermore, the experiments also deal with the question whether using many ant solutions in one iteration can decrease the total runtime....

Automatic fault extraction using a modified ant-colony algorithm

International Nuclear Information System (INIS)

Zhao, Junsheng; Sun, Sam Zandong

2013-01-01

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)

ANTS/PAM: Future Exploration of the Asteroid Belt

Science.gov (United States)

Clark, P. E.; Curtis, S. A.; Rilee, M. L.; Cheung, C. Y.

2004-05-01

The Autonomous Nano-Technology Swarm (ANTS) is applied to the Prospecting Asteroid Mission (PAM) concept, as part of a NASA RASC study. The ANTS architecture is inspired by success of social insect colonies, based on the division of labor within the colonies: 1) within their specialties, individual specialists generally outperform general-ists, and 2) with sufficiently efficient social interaction and coordination, the group of specialists generally outper-forms the group of generalists. ANTS as applied to PAM involves a thousand individual specialist `sciencecraft', one subswarm per target, in an environment where detection and tracking of irregular, infrequent targets is a major chal-lenge. Workers, carry and operate eight to nine different scientific instruments, including spectrometers, ranging and radio science devices, imagers. The remaining specialists, Messenger/Rulers, provide communication and coordina-tion. The non-expendable propulsion system is based on autonomously deployable and configurable solar sails, a system suitable to a low gravity environment. The design of the neural basis function requires a minimum of 4 or 5 specialists for collective decision making. Allowing for ten instrument specialist teams and compensating for antici-pated high attrition, we calculate an initial minimum of 100 per subswarm should allow characterization of hundreds of asteroids. The difficulty in observing irregular, rapidly moving, poorly illuminated objects is largely overcome by the ANT sciencecraft capability to optimize conditions for each instrument. Components are composed of carbon nanotubules reversibly deployable from NEMS nodes, allowing 100 times decrease in packaging volume. 1000 smart 10 centimeter, 1 kg cubic boxes create a 1000 kg 1 meter cube.

Case series of ante-grade biliary stenting: An option during bile duct exploration

Directory of Open Access Journals (Sweden)

Qaiser Jalal

Full Text Available Background: Managing choledochotomy after bile duct clearance is an ongoing debate. T-tube insertion is not without complication and morbidity, requires significant post-operative care. Primary closure alone can result in a high pressure biliary system and bile leak. The placement of an ante-grade stent through the choledochotomy prior to primary closure is an option for ensuring biliary drainage after bile duct exploration. We reviewed our series of open bile duct explorations, where an ante-grade stent was placed when managing choledochotomy. Methods: Patients who had ante-grade stent placement, all performed by same senior hepatobiliary surgeon, were identified retrospectively. Case note review was used to gather demographic, complication, length of stay, post-operative clinic visits and readmission data. Results: 22 (M:F, 7:15 patients with a median age of 64 years (22–82. The indication for surgical stone clearance was failed ERCP in 20.2 patients were not suitable for ERCP. The median post-operative stay was 8 days (379 with the abdominal drain remaining for a median of 4 days (137. 16 (73% patients had no complications. 4 (18% had bile leaks, 5 (22% wound infections, 1 (5% cholangitis and 1 (5% pancreatitis. All complications were Clavien-Dindo grade 3 or less. Conclusion: In situations where primary CBD closure is not safe due to concern over high pressure in the biliary tree the placement of ante-grade stent may be preferred to T-tube placement. Keywords: Choledocholithiasis, Ante-grade stenting, Choledochotomy

Autonomous Agents on Expedition: Humans and Progenitor Ants and Planetary Exploration

Science.gov (United States)

Rilee, M. L.; Clark, P. E.; Curtis, S. A.; Truszkowski, W. F.

2002-01-01

The Autonomous Nano-Technology Swarm (ANTS) is an advanced mission architecture based on a social insect analog of many specialized spacecraft working together to achieve mission goals. The principal mission concept driving the ANTS architecture is a Main Belt Asteroid Survey in the 2020s that will involve a thousand or more nano-technology enabled, artificially intelligent, autonomous pico-spacecraft (architecture. High level, mission-oriented behaviors are to be managed by a control / communications layer of the swarm, whereas common low level functions required of all spacecraft, e.g. attitude control and guidance and navigation, are handled autonomically on each spacecraft. At the higher levels of mission planning and social interaction deliberative techniques are to be used. For the asteroid survey, ANTS acts as a large community of cooperative agents while for precursor missions there arises the intriguing possibility of Progenitor ANTS and humans acting together as agents. For optimal efficiency and responsiveness for individual spacecraft at the lowest levels of control we have been studying control methods based on nonlinear dynamical systems. We describe the critically important autonomous control architecture of the ANTS mission concept and a sequence of partial implementations that feature increasingly autonomous behaviors. The scientific and engineering roles that these Progenitor ANTS could play in human missions or remote missions with near real time human interactions, particularly to the Moon and Mars, will be discussed.

Gas ultrasonic flow rate measurement through genetic-ant colony optimization based on the ultrasonic pulse received signal model

International Nuclear Information System (INIS)

Hou, Huirang; Zheng, Dandan; Nie, Laixiao

2015-01-01

For gas ultrasonic flowmeters, the signals received by ultrasonic sensors are susceptible to noise interference. If signals are mingled with noise, a large error in flow measurement can be caused by triggering mistakenly using the traditional double-threshold method. To solve this problem, genetic-ant colony optimization (GACO) based on the ultrasonic pulse received signal model is proposed. Furthermore, in consideration of the real-time performance of the flow measurement system, the improvement of processing only the first three cycles of the received signals rather than the whole signal is proposed. Simulation results show that the GACO algorithm has the best estimation accuracy and ant-noise ability compared with the genetic algorithm, ant colony optimization, double-threshold and enveloped zero-crossing. Local convergence doesn’t appear with the GACO algorithm until –10 dB. For the GACO algorithm, the converging accuracy and converging speed and the amount of computation are further improved when using the first three cycles (called GACO-3cycles). Experimental results involving actual received signals show that the accuracy of single-gas ultrasonic flow rate measurement can reach 0.5% with GACO-3 cycles, which is better than with the double-threshold method. (paper)

Application of chaotic ant swarm optimization in electric load forecasting

International Nuclear Information System (INIS)

Hong, W.-C.

2010-01-01

Support vector regression (SVR) had revealed strong potential in accurate electric load forecasting, particularly by employing effective evolutionary algorithms to determine suitable values of its three parameters. Based on previous research results, however, these employed evolutionary algorithms themselves have several drawbacks, such as converging prematurely, reaching slowly the global optimal solution, and trapping into a local optimum. This investigation presents an SVR-based electric load forecasting model that applied a novel algorithm, namely chaotic ant swarm optimization (CAS), to improve the forecasting performance by searching its suitable parameters combination. The proposed CAS combines with the chaotic behavior of single ant and self-organization behavior of ant colony in the foraging process to overcome premature local optimum. The empirical results indicate that the SVR model with CAS (SVRCAS) results in better forecasting performance than the other alternative methods, namely SVRCPSO (SVR with chaotic PSO), SVRCGA (SVR with chaotic GA), regression model, and ANN model.

A Novel Path Planning for Robots Based on Rapidly-Exploring Random Tree and Particle Swarm Optimizer Algorithm

Directory of Open Access Journals (Sweden)

Zhou Feng

2013-09-01

Full Text Available A based on Rapidly-exploring Random Tree(RRT and Particle Swarm Optimizer (PSO for path planning of the robot is proposed.First the grid method is built to describe the working space of the mobile robot,then the Rapidly-exploring Random Tree algorithm is used to obtain the global navigation path,and the Particle Swarm Optimizer algorithm is adopted to get the better path.Computer experiment results demonstrate that this novel algorithm can plan an optimal path rapidly in a cluttered environment.The successful obstacle avoidance is achieved,and the model is robust and performs reliably.

A Novel Quad Harmony Search Algorithm for Grid-Based Path Finding

Directory of Open Access Journals (Sweden)

Saso Koceski

2014-09-01

Full Text Available A novel approach to the problem of grid-based path finding has been introduced. The method is a block-based search algorithm, founded on the bases of two algorithms, namely the quad-tree algorithm, which offered a great opportunity for decreasing the time needed to compute the solution, and the harmony search (HS algorithm, a meta-heuristic algorithm used to obtain the optimal solution. This quad HS algorithm uses the quad-tree decomposition of free space in the grid to mark the free areas and treat them as a single node, which greatly improves the execution. The results of the quad HS algorithm have been compared to other meta-heuristic algorithms, i.e., ant colony, genetic algorithm, particle swarm optimization and simulated annealing, and it was proved to obtain the best results in terms of time and giving the optimal path.

Software Piracy Detection Model Using Ant Colony Optimization Algorithm

Science.gov (United States)

Astiqah Omar, Nor; Zakuan, Zeti Zuryani Mohd; Saian, Rizauddin

2017-06-01

Internet enables information to be accessible anytime and anywhere. This scenario creates an environment whereby information can be easily copied. Easy access to the internet is one of the factors which contribute towards piracy in Malaysia as well as the rest of the world. According to a survey conducted by Compliance Gap BSA Global Software Survey in 2013 on software piracy, found out that 43 percent of the software installed on PCs around the world was not properly licensed, the commercial value of the unlicensed installations worldwide was reported to be 62.7 billion. Piracy can happen anywhere including universities. Malaysia as well as other countries in the world is faced with issues of piracy committed by the students in universities. Piracy in universities concern about acts of stealing intellectual property. It can be in the form of software piracy, music piracy, movies piracy and piracy of intellectual materials such as books, articles and journals. This scenario affected the owner of intellectual property as their property is in jeopardy. This study has developed a classification model for detecting software piracy. The model was developed using a swarm intelligence algorithm called the Ant Colony Optimization algorithm. The data for training was collected by a study conducted in Universiti Teknologi MARA (Perlis). Experimental results show that the model detection accuracy rate is better as compared to J48 algorithm.

Inverse estimation of the spheroidal particle size distribution using Ant Colony Optimization algorithms in multispectral extinction technique

Science.gov (United States)

He, Zhenzong; Qi, Hong; Wang, Yuqing; Ruan, Liming

2014-10-01

Four improved Ant Colony Optimization (ACO) algorithms, i.e. the probability density function based ACO (PDF-ACO) algorithm, the Region ACO (RACO) algorithm, Stochastic ACO (SACO) algorithm and Homogeneous ACO (HACO) algorithm, are employed to estimate the particle size distribution (PSD) of the spheroidal particles. The direct problems are solved by the extended Anomalous Diffraction Approximation (ADA) and the Lambert-Beer law. Three commonly used monomodal distribution functions i.e. the Rosin-Rammer (R-R) distribution function, the normal (N-N) distribution function, and the logarithmic normal (L-N) distribution function are estimated under dependent model. The influence of random measurement errors on the inverse results is also investigated. All the results reveal that the PDF-ACO algorithm is more accurate than the other three ACO algorithms and can be used as an effective technique to investigate the PSD of the spheroidal particles. Furthermore, the Johnson's SB (J-SB) function and the modified beta (M-β) function are employed as the general distribution functions to retrieve the PSD of spheroidal particles using PDF-ACO algorithm. The investigation shows a reasonable agreement between the original distribution function and the general distribution function when only considering the variety of the length of the rotational semi-axis.

VLSI PARTITIONING ALGORITHM WITH ADAPTIVE CONTROL PARAMETER

Directory of Open Access Journals (Sweden)

P. N. Filippenko

2013-03-01

Full Text Available The article deals with the problem of very large-scale integration circuit partitioning. A graph is selected as a mathematical model describing integrated circuit. Modification of ant colony optimization algorithm is presented, which is used to solve graph partitioning problem. Ant colony optimization algorithm is an optimization method based on the principles of self-organization and other useful features of the ants’ behavior. The proposed search system is based on ant colony optimization algorithm with the improved method of the initial distribution and dynamic adjustment of the control search parameters. The experimental results and performance comparison show that the proposed method of very large-scale integration circuit partitioning provides the better search performance over other well known algorithms.

Hybrid Optimization of Object-Based Classification in High-Resolution Images Using Continous ANT Colony Algorithm with Emphasis on Building Detection

Science.gov (United States)

Tamimi, E.; Ebadi, H.; Kiani, A.

2017-09-01

Automatic building detection from High Spatial Resolution (HSR) images is one of the most important issues in Remote Sensing (RS). Due to the limited number of spectral bands in HSR images, using other features will lead to improve accuracy. By adding these features, the presence probability of dependent features will be increased, which leads to accuracy reduction. In addition, some parameters should be determined in Support Vector Machine (SVM) classification. Therefore, it is necessary to simultaneously determine classification parameters and select independent features according to image type. Optimization algorithm is an efficient method to solve this problem. On the other hand, pixel-based classification faces several challenges such as producing salt-paper results and high computational time in high dimensional data. Hence, in this paper, a novel method is proposed to optimize object-based SVM classification by applying continuous Ant Colony Optimization (ACO) algorithm. The advantages of the proposed method are relatively high automation level, independency of image scene and type, post processing reduction for building edge reconstruction and accuracy improvement. The proposed method was evaluated by pixel-based SVM and Random Forest (RF) classification in terms of accuracy. In comparison with optimized pixel-based SVM classification, the results showed that the proposed method improved quality factor and overall accuracy by 17% and 10%, respectively. Also, in the proposed method, Kappa coefficient was improved by 6% rather than RF classification. Time processing of the proposed method was relatively low because of unit of image analysis (image object). These showed the superiority of the proposed method in terms of time and accuracy.

Energy Efficient Routing in Wireless Sensor Networks based on Ant ...

African Journals Online (AJOL)

PROF. O. E. OSUAGWU

2013-09-01

Sep 1, 2013 ... improved Ant System and their application in WSN routing process. The simulation results show ... and Mobile ad-hoc networks (MANETs) are inappropriate for ... Dorigo in 1992 in his PhD thesis, the first algorithm was aiming ...

A flooding algorithm for multirobot exploration.

Science.gov (United States)

Cabrera-Mora, Flavio; Xiao, Jizhong

2012-06-01

In this paper, we present a multirobot exploration algorithm that aims at reducing the exploration time and to minimize the overall traverse distance of the robots by coordinating the movement of the robots performing the exploration. Modeling the environment as a tree, we consider a coordination model that restricts the number of robots allowed to traverse an edge and to enter a vertex during each step. This coordination is achieved in a decentralized manner by the robots using a set of active landmarks that are dropped by them at explored vertices. We mathematically analyze the algorithm on trees, obtaining its main properties and specifying its bounds on the exploration time. We also define three metrics of performance for multirobot algorithms. We simulate and compare the performance of this new algorithm with those of our multirobot depth first search (MR-DFS) approach presented in our recent paper and classic single-robot DFS.

Ant colony optimization as a descriptor selection in QSPR modeling: Estimation of the λmax of anthraquinones-based dyes

OpenAIRE

Morteza Atabati; Kobra Zarei; Azam Borhani

2016-01-01

Quantitative structure–property relationship (QSPR) studies based on ant colony optimization (ACO) were carried out for the prediction of λmax of 9,10-anthraquinone derivatives. ACO is a meta-heuristic algorithm, which is derived from the observation of real ants and proposed to feature selection. After optimization of 3D geometry of structures by the semi-empirical quantum-chemical calculation at AM1 level, different descriptors were calculated by the HyperChem and Dragon softwares (1514 des...

Adaptive algorithms of position and energy reconstruction in Anger-camera type detectors: experimental data processing in ANTS

Energy Technology Data Exchange (ETDEWEB)

Morozov, A; Fraga, F A F; Fraga, M M F R; Margato, L M S; Pereira, L [LIP-Coimbra and Departamento de Física, Universidade de Coimbra, Rua Larga, Coimbra (Portugal); Defendi, I; Jurkovic, M [Forschungs-Neutronenquelle Heinz Maier-Leibnitz (FRM II), TUM, Lichtenbergstr. 1, Garching (Germany); Engels, R; Kemmerling, G [Zentralinstitut für Elektronik, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich (Germany); Gongadze, A; Guerard, B; Manzin, G; Niko, H; Peyaud, A; Piscitelli, F [Institut Laue Langevin, 6 Rue Jules Horowitz, Grenoble (France); Petrillo, C; Sacchetti, F [Istituto Nazionale per la Fisica della Materia, Unità di Perugia, Via A. Pascoli, Perugia (Italy); Raspino, D; Rhodes, N J; Schooneveld, E M, E-mail: andrei@coimbra.lip.pt [Science and Technology Facilities Council, Rutherford Appleton Laboratory, Harwell Oxford, Didcot (United Kingdom); others, and

2013-05-01

The software package ANTS (Anger-camera type Neutron detector: Toolkit for Simulations), developed for simulation of Anger-type gaseous detectors for thermal neutron imaging was extended to include a module for experimental data processing. Data recorded with a sensor array containing up to 100 photomultiplier tubes (PMT) or silicon photomultipliers (SiPM) in a custom configuration can be loaded and the positions and energies of the events can be reconstructed using the Center-of-Gravity, Maximum Likelihood or Least Squares algorithm. A particular strength of the new module is the ability to reconstruct the light response functions and relative gains of the photomultipliers from flood field illumination data using adaptive algorithms. The performance of the module is demonstrated with simulated data generated in ANTS and experimental data recorded with a 19 PMT neutron detector. The package executables are publicly available at http://coimbra.lip.pt/∼andrei/.

A Hybrid Ant Colony Optimization Algorithm for the Extended Capacitated Arc Routing Problem.

Science.gov (United States)

Li-Ning Xing; Rohlfshagen, P; Ying-Wu Chen; Xin Yao

2011-08-01

The capacitated arc routing problem (CARP) is representative of numerous practical applications, and in order to widen its scope, we consider an extended version of this problem that entails both total service time and fixed investment costs. We subsequently propose a hybrid ant colony optimization (ACO) algorithm (HACOA) to solve instances of the extended CARP. This approach is characterized by the exploitation of heuristic information, adaptive parameters, and local optimization techniques: Two kinds of heuristic information, arc cluster information and arc priority information, are obtained continuously from the solutions sampled to guide the subsequent optimization process. The adaptive parameters ease the burden of choosing initial values and facilitate improved and more robust results. Finally, local optimization, based on the two-opt heuristic, is employed to improve the overall performance of the proposed algorithm. The resulting HACOA is tested on four sets of benchmark problems containing a total of 87 instances with up to 140 nodes and 380 arcs. In order to evaluate the effectiveness of the proposed method, some existing capacitated arc routing heuristics are extended to cope with the extended version of this problem; the experimental results indicate that the proposed ACO method outperforms these heuristics.

Proposed algorithm to improve job shop production scheduling using ant colony optimization method

Science.gov (United States)

Pakpahan, Eka KA; Kristina, Sonna; Setiawan, Ari

2017-12-01

This paper deals with the determination of job shop production schedule on an automatic environment. On this particular environment, machines and material handling system are integrated and controlled by a computer center where schedule were created and then used to dictate the movement of parts and the operations at each machine. This setting is usually designed to have an unmanned production process for a specified interval time. We consider here parts with various operations requirement. Each operation requires specific cutting tools. These parts are to be scheduled on machines each having identical capability, meaning that each machine is equipped with a similar set of cutting tools therefore is capable of processing any operation. The availability of a particular machine to process a particular operation is determined by the remaining life time of its cutting tools. We proposed an algorithm based on the ant colony optimization method and embedded them on matlab software to generate production schedule which minimize the total processing time of the parts (makespan). We test the algorithm on data provided by real industry and the process shows a very short computation time. This contributes a lot to the flexibility and timelines targeted on an automatic environment.

Integrating fuzzy object based image analysis and ant colony optimization for road extraction from remotely sensed images

Science.gov (United States)

Maboudi, Mehdi; Amini, Jalal; Malihi, Shirin; Hahn, Michael

2018-04-01

Updated road network as a crucial part of the transportation database plays an important role in various applications. Thus, increasing the automation of the road extraction approaches from remote sensing images has been the subject of extensive research. In this paper, we propose an object based road extraction approach from very high resolution satellite images. Based on the object based image analysis, our approach incorporates various spatial, spectral, and textural objects' descriptors, the capabilities of the fuzzy logic system for handling the uncertainties in road modelling, and the effectiveness and suitability of ant colony algorithm for optimization of network related problems. Four VHR optical satellite images which are acquired by Worldview-2 and IKONOS satellites are used in order to evaluate the proposed approach. Evaluation of the extracted road networks shows that the average completeness, correctness, and quality of the results can reach 89%, 93% and 83% respectively, indicating that the proposed approach is applicable for urban road extraction. We also analyzed the sensitivity of our algorithm to different ant colony optimization parameter values. Comparison of the achieved results with the results of four state-of-the-art algorithms and quantifying the robustness of the fuzzy rule set demonstrate that the proposed approach is both efficient and transferable to other comparable images.

HYBRID OPTIMIZATION OF OBJECT-BASED CLASSIFICATION IN HIGH-RESOLUTION IMAGES USING CONTINOUS ANT COLONY ALGORITHM WITH EMPHASIS ON BUILDING DETECTION

Directory of Open Access Journals (Sweden)

E. Tamimi

2017-09-01

Full Text Available Automatic building detection from High Spatial Resolution (HSR images is one of the most important issues in Remote Sensing (RS. Due to the limited number of spectral bands in HSR images, using other features will lead to improve accuracy. By adding these features, the presence probability of dependent features will be increased, which leads to accuracy reduction. In addition, some parameters should be determined in Support Vector Machine (SVM classification. Therefore, it is necessary to simultaneously determine classification parameters and select independent features according to image type. Optimization algorithm is an efficient method to solve this problem. On the other hand, pixel-based classification faces several challenges such as producing salt-paper results and high computational time in high dimensional data. Hence, in this paper, a novel method is proposed to optimize object-based SVM classification by applying continuous Ant Colony Optimization (ACO algorithm. The advantages of the proposed method are relatively high automation level, independency of image scene and type, post processing reduction for building edge reconstruction and accuracy improvement. The proposed method was evaluated by pixel-based SVM and Random Forest (RF classification in terms of accuracy. In comparison with optimized pixel-based SVM classification, the results showed that the proposed method improved quality factor and overall accuracy by 17% and 10%, respectively. Also, in the proposed method, Kappa coefficient was improved by 6% rather than RF classification. Time processing of the proposed method was relatively low because of unit of image analysis (image object. These showed the superiority of the proposed method in terms of time and accuracy.

Novel Rock Detection Intelligence for Space Exploration Based on Non-Symbolic Algorithms and Concepts

Science.gov (United States)

Yildirim, Sule; Beachell, Ronald L.; Veflingstad, Henning

2007-01-01

Future space exploration can utilize artificial intelligence as an integral part of next generation space rover technology to make the rovers more autonomous in performing mission objectives. The main advantage of the increased autonomy through a higher degree of intelligence is that it allows for greater utilization of rover resources by reducing the frequency of time consuming communications between rover and earth. In this paper, we propose a space exploration application of our research on a non-symbolic algorithm and concepts model. This model is based on one of the most recent approaches of cognitive science and artificial intelligence research, a parallel distributed processing approach. We use the Mars rovers. Sprit and Opportunity, as a starting point for proposing what rovers in the future could do if the presented model of non-symbolic algorithms and concepts is embedded in a future space rover. The chosen space exploration application for this paper, novel rock detection, is only one of many potential space exploration applications which can be optimized (through reduction of the frequency of rover-earth communications. collection and transmission of only data that is distinctive/novel) through the use of artificial intelligence technology compared to existing approaches.

A Flocking Based algorithm for Document Clustering Analysis

Energy Technology Data Exchange (ETDEWEB)

Cui, Xiaohui [ORNL; Gao, Jinzhu [ORNL; Potok, Thomas E [ORNL

2006-01-01

Social animals or insects in nature often exhibit a form of emergent collective behavior known as flocking. In this paper, we present a novel Flocking based approach for document clustering analysis. Our Flocking clustering algorithm uses stochastic and heuristic principles discovered from observing bird flocks or fish schools. Unlike other partition clustering algorithm such as K-means, the Flocking based algorithm does not require initial partitional seeds. The algorithm generates a clustering of a given set of data through the embedding of the high-dimensional data items on a two-dimensional grid for easy clustering result retrieval and visualization. Inspired by the self-organized behavior of bird flocks, we represent each document object with a flock boid. The simple local rules followed by each flock boid result in the entire document flock generating complex global behaviors, which eventually result in a clustering of the documents. We evaluate the efficiency of our algorithm with both a synthetic dataset and a real document collection that includes 100 news articles collected from the Internet. Our results show that the Flocking clustering algorithm achieves better performance compared to the K- means and the Ant clustering algorithm for real document clustering.

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

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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.

X-Ray microtomography for ant taxonomy: An exploration and case study with two new Terataner (Hymenoptera, Formicidae, Myrmicinae species from Madagascar.

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Francisco Hita Garcia

Full Text Available We explore the potential of x-ray micro computed tomography (μCT for the field of ant taxonomy by using it to enhance the descriptions of two remarkable new species of the ant genus Terataner: T. balrog sp. n. and T. nymeria sp. n.. We provide an illustrated worker-based species identification key for all species found on Madagascar, as well as detailed taxonomic descriptions, which include diagnoses, discussions, measurements, natural history data, high-quality montage images and distribution maps for both new species. In addition to conventional morphological examination, we have used virtual reconstructions based on volumetric μCT scanning data for the species descriptions. We also include 3D PDFs, still images of virtual reconstructions, and 3D rotation videos for both holotype workers and one paratype queen. The complete μCT datasets have been made available online (Dryad, https://datadryad.org and represent the first cybertypes in ants (and insects. We discuss the potential of μCT scanning and critically assess the usefulness of cybertypes for ant taxonomy.

Ant-based extraction of rules in simple decision systems over ontological graphs

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Pancerz Krzysztof

2015-06-01

Full Text Available In the paper, the problem of extraction of complex decision rules in simple decision systems over ontological graphs is considered. The extracted rules are consistent with the dominance principle similar to that applied in the dominancebased rough set approach (DRSA. In our study, we propose to use a heuristic algorithm, utilizing the ant-based clustering approach, searching the semantic spaces of concepts presented by means of ontological graphs. Concepts included in the semantic spaces are values of attributes describing objects in simple decision systems

DESIGNING APPLICATION OF ANT COLONY SYSTEM ALGORITHM FOR THE SHORTEST ROUTE OF BANDA ACEH CITY AND ACEH BESAR REGENCY TOURISM BY USING GRAPHICAL USER INTERFACE MATLAB

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Durisman Durisman

2017-09-01

Full Text Available Banda Aceh city and Aceh Besar Regency are two of the leading tourism areas located in the province of Aceh. For travelling, there are some important things to be considered, such as determining schedule and distance of tourism. Every tourist certainly chooses the shortest route to reach the destination since it can save time, energy, and money. The purpose of this reserach is to develop a method that can be used in calculating the shortest route and applied to the tourism of Banda Aceh city and Aceh Besar regency. In this reserach, Ant Colony Optimization algorithm is used to determine the shortest route to tourism of Banda Aceh city and Aceh Besar regency. From the analysis made by using both manual calculation and  GUI MATLAB program application test, the shortest route can be obtained with a minimum distance of 120.85 km in one travel. Based on the test result, the application for tourism (in Banda Aceh city and Aceh Besar regency shortest route searching built by utilizing the Ant Colony Optimization algorithm can find optimal route.  Keyword: tourism, the shortest route, Ant Colony Optimization

Ant Colony Optimization Algorithm for Centralized Dynamic Channel Allocation in Multi-Cell OFDMA Systems

Science.gov (United States)

Kim, Hyo-Su; Kim, Dong-Hoi

The dynamic channel allocation (DCA) scheme in multi-cell systems causes serious inter-cell interference (ICI) problem to some existing calls when channels for new calls are allocated. Such a problem can be addressed by advanced centralized DCA design that is able to minimize ICI. Thus, in this paper, a centralized DCA is developed for the downlink of multi-cell orthogonal frequency division multiple access (OFDMA) systems with full spectral reuse. However, in practice, as the search space of channel assignment for centralized DCA scheme in multi-cell systems grows exponentially with the increase of the number of required calls, channels, and cells, it becomes an NP-hard problem and is currently too complicated to find an optimum channel allocation. In this paper, we propose an ant colony optimization (ACO) based DCA scheme using a low-complexity ACO algorithm which is a kind of heuristic algorithm in order to solve the aforementioned problem. Simulation results demonstrate significant performance improvements compared to the existing schemes in terms of the grade of service (GoS) performance and the forced termination probability of existing calls without degrading the system performance of the average throughput.

Comprehensive Evaluation for Operating Efficiency of Electricity Retail Companies Based on the Improved TOPSIS Method and LSSVM Optimized by Modified Ant Colony Algorithm from the View of Sustainable Development

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Dongxiao Niu

2018-03-01

Full Text Available The electricity market of China is currently in the process of a new institutional reform. Diversified electricity retail entities are gradually being established with the opening of the marketing electricity side. In the face of a complex market environment and fierce competition, the operating efficiency can directly reflect the current market position and development of electricity retail companies. TOPSIS method can make full use of the information of original data, calculate the distance between evaluated objects and the ideal solutions and get the relative proximity, which is generally used in the overall department and comprehensive evaluation of the benefits. Least squares support vector machine (LSSVM, with high convergence precision, helps save the training time of algorithm by solving linear equations and is used to predict the comprehensive evaluation value. Considering the ultimate goal of sustainable development, a comprehensive evaluation model on operating efficiency of electricity retail companies based on the improved TOPSIS method and LSSVM optimized by modified ant colony algorithm is proposed in this paper. Firstly, from the view of sustainable development, an operating efficiency evaluation indicator system is constructed. Secondly, the entropy weight method is applied to empower the indicators objectively. After that, based on the improved TOPSIS method, the reverse problem in the evaluation process is eliminated. According to the relative proximity between the evaluated objects and the absolute ideal solutions, the scores of comprehensive evaluation for operating efficiency can then be ranked. Finally, the LSSVM optimized by modified ant colony algorithm is introduced to realize the simplified expert scoring process and fast calculation in the comprehensive evaluation process, and its improved learning and generalization ability can be used in the comprehensive evaluation of similar projects. The example analysis proves

PARAMETER ESTIMATION OF VALVE STICTION USING ANT COLONY OPTIMIZATION

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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.

Multi-criteria ACO-based Algorithm for Ship’s Trajectory Planning

OpenAIRE

Agnieszka Lazarowska

2017-01-01

The paper presents a new approach for solving a path planning problem for ships in the environment with static and dynamic obstacles. The algorithm utilizes a heuristic method, classified to the group of Swarm Intelligence approaches, called the Ant Colony Optimization. The method is inspired by a collective behaviour of ant colonies. A group of agents - artificial ants searches through the solution space in order to find a safe, optimal trajectory for a ship. The problem is considered as a ...

Optimum Layout for Water Quality Monitoring Stations through Ant Colony Algorithm

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Amin Afshar

2006-09-01

Full Text Available Due to the high cost of monitoring systems, budget limitations, and high priority given to water quality control in municipal networks, especially for unexpected events, optimum location of monitoring stations has received considerable attention during the last decade. An optimization model needs to be developed for the desirable location of monitoring stations. This research attempts to develop such a model using Ant Colony Optimization (ACO algorithm and tires to verify it through a bench-mark classical example used in previous researches. Selection of ACO as optimizer was fully justified due to discrete decision space and extensive number of binary variables in modeling system. Diversity of the policies derived from ACO may facilitate the process of decision making considering the social, physical, and economical conditions.

Tree-Dwelling Ants: Contrasting Two Brazilian Cerrado Plant Species without Extrafloral Nectaries

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Jonas Maravalhas

2012-01-01

Full Text Available Ants dominate vegetation stratum, exploiting resources like extrafloral nectaries (EFNs and insect honeydew. These interactions are frequent in Brazilian cerrado and are well known, but few studies compare ant fauna and explored resources between plant species. We surveyed two cerrado plants without EFNs, Roupala montana (found on preserved environments of our study area and Solanum lycocarpum (disturbed ones. Ants were collected and identified, and resources on each plant noted. Ant frequency and richness were higher on R. montana (67%; 35 spp than S. lycocarpum (52%; 26, the occurrence of the common ant species varied between them, and similarity was low. Resources were explored mainly by Camponotus crassus and consisted of scale insects, aphids, and floral nectaries on R. montana and two treehopper species on S. lycocarpum. Ants have a high diversity on cerrado plants, exploring liquid and prey-based resources that vary in time and space and affect their presence on plants.

Explorations of the implementation of a parallel IDW interpolation algorithm in a Linux cluster-based parallel GIS

Science.gov (United States)

Huang, Fang; Liu, Dingsheng; Tan, Xicheng; Wang, Jian; Chen, Yunping; He, Binbin

2011-04-01

To design and implement an open-source parallel GIS (OP-GIS) based on a Linux cluster, the parallel inverse distance weighting (IDW) interpolation algorithm has been chosen as an example to explore the working model and the principle of algorithm parallel pattern (APP), one of the parallelization patterns for OP-GIS. Based on an analysis of the serial IDW interpolation algorithm of GRASS GIS, this paper has proposed and designed a specific parallel IDW interpolation algorithm, incorporating both single process, multiple data (SPMD) and master/slave (M/S) programming modes. The main steps of the parallel IDW interpolation algorithm are: (1) the master node packages the related information, and then broadcasts it to the slave nodes; (2) each node calculates its assigned data extent along one row using the serial algorithm; (3) the master node gathers the data from all nodes; and (4) iterations continue until all rows have been processed, after which the results are outputted. According to the experiments performed in the course of this work, the parallel IDW interpolation algorithm can attain an efficiency greater than 0.93 compared with similar algorithms, which indicates that the parallel algorithm can greatly reduce processing time and maximize speed and performance.

The regulation of ant colony foraging activity without spatial information.

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Balaji Prabhakar

Full Text Available Many dynamical networks, such as the ones that produce the collective behavior of social insects, operate without any central control, instead arising from local interactions among individuals. A well-studied example is the formation of recruitment trails in ant colonies, but many ant species do not use pheromone trails. We present a model of the regulation of foraging by harvester ant (Pogonomyrmex barbatus colonies. This species forages for scattered seeds that one ant can retrieve on its own, so there is no need for spatial information such as pheromone trails that lead ants to specific locations. Previous work shows that colony foraging activity, the rate at which ants go out to search individually for seeds, is regulated in response to current food availability throughout the colony's foraging area. Ants use the rate of brief antennal contacts inside the nest between foragers returning with food and outgoing foragers available to leave the nest on the next foraging trip. Here we present a feedback-based algorithm that captures the main features of data from field experiments in which the rate of returning foragers was manipulated. The algorithm draws on our finding that the distribution of intervals between successive ants returning to the nest is a Poisson process. We fitted the parameter that estimates the effect of each returning forager on the rate at which outgoing foragers leave the nest. We found that correlations between observed rates of returning foragers and simulated rates of outgoing foragers, using our model, were similar to those in the data. Our simple stochastic model shows how the regulation of ant colony foraging can operate without spatial information, describing a process at the level of individual ants that predicts the overall foraging activity of the colony.

Individual-based ant-plant networks: diurnal-nocturnal structure and species-area relationship.

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Wesley Dáttilo

Full Text Available Despite the importance and increasing knowledge of ecological networks, sampling effort and intrapopulation variation has been widely overlooked. Using continuous daily sampling of ants visiting three plant species in the Brazilian Neotropical savanna, we evaluated for the first time the topological structure over 24 h and species-area relationships (based on the number of extrafloral nectaries available in individual-based ant-plant networks. We observed that diurnal and nocturnal ant-plant networks exhibited the same pattern of interactions: a nested and non-modular pattern and an average level of network specialization. Despite the high similarity in the ants' composition between the two collection periods, ant species found in the central core of highly interacting species totally changed between diurnal and nocturnal sampling for all plant species. In other words, this "night-turnover" suggests that the ecological dynamics of these ant-plant interactions can be temporally partitioned (day and night at a small spatial scale. Thus, it is possible that in some cases processes shaping mutualistic networks formed by protective ants and plants may be underestimated by diurnal sampling alone. Moreover, we did not observe any effect of the number of extrafloral nectaries on ant richness and their foraging on such plants in any of the studied ant-plant networks. We hypothesize that competitively superior ants could monopolize individual plants and allow the coexistence of only a few other ant species, however, other alternative hypotheses are also discussed. Thus, sampling period and species-area relationship produces basic information that increases our confidence in how individual-based ant-plant networks are structured, and the need to consider nocturnal records in ant-plant network sampling design so as to decrease inappropriate inferences.

Exploring agency beyond humans: the compatibility of Actor-Network Theory (ANT and resilience thinking

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Angga Dwiartama

2014-09-01

Full Text Available At first glance, the compatibility of social theory and resilience thinking is not entirely evident, in part because the ontology of the former is rooted in social interactions among human beings rather than ecological process. Despite this difference, resilience thinking engages with particular aspects of social organization that have generated intense debates within social science, namely the role of humans as integral elements of social-ecological systems and the processes through which given social structures (including material relations are either maintained or transformed. Among social theoretical approaches, Actor-Network Theory (ANT is noted for its distinctive approach to these aspects. ANT proposes that human and nonhuman components (both referred to as actants have the same capacity to influence the development of social-ecological systems (represented as actor-networks by enacting relations and enrolling other actors. We explore the notion of agency that is employed in resilience thinking and ANT in order to extend our understandings of human-environment relationships through complementary insights from each approach. The discussion is illustrated by reference to ongoing assessment of resilience as it is experienced and expressed in two distinctive agricultural production systems: Indonesian rice and New Zealand kiwifruit. We conclude by establishing the potential for ANT to provide more profound theoretical conceptualizations of agency, both human and nonhuman, in analyses of social ecological systems.

The organization of societal conflicts by pavement ants Tetramorium caespitum: an agent-based model of amine-mediated decision making.

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Hoover, Kevin M; Bubak, Andrew N; Law, Isaac J; Yaeger, Jazmine D W; Renner, Kenneth J; Swallow, John G; Greene, Michael J

2016-06-01

Ant colonies self-organize to solve complex problems despite the simplicity of an individual ant's brain. Pavement ant Tetramorium caespitum colonies must solve the problem of defending the territory that they patrol in search of energetically rich forage. When members of 2 colonies randomly interact at the territory boundary a decision to fight occurs when: 1) there is a mismatch in nestmate recognition cues and 2) each ant has a recent history of high interaction rates with nestmate ants. Instead of fighting, some ants will decide to recruit more workers from the nest to the fighting location, and in this way a positive feedback mediates the development of colony wide wars. In ants, the monoamines serotonin (5-HT) and octopamine (OA) modulate many behaviors associated with colony organization and in particular behaviors associated with nestmate recognition and aggression. In this article, we develop and explore an agent-based model that conceptualizes how individual changes in brain concentrations of 5-HT and OA, paired with a simple threshold-based decision rule, can lead to the development of colony wide warfare. Model simulations do lead to the development of warfare with 91% of ants fighting at the end of 1 h. When conducting a sensitivity analysis, we determined that uncertainty in monoamine concentration signal decay influences the behavior of the model more than uncertainty in the decision-making rule or density. We conclude that pavement ant behavior is consistent with the detection of interaction rate through a single timed interval rather than integration of multiple interactions.

Advanced Harmony Search with Ant Colony Optimization for Solving the Traveling Salesman Problem

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Ho-Yoeng Yun

2013-01-01

Full Text Available We propose a novel heuristic algorithm based on the methods of advanced Harmony Search and Ant Colony Optimization (AHS-ACO to effectively solve the Traveling Salesman Problem (TSP. The TSP, in general, is well known as an NP-complete problem, whose computational complexity increases exponentially by increasing the number of cities. In our algorithm, Ant Colony Optimization (ACO is used to search the local optimum in the solution space, followed by the use of the Harmony Search to escape the local optimum determined by the ACO and to move towards a global optimum. Experiments were performed to validate the efficiency of our algorithm through a comparison with other algorithms and the optimum solutions presented in the TSPLIB. The results indicate that our algorithm is capable of generating the optimum solution for most instances in the TSPLIB; moreover, our algorithm found better solutions in two cases (kroB100 and pr144 when compared with the optimum solution presented in the TSPLIB.

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

International Nuclear Information System (INIS)

Chrysostomou, Dimitrios; Gasteratos, Antonios; Nalpantidis, Lazaros; Sirakoulis, Georgios C

2012-01-01

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)

Search and optimization by metaheuristics techniques and algorithms inspired by nature

CERN Document Server

Du, Ke-Lin

2016-01-01

This textbook provides a comprehensive introduction to nature-inspired metaheuristic methods for search and optimization, including the latest trends in evolutionary algorithms and other forms of natural computing. Over 100 different types of these methods are discussed in detail. The authors emphasize non-standard optimization problems and utilize a natural approach to the topic, moving from basic notions to more complex ones. An introductory chapter covers the necessary biological and mathematical backgrounds for understanding the main material. Subsequent chapters then explore almost all of the major metaheuristics for search and optimization created based on natural phenomena, including simulated annealing, recurrent neural networks, genetic algorithms and genetic programming, differential evolution, memetic algorithms, particle swarm optimization, artificial immune systems, ant colony optimization, tabu search and scatter search, bee and bacteria foraging algorithms, harmony search, biomolecular computin...

Dynamic routing and spectrum assignment based on multilayer virtual topology and ant colony optimization in elastic software-defined optical networks

Science.gov (United States)

Wang, Fu; Liu, Bo; Zhang, Lijia; Zhang, Qi; Tian, Qinghua; Tian, Feng; Rao, Lan; Xin, Xiangjun

2017-07-01

Elastic software-defined optical networks greatly improve the flexibility of the optical switching network while it has brought challenges to the routing and spectrum assignment (RSA). A multilayer virtual topology model is proposed to solve RSA problems. Two RSA algorithms based on the virtual topology are proposed, which are the ant colony optimization (ACO) algorithm of minimum consecutiveness loss and the ACO algorithm of maximum spectrum consecutiveness. Due to the computing power of the control layer in the software-defined network, the routing algorithm avoids the frequent link-state information between routers. Based on the effect of the spectrum consecutiveness loss on the pheromone in the ACO, the path and spectrum of the minimal impact on the network are selected for the service request. The proposed algorithms have been compared with other algorithms. The results show that the proposed algorithms can reduce the blocking rate by at least 5% and perform better in spectrum efficiency. Moreover, the proposed algorithms can effectively decrease spectrum fragmentation and enhance available spectrum consecutiveness.

Improving the Power Quality in Tehran Metro Line-Two Using the Ant Colony Algorithm

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H. Ehteshami

2017-12-01

Full Text Available This research aims to survey the improvement of power quality in Tehran metro line 2 using the ant colony algorithm and to investigate all the factors affecting the achievement of this goal. In order to put Tehran on the road of sustainable development, finding a solution for dealing with air pollution is essential. The use of public transportation, especially metro, is one of the ways to achieve this goal. Since the highest share of pollutants in Tehran belongs to cars and mobile sources, relative statistical indicators are estimated through assuming the effect of metro lines development and subsequently reduction of traffic on power quality index.

Improved ant Colony Optimization for Virtual Teams Building in Collaborative Process Planning

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Yingying Su

2014-02-01

Full Text Available Virtual teams have been adopted by organizations to gain competitive advantages in this global economy. Virtual teams are a ubiquitous part of getting work done in almost every organization. For the purpose of building virtual teams in collaborative process planning, the method based on improved ant colony algorithm (IMACO was proposed. The concept of virtual team was illustrated and the necessity of building virtual teams in collaborative process planning was analyzed. The sub tasks with certain timing relationship were described and the model of building virtual teams in collaborative process planning was established, which was solved by improved ant colony algorithm. In this paper applications of the IMACO and ACO are compared and demonstrate that the use of the IMACO algorithm performs better. An example was studied to illustrate the effectiveness of the strategy.

Function-Oriented Networking and On-Demand Routing System in Network Using Ant Colony Optimization Algorithm

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Young-Bo Sim

2017-11-01

Full Text Available In this paper, we proposed and developed Function-Oriented Networking (FON, a platform for network users. It has a different philosophy as opposed to technologies for network managers of Software-Defined Networking technology, OpenFlow. It is a technology that can immediately reflect the demands of the network users in the network, unlike the existing OpenFlow and Network Functions Virtualization (NFV, which do not reflect directly the needs of the network users. It allows the network user to determine the policy of the direct network, so it can be applied more precisely than the policy applied by the network manager. This is expected to increase the satisfaction of the service users when the network users try to provide new services. We developed FON function that performs on-demand routing for Low-Delay Required service. We analyzed the characteristics of the Ant Colony Optimization (ACO algorithm and found that the algorithm is suitable for low-delay required services. It was also the first in the world to implement the routing software using ACO Algorithm in the real Ethernet network. In order to improve the routing performance, several algorithms of the ACO Algorithm have been developed to enable faster path search-routing and path recovery. The relationship between the network performance index and the ACO routing parameters is derived, and the results are compared and analyzed. Through this, it was possible to develop the ACO algorithm.

ANT, tourism and situated globality

DEFF Research Database (Denmark)

Jóhannesson, Gunnar Thór; Ren, Carina Bregnholm; van der Duim, René

2015-01-01

viable descriptions of the collective condition of humans and more-than-humans in the Anthropocene. Also and moving past a merely descriptive approach, it discusses it as a useful tool to engage with the situated globalities which come into being through the socio-spatial coupling of tourism......In recent years Actor-network theory (ANT) has increasingly been felt in the field of tourism studies (Van der Duim, Ren, & Jóhannesson, 2012). An important implication of the meeting between ANT and tourism studies is the notion of tourism being described as a heterogeneous assemblage of what we...... are used to define as the separate spheres of nature and culture. This paper explores and relates the central tenets of ANT in tourism with regard to the concept of the Anthropocene. It presents the ANT approach as a flat and object-oriented ontology and methodology and explores its potentials to carve out...

Coupling ant colony and the degraded ceiling algorithm for the redundancy allocation problem of series-parallel systems

International Nuclear Information System (INIS)

Nahas, Nabil; Nourelfath, Mustapha; Ait-Kadi, Daoud

2007-01-01

The redundancy allocation problem (RAP) is a well known NP-hard problem which involves the selection of elements and redundancy levels to maximize system reliability given various system-level constraints. As telecommunications and internet protocol networks, manufacturing and power systems are becoming more and more complex, while requiring short developments schedules and very high reliability, it is becoming increasingly important to develop efficient solutions to the RAP. This paper presents an efficient algorithm to solve this reliability optimization problem. The idea of a heuristic approach design is inspired from the ant colony meta-heuristic optimization method and the degraded ceiling local search technique. Our hybridization of the ant colony meta-heuristic with the degraded ceiling performs well and is competitive with the best-known heuristics for redundancy allocation. Numerical results for the 33 test problems from previous research are reported and compared. The solutions found by our approach are all better than or are in par with the well-known best solutions

The Algorithm for Algorithms: An Evolutionary Algorithm Based on Automatic Designing of Genetic Operators

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Dazhi Jiang

2015-01-01

Full Text Available At present there is a wide range of evolutionary algorithms available to researchers and practitioners. Despite the great diversity of these algorithms, virtually all of the algorithms share one feature: they have been manually designed. A fundamental question is “are there any algorithms that can design evolutionary algorithms automatically?” A more complete definition of the question is “can computer construct an algorithm which will generate algorithms according to the requirement of a problem?” In this paper, a novel evolutionary algorithm based on automatic designing of genetic operators is presented to address these questions. The resulting algorithm not only explores solutions in the problem space like most traditional evolutionary algorithms do, but also automatically generates genetic operators in the operator space. In order to verify the performance of the proposed algorithm, comprehensive experiments on 23 well-known benchmark optimization problems are conducted. The results show that the proposed algorithm can outperform standard differential evolution algorithm in terms of convergence speed and solution accuracy which shows that the algorithm designed automatically by computers can compete with the algorithms designed by human beings.

Cloud computing task scheduling strategy based on differential evolution and ant colony optimization

Science.gov (United States)

Ge, Junwei; Cai, Yu; Fang, Yiqiu

2018-05-01

This paper proposes a task scheduling strategy DEACO based on the combination of Differential Evolution (DE) and Ant Colony Optimization (ACO), aiming at the single problem of optimization objective in cloud computing task scheduling, this paper combines the shortest task completion time, cost and load balancing. DEACO uses the solution of the DE to initialize the initial pheromone of ACO, reduces the time of collecting the pheromone in ACO in the early, and improves the pheromone updating rule through the load factor. The proposed algorithm is simulated on cloudsim, and compared with the min-min and ACO. The experimental results show that DEACO is more superior in terms of time, cost, and load.

An ant colony optimization based feature selection for web page classification.

Science.gov (United States)

Saraç, Esra; Özel, Selma Ayşe

2014-01-01

The increased popularity of the web has caused the inclusion of huge amount of information to the web, and as a result of this explosive information growth, automated web page classification systems are needed to improve search engines' performance. Web pages have a large number of features such as HTML/XML tags, URLs, hyperlinks, and text contents that should be considered during an automated classification process. The aim of this study is to reduce the number of features to be used to improve runtime and accuracy of the classification of web pages. In this study, we used an ant colony optimization (ACO) algorithm to select the best features, and then we applied the well-known C4.5, naive Bayes, and k nearest neighbor classifiers to assign class labels to web pages. We used the WebKB and Conference datasets in our experiments, and we showed that using the ACO for feature selection improves both accuracy and runtime performance of classification. We also showed that the proposed ACO based algorithm can select better features with respect to the well-known information gain and chi square feature selection methods.

Hybrid chaotic ant swarm optimization

International Nuclear Information System (INIS)

Li Yuying; Wen Qiaoyan; Li Lixiang; Peng Haipeng

2009-01-01

Chaotic ant swarm optimization (CASO) is a powerful chaos search algorithm that is used to find the global optimum solution in search space. However, the CASO algorithm has some disadvantages, such as lower solution precision and longer computational time, when solving complex optimization problems. To resolve these problems, an improved CASO, called hybrid chaotic swarm optimization (HCASO), is proposed in this paper. The new algorithm introduces preselection operator and discrete recombination operator into the CASO; meanwhile it replaces the best position found by own and its neighbors' ants with the best position found by preselection operator and discrete recombination operator in evolution equation. Through testing five benchmark functions with large dimensionality, the experimental results show the new method enhances the solution accuracy and stability greatly, as well as reduces the computational time and computer memory significantly when compared to the CASO. In addition, we observe the results can become better with swarm size increasing from the sensitivity study to swarm size. And we gain some relations between problem dimensions and swam size according to scalability study.

Multi-Objective Ant Colony Optimization Based on the Physarum-Inspired Mathematical Model for Bi-Objective Traveling Salesman Problems.

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Zili Zhang

Full Text Available Bi-objective Traveling Salesman Problem (bTSP is an important field in the operations research, its solutions can be widely applied in the real world. Many researches of Multi-objective Ant Colony Optimization (MOACOs have been proposed to solve bTSPs. However, most of MOACOs suffer premature convergence. This paper proposes an optimization strategy for MOACOs by optimizing the initialization of pheromone matrix with the prior knowledge of Physarum-inspired Mathematical Model (PMM. PMM can find the shortest route between two nodes based on the positive feedback mechanism. The optimized algorithms, named as iPM-MOACOs, can enhance the pheromone in the short paths and promote the search ability of ants. A series of experiments are conducted and experimental results show that the proposed strategy can achieve a better compromise solution than the original MOACOs for solving bTSPs.

Optimization on Paddy Crops in Central Java (with Solver, SVD on Least Square and ACO (Ant Colony Algorithm))

Science.gov (United States)

Parhusip, H. A.; Trihandaru, S.; Susanto, B.; Prasetyo, S. Y. J.; Agus, Y. H.; Simanjuntak, B. H.

2017-03-01

Several algorithms and objective functions on paddy crops have been studied to get optimal paddy crops in Central Java based on the data given from Surakarta and Boyolali. The algorithms are linear solver, least square and Ant Colony Algorithms (ACO) to develop optimization procedures on paddy crops modelled with Modified GSTAR (Generalized Space-Time Autoregressive) and nonlinear models where the nonlinear models are quadratic and power functions. The studied data contain paddy crops from Surakarta and Boyolali determining the best period of planting in the year 1992-2012 for Surakarta where 3 periods for planting are known and the optimal amount of paddy crops in Boyolali in the year 2008-2013. Having these analyses may guide the local agriculture government to give a decision on rice sustainability in its region. The best period for planting in Surakarta is observed, i.e. the best period is in September-December based on the data 1992-2012 by considering the planting area, the cropping area, and the paddy crops are the most important factors to be taken into account. As a result, we can refer the paddy crops in this best period (about 60.4 thousand tons per year) as the optimal results in 1992-2012 where the used objective function is quadratic. According to the research, the optimal paddy crops in Boyolali about 280 thousand tons per year where the studied factors are the amount of rainfalls, the harvested area and the paddy crops in 2008-2013. In this case, linear and power functions are studied to be the objective functions. Compared to all studied algorithms, the linear solver is still recommended to be an optimization tool for a local agriculture government to predict paddy crops in future.

Solving a Closed-Loop Location-Inventory-Routing Problem with Mixed Quality Defects Returns in E-Commerce by Hybrid Ant Colony Optimization Algorithm

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Shuai Deng

2016-01-01

Full Text Available This paper presents a closed-loop location-inventory-routing problem model considering both quality defect returns and nondefect returns in e-commerce supply chain system. The objective is to minimize the total cost produced in both forward and reverse logistics networks. We propose a combined optimization algorithm named hybrid ant colony optimization algorithm (HACO to address this model that is an NP-hard problem. Our experimental results show that the proposed HACO is considerably efficient and effective in solving this model.

Calibration of Water Supply Systems Based on Ant Colony Optimization

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Mahmoud Faghfoor Maghrebi

2013-03-01

Full Text Available Leakage is one of the main problems in the water supply systems and due to the limitations in water supply and its costly process, reduction of leak in water distribution networks can be considered as one of the main goals of the water supply authorities. One of the leak detection techniques in water distribution system is the usage of the recorded node pressures at some locations to calibrate the whole system node pressures. Calibration process is accomplished by the optimization of a constrained objective function. Therefore, in addition to performing a hydraulic analysis of the network, application of an optimization technique is needed. In the current paper, a comparsion between the ant colony and genetic algorithm methodes, in calibration of the node pressures and leak detections was investigated. To examine the workability and the way of leak detection, analysis of the network with an assumed leak was carried out. The results showed that the effectiveness of the ant colony optimization in the detection of the position and magnitude of leak in a water network.

Symbiont interactions in a tripartite mutualism: exploring the presence and impact of antagonism between two fungus-growing ant mutualists.

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Michael Poulsen

Full Text Available Mutualistic associations are shaped by the interplay of cooperation and conflict among the partners involved, and it is becoming increasingly clear that within many mutualisms multiple partners simultaneously engage in beneficial interactions. Consequently, a more complete understanding of the dynamics within multipartite mutualism communities is essential for understanding the origin, specificity, and stability of mutualisms. Fungus-growing ants cultivate fungi for food and maintain antibiotic-producing Pseudonocardia actinobacteria on their cuticle that help defend the cultivar fungus from specialized parasites. Within both ant-fungus and ant-bacterium mutualisms, mixing of genetically distinct strains can lead to antagonistic interactions (i.e., competitive conflict, which may prevent the ants from rearing multiple strains of either of the mutualistic symbionts within individual colonies. The success of different ant-cultivar-bacterium combinations could ultimately be governed by antagonistic interactions between the two mutualists, either as inhibition of the cultivar by Pseudonocardia or vice versa. Here we explore cultivar-Pseudonocardia antagonism by evaluating in vitro interactions between strains of the two mutualists, and find frequent antagonistic interactions both from cultivars towards Pseudonocardia and vice versa. To test whether such in vitro antagonistic interactions affect ant colonies in vivo, we performed sub-colony experiments using species of Acromyrmex leaf-cutting ants. We created novel ant-fungus-bacterium pairings in which there was antagonism from one, both, or neither of the ants' microbial mutualists, and evaluated the effect of directional antagonism on cultivar biomass and Pseudonocardia abundance on the cuticle of workers within sub-colonies. Despite the presence of frequent in vitro growth suppression between cultivars and Pseudonocardia, antagonism from Pseudonocardia towards the cultivar did not reduce sub

Ant colony optimization as a descriptor selection in QSPR modeling: Estimation of the λmax of anthraquinones-based dyes

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Morteza Atabati

2016-09-01

Full Text Available Quantitative structure–property relationship (QSPR studies based on ant colony optimization (ACO were carried out for the prediction of λmax of 9,10-anthraquinone derivatives. ACO is a meta-heuristic algorithm, which is derived from the observation of real ants and proposed to feature selection. After optimization of 3D geometry of structures by the semi-empirical quantum-chemical calculation at AM1 level, different descriptors were calculated by the HyperChem and Dragon softwares (1514 descriptors. A major problem of QSPR is the high dimensionality of the descriptor space; therefore, descriptor selection is the most important step. In this paper, an ACO algorithm was used to select the best descriptors. Then selected descriptors were applied for model development using multiple linear regression. The average absolute relative deviation and correlation coefficient for the calibration set were obtained as 3.3% and 0.9591, respectively, while the average absolute relative deviation and correlation coefficient for the prediction set were obtained as 5.0% and 0.9526, respectively. The results showed that the applied procedure is suitable for prediction of λmax of 9,10-anthraquinone derivatives.

Multi-objective ant algorithm for wireless sensor network positioning

International Nuclear Information System (INIS)

Fidanova, S.; Shindarov, M.; Marinov, P.

2013-01-01

It is impossible to imagine our modern life without telecommunications. Wireless networks are a part of telecommunications. Wireless sensor networks (WSN) consist of spatially distributed sensors, which communicate in wireless way. This network monitors physical or environmental conditions. The objective is the full coverage of the monitoring region and less energy consumption of the network. The most appropriate approach to solve the problem is metaheuristics. In this paper the full coverage of the area is treated as a constrain. The objectives which are optimized are a minimal number of sensors and energy (lifetime) of the network. We apply multi-objective Ant Colony Optimization to solve this important telecommunication problem. We chose MAX-MIN Ant System approach, because it is proven to converge to the global optima

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 prov...

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)

Kefayat, M.; Lashkar Ara, A.; Nabavi Niaki, S.A.

2015-01-01

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

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.

Real-Coded Quantum-Inspired Genetic Algorithm-Based BP Neural Network Algorithm

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Jianyong Liu

2015-01-01

Full Text Available The method that the real-coded quantum-inspired genetic algorithm (RQGA used to optimize the weights and threshold of BP neural network is proposed to overcome the defect that the gradient descent method makes the algorithm easily fall into local optimal value in the learning process. Quantum genetic algorithm (QGA is with good directional global optimization ability, but the conventional QGA is based on binary coding; the speed of calculation is reduced by the coding and decoding processes. So, RQGA is introduced to explore the search space, and the improved varied learning rate is adopted to train the BP neural network. Simulation test shows that the proposed algorithm is effective to rapidly converge to the solution conformed to constraint conditions.

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

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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.

A new fire ant (Hymenoptera: Formicidae) bait base carrier for moist conditions.

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Kafle, Lekhnath; Wu, Wen-Jer; Shih, Cheng-Jen

2010-10-01

A new water-resistant fire ant bait (T-bait; cypermethrin 0.128%) consisting of dried distillers grains with solubles (DDGS) as a carrier was developed and evaluated against a standard commercial bait (Advion; indoxacarb 0.045%) under both laboratory and field conditions. When applying the normal T-bait or Advion in the laboratory, 100% of Solenopsis invicta Buren worker ants were killed within 4 days. However, when the T-bait and Advion were wetted, 70.6 and 39.7% of the ants were killed respectively. Under field conditions, dry T-bait and dry Advion had almost the same efficacy against ant colonies. However, when T-bait and Advion came in contact with water, the former's ability to kill S. invicta colonies in the field was only marginally reduced, while Advion lost virtually all of its activity. In addition, DDGS was also shown to be compatible with a number of other insecticides, such as d-allethrin, permethrin and pyrethrin. Based on its properties of remaining attractive to the fire ants when wetted, combined with its ant-killing abilities both in the laboratory and in the field, T-bait is an efficient fire ant bait, especially under moist conditions.

Pest repelling properties of ant pheromones

DEFF Research Database (Denmark)

Offenberg, Joachim

2014-01-01

Ants control pests via predation and physical deterrence; however, ant communication is based on chemical cues which may serve as warning signals to potential prey and other intruders. The presence of ant pheromones may, thus, be sufficient to repel pests from ant territories. This mini-review sh......-review shows that four out of five tested ant species deposit pheromones that repel herbivorous prey from their host plants.......Ants control pests via predation and physical deterrence; however, ant communication is based on chemical cues which may serve as warning signals to potential prey and other intruders. The presence of ant pheromones may, thus, be sufficient to repel pests from ant territories. This mini...

An ant colony based resilience approach to cascading failures in cluster supply network

Science.gov (United States)

Wang, Yingcong; Xiao, Renbin

2016-11-01

Cluster supply chain network is a typical complex network and easily suffers cascading failures under disruption events, which is caused by the under-load of enterprises. Improving network resilience can increase the ability of recovery from cascading failures. Social resilience is found in ant colony and comes from ant's spatial fidelity zones (SFZ). Starting from the under-load failures, this paper proposes a resilience method to cascading failures in cluster supply chain network by leveraging on social resilience of ant colony. First, the mapping between ant colony SFZ and cluster supply chain network SFZ is presented. Second, a new cascading model for cluster supply chain network is constructed based on under-load failures. Then, the SFZ-based resilience method and index to cascading failures are developed according to ant colony's social resilience. Finally, a numerical simulation and a case study are used to verify the validity of the cascading model and the resilience method. Experimental results show that, the cluster supply chain network becomes resilient to cascading failures under the SFZ-based resilience method, and the cluster supply chain network resilience can be enhanced by improving the ability of enterprises to recover and adjust.

Ant-plant-homopteran mutualism: how the third partner affects the interaction between a plant-specialist ant and its myrmecophyte host

Science.gov (United States)

Gaume, L.; McKey, D.; Terrin, S.

1998-01-01

By estimating relative costs and benefits, we explored the role of the homopteran partner in the protection mutualism between the myrmecophyte Leonardoxa africana T3, the ant Aphomomyrmex afer, and sap-sucking homopterans tended by ants in the tree's swollen hollow twigs. The ants obtain nest sites and food from their host-plant (food is obtained either directly by extrafloral nectar or indirectly via homopterans). Aphomomyrmex workers patrol the young leaves of L. africana T3 and protect them against phytophagous insects. Because ants tended, either solely or primarily, coccids in some trees and pseudococcids in others, we were able to study whether the nature of the interaction was dependent on the identity of the third partner. First, the type of homopteran affects the benefits to the tree of maintaining a large ant colony. Larger colony size (relative to tree size) confers greater protection against herbivory; this relationship is more pronounced for trees whose ants tend pseudococcids than for those in which ants tend coccids. Second, for trees (and associated ant colonies) of comparable size, homopteran biomass was much larger in trees harbouring coccids than in trees with pseudococcids. Thus, the cost to the tree of maintaining ants may be greater when ants are associated with coccids. The net benefits to the plant of maintaining ants appear to be much greater with pseudococcids as the third partner. To explore how the type of homopteran affects functioning of the system, we attempted to determine which of the resources (nest sites, extrafloral nectar, and homopterans) is likely to limit ant colony size. In trees where ants tended coccids, ant-colony biomass was strongly dependent on the number of extrafloral nectaries. In contrast, in trees whose ants tended only pseudococcids, colony biomass was not related to the number of nectaries and was most strongly determined by the volume of available nest sites. We present hypotheses to explain how the type of

Diversity of Species and Behavior of Hymenopteran Parasitoids of Ants: A Review

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Jean-Paul Lachaud

2012-01-01

Full Text Available Reports of hymenopterans associated with ants involve more than 500 species, but only a fraction unambiguously pertain to actual parasitoids. In this paper, we attempt to provide an overview of both the diversity of these parasitoid wasps and the diversity of the types of interactions they have formed with their ant hosts. The reliable list of parasitoid wasps using ants as primary hosts includes at least 138 species, reported between 1852 and 2011, distributed among 9 families from 3 superfamilies. These parasitoids exhibit a wide array of biologies and developmental strategies: ecto- or endoparasitism, solitary or gregarious, and idio- or koinobiosis. All castes of ants and all developmental stages, excepting eggs, are possible targets. Some species parasitize adult worker ants while foraging or performing other activities outside the nest; however, in most cases, parasitoids attack ant larvae either inside or outside their nests. Based on their abundance and success in attacking ants, some parasitoid wasps like diapriids and eucharitids seem excellent potential models to explore how parasitoids impact ant colony demography, population biology, and ant community structure. Despite a significant increase in our knowledge of hymenopteran parasitoids of ants, most of them remain to be discovered.

Processing time tolerance-based ACO algorithm for solving job-shop scheduling problem

Science.gov (United States)

Luo, Yabo; Waden, Yongo P.

2017-06-01

Ordinarily, Job Shop Scheduling Problem (JSSP) is known as NP-hard problem which has uncertainty and complexity that cannot be handled by a linear method. Thus, currently studies on JSSP are concentrated mainly on applying different methods of improving the heuristics for optimizing the JSSP. However, there still exist many problems for efficient optimization in the JSSP, namely, low efficiency and poor reliability, which can easily trap the optimization process of JSSP into local optima. Therefore, to solve this problem, a study on Ant Colony Optimization (ACO) algorithm combined with constraint handling tactics is carried out in this paper. Further, the problem is subdivided into three parts: (1) Analysis of processing time tolerance-based constraint features in the JSSP which is performed by the constraint satisfying model; (2) Satisfying the constraints by considering the consistency technology and the constraint spreading algorithm in order to improve the performance of ACO algorithm. Hence, the JSSP model based on the improved ACO algorithm is constructed; (3) The effectiveness of the proposed method based on reliability and efficiency is shown through comparative experiments which are performed on benchmark problems. Consequently, the results obtained by the proposed method are better, and the applied technique can be used in optimizing JSSP.

A Dynamic Neighborhood Learning-Based Gravitational Search Algorithm.

Science.gov (United States)

Zhang, Aizhu; Sun, Genyun; Ren, Jinchang; Li, Xiaodong; Wang, Zhenjie; Jia, Xiuping

2018-01-01

Balancing exploration and exploitation according to evolutionary states is crucial to meta-heuristic search (M-HS) algorithms. Owing to its simplicity in theory and effectiveness in global optimization, gravitational search algorithm (GSA) has attracted increasing attention in recent years. However, the tradeoff between exploration and exploitation in GSA is achieved mainly by adjusting the size of an archive, named , which stores those superior agents after fitness sorting in each iteration. Since the global property of remains unchanged in the whole evolutionary process, GSA emphasizes exploitation over exploration and suffers from rapid loss of diversity and premature convergence. To address these problems, in this paper, we propose a dynamic neighborhood learning (DNL) strategy to replace the model and thereby present a DNL-based GSA (DNLGSA). The method incorporates the local and global neighborhood topologies for enhancing the exploration and obtaining adaptive balance between exploration and exploitation. The local neighborhoods are dynamically formed based on evolutionary states. To delineate the evolutionary states, two convergence criteria named limit value and population diversity, are introduced. Moreover, a mutation operator is designed for escaping from the local optima on the basis of evolutionary states. The proposed algorithm was evaluated on 27 benchmark problems with different characteristic and various difficulties. The results reveal that DNLGSA exhibits competitive performances when compared with a variety of state-of-the-art M-HS algorithms. Moreover, the incorporation of local neighborhood topology reduces the numbers of calculations of gravitational force and thus alleviates the high computational cost of GSA.

An Improved Routing Optimization Algorithm Based on Travelling Salesman Problem for Social Networks

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Naixue Xiong

2017-06-01

Full Text Available A social network is a social structure, which is organized by the relationships or interactions between individuals or groups. Humans link the physical network with social network, and the services in the social world are based on data and analysis, which directly influence decision making in the physical network. In this paper, we focus on a routing optimization algorithm, which solves a well-known and popular problem. Ant colony algorithm is proposed to solve this problem effectively, but random selection strategy of the traditional algorithm causes evolution speed to be slow. Meanwhile, positive feedback and distributed computing model make the algorithm quickly converge. Therefore, how to improve convergence speed and search ability of algorithm is the focus of the current research. The paper proposes the improved scheme. Considering the difficulty about searching for next better city, new parameters are introduced to improve probability of selection, and delay convergence speed of algorithm. To avoid the shortest path being submerged, and improve sensitive speed of finding the shortest path, it updates pheromone regulation formula. The results show that the improved algorithm can effectively improve convergence speed and search ability for achieving higher accuracy and optimal results.

Algorithms bio-inspired for the pattern obtention of control bars in BWR reactors

International Nuclear Information System (INIS)

Ortiz, J.J.; Perusquia, R.; Montes, J.L.

2003-01-01

In this work methods based on Genetic Algorithms and Systems based on ant colonies for the obtention of the patterns of control bars of an equilibrium cycle of 18 months for the Laguna Verde nuclear power station are presented. A comparison of obtained results with the methods and with those of design of such equilibrium cycle is presented. As consequence of the study, it was found that the algorithm based on the ant colonies reached to diminish the coast down period (decrease of power at the end of the cycle) in five and half days with respect to the original design what represents an annual saving of $US 100,000. (Author)

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.

ANTQ evolutionary algorithm applied to nuclear fuel reload problem

International Nuclear Information System (INIS)

Machado, Liana; Schirru, Roberto

2000-01-01

Nuclear fuel reload optimization is a NP-complete combinatorial optimization problem where the aim is to find fuel rods' configuration that maximizes burnup or minimizes the power peak factor. For decades this problem was solved exclusively using an expert's knowledge. From the eighties, however, there have been efforts to automatize fuel reload. The first relevant effort used Simulated Annealing, but more recent publications show Genetic Algorithm's (GA) efficiency on this problem's solution. Following this direction, our aim is to optimize nuclear fuel reload using Ant-Q, a reinforcement learning algorithm based on the Cellular Computing paradigm. Ant-Q's results on the Travelling Salesmen Problem, which is conceptually similar to fuel reload, are better than the GA's ones. Ant-Q was tested on fuel reload by the simulation of the first cycle in-out reload of Bibils, a 193 fuel element PWR. Comparing An-Q's result with the GA's ones, it can b seen that even without a local heuristics, the former evolutionary algorithm can be used to solve the nuclear fuel reload problem. (author)

Nature-inspired optimization algorithms

CERN Document Server

Yang, Xin-She

2014-01-01

Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning

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

International Nuclear Information System (INIS)

Lima, Alan M.M. de; Schirru, Roberto; Carvalho da Silva, Fernando; Medeiros, Jose Antonio Carlos Canedo

2008-01-01

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

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

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.

Dual ant colony operational modal analysis parameter estimation method

Science.gov (United States)

Sitarz, Piotr; Powałka, Bartosz

2018-01-01

Operational Modal Analysis (OMA) is a common technique used to examine the dynamic properties of a system. Contrary to experimental modal analysis, the input signal is generated in object ambient environment. Operational modal analysis mainly aims at determining the number of pole pairs and at estimating modal parameters. Many methods are used for parameter identification. Some methods operate in time while others in frequency domain. The former use correlation functions, the latter - spectral density functions. However, while some methods require the user to select poles from a stabilisation diagram, others try to automate the selection process. Dual ant colony operational modal analysis parameter estimation method (DAC-OMA) presents a new approach to the problem, avoiding issues involved in the stabilisation diagram. The presented algorithm is fully automated. It uses deterministic methods to define the interval of estimated parameters, thus reducing the problem to optimisation task which is conducted with dedicated software based on ant colony optimisation algorithm. The combination of deterministic methods restricting parameter intervals and artificial intelligence yields very good results, also for closely spaced modes and significantly varied mode shapes within one measurement point.

Optimal Pid Controller Design Using Adaptive Vurpso Algorithm

Science.gov (United States)

Zirkohi, Majid Moradi

2015-04-01

The purpose of this paper is to improve theVelocity Update Relaxation Particle Swarm Optimization algorithm (VURPSO). The improved algorithm is called Adaptive VURPSO (AVURPSO) algorithm. Then, an optimal design of a Proportional-Integral-Derivative (PID) controller is obtained using the AVURPSO algorithm. An adaptive momentum factor is used to regulate a trade-off between the global and the local exploration abilities in the proposed algorithm. This operation helps the system to reach the optimal solution quickly and saves the computation time. Comparisons on the optimal PID controller design confirm the superiority of AVURPSO algorithm to the optimization algorithms mentioned in this paper namely the VURPSO algorithm, the Ant Colony algorithm, and the conventional approach. Comparisons on the speed of convergence confirm that the proposed algorithm has a faster convergence in a less computation time to yield a global optimum value. The proposed AVURPSO can be used in the diverse areas of optimization problems such as industrial planning, resource allocation, scheduling, decision making, pattern recognition and machine learning. The proposed AVURPSO algorithm is efficiently used to design an optimal PID controller.

Topology optimum design of compliant mechanisms using modified ant colony optimization

Energy Technology Data Exchange (ETDEWEB)

Yoo, Kwang Seon; Han, Seog Young [Hanyang University, Seoul (Korea, Republic of)

2015-08-15

A Modified ant colony optimization (MACO) algorithm was suggested for topology optimal design of compliant mechanisms since standard ACO cannot provide an appropriate optimal topology. In order to improve computational efficiency and suitability of standard ACO algorithm in topology optimization for compliant mechanisms, a continuous variable, called the 'Element contribution significance (ECS),'is employed, which serves to replace the positions of ants in the standard ACO algorithm, and assess the importance of each element in the optimization process. MACO algorithm was applied to topology optimizations of both linear and geometrically nonlinear compliant mechanisms using three kinds of objective functions, and optimized topologies were compared each other. From the comparisons, it was concluded that MACO algorithm can effectively be applied to topology optimizations of linear and geometrically nonlinear compliant mechanisms, and the ratio of Mutual potential energy (MPE) to Strain energy (SE) type of objective function is the best for topology optimal design of compliant mechanisms.

Pest repellent properties of ant pheromones

DEFF Research Database (Denmark)

Offenberg, Joachim

2012-01-01

of ant pheromones may be sufficient to repel pest insects from ant territories. The study of ant semiochemicals is in its infancy, yet, evidence for their potential use in pest management is starting to build up. Pheromones from four of five tested ant species have been shown to deter herbivorous insect...... prey and competing ant species are also deterred by ant deposits, whereas ant symbionts may be attracted to them. Based on these promising initial findings, it seems advisable to further elucidate the signaling properties of ant pheromones and to test and develop their use in future pest management....

The dynamics of ant mosaics in tropical rainforests characterized using the Self-Organizing Map algorithm.

Science.gov (United States)

Dejean, Alain; Azémar, Frédéric; Céréghino, Régis; Leponce, Maurice; Corbara, Bruno; Orivel, Jérôme; Compin, Arthur

2016-08-01

Ants, the most abundant taxa among canopy-dwelling animals in tropical rainforests, are mostly represented by territorially dominant arboreal ants (TDAs) whose territories are distributed in a mosaic pattern (arboreal ant mosaics). Large TDA colonies regulate insect herbivores, with implications for forestry and agronomy. What generates these mosaics in vegetal formations, which are dynamic, still needs to be better understood. So, from empirical research based on 3 Cameroonian tree species (Lophira alata, Ochnaceae; Anthocleista vogelii, Gentianaceae; and Barteria fistulosa, Passifloraceae), we used the Self-Organizing Map (SOM, neural network) to illustrate the succession of TDAs as their host trees grow and age. The SOM separated the trees by species and by size for L. alata, which can reach 60 m in height and live several centuries. An ontogenic succession of TDAs from sapling to mature trees is shown, and some ecological traits are highlighted for certain TDAs. Also, because the SOM permits the analysis of data with many zeroes with no effect of outliers on the overall scatterplot distributions, we obtained ecological information on rare species. Finally, the SOM permitted us to show that functional groups cannot be selected at the genus level as congeneric species can have very different ecological niches, something particularly true for Crematogaster spp., which include a species specifically associated with B. fistulosa, nondominant species and TDAs. Therefore, the SOM permitted the complex relationships between TDAs and their growing host trees to be analyzed, while also providing new information on the ecological traits of the ant species involved. © 2015 Institute of Zoology, Chinese Academy of Sciences.

Complex-based OCT angiography algorithm recovers microvascular information better than amplitude- or phase-based algorithms in phase-stable systems.

Science.gov (United States)

Xu, Jingjiang; Song, Shaozhen; Li, Yuandong; Wang, Ruikang K

2017-12-19

Optical coherence tomography angiography (OCTA) is increasingly becoming a popular inspection tool for biomedical imaging applications. By exploring the amplitude, phase and complex information available in OCT signals, numerous algorithms have been proposed that contrast functional vessel networks within microcirculatory tissue beds. However, it is not clear which algorithm delivers optimal imaging performance. Here, we investigate systematically how amplitude and phase information have an impact on the OCTA imaging performance, to establish the relationship of amplitude and phase stability with OCT signal-to-noise ratio (SNR), time interval and particle dynamics. With either repeated A-scan or repeated B-scan imaging protocols, the amplitude noise increases with the increase of OCT SNR; however, the phase noise does the opposite, i.e. it increases with the decrease of OCT SNR. Coupled with experimental measurements, we utilize a simple Monte Carlo (MC) model to simulate the performance of amplitude-, phase- and complex-based algorithms for OCTA imaging, the results of which suggest that complex-based algorithms deliver the best performance when the phase noise is  algorithm delivers better performance than either the amplitude- or phase-based algorithms for both the repeated A-scan and the B-scan imaging protocols, which agrees well with the conclusion drawn from the MC simulations.

Ant system for reliability optimization of a series system with multiple-choice and budget constraints

International Nuclear Information System (INIS)

Nahas, Nabil; Nourelfath, Mustapha

2005-01-01

Many researchers have shown that insect colonies behavior can be seen as a natural model of collective problem solving. The analogy between the way ants look for food and combinatorial optimization problems has given rise to a new computational paradigm, which is called ant system. This paper presents an application of ant system in a reliability optimization problem for a series system with multiple-choice constraints incorporated at each subsystem, to maximize the system reliability subject to the system budget. The problem is formulated as a nonlinear binary integer programming problem and characterized as an NP-hard problem. This problem is solved by developing and demonstrating a problem-specific ant system algorithm. In this algorithm, solutions of the reliability optimization problem are repeatedly constructed by considering the trace factor and the desirability factor. A local search is used to improve the quality of the solutions obtained by each ant. A penalty factor is introduced to deal with the budget constraint. Simulations have shown that the proposed ant system is efficient with respect to the quality of solutions and the computing time

ANT

DEFF Research Database (Denmark)

van der Duim, René; Ren, Carina Bregnholm; Jóhannesson, Gunnar Thór

2017-01-01

Ten years ago actor-network theory (ANT) entered this journal. To illustrate how the relational ontology and sensibilities of ANT lend themselves to particular kinds of research, we first interrogate the main controversies as a way to open up and discuss the main premises of ANT. These debates...... concern the status and agency of objects and non-humans, ANT’s denial of the explanatory power of social structures, and the political implications of ANT. Second we present ANT’s relevance for tourism studies and discuss what ANT ‘does’ in practice. After summarizing a decade of relations between ANT...... and tourism, we conclude by tracing three future trajectories of how we have ‘moved away with’ ANT into new areas of discovery....

Learning from nature: Nature-inspired algorithms

DEFF Research Database (Denmark)

Albeanu, Grigore; Madsen, Henrik; Popentiu-Vladicescu, Florin

2016-01-01

.), genetic and evolutionary strategies, artificial immune systems etc. Well-known examples of applications include: aircraft wing design, wind turbine design, bionic car, bullet train, optimal decisions related to traffic, appropriate strategies to survive under a well-adapted immune system etc. Based......During last decade, the nature has inspired researchers to develop new algorithms. The largest collection of nature-inspired algorithms is biology-inspired: swarm intelligence (particle swarm optimization, ant colony optimization, cuckoo search, bees' algorithm, bat algorithm, firefly algorithm etc...... on collective social behaviour of organisms, researchers have developed optimization strategies taking into account not only the individuals, but also groups and environment. However, learning from nature, new classes of approaches can be identified, tested and compared against already available algorithms...

Disease dynamics in a specialized parasite of ant societies

DEFF Research Database (Denmark)

Andersen, Sandra Breum; Ferrari, Matthew; Evans, Harry C.

2012-01-01

Coevolution between ant colonies and their rare specialized parasites are intriguing, because lethal infections of workers may correspond to tolerable chronic diseases of colonies, but the parasite adaptations that allow stable coexistence with ants are virtually unknown. We explore the trade......-offs experienced by Ophiocordyceps parasites manipulating ants into dying in nearby graveyards. We used field data from Brazil and Thailand to parameterize and fit a model for the growth rate of graveyards. We show that parasite pressure is much lower than the abundance of ant cadavers suggests...

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

International Nuclear Information System (INIS)

Ketabi, Abbas; Alibabaee, Ahmad; Feuillet, R.

2010-01-01

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)

Ant colony system (ACS with hybrid local search to solve vehicle routing problems

Directory of Open Access Journals (Sweden)

Suphan Sodsoon

2016-02-01

Full Text Available This research applied an Ant Colony System algorithm with a Hybrid Local Search to solve Vehicle Routing Problems (VRP from a single depot when the customers’ requirements are known. VRP is an NP-hard optimization problem and has usually been successfully solved optimum by heuristics. A fleet of vehicles of a specific capacity are used to serve a number of customers at minimum cost, without violating the constraints of vehicle capacity. There are meta-heuristic approaches to solve these problems, such as Simulated Annealing, Genetic Algorithm, Tabu Search and the Ant Colony System algorithm. In this case a hybrid local search was used (Cross-Exchange, Or-Opt and 2-Opt algorithm with an Ant Colony System algorithm. The Experimental Design was tested on 7 various problems from the data set online in the OR-Library. There are five different problems in which customers are randomly distributed with the depot in an approximately central location. The customers were grouped into clusters. The results are evaluated in terms of optimal routes using optimal distances. The experimental results are compared with those obtained from meta-heuristics and they show that the proposed method outperforms six meta-heuristics in the literature.

Log-Linear Model Based Behavior Selection Method for Artificial Fish Swarm Algorithm

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Zhehuang Huang

2015-01-01

Full Text Available Artificial fish swarm algorithm (AFSA is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.

Log-linear model based behavior selection method for artificial fish swarm algorithm.

Science.gov (United States)

Huang, Zhehuang; Chen, Yidong

2015-01-01

Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.

Aeromagnetic Compensation Algorithm Based on Principal Component Analysis

Directory of Open Access Journals (Sweden)

Peilin Wu

2018-01-01

Full Text Available Aeromagnetic exploration is an important exploration method in geophysics. The data is typically measured by optically pumped magnetometer mounted on an aircraft. But any aircraft produces significant levels of magnetic interference. Therefore, aeromagnetic compensation is important in aeromagnetic exploration. However, multicollinearity of the aeromagnetic compensation model degrades the performance of the compensation. To address this issue, a novel aeromagnetic compensation method based on principal component analysis is proposed. Using the algorithm, the correlation in the feature matrix is eliminated and the principal components are using to construct the hyperplane to compensate the platform-generated magnetic fields. The algorithm was tested using a helicopter, and the obtained improvement ratio is 9.86. The compensated quality is almost the same or slightly better than the ridge regression. The validity of the proposed method was experimentally demonstrated.

A Turn-Projected State-Based Conflict Resolution Algorithm

Science.gov (United States)

Butler, Ricky W.; Lewis, Timothy A.

2013-01-01

State-based conflict detection and resolution (CD&R) algorithms detect conflicts and resolve them on the basis on current state information without the use of additional intent information from aircraft flight plans. Therefore, the prediction of the trajectory of aircraft is based solely upon the position and velocity vectors of the traffic aircraft. Most CD&R algorithms project the traffic state using only the current state vectors. However, the past state vectors can be used to make a better prediction of the future trajectory of the traffic aircraft. This paper explores the idea of using past state vectors to detect traffic turns and resolve conflicts caused by these turns using a non-linear projection of the traffic state. A new algorithm based on this idea is presented and validated using a fast-time simulator developed for this study.

Performance of humans vs. exploration algorithms on the Tower of London Test.

Directory of Open Access Journals (Sweden)

Eric Fimbel

Full Text Available The Tower of London Test (TOL used to assess executive functions was inspired in Artificial Intelligence tasks used to test problem-solving algorithms. In this study, we compare the performance of humans and of exploration algorithms. Instead of absolute execution times, we focus on how the execution time varies with the tasks and/or the number of moves. This approach used in Algorithmic Complexity provides a fair comparison between humans and computers, although humans are several orders of magnitude slower. On easy tasks (1 to 5 moves, healthy elderly persons performed like exploration algorithms using bounded memory resources, i.e., the execution time grew exponentially with the number of moves. This result was replicated with a group of healthy young participants. However, for difficult tasks (5 to 8 moves the execution time of young participants did not increase significantly, whereas for exploration algorithms, the execution time keeps on increasing exponentially. A pre-and post-test control task showed a 25% improvement of visuo-motor skills but this was insufficient to explain this result. The findings suggest that naive participants used systematic exploration to solve the problem but under the effect of practice, they developed markedly more efficient strategies using the information acquired during the test.

Individual Rules for Trail Pattern Formation in Argentine Ants (Linepithema humile)

OpenAIRE

Perna, Andrea; Granovskiy, Boris; Garnier, Simon; Nicolis, Stamatios C.; Labédan, Marjorie; Theraulaz, Guy; Fourcassié, Vincent; Sumpter, David J. T.

2012-01-01

We studied the formation of trail patterns by Argentine ants exploring an empty arena. Using a novel imaging and analysis technique we estimated pheromone concentrations at all spatial positions in the experimental arena and at different times. Then we derived the response function of individual ants to pheromone concentrations by looking at correlations between concentrations and changes in speed or direction of the ants. Ants were found to turn in response to local pheromone concentrations,...

Monte Carlo simulation using the PENELOPE code with an ant colony algorithm to study MOSFET detectors

Energy Technology Data Exchange (ETDEWEB)

Carvajal, M A; Palma, A J [Departamento de Electronica y Tecnologia de Computadores, Universidad de Granada, E-18071 Granada (Spain); Garcia-Pareja, S [Servicio de Radiofisica Hospitalaria, Hospital Regional Universitario ' Carlos Haya' , Avda Carlos Haya, s/n, E-29010 Malaga (Spain); Guirado, D [Servicio de RadiofIsica, Hospital Universitario ' San Cecilio' , Avda Dr Oloriz, 16, E-18012 Granada (Spain); Vilches, M [Servicio de Fisica y Proteccion Radiologica, Hospital Regional Universitario ' Virgen de las Nieves' , Avda Fuerzas Armadas, 2, E-18014 Granada (Spain); Anguiano, M; Lallena, A M [Departamento de Fisica Atomica, Molecular y Nuclear, Universidad de Granada, E-18071 Granada (Spain)], E-mail: carvajal@ugr.es, E-mail: garciapareja@gmail.com, E-mail: dguirado@ugr.es, E-mail: mvilches@ugr.es, E-mail: mangui@ugr.es, E-mail: ajpalma@ugr.es, E-mail: lallena@ugr.es

2009-10-21

In this work we have developed a simulation tool, based on the PENELOPE code, to study the response of MOSFET devices to irradiation with high-energy photons. The energy deposited in the extremely thin silicon dioxide layer has been calculated. To reduce the statistical uncertainties, an ant colony algorithm has been implemented to drive the application of splitting and Russian roulette as variance reduction techniques. In this way, the uncertainty has been reduced by a factor of {approx}5, while the efficiency is increased by a factor of above 20. As an application, we have studied the dependence of the response of the pMOS transistor 3N163, used as a dosimeter, with the incidence angle of the radiation for three common photons sources used in radiotherapy: a {sup 60}Co Theratron-780 and the 6 and 18 MV beams produced by a Mevatron KDS LINAC. Experimental and simulated results have been obtained for gantry angles of 0 deg., 15 deg., 30 deg., 45 deg., 60 deg. and 75 deg. The agreement obtained has permitted validation of the simulation tool. We have studied how to reduce the angular dependence of the MOSFET response by using an additional encapsulation made of brass in the case of the two LINAC qualities considered.

Individual Rules for Trail Pattern Formation in Argentine Ants (Linepithema humile)

Science.gov (United States)

Perna, Andrea; Granovskiy, Boris; Garnier, Simon; Nicolis, Stamatios C.; Labédan, Marjorie; Theraulaz, Guy; Fourcassié, Vincent; Sumpter, David J. T.

2012-01-01

We studied the formation of trail patterns by Argentine ants exploring an empty arena. Using a novel imaging and analysis technique we estimated pheromone concentrations at all spatial positions in the experimental arena and at different times. Then we derived the response function of individual ants to pheromone concentrations by looking at correlations between concentrations and changes in speed or direction of the ants. Ants were found to turn in response to local pheromone concentrations, while their speed was largely unaffected by these concentrations. Ants did not integrate pheromone concentrations over time, with the concentration of pheromone in a 1 cm radius in front of the ant determining the turning angle. The response to pheromone was found to follow a Weber's Law, such that the difference between quantities of pheromone on the two sides of the ant divided by their sum determines the magnitude of the turning angle. This proportional response is in apparent contradiction with the well-established non-linear choice function used in the literature to model the results of binary bridge experiments in ant colonies (Deneubourg et al. 1990). However, agent based simulations implementing the Weber's Law response function led to the formation of trails and reproduced results reported in the literature. We show analytically that a sigmoidal response, analogous to that in the classical Deneubourg model for collective decision making, can be derived from the individual Weber-type response to pheromone concentrations that we have established in our experiments when directional noise around the preferred direction of movement of the ants is assumed. PMID:22829756

Individual rules for trail pattern formation in Argentine ants (Linepithema humile.

Directory of Open Access Journals (Sweden)

Andrea Perna

Full Text Available We studied the formation of trail patterns by Argentine ants exploring an empty arena. Using a novel imaging and analysis technique we estimated pheromone concentrations at all spatial positions in the experimental arena and at different times. Then we derived the response function of individual ants to pheromone concentrations by looking at correlations between concentrations and changes in speed or direction of the ants. Ants were found to turn in response to local pheromone concentrations, while their speed was largely unaffected by these concentrations. Ants did not integrate pheromone concentrations over time, with the concentration of pheromone in a 1 cm radius in front of the ant determining the turning angle. The response to pheromone was found to follow a Weber's Law, such that the difference between quantities of pheromone on the two sides of the ant divided by their sum determines the magnitude of the turning angle. This proportional response is in apparent contradiction with the well-established non-linear choice function used in the literature to model the results of binary bridge experiments in ant colonies (Deneubourg et al. 1990. However, agent based simulations implementing the Weber's Law response function led to the formation of trails and reproduced results reported in the literature. We show analytically that a sigmoidal response, analogous to that in the classical Deneubourg model for collective decision making, can be derived from the individual Weber-type response to pheromone concentrations that we have established in our experiments when directional noise around the preferred direction of movement of the ants is assumed.

Fungal enzymes in the attine ant symbiosis

DEFF Research Database (Denmark)

de Fine Licht, Henrik Hjarvard; Schiøtt, Morten; Boomsma, Jacobus Jan

the more basal attine genera use substrates such as flowers, plant debris, small twigs, insect feces and insect carcasses. This diverse array of fungal substrates across the attine lineage implies that the symbiotic fungus needs different enzymes to break down the plant material that the ants provide...... or different efficiencies of enzyme function. Fungal enzymes that degrade plant cell walls may have functionally co-evolved with the ants in this scenario. We explore this hypothesis with direct measurements of enzyme activity in fungus gardens in 12 species across 8 genera spanning the entire phylogeny...... and diversity of life-styles within the attine clade. We find significant differences in enzyme activity between different genera and life-styles of the ants. How these findings relate to attine ant coevolution and crop optimization are discussed....

PMU Placement Methods in Power Systems based on Evolutionary Algorithms and GPS Receiver

Directory of Open Access Journals (Sweden)

M. R. Mosavi

2013-06-01

Full Text Available In this paper, optimal placement of Phasor Measurement Unit (PMU using Global Positioning System (GPS is discussed. Ant Colony Optimization (ACO, Simulated Annealing (SA, Particle Swarm Optimization (PSO and Genetic Algorithm (GA are used for this problem. Pheromone evaporation coefficient and the probability of moving from state x to state y by ant are introduced into the ACO. The modified algorithm overcomes the ACO in obtaining global optimal solution and convergence speed, when applied to optimizing the PMU placement problem. We also compare this simulink with SA, PSO and GA that to find capability of ACO in the search of optimal solution. The fitness function includes observability, redundancy and number of PMU. Logarithmic Least Square Method (LLSM is used to calculate the weights of fitness function. The suggested optimization method is applied in 30-bus IEEE system and the simulation results show modified ACO find results better than PSO and SA, but same result with GA.

A tunable algorithm for collective decision-making.

Science.gov (United States)

Pratt, Stephen C; Sumpter, David J T

2006-10-24

Complex biological systems are increasingly understood in terms of the algorithms that guide the behavior of system components and the information pathways that link them. Much attention has been given to robust algorithms, or those that allow a system to maintain its functions in the face of internal or external perturbations. At the same time, environmental variation imposes a complementary need for algorithm versatility, or the ability to alter system function adaptively as external circumstances change. An important goal of systems biology is thus the identification of biological algorithms that can meet multiple challenges rather than being narrowly specified to particular problems. Here we show that emigrating colonies of the ant Temnothorax curvispinosus tune the parameters of a single decision algorithm to respond adaptively to two distinct problems: rapid abandonment of their old nest in a crisis and deliberative selection of the best available new home when their old nest is still intact. The algorithm uses a stepwise commitment scheme and a quorum rule to integrate information gathered by numerous individual ants visiting several candidate homes. By varying the rates at which they search for and accept these candidates, the ants yield a colony-level response that adaptively emphasizes either speed or accuracy. We propose such general but tunable algorithms as a design feature of complex systems, each algorithm providing elegant solutions to a wide range of problems.

Image Edge Tracking via Ant Colony Optimization

Science.gov (United States)

Li, Ruowei; Wu, Hongkun; Liu, Shilong; Rahman, M. A.; Liu, Sanchi; Kwok, Ngai Ming

2018-04-01

A good edge plot should use continuous thin lines to describe the complete contour of the captured object. However, the detection of weak edges is a challenging task because of the associated low pixel intensities. Ant Colony Optimization (ACO) has been employed by many researchers to address this problem. The algorithm is a meta-heuristic method developed by mimicking the natural behaviour of ants. It uses iterative searches to find the optimal solution that cannot be found via traditional optimization approaches. In this work, ACO is employed to track and repair broken edges obtained via conventional Sobel edge detector to produced a result with more connected edges.

Intelligent Hypothermia Care System using Ant ‎Colony Optimization for Rules Prediction

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Hayder Naser Khraibet

2017-12-01

Full Text Available Intelligent Hypothermia Care System (IHCS is an intelligence system uses set of methodologies, algorithms, architectures and processes to determine where patients in a postoperative recovery area must be sent. Hypothermia is a significant concern after surgery. This paper utilizes the classification task in data mining to propose an intelligent technique to predict where to send a patient after surgery: intensive care unit, general floor or home. To achieve this goal, this paper evaluates the performance of decision tree algorithm, exemplifying the deterministic approach, against the AntMiner algorithm, exemplifying the heuristic approach, to choose the best approach in detecting the patient’s status. Results show the outperformance of the heuristic approach. The implication of this proposal will be twofold: in hypothermia treatment and in the application of ant colony optimization

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

International Nuclear Information System (INIS)

Silva, Marcio H.; Schirru, Roberto; Medeiros, Jose A.C.C.

2009-01-01

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 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)

Vegetation structure of plantain-based agrosystems determines numerical dominance in community of ground-dwelling ants.

Science.gov (United States)

Dassou, Anicet Gbéblonoudo; Tixier, Philippe; Dépigny, Sylvain; Carval, Dominique

2017-01-01

In tropics, ants can represent an important part of animal biomass and are known to be involved in ecosystem services, such as pest regulation. Understanding the mechanisms underlying the structuring of local ant communities is therefore important in agroecology. In the humid tropics of Africa, plantains are cropped in association with many other annual and perennial crops. Such agrosystems differ greatly in vegetation diversity and structure and are well-suited for studying how habitat-related factors affect the ant community. We analysed abundance data for the six numerically dominant ant taxa in 500 subplots located in 20 diversified, plantain-based fields. We found that the density of crops with foliage at intermediate and high canopy strata determined the numerical dominance of species. We found no relationship between the numerical dominance of each ant taxon with the crop diversity. Our results indicate that the manipulation of the densities of crops with leaves in the intermediate and high strata may help maintain the coexistence of ant species by providing different habitat patches. Further research in such agrosystems should be performed to assess if the effect of vegetation structure on ant abundance could result in efficient pest regulation.

Adaptive Bio-Inspired Wireless Network Routing for Planetary Surface Exploration

Science.gov (United States)

Alena, Richard I.; Lee, Charles

2004-01-01

Wireless mobile networks suffer connectivity loss when used in a terrain that has hills, and valleys when line of sight is interrupted or range is exceeded. To resolve this problem and achieve acceptable network performance, we have designed an adaptive, configurable, hybrid system to automatically route network packets along the best path between multiple geographically dispersed modules. This is very useful in planetary surface exploration, especially for ad-hoc mobile networks, where computational devices take an active part in creating a network infrastructure, and can actually be used to route data dynamically and even store data for later transmission between networks. Using inspiration from biological systems, this research proposes to use ant trail algorithms with multi-layered information maps (topographic maps, RF coverage maps) to determine the best route through ad-hoc network at real time. The determination of best route is a complex one, and requires research into the appropriate metrics, best method to identify the best path, optimizing traffic capacity, network performance, reliability, processing capabilities and cost. Real ants are capable of finding the shortest path from their nest to a food source without visual sensing through the use of pheromones. They are also able to adapt to changes in the environment using subtle clues. To use ant trail algorithms, we need to define the probability function. The artificial ant is, in this case, a software agent that moves from node to node on a network graph. The function to calculate the fitness (evaluate the better path) includes: length of the network edge, the coverage index, topology graph index, and pheromone trail left behind by other ant agents. Each agent modifies the environment in two different ways: 1) Local trail updating: As the ant moves between nodes it updates the amount of pheromone on the edge; and 2) Global trail updating: When all ants have completed a tour the ant that found the

(MBO) algorithm in multi-reservoir system optimisation

African Journals Online (AJOL)

A comparative study of marriage in honey bees optimisation (MBO) algorithm in ... A practical application of the marriage in honey bees optimisation (MBO) ... to those of other evolutionary algorithms, such as the genetic algorithm (GA), ant ...

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.

Pericarpial nectary-visiting ants do not provide fruit protection against pre-dispersal seed predators regardless of ant species composition and resource availability.

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Priscila Andre Sanz-Veiga

Full Text Available Extrafloral nectaries can occur in both vegetative and reproductive plant structures. In many Rubiaceae species in the Brazilian Cerrado, after corolla abscission, the floral nectary continues to secret nectar throughout fruit development originating post-floral pericarpial nectaries which commonly attract many ant species. The occurrence of such nectar secreting structures might be strategic for fruit protection against seed predators, as plants are expected to invest higher on more valuable and vulnerable parts. Here, we performed ant exclusion experiments to investigate whether the interaction with ants mediated by the pericarpial nectaries of Tocoyena formosa affects plant reproductive success by reducing the number of pre-dispersal seed predators. We also assessed whether ant protection was dependent on ant species composition and resource availability. Although most of the plants were visited by large and aggressive ant species, such as Ectatomma tuberculatum and species of the genus Camponotus, ants did not protect fruits against seed predators. Furthermore, the result of the interaction was neither related to ant species composition nor to the availability of resources. We suggest that these results may be related to the nature and behavior of the most important seed predators, like Hemicolpus abdominalis weevil which the exoskeleton toughness prevent it from being predated by most ant species. On the other hand, not explored factors, such as reward quality, local ant abundance, ant colony characteristics and/or the presence of alternative energetic sources could also account for variations in ant frequency, composition, and finally ant protective effects, highlighting the conditionality of facultative plant-ant mutualisms.

Exploring SWOT discharge algorithm accuracy on the Sacramento River

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Durand, M. T.; Yoon, Y.; Rodriguez, E.; Minear, J. T.; Andreadis, K.; Pavelsky, T. M.; Alsdorf, D. E.; Smith, L. C.; Bales, J. D.

2012-12-01

Scheduled for launch in 2019, the Surface Water and Ocean Topography (SWOT) satellite mission will utilize a Ka-band radar interferometer to measure river heights, widths, and slopes, globally, as well as characterize storage change in lakes and ocean surface dynamics with a spatial resolution ranging from 10 - 70 m, with temporal revisits on the order of a week. A discharge algorithm has been formulated to solve the inverse problem of characterizing river bathymetry and the roughness coefficient from SWOT observations. The algorithm uses a Bayesian Markov Chain estimation approach, treats rivers as sets of interconnected reaches (typically 5 km - 10 km in length), and produces best estimates of river bathymetry, roughness coefficient, and discharge, given SWOT observables. AirSWOT (the airborne version of SWOT) consists of a radar interferometer similar to SWOT, but mounted aboard an aircraft. AirSWOT spatial resolution will range from 1 - 35 m. In early 2013, AirSWOT will perform several flights over the Sacramento River, capturing river height, width, and slope at several different flow conditions. The Sacramento River presents an excellent target given that the river includes some stretches heavily affected by management (diversions, bypasses, etc.). AirSWOT measurements will be used to validate SWOT observation performance, but are also a unique opportunity for testing and demonstrating the capabilities and limitations of the discharge algorithm. This study uses HEC-RAS simulations of the Sacramento River to first, characterize expected discharge algorithm accuracy on the Sacramento River, and second to explore the required AirSWOT measurements needed to perform a successful inverse with the discharge algorithm. We focus on several specific research questions affecting algorithm performance: 1) To what extent do lateral inflows confound algorithm performance? We examine the ~100 km stretch of river from Colusa, CA to the Yolo Bypass, and investigate how the

2D Tsallis Entropy for Image Segmentation Based on Modified Chaotic Bat Algorithm

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Zhiwei Ye

2018-03-01

Full Text Available Image segmentation is a significant step in image analysis and computer vision. Many entropy based approaches have been presented in this topic; among them, Tsallis entropy is one of the best performing methods. However, 1D Tsallis entropy does not consider make use of the spatial correlation information within the neighborhood results might be ruined by noise. Therefore, 2D Tsallis entropy is proposed to solve the problem, and results are compared with 1D Fisher, 1D maximum entropy, 1D cross entropy, 1D Tsallis entropy, fuzzy entropy, 2D Fisher, 2D maximum entropy and 2D cross entropy. On the other hand, due to the existence of huge computational costs, meta-heuristics algorithms like genetic algorithm (GA, particle swarm optimization (PSO, ant colony optimization algorithm (ACO and differential evolution algorithm (DE are used to accelerate the 2D Tsallis entropy thresholding method. In this paper, considering 2D Tsallis entropy as a constrained optimization problem, the optimal thresholds are acquired by maximizing the objective function using a modified chaotic Bat algorithm (MCBA. The proposed algorithm has been tested on some actual and infrared images. The results are compared with that of PSO, GA, ACO and DE and demonstrate that the proposed method outperforms other approaches involved in the paper, which is a feasible and effective option for image segmentation.

Behind every great ant, there is a great gut

DEFF Research Database (Denmark)

Poulsen, Michael; Sapountzis, Panagiotis

2012-01-01

on the potential contribution of the ants’ gut symbionts. This issue of Molecular Ecology contains a study by Anderson et al. (2012), who take a comparative approach to explore the link between trophic levels and ant microbiomes, specifically, to address three main questions: (i) Do closely related herbivorous...... conserved gut microbiomes, suggesting symbiont functions that directly relate to dietary preference of the ant host. These findings suggest an ecological role of gut symbionts in ants, for example, in metabolism and/or protection, and the comparative approach taken supports a model of co-evolution between...... ant species and specific core symbiont microbiomes. This study, thereby, highlights the omnipresence and importance of gut symbioses—also in the Hymenoptera—and suggests that these hitherto overlooked microbes likely have contributed to the ecological success of the ants....

Introduction to Evolutionary Algorithms

CERN Document Server

Yu, Xinjie

2010-01-01

Evolutionary algorithms (EAs) are becoming increasingly attractive for researchers from various disciplines, such as operations research, computer science, industrial engineering, electrical engineering, social science, economics, etc. This book presents an insightful, comprehensive, and up-to-date treatment of EAs, such as genetic algorithms, differential evolution, evolution strategy, constraint optimization, multimodal optimization, multiobjective optimization, combinatorial optimization, evolvable hardware, estimation of distribution algorithms, ant colony optimization, particle swarm opti

TACD: a transportable ant colony discrimination model for corporate bankruptcy prediction

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Lalbakhsh, Pooia; Chen, Yi-Ping Phoebe

2017-05-01

This paper presents a transportable ant colony discrimination strategy (TACD) to predict corporate bankruptcy, a topic of vital importance that is attracting increasing interest in the field of economics. The proposed algorithm uses financial ratios to build a binary prediction model for companies with the two statuses of bankrupt and non-bankrupt. The algorithm takes advantage of an improved version of continuous ant colony optimisation (CACO) at the core, which is used to create an accurate, simple and understandable linear model for discrimination. This also enables the algorithm to work with continuous values, leading to more efficient learning and adaption by avoiding data discretisation. We conduct a comprehensive performance evaluation on three real-world data sets under a stratified cross-validation strategy. In three different scenarios, TACD is compared with 11 other bankruptcy prediction strategies. We also discuss the efficiency of the attribute selection methods used in the experiments. In addition to its simplicity and understandability, statistical significance tests prove the efficiency of TACD against the other prediction algorithms in both measures of AUC and accuracy.

Ant Foraging Behavior for Job Shop Problem

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Mahad Diyana Abdul

2016-01-01

Full Text Available Ant Colony Optimization (ACO is a new algorithm approach, inspired by the foraging behavior of real ants. It has frequently been applied to many optimization problems and one such problem is in solving the job shop problem (JSP. The JSP is a finite set of jobs processed on a finite set of machine where once a job initiates processing on a given machine, it must complete processing and uninterrupted. In solving the Job Shop Scheduling problem, the process is measure by the amount of time required in completing a job known as a makespan and minimizing the makespan is the main objective of this study. In this paper, we developed an ACO algorithm to minimize the makespan. A real set of problems from a metal company in Johor bahru, producing 20 parts with jobs involving the process of clinching, tapping and power press respectively. The result from this study shows that the proposed ACO heuristics managed to produce a god result in a short time.

Hybrid real-code ant colony optimisation for constrained mechanical design

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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.

Exploring high dimensional data with Butterfly: a novel classification algorithm based on discrete dynamical systems.

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Geraci, Joseph; Dharsee, Moyez; Nuin, Paulo; Haslehurst, Alexandria; Koti, Madhuri; Feilotter, Harriet E; Evans, Ken

2014-03-01

We introduce a novel method for visualizing high dimensional data via a discrete dynamical system. This method provides a 2D representation of the relationship between subjects according to a set of variables without geometric projections, transformed axes or principal components. The algorithm exploits a memory-type mechanism inherent in a certain class of discrete dynamical systems collectively referred to as the chaos game that are closely related to iterative function systems. The goal of the algorithm was to create a human readable representation of high dimensional patient data that was capable of detecting unrevealed subclusters of patients from within anticipated classifications. This provides a mechanism to further pursue a more personalized exploration of pathology when used with medical data. For clustering and classification protocols, the dynamical system portion of the algorithm is designed to come after some feature selection filter and before some model evaluation (e.g. clustering accuracy) protocol. In the version given here, a univariate features selection step is performed (in practice more complex feature selection methods are used), a discrete dynamical system is driven by this reduced set of variables (which results in a set of 2D cluster models), these models are evaluated for their accuracy (according to a user-defined binary classification) and finally a visual representation of the top classification models are returned. Thus, in addition to the visualization component, this methodology can be used for both supervised and unsupervised machine learning as the top performing models are returned in the protocol we describe here. Butterfly, the algorithm we introduce and provide working code for, uses a discrete dynamical system to classify high dimensional data and provide a 2D representation of the relationship between subjects. We report results on three datasets (two in the article; one in the appendix) including a public lung cancer

Metaheuristic algorithms for building Covering Arrays: A review

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Jimena Adriana Timaná-Peña

2016-09-01

Full Text Available Covering Arrays (CA are mathematical objects used in the functional testing of software components. They enable the testing of all interactions of a given size of input parameters in a procedure, function, or logical unit in general, using the minimum number of test cases. Building CA is a complex task (NP-complete problem that involves lengthy execution times and high computational loads. The most effective methods for building CAs are algebraic, Greedy, and metaheuristic-based. The latter have reported the best results to date. This paper presents a description of the major contributions made by a selection of different metaheuristics, including simulated annealing, tabu search, genetic algorithms, ant colony algorithms, particle swarm algorithms, and harmony search algorithms. It is worth noting that simulated annealing-based algorithms have evolved as the most competitive, and currently form the state of the art.

Meta-Heuristics in Short Scale Construction: Ant Colony Optimization and Genetic Algorithm.

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Schroeders, Ulrich; Wilhelm, Oliver; Olaru, Gabriel

2016-01-01

The advent of large-scale assessment, but also the more frequent use of longitudinal and multivariate approaches to measurement in psychological, educational, and sociological research, caused an increased demand for psychometrically sound short scales. Shortening scales economizes on valuable administration time, but might result in inadequate measures because reducing an item set could: a) change the internal structure of the measure, b) result in poorer reliability and measurement precision, c) deliver measures that cannot effectively discriminate between persons on the intended ability spectrum, and d) reduce test-criterion relations. Different approaches to abbreviate measures fare differently with respect to the above-mentioned problems. Therefore, we compare the quality and efficiency of three item selection strategies to derive short scales from an existing long version: a Stepwise COnfirmatory Factor Analytical approach (SCOFA) that maximizes factor loadings and two metaheuristics, specifically an Ant Colony Optimization (ACO) with a tailored user-defined optimization function and a Genetic Algorithm (GA) with an unspecific cost-reduction function. SCOFA compiled short versions were highly reliable, but had poor validity. In contrast, both metaheuristics outperformed SCOFA and produced efficient and psychometrically sound short versions (unidimensional, reliable, sensitive, and valid). We discuss under which circumstances ACO and GA produce equivalent results and provide recommendations for conditions in which it is advisable to use a metaheuristic with an unspecific out-of-the-box optimization function.

Identifying Ant-Mirid Spatial Interactions to Improve Biological Control in Cacao-Based Agroforestry System.

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Bagny Beilhe, Leïla; Piou, Cyril; Tadu, Zéphirin; Babin, Régis

2018-06-06

The use of ants for biological control of insect pests was the first reported case of conservation biological control. Direct and indirect community interactions between ants and pests lead to differential spatial pattern. We investigated spatial interactions between mirids, the major cocoa pest in West Africa and numerically dominant ant species, using bivariate point pattern analysis to identify potential biological control agents. We assume that potential biological control agents should display negative spatial interactions with mirids considering their niche overlap. The mirid/ant data were collected in complex cacao-based agroforestry systems sampled in three agroecological areas over a forest-savannah gradient in Cameroon. Three species, Crematogaster striatula Emery (Hymenoptera: Formicidae), Crematogaster clariventris Mayr (Hymenoptera: Formicidae), and Oecophylla longinoda Latreille (Hymenoptera: Formicidae) with high predator and aggressive behaviors were identified as dominant and showed negative spatial relationships with mirids. The weaver ant, O. longinoda was identified as the only potential biological control agent, considering its ubiquity in the plots, the similarity in niche requirements, and the spatial segregation with mirids resulting probably from exclusion mechanisms. Combining bivariate point pattern analysis to good knowledge of insect ecology was an effective method to identify a potentially good biological control agent.

Algorithm for Wireless Sensor Networks Based on Grid Management

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Geng Zhang

2014-05-01

Full Text Available This paper analyzes the key issues for wireless sensor network trust model and describes a method to build a wireless sensor network, such as the definition of trust for wireless sensor networks, computing and credibility of trust model application. And for the problem that nodes are vulnerable to attack, this paper proposed a grid-based trust algorithm by deep exploration trust model within the framework of credit management. Algorithm for node reliability screening and rotation schedule to cover parallel manner based on the implementation of the nodes within the area covered by trust. And analyze the results of the size of trust threshold has great influence on the safety and quality of coverage throughout the coverage area. The simulation tests the validity and correctness of the algorithm.

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

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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.

Hybrid ANFIS with ant colony optimization algorithm for prediction of shear wave velocity from a carbonate reservoir in Iran

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Hadi Fattahi

2016-12-01

Full Text Available Shear wave velocity (Vs data are key information for petrophysical, geophysical and geomechanical studies. Although compressional wave velocity (Vp measurements exist in almost all wells, shear wave velocity is not recorded for most of elderly wells due to lack of technologic tools. Furthermore, measurement of shear wave velocity is to some extent costly. This study proposes a novel methodology to remove aforementioned problems by use of hybrid adaptive neuro fuzzy inference system (ANFIS with ant colony optimization algorithm (ACO based on fuzzy c–means clustering (FCM and subtractive clustering (SCM. The ACO is combined with two ANFIS models for determining the optimal value of its user–defined parameters. The optimization implementation by the ACO significantly improves the generalization ability of the ANFIS models. These models are used in this study to formulate conventional well log data into Vs in a quick, cheap, and accurate manner. A total of 3030 data points was used for model construction and 833 data points were employed for assessment of ANFIS models. Finally, a comparison among ANFIS models, and six well–known empirical correlations demonstrated ANFIS models outperformed other methods. This strategy was successfully applied in the Marun reservoir, Iran.

Are ant feces nutrients for plants? A metabolomics approach to elucidate the nutritional effects on plants hosting weaver ants

DEFF Research Database (Denmark)

Vidkjær, Nanna Hjort; Wollenweber, Bernd; Gislum, René

2015-01-01

Weaver ants (genus Oecophylla) are tropical carnivorous ant species living in high numbers in the canopies of trees. The ants excrete copious amounts of fecal matter on leaf surfaces, and these feces may provide nutrients to host trees. This hypothesis is supported by studies of ant......-plant interactions involving other ant species that have demonstrated the transfer of nutrients from ants to plants. In this 7-months study, a GC–MS-based metabolomics approach along with an analysis of total nitrogen and carbon levels was used to study metabolic changes in ant-hosting Coffea arabica plants compared...... with control plants. The results showed elevated levels of total nitrogen, amino acids, fatty acids, caffeine, and secondary metabolites of the phenylpropanoid pathway in leaves from ant-hosting plants. Minor effects were observed for sugars, whereas little or no effect was observed for organic acids, despite...

JOB SHOP METHODOLOGY BASED ON AN ANT COLONY

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OMAR CASTRILLON

2009-01-01

Full Text Available The purpose of this study is to reduce the total process time (Makespan and to increase the machines working time, in a job shop environment, using a heuristic based on ant colony optimization. This work is developed in two phases: The first stage describes the identification and definition of heuristics for the sequential processes in the job shop. The second stage shows the effectiveness of the system in the traditional programming of production. A good solution, with 99% efficiency is found using this technique.

Leader-based and self-organized communication: modelling group-mass recruitment in ants.

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Collignon, Bertrand; Deneubourg, Jean Louis; Detrain, Claire

2012-11-21

For collective decisions to be made, the information acquired by experienced individuals about resources' location has to be shared with naïve individuals through recruitment. Here, we investigate the properties of collective responses arising from a leader-based recruitment and a self-organized communication by chemical trails. We develop a generalized model based on biological data drawn from Tetramorium caespitum ant species of which collective foraging relies on the coupling of group leading and trail recruitment. We show that for leader-based recruitment, small groups of recruits have to be guided in a very efficient way to allow a collective exploitation of food while large group requires less attention from their leader. In the case of self-organized recruitment through a chemical trail, a critical value of trail amount has to be laid per forager in order to launch collective food exploitation. Thereafter, ants can maintain collective foraging by emitting signal intensity below this threshold. Finally, we demonstrate how the coupling of both recruitment mechanisms may benefit to collectively foraging species. These theoretical results are then compared with experimental data from recruitment by T. caespitum ant colonies performing group-mass recruitment towards a single food source. We evidence the key role of leaders as initiators and catalysts of recruitment before this leader-based process is overtaken by self-organised communication through trails. This model brings new insights as well as a theoretical background to empirical studies about cooperative foraging in group-living species. Copyright © 2012 Elsevier Ltd. All rights reserved.

MGA trajectory planning with an ACO-inspired algorithm

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Ceriotti, Matteo; Vasile, Massimiliano

2010-11-01

Given a set of celestial bodies, the problem of finding an optimal sequence of swing-bys, deep space manoeuvres (DSM) and transfer arcs connecting the elements of the set is combinatorial in nature. The number of possible paths grows exponentially with the number of celestial bodies. Therefore, the design of an optimal multiple gravity assist (MGA) trajectory is a NP-hard mixed combinatorial-continuous problem. Its automated solution would greatly improve the design of future space missions, allowing the assessment of a large number of alternative mission options in a short time. This work proposes to formulate the complete automated design of a multiple gravity assist trajectory as an autonomous planning and scheduling problem. The resulting scheduled plan will provide the optimal planetary sequence and a good estimation of the set of associated optimal trajectories. The trajectory model consists of a sequence of celestial bodies connected by two-dimensional transfer arcs containing one DSM. For each transfer arc, the position of the planet and the spacecraft, at the time of arrival, are matched by varying the pericentre of the preceding swing-by, or the magnitude of the launch excess velocity, for the first arc. For each departure date, this model generates a full tree of possible transfers from the departure to the destination planet. Each leaf of the tree represents a planetary encounter and a possible way to reach that planet. An algorithm inspired by ant colony optimization (ACO) is devised to explore the space of possible plans. The ants explore the tree from departure to destination adding one node at the time: every time an ant is at a node, a probability function is used to select a feasible direction. This approach to automatic trajectory planning is applied to the design of optimal transfers to Saturn and among the Galilean moons of Jupiter. Solutions are compared to those found through more traditional genetic-algorithm techniques.

Swarm controlled emergence for ant clustering

DEFF Research Database (Denmark)

Scheidler, Alexander; Merkle, Daniel; Middendorf, Martin

2013-01-01

.g. moving robots, and clustering algorithms. Design/methodology/approach: Different types of control agents for that ant clustering model are designed by introducing slight changes to the behavioural rules of the normal agents. The clustering behaviour of the resulting swarms is investigated by extensive...... for future research to investigate the application of the method in other swarm systems. Swarm controlled emergence might be applied to control emergent effects in computing systems that consist of many autonomous components which make decentralized decisions based on local information. Practical...... simulation studies. Findings: It is shown that complex behavior can emerge in systems with two types of agents (normal agents and control agents). For a particular behavior of the control agents, an interesting swarm size dependent effect was found. The behaviour prevents clustering when the number...

Efficient sequential and parallel algorithms for finding edit distance based motifs.

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Pal, Soumitra; Xiao, Peng; Rajasekaran, Sanguthevar

2016-08-18

Motif search is an important step in extracting meaningful patterns from biological data. The general problem of motif search is intractable and there is a pressing need to develop efficient, exact and approximation algorithms to solve this problem. In this paper, we present several novel, exact, sequential and parallel algorithms for solving the (l,d) Edit-distance-based Motif Search (EMS) problem: given two integers l,d and n biological strings, find all strings of length l that appear in each input string with atmost d errors of types substitution, insertion and deletion. One popular technique to solve the problem is to explore for each input string the set of all possible l-mers that belong to the d-neighborhood of any substring of the input string and output those which are common for all input strings. We introduce a novel and provably efficient neighborhood exploration technique. We show that it is enough to consider the candidates in neighborhood which are at a distance exactly d. We compactly represent these candidate motifs using wildcard characters and efficiently explore them with very few repetitions. Our sequential algorithm uses a trie based data structure to efficiently store and sort the candidate motifs. Our parallel algorithm in a multi-core shared memory setting uses arrays for storing and a novel modification of radix-sort for sorting the candidate motifs. The algorithms for EMS are customarily evaluated on several challenging instances such as (8,1), (12,2), (16,3), (20,4), and so on. The best previously known algorithm, EMS1, is sequential and in estimated 3 days solves up to instance (16,3). Our sequential algorithms are more than 20 times faster on (16,3). On other hard instances such as (9,2), (11,3), (13,4), our algorithms are much faster. Our parallel algorithm has more than 600 % scaling performance while using 16 threads. Our algorithms have pushed up the state-of-the-art of EMS solvers and we believe that the techniques introduced in

Solving multi-objective job shop problem using nature-based algorithms: new Pareto approximation features

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Jarosław Rudy

2015-01-01

Full Text Available In this paper the job shop scheduling problem (JSP with minimizing two criteria simultaneously is considered. JSP is frequently used model in real world applications of combinatorial optimization. Multi-objective job shop problems (MOJSP were rarely studied. We implement and compare two multi-agent nature-based methods, namely ant colony optimization (ACO and genetic algorithm (GA for MOJSP. Both of those methods employ certain technique, taken from the multi-criteria decision analysis in order to establish ranking of solutions. ACO and GA differ in a method of keeping information about previously found solutions and their quality, which affects the course of the search. In result, new features of Pareto approximations provided by said algorithms are observed: aside from the slight superiority of the ACO method the Pareto frontier approximations provided by both methods are disjoint sets. Thus, both methods can be used to search mutually exclusive areas of the Pareto frontier.

Acanthopria and Mimopriella parasitoid wasps (Diapriidae) attack Cyphomyrmex fungus-growing ants (Formicidae, Attini)

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Fernández-Marín, Hermógenes; Zimmerman, Jess K.; Wcislo, William T.

2006-01-01

New World diapriine wasps are abundant and diverse, but the biology of most species is unknown. We provide the first description of the biology of diapriine wasps, Acanthopria spp. and Mimopriella sp., which attack the larvae of Cyphomyrmex fungus-growing ants. In Puerto Rico, the koinobiont parasitoids Acanthopria attack Cyphomyrmex minutus, while in Panama at least four morphospecies of Acanthopria and one of Mimopriella attack Cyphomyrmex rimosus. Of the total larvae per colony, 0 100% were parasitized, and 27 70% of the colonies per population were parasitized. Parasitism rate and colony size were negatively correlated for C. rimosus but not for C. minutus. Worker ants grasped at, bit, and in some cases, killed adult wasps that emerged in artificial nests or tried to enter natural nests. Parasitoid secondary sex ratios were female-biased for eclosing wasps, while field collections showed a male-biased sex ratio. Based on their abundance and success in attacking host ants, these minute wasps present excellent opportunities to explore how natural enemies impact ant colony demography and population biology.

Cloud Service Scheduling Algorithm Research and Optimization

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Hongyan Cui

2017-01-01

Full Text Available We propose a cloud service scheduling model that is referred to as the Task Scheduling System (TSS. In the user module, the process time of each task is in accordance with a general distribution. In the task scheduling module, we take a weighted sum of makespan and flowtime as the objective function and use an Ant Colony Optimization (ACO and a Genetic Algorithm (GA to solve the problem of cloud task scheduling. Simulation results show that the convergence speed and output performance of our Genetic Algorithm-Chaos Ant Colony Optimization (GA-CACO are optimal.

An explicit semantic relatedness measure based on random walk

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HU Sihui

2016-10-01

Full Text Available The semantic relatedness calculation of open domain knowledge network is a significant issue.In this paper,pheromone strategy is drawn from the thought of ant colony algorithm and is integrated into the random walk which is taken as the basic framework of calculating the semantic relatedness degree.The pheromone distribution is taken as a criterion of determining the tightness degree of semantic relatedness.A method of calculating semantic relatedness degree based on random walk is proposed and the exploration process of calculating the semantic relatedness degree is presented in a dominant way.The method mainly contains Path Select Model(PSM and Semantic Relatedness Computing Model(SRCM.PSM is used to simulate the path selection of ants and pheromone release.SRCM is used to calculate the semantic relatedness by utilizing the information returned by ants.The result indicates that the method could complete semantic relatedness calculation in linear complexity and extend the feasible strategy of semantic relatedness calculation.

Ant colony optimisation for scheduling of flexible job shop with multi-resources requirements

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Kalinowski Krzysztof

2017-01-01

Full Text Available The paper presents application of ant colony optimisation algorithm for scheduling multi-resources operations in flexible job shop type of production systems. Operations that require the participation of two or more resources are common in industrial practice, when planning are subject not only machines, but also other additional resources (personnel, tools, etc.. Resource requirements of operation are indicated indirectly by resource groups. The most important parameters of the resource model and resource groups are also described. A basic assumptions for ant colony algorithm used for scheduling in the considered model with multiresources requirements of operations is discussed. The main result of the research is the schema of metaheuristic that enables searching best-score solutions in manufacturing systems satisfying presented constraints.

Hybrid fuzzy charged system search algorithm based state estimation in distribution networks

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Sachidananda Prasad

2017-06-01

Full Text Available This paper proposes a new hybrid charged system search (CSS algorithm based state estimation in radial distribution networks in fuzzy framework. The objective of the optimization problem is to minimize the weighted square of the difference between the measured and the estimated quantity. The proposed method of state estimation considers bus voltage magnitude and phase angle as state variable along with some equality and inequality constraints for state estimation in distribution networks. A rule based fuzzy inference system has been designed to control the parameters of the CSS algorithm to achieve better balance between the exploration and exploitation capability of the algorithm. The efficiency of the proposed fuzzy adaptive charged system search (FACSS algorithm has been tested on standard IEEE 33-bus system and Indian 85-bus practical radial distribution system. The obtained results have been compared with the conventional CSS algorithm, weighted least square (WLS algorithm and particle swarm optimization (PSO for feasibility of the algorithm.

Entropic algorithms and the lid method as exploration tools for complex landscapes

DEFF Research Database (Denmark)

Barettin, Daniele; Sibani, Paolo

2011-01-01

to a single valley, are key to understand the dynamical properties of such systems. In this paper we combine the lid algorithm, a tool for landscape exploration previously applied to a range of models, with the Wang-Swendsen algorithm. To test this improved exploration tool, we consider a paradigmatic complex...... system, the Edwards-Andersom model in two and three spatial dimension. We find a striking difference between the energy dependence of the local density of states in the two cases: nearly flat in the first case, and nearly exponential in the second. The lid dependence of the data is analyzed to estimate...

Optimization of Nano-Process Deposition Parameters Based on Gravitational Search Algorithm

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Norlina Mohd Sabri

2016-06-01

Full Text Available This research is focusing on the radio frequency (RF magnetron sputtering process, a physical vapor deposition technique which is widely used in thin film production. This process requires the optimized combination of deposition parameters in order to obtain the desirable thin film. The conventional method in the optimization of the deposition parameters had been reported to be costly and time consuming due to its trial and error nature. Thus, gravitational search algorithm (GSA technique had been proposed to solve this nano-process parameters optimization problem. In this research, the optimized parameter combination was expected to produce the desirable electrical and optical properties of the thin film. The performance of GSA in this research was compared with that of Particle Swarm Optimization (PSO, Genetic Algorithm (GA, Artificial Immune System (AIS and Ant Colony Optimization (ACO. Based on the overall results, the GSA optimized parameter combination had generated the best electrical and an acceptable optical properties of thin film compared to the others. This computational experiment is expected to overcome the problem of having to conduct repetitive laboratory experiments in obtaining the most optimized parameter combination. Based on this initial experiment, the adaptation of GSA into this problem could offer a more efficient and productive way of depositing quality thin film in the fabrication process.

Ex-ante Evaluation of Publicly Funded R&D Projects

DEFF Research Database (Denmark)

Bulathsinhala, Nadika Anuruddhi

2015-01-01

can contribute to the further development of explorative and existing energy technologies that meet climate challenges. The aim of this paper is to investigate the ex-ante evaluation process of a Danish energy program with regard to exploration. The paper applies a qualitative approach, combining in...

Optimum Layout for Sensors in Water Distribution Networks through Ant Colony Algorithm: A Dual Use Vision

Directory of Open Access Journals (Sweden)

Seyed Mehdi Miri

2014-07-01

Full Text Available The accidental or intentional entry of contaminants or self-deterioration of the water quality within the network itself can severely harm public health. Efficient water quality monitoring is one of the most important tools to guarantee a reliable potable water supply to consumers of drinking water distribution systems. Considering the high purchase, installation and maintenance cost of sensors in water distribution networks deploying two independent sensor networks within one distribution system is not only bounded by physical constraints but also is not a cost-effective approach. Therefore, need for combining different objectives and designing sensor network to simultaneity satisfying these objectives is felt. Sensors should comply with dual use benefits. Sensor locations and types should be integrated not only for achieving water security goals but also for accomplishing other water utility objectives, such as satisfying regulatory monitoring requirements or collecting information to solve water quality problems. In this study, a dual use vision for the sensor layout problem in the municipal water networks, is formulated and solved with the ant colony algorithm.

A New Augmentation Based Algorithm for Extracting Maximal Chordal Subgraphs.

Science.gov (United States)

Bhowmick, Sanjukta; Chen, Tzu-Yi; Halappanavar, Mahantesh

2015-02-01

A graph is chordal if every cycle of length greater than three contains an edge between non-adjacent vertices. Chordal graphs are of interest both theoretically, since they admit polynomial time solutions to a range of NP-hard graph problems, and practically, since they arise in many applications including sparse linear algebra, computer vision, and computational biology. A maximal chordal subgraph is a chordal subgraph that is not a proper subgraph of any other chordal subgraph. Existing algorithms for computing maximal chordal subgraphs depend on dynamically ordering the vertices, which is an inherently sequential process and therefore limits the algorithms' parallelizability. In this paper we explore techniques to develop a scalable parallel algorithm for extracting a maximal chordal subgraph. We demonstrate that an earlier attempt at developing a parallel algorithm may induce a non-optimal vertex ordering and is therefore not guaranteed to terminate with a maximal chordal subgraph. We then give a new algorithm that first computes and then repeatedly augments a spanning chordal subgraph. After proving that the algorithm terminates with a maximal chordal subgraph, we then demonstrate that this algorithm is more amenable to parallelization and that the parallel version also terminates with a maximal chordal subgraph. That said, the complexity of the new algorithm is higher than that of the previous parallel algorithm, although the earlier algorithm computes a chordal subgraph which is not guaranteed to be maximal. We experimented with our augmentation-based algorithm on both synthetic and real-world graphs. We provide scalability results and also explore the effect of different choices for the initial spanning chordal subgraph on both the running time and on the number of edges in the maximal chordal subgraph.

Long-term olfactory memories are stabilised via protein synthesis in Camponotus fellah ants

DEFF Research Database (Denmark)

Guerrieri, Fernando Javier; D'Ettorre, Patrizia; Deveaud, J-M.

2011-01-01

-chain hydrocarbons, one paired with sucrose and the other with quinine solution. Differential conditioning leads to the formation of a long-term memory retrievable at least 72¿h after training. Long-term memory consolidation was impaired by the ingestion of cycloheximide, a protein synthesis blocker, prior...... to conditioning. Cycloheximide did not impair acquisition of either short-term memory (10¿min) or early and late mid-term memories (1 or 12¿h). These results show that, upon olfactory learning, ants form different memories with variable molecular bases. While short- and mid-term memories do not require protein...... synthesis, long-term memories are stabilised via protein synthesis. Our behavioural protocol opens interesting research avenues to explore the cellular and molecular bases of olfactory learning and memory in ants....

Fracture enhancement based on artificial ants and fuzzy c-means clustering (FCMC) in Dezful Embayment of Iran

International Nuclear Information System (INIS)

Nasseri, Aynur; Mohammadzadeh, Mohammad Jafar; Tabatabaei Raeisi, S Hashem

2015-01-01

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

Ant-plant mutualism: a dietary by-product of a tropical ant's macronutrient requirements.

Science.gov (United States)

Arcila Hernández, Lina M; Sanders, Jon G; Miller, Gabriel A; Ravenscraft, Alison; Frederickson, Megan E

2017-12-01

Many arboreal ants depend on myrmecophytic plants for both food and shelter; in return, these ants defend their host plants against herbivores, which are often insects. Ant-plant and other mutualisms do not necessarily involve the exchange of costly rewards or services; they may instead result from by-product benefits, or positive outcomes that do not entail a cost for one or both partners. Here, we examined whether the plant-ant Allomerus octoarticulatus pays a short-term cost to defend their host plants against herbivores, or whether plant defense is a by-product benefit of ant foraging for insect prey. Because the food offered by ant-plants is usually nitrogen-poor, arboreal ants may balance their diets by consuming insect prey or associating with microbial symbionts to acquire nitrogen, potentially shifting the costs and benefits of plant defense for the ant partner. To determine the effect of ant diet on an ant-plant mutualism, we compared the behavior, morphology, fitness, stable isotope signatures, and gaster microbiomes of A. octoarticulatus ants nesting in Cordia nodosa trees maintained for nearly a year with or without insect herbivores. At the end of the experiment, ants from herbivore exclosures preferred protein-rich baits more than ants in the control (i.e., herbivores present) treatment. Furthermore, workers in the control treatment were heavier than in the herbivore-exclusion treatment, and worker mass predicted reproductive output, suggesting that foraging for insect prey directly increased ant colony fitness. The gaster microbiome of ants was not significantly affected by the herbivore exclusion treatment. We conclude that the defensive behavior of some phytoecious ants is a by-product of their need for external protein sources; thus, the consumption of insect herbivores by ants benefits both the ant colony and the host plant. © 2017 by the Ecological Society of America.

COOBBO: A Novel Opposition-Based Soft Computing Algorithm for TSP Problems

Directory of Open Access Journals (Sweden)

Qingzheng Xu

2014-12-01

Full Text Available In this paper, we propose a novel definition of opposite path. Its core feature is that the sequence of candidate paths and the distances between adjacent nodes in the tour are considered simultaneously. In a sense, the candidate path and its corresponding opposite path have the same (or similar at least distance to the optimal path in the current population. Based on an accepted framework for employing opposition-based learning, Oppositional Biogeography-Based Optimization using the Current Optimum, called COOBBO algorithm, is introduced to solve traveling salesman problems. We demonstrate its performance on eight benchmark problems and compare it with other optimization algorithms. Simulation results illustrate that the excellent performance of our proposed algorithm is attributed to the distinct definition of opposite path. In addition, its great strength lies in exploitation for enhancing the solution accuracy, not exploration for improving the population diversity. Finally, by comparing different version of COOBBO, another conclusion is that each successful opposition-based soft computing algorithm needs to adjust and remain a good balance between backward adjacent node and forward adjacent node.

Host ant independent oviposition in the parasitic butterfly Maculinea alcon

DEFF Research Database (Denmark)

Fürst, Matthias A; Nash, David Richard

2010-01-01

to host-ant nests and non-host-ant nests, and the number and position of eggs attached were assessed. Our results show no evidence for host-ant-based oviposition in M. alcon, but support an oviposition strategy based on plant characteristics. This suggests that careful management of host-ant distribution......Parasitic Maculinea alcon butterflies can only develop in nests of a subset of available Myrmica ant species, so female butterflies have been hypothesized to preferentially lay eggs on plants close to colonies of the correct host ants. Previous correlational investigations of host......-ant-dependent oviposition in this and other Maculinea species have, however, shown equivocal results, leading to a long-term controversy over support for this hypothesis. We therefore conducted a controlled field experiment to study the egg-laying behaviour of M. alcon. Matched potted Gentiana plants were set out close...

Fuzzy Expert System based on a Novel Hybrid Stem Cell (HSC) Algorithm for Classification of Micro Array Data.

Science.gov (United States)

Vijay, S Arul Antran; GaneshKumar, P

2018-02-21

In the growing scenario, microarray data is extensively used since it provides a more comprehensive understanding of genetic variants among diseases. As the gene expression samples have high dimensionality it becomes tedious to analyze the samples manually. Hence an automated system is needed to analyze these samples. The fuzzy expert system offers a clear classification when compared to the machine learning and statistical methodologies. In fuzzy classification, knowledge acquisition would be a major concern. Despite several existing approaches for knowledge acquisition much effort is necessary to enhance the learning process. This paper proposes an innovative Hybrid Stem Cell (HSC) algorithm that utilizes Ant Colony optimization and Stem Cell algorithm for designing fuzzy classification system to extract the informative rules to form the membership functions from the microarray dataset. The HSC algorithm uses a novel Adaptive Stem Cell Optimization (ASCO) to improve the points of membership function and Ant Colony Optimization to produce the near optimum rule set. In order to extract the most informative genes from the large microarray dataset a method called Mutual Information is used. The performance results of the proposed technique evaluated using the five microarray datasets are simulated. These results prove that the proposed Hybrid Stem Cell (HSC) algorithm produces a precise fuzzy system than the existing methodologies.

Application of ant colony optimization in NPP classification fault location

International Nuclear Information System (INIS)

Xie Chunli; Liu Yongkuo; Xia Hong

2009-01-01

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)

EAGLE: 'EAGLE'Is an' Algorithmic Graph Library for Exploration

Energy Technology Data Exchange (ETDEWEB)

2015-01-16

The Resource Description Framework (RDF) and SPARQL Protocol and RDF Query Language (SPARQL) were introduced about a decade ago to enable flexible schema-free data interchange on the Semantic Web. Today data scientists use the framework as a scalable graph representation for integrating, querying, exploring and analyzing data sets hosted at different sources. With increasing adoption, the need for graph mining capabilities for the Semantic Web has emerged. Today there is no tools to conduct "graph mining" on RDF standard data sets. We address that need through implementation of popular iterative Graph Mining algorithms (Triangle count, Connected component analysis, degree distribution, diversity degree, PageRank, etc.). We implement these algorithms as SPARQL queries, wrapped within Python scripts and call our software tool as EAGLE. In RDF style, EAGLE stands for "EAGLE 'Is an' algorithmic graph library for exploration. EAGLE is like 'MATLAB' for 'Linked Data.'

Tracing the rise of ants - out of the ground.

Directory of Open Access Journals (Sweden)

Andrea Lucky

Full Text Available The evolution of ants (Hymenoptera: Formicidae is increasingly well-understood due to recent phylogenetic analyses, along with estimates of divergence times and diversification rates. Yet, leading hypotheses regarding the ancestral habitat of ants conflict with new findings that early ant lineages are cryptic and subterranean. Where the ants evolved, in respect to habitat, and how habitat shifts took place over time have not been formally tested. Here, we reconstruct the habitat transitions of crown-group ants through time, focusing on where they nest and forage (in the canopy, litter, or soil. Based on ancestral character reconstructions, we show that in contrast to the current consensus based on verbal arguments that ants evolved in tropical leaf litter, the soil is supported as the ancestral stratum of all ants. We also find subsequent movements up into the litter and, in some cases, into the canopy. Given the global importance of ants, because of their diversity, ecological influence and status as the most successful eusocial lineage on Earth, understanding the early evolution of this lineage provides insight into the factors that made this group so successful today.

Ant Larval Demand Reduces Aphid Colony Growth Rates in an Ant-Aphid Interaction

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James M. Cook

2012-02-01

Full Text Available 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 experiment, we tested whether the presence of larvae in Lasius niger ant colonies affected the growth rate of Aphis fabae colonies. Other explanatory variables tested were the origin of ant colonies (two separate colonies were used and previous diet (sugar only or sugar and protein. We found that the presence of larvae in the ant colony significantly reduced the growth rate of aphid colonies. Previous diet and colony origin did not affect aphid colony growth rates. Our results suggest that ant colonies balance the flow of two separate resources from aphid colonies- renewable sugars or a protein-rich meal, depending on demand from ant larvae within the nest. Aphid payoffs from the ant-aphid interaction may change on a seasonal basis, as the demand from larvae within the ant colony waxes and wanes.

Visualization of Metabolic Interaction Networks in Microbial Communities Using VisANT 5.0.

Directory of Open Access Journals (Sweden)

Brian R Granger

2016-04-01

Full Text Available The complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space, a dynamic stoichiometric modeling framework. Using VisANT's unique metagraph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues. VisANT is freely available at: http://visant.bu.edu and COMETS at http://comets.bu.edu.

Visualization of Metabolic Interaction Networks in Microbial Communities Using VisANT 5.0.

Science.gov (United States)

Granger, Brian R; Chang, Yi-Chien; Wang, Yan; DeLisi, Charles; Segrè, Daniel; Hu, Zhenjun

2016-04-01

The complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space), a dynamic stoichiometric modeling framework. Using VisANT's unique metagraph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues. VisANT is freely available at: http://visant.bu.edu and COMETS at http://comets.bu.edu.

Parameter estimation of Lorenz chaotic system using a hybrid swarm intelligence algorithm

International Nuclear Information System (INIS)

Lazzús, Juan A.; Rivera, Marco; López-Caraballo, Carlos H.

2016-01-01

A novel hybrid swarm intelligence algorithm for chaotic system parameter estimation is present. For this purpose, the parameters estimation on Lorenz systems is formulated as a multidimensional problem, and a hybrid approach based on particle swarm optimization with ant colony optimization (PSO–ACO) is implemented to solve this problem. Firstly, the performance of the proposed PSO–ACO algorithm is tested on a set of three representative benchmark functions, and the impact of the parameter settings on PSO–ACO efficiency is studied. Secondly, the parameter estimation is converted into an optimization problem on a three-dimensional Lorenz system. Numerical simulations on Lorenz model and comparisons with results obtained by other algorithms showed that PSO–ACO is a very powerful tool for parameter estimation with high accuracy and low deviations. - Highlights: • PSO–ACO combined particle swarm optimization with ant colony optimization. • This study is the first research of PSO–ACO to estimate parameters of chaotic systems. • PSO–ACO algorithm can identify the parameters of the three-dimensional Lorenz system with low deviations. • PSO–ACO is a very powerful tool for the parameter estimation on other chaotic system.

Parameter estimation of Lorenz chaotic system using a hybrid swarm intelligence algorithm

Energy Technology Data Exchange (ETDEWEB)

Lazzús, Juan A., E-mail: jlazzus@dfuls.cl; Rivera, Marco; López-Caraballo, Carlos H.

2016-03-11

A novel hybrid swarm intelligence algorithm for chaotic system parameter estimation is present. For this purpose, the parameters estimation on Lorenz systems is formulated as a multidimensional problem, and a hybrid approach based on particle swarm optimization with ant colony optimization (PSO–ACO) is implemented to solve this problem. Firstly, the performance of the proposed PSO–ACO algorithm is tested on a set of three representative benchmark functions, and the impact of the parameter settings on PSO–ACO efficiency is studied. Secondly, the parameter estimation is converted into an optimization problem on a three-dimensional Lorenz system. Numerical simulations on Lorenz model and comparisons with results obtained by other algorithms showed that PSO–ACO is a very powerful tool for parameter estimation with high accuracy and low deviations. - Highlights: • PSO–ACO combined particle swarm optimization with ant colony optimization. • This study is the first research of PSO–ACO to estimate parameters of chaotic systems. • PSO–ACO algorithm can identify the parameters of the three-dimensional Lorenz system with low deviations. • PSO–ACO is a very powerful tool for the parameter estimation on other chaotic system.

Survey on Recent Research and Implementation of Ant Colony Optimization in Various Engineering Applications

Directory of Open Access Journals (Sweden)

Mohan B. Chandra

2011-08-01

Full Text Available Ant colony optimization (ACO takes inspiration from the foraging behaviour of real ant species. This ACO exploits a similar mechanism for solving optimization problems for the various engineering field of study. Many successful implementations using ACO are now available in many applications. This paper reviewing varies systematic approach on recent research and implementation of ACO. Finally it presents the experimental result of ACO which is applied for routing problem and compared with existing algorithms.

Disease dynamics in a specialized parasite of ant societies.

Directory of Open Access Journals (Sweden)

Sandra B Andersen

Full Text Available Coevolution between ant colonies and their rare specialized parasites are intriguing, because lethal infections of workers may correspond to tolerable chronic diseases of colonies, but the parasite adaptations that allow stable coexistence with ants are virtually unknown. We explore the trade-offs experienced by Ophiocordyceps parasites manipulating ants into dying in nearby graveyards. We used field data from Brazil and Thailand to parameterize and fit a model for the growth rate of graveyards. We show that parasite pressure is much lower than the abundance of ant cadavers suggests and that hyperparasites often castrate Ophiocordyceps. However, once fruiting bodies become sexually mature they appear robust. Such parasite life-history traits are consistent with iteroparity--a reproductive strategy rarely considered in fungi. We discuss how tropical habitats with high biodiversity of hyperparasites and high spore mortality has likely been crucial for the evolution and maintenance of iteroparity in parasites with low dispersal potential.

Opposition-Based Adaptive Fireworks Algorithm

Directory of Open Access Journals (Sweden)

Chibing Gong

2016-07-01

Full Text Available A fireworks algorithm (FWA is a recent swarm intelligence algorithm that is inspired by observing fireworks explosions. An adaptive fireworks algorithm (AFWA proposes additional adaptive amplitudes to improve the performance of the enhanced fireworks algorithm (EFWA. The purpose of this paper is to add opposition-based learning (OBL to AFWA with the goal of further boosting performance and achieving global optimization. Twelve benchmark functions are tested in use of an opposition-based adaptive fireworks algorithm (OAFWA. The final results conclude that OAFWA significantly outperformed EFWA and AFWA in terms of solution accuracy. Additionally, OAFWA was compared with a bat algorithm (BA, differential evolution (DE, self-adapting control parameters in differential evolution (jDE, a firefly algorithm (FA, and a standard particle swarm optimization 2011 (SPSO2011 algorithm. The research results indicate that OAFWA ranks the highest of the six algorithms for both solution accuracy and runtime cost.

Opposition-Based Adaptive Fireworks Algorithm

OpenAIRE

Chibing Gong

2016-01-01

A fireworks algorithm (FWA) is a recent swarm intelligence algorithm that is inspired by observing fireworks explosions. An adaptive fireworks algorithm (AFWA) proposes additional adaptive amplitudes to improve the performance of the enhanced fireworks algorithm (EFWA). The purpose of this paper is to add opposition-based learning (OBL) to AFWA with the goal of further boosting performance and achieving global optimization. Twelve benchmark functions are tested in use of an opposition-based a...

Genetic algorithm based separation cascade optimization

International Nuclear Information System (INIS)

Mahendra, A.K.; Sanyal, A.; Gouthaman, G.; Bera, T.K.

2008-01-01

The conventional separation cascade design procedure does not give an optimum design because of squaring-off, variation of flow rates and separation factor of the element with respect to stage location. Multi-component isotope separation further complicates the design procedure. Cascade design can be stated as a constrained multi-objective optimization. Cascade's expectation from the separating element is multi-objective i.e. overall separation factor, cut, optimum feed and separative power. Decision maker may aspire for more comprehensive multi-objective goals where optimization of cascade is coupled with the exploration of separating element optimization vector space. In real life there are many issues which make it important to understand the decision maker's perception of cost-quality-speed trade-off and consistency of preferences. Genetic algorithm (GA) is one such evolutionary technique that can be used for cascade design optimization. This paper addresses various issues involved in the GA based multi-objective optimization of the separation cascade. Reference point based optimization methodology with GA based Pareto optimality concept for separation cascade was found pragmatic and promising. This method should be explored, tested, examined and further developed for binary as well as multi-component separations. (author)

A Multipath Routing Protocol Based on Clustering and Ant Colony Optimization for Wireless Sensor Networks

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Jing Yang

2010-05-01

Full Text Available For monitoring burst events in a kind of reactive wireless sensor networks (WSNs, a multipath routing protocol (MRP based on dynamic clustering and ant colony optimization (ACO is proposed.. Such an approach can maximize the network lifetime and reduce the energy consumption. An important attribute of WSNs is their limited power supply, and therefore some metrics (such as energy consumption of communication among nodes, residual energy, path length were considered as very important criteria while designing routing in the MRP. Firstly, a cluster head (CH is selected among nodes located in the event area according to some parameters, such as residual energy. Secondly, an improved ACO algorithm is applied in the search for multiple paths between the CH and sink node. Finally, the CH dynamically chooses a route to transmit data with a probability that depends on many path metrics, such as energy consumption. The simulation results show that MRP can prolong the network lifetime, as well as balance of energy consumption among nodes and reduce the average energy consumption effectively.

Evolving Stochastic Learning Algorithm based on Tsallis entropic index

Science.gov (United States)

Anastasiadis, A. D.; Magoulas, G. D.

2006-03-01

In this paper, inspired from our previous algorithm, which was based on the theory of Tsallis statistical mechanics, we develop a new evolving stochastic learning algorithm for neural networks. The new algorithm combines deterministic and stochastic search steps by employing a different adaptive stepsize for each network weight, and applies a form of noise that is characterized by the nonextensive entropic index q, regulated by a weight decay term. The behavior of the learning algorithm can be made more stochastic or deterministic depending on the trade off between the temperature T and the q values. This is achieved by introducing a formula that defines a time-dependent relationship between these two important learning parameters. Our experimental study verifies that there are indeed improvements in the convergence speed of this new evolving stochastic learning algorithm, which makes learning faster than using the original Hybrid Learning Scheme (HLS). In addition, experiments are conducted to explore the influence of the entropic index q and temperature T on the convergence speed and stability of the proposed method.

Fire Ant Bites

Science.gov (United States)

... Favorite Name: Category: Share: Yes No, Keep Private Fire Ant Bites Share | Fire ants are aggressive, venomous insects that have pinching ... across the United States, even into Puerto Rico. Fire ant stings usually occur on the feet or ...

Algorithms bio-inspired for the pattern obtention of control bars in BWR reactors; Algoritmos bio-inspirados para la obtencion de patrones de barras de control en reactores BWR

Energy Technology Data Exchange (ETDEWEB)

Ortiz, J.J.; Perusquia, R.; Montes, J.L. [ININ, 52045 Ocoyoacac, Estado de Mexico (Mexico)

2003-07-01

In this work methods based on Genetic Algorithms and Systems based on ant colonies for the obtention of the patterns of control bars of an equilibrium cycle of 18 months for the Laguna Verde nuclear power station are presented. A comparison of obtained results with the methods and with those of design of such equilibrium cycle is presented. As consequence of the study, it was found that the algorithm based on the ant colonies reached to diminish the coast down period (decrease of power at the end of the cycle) in five and half days with respect to the original design what represents an annual saving of $US 100,000. (Author)

Algorithmic strategies for FPGA-based vision

OpenAIRE

Lim, Yoong Kang

2016-01-01

As demands for real-time computer vision applications increase, implementations on alternative architectures have been explored. These architectures include Field-Programmable Gate Arrays (FPGAs), which offer a high degree of flexibility and parallelism. A problem with this is that many computer vision algorithms have been optimized for serial processing, and this often does not map well to FPGA implementation. This thesis introduces the concept of FPGA-tailored computer vision algorithms...

Dynamic route guidance algorithm based algorithm based on artificial immune system

Institute of Scientific and Technical Information of China (English)

无

2007-01-01

To improve the performance of the K-shortest paths search in intelligent traffic guidance systems,this paper proposes an optimal search algorithm based on the intelligent optimization search theory and the memphor mechanism of vertebrate immune systems.This algorithm,applied to the urban traffic network model established by the node-expanding method,can expediently realize K-shortest paths search in the urban traffic guidance systems.Because of the immune memory and global parallel search ability from artificial immune systems,K shortest paths can be found without any repeat,which indicates evidently the superiority of the algorithm to the conventional ones.Not only does it perform a better parallelism,the algorithm also prevents premature phenomenon that often occurs in genetic algorithms.Thus,it is especially suitable for real-time requirement of the traffic guidance system and other engineering optimal applications.A case study verifies the efficiency and the practicability of the algorithm aforementioned.

Multithreshold Segmentation by Using an Algorithm Based on the Behavior of Locust Swarms

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Erik Cuevas

2015-01-01

Full Text Available As an alternative to classical techniques, the problem of image segmentation has also been handled through evolutionary methods. Recently, several algorithms based on evolutionary principles have been successfully applied to image segmentation with interesting performances. However, most of them maintain two important limitations: (1 they frequently obtain suboptimal results (misclassifications as a consequence of an inappropriate balance between exploration and exploitation in their search strategies; (2 the number of classes is fixed and known in advance. This paper presents an algorithm for the automatic selection of pixel classes for image segmentation. The proposed method combines a novel evolutionary method with the definition of a new objective function that appropriately evaluates the segmentation quality with respect to the number of classes. The new evolutionary algorithm, called Locust Search (LS, is based on the behavior of swarms of locusts. Different to the most of existent evolutionary algorithms, it explicitly avoids the concentration of individuals in the best positions, avoiding critical flaws such as the premature convergence to suboptimal solutions and the limited exploration-exploitation balance. Experimental tests over several benchmark functions and images validate the efficiency of the proposed technique with regard to accuracy and robustness.

Oecophylla smaragdina food conversion efficiency: prospects for ant farming

DEFF Research Database (Denmark)

Offenberg, Hans Joachim

2011-01-01

can be combined with the use of the ants in biological control programmes in tropical plantations where pest insects are converted into ant biomass. To assess the cost-benefits of ant farming based on artificial feeding, food consumption and food conversion efficiency (ECI) of Oecophylla smaragdina......Oecophylla ants are sold at high prices on several commercial markets as a human delicacy, as pet food or as traditional medicine. Currently markets are supplied by ants collected from the wild; however, an increasing interest in ant farming exists as all harvest is easily sold and as ant farming...... selling prices these efficiencies led to rates of return from 1.52 to 4.56, respectively, if: (i) protein is supplied from commercial products; or (ii) alternatively supplied from free sources such as insects and kitchen waste. These results suggest that Oecophylla ant farming may become highly profitable...

A fast identification algorithm for Box-Cox transformation based radial basis function neural network.

Science.gov (United States)

Hong, Xia

2006-07-01

In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.

Research of Ant Colony Optimized Adaptive Control Strategy for Hybrid Electric Vehicle

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Linhui Li

2014-01-01

Full Text Available Energy management control strategy of hybrid electric vehicle has a great influence on the vehicle fuel consumption with electric motors adding to the traditional vehicle power system. As vehicle real driving cycles seem to be uncertain, the dynamic driving cycles will have an impact on control strategy’s energy-saving effect. In order to better adapt the dynamic driving cycles, control strategy should have the ability to recognize the real-time driving cycle and adaptively adjust to the corresponding off-line optimal control parameters. In this paper, four types of representative driving cycles are constructed based on the actual vehicle operating data, and a fuzzy driving cycle recognition algorithm is proposed for online recognizing the type of actual driving cycle. Then, based on the equivalent fuel consumption minimization strategy, an ant colony optimization algorithm is utilized to search the optimal control parameters “charge and discharge equivalent factors” for each type of representative driving cycle. At last, the simulation experiments are conducted to verify the accuracy of the proposed fuzzy recognition algorithm and the validity of the designed control strategy optimization method.

An improved feature extraction algorithm based on KAZE for multi-spectral image

Science.gov (United States)

Yang, Jianping; Li, Jun

2018-02-01

Multi-spectral image contains abundant spectral information, which is widely used in all fields like resource exploration, meteorological observation and modern military. Image preprocessing, such as image feature extraction and matching, is indispensable while dealing with multi-spectral remote sensing image. Although the feature matching algorithm based on linear scale such as SIFT and SURF performs strong on robustness, the local accuracy cannot be guaranteed. Therefore, this paper proposes an improved KAZE algorithm, which is based on nonlinear scale, to raise the number of feature and to enhance the matching rate by using the adjusted-cosine vector. The experiment result shows that the number of feature and the matching rate of the improved KAZE are remarkably than the original KAZE algorithm.

Archimedean copula estimation of distribution algorithm based on artificial bee colony algorithm

Institute of Scientific and Technical Information of China (English)

Haidong Xu; Mingyan Jiang; Kun Xu

2015-01-01

The artificial bee colony (ABC) algorithm is a com-petitive stochastic population-based optimization algorithm. How-ever, the ABC algorithm does not use the social information and lacks the knowledge of the problem structure, which leads to in-sufficiency in both convergent speed and searching precision. Archimedean copula estimation of distribution algorithm (ACEDA) is a relatively simple, time-economic and multivariate correlated EDA. This paper proposes a novel hybrid algorithm based on the ABC algorithm and ACEDA cal ed Archimedean copula estima-tion of distribution based on the artificial bee colony (ACABC) algorithm. The hybrid algorithm utilizes ACEDA to estimate the distribution model and then uses the information to help artificial bees to search more efficiently in the search space. Six bench-mark functions are introduced to assess the performance of the ACABC algorithm on numerical function optimization. Experimen-tal results show that the ACABC algorithm converges much faster with greater precision compared with the ABC algorithm, ACEDA and the global best (gbest)-guided ABC (GABC) algorithm in most of the experiments.

Ant aggression and evolutionary stability in plant-ant and plant-pollinator mutualistic interactions.

Science.gov (United States)

Oña, L; Lachmann, M

2011-03-01

Mutualistic partners derive a benefit from their interaction, but this benefit can come at a cost. This is the case for plant-ant and plant-pollinator mutualistic associations. In exchange for protection from herbivores provided by the resident ants, plants supply various kinds of resources or nests to the ants. Most ant-myrmecophyte mutualisms are horizontally transmitted, and therefore, partners share an interest in growth but not in reproduction. This lack of alignment in fitness interests between plants and ants drives a conflict between them: ants can attack pollinators that cross-fertilize the host plants. Using a mathematical model, we define a threshold in ant aggressiveness determining pollinator survival or elimination on the host plant. In our model we observed that, all else being equal, facultative interactions result in pollinator extinction for lower levels of ant aggressiveness than obligatory interactions. We propose that the capacity to discriminate pollinators from herbivores should not often evolve in ants, and when it does it will be when the plants exhibit limited dispersal in an environment that is not seed saturated so that each seed produced can effectively generate a new offspring or if ants acquire an extra benefit from pollination (e.g. if ants eat fruit). We suggest specific mutualism examples where these hypotheses can be tested empirically. © 2010 The Authors. Journal of Evolutionary Biology © 2010 European Society For Evolutionary Biology.

Pattern Nulling of Linear Antenna Arrays Using Backtracking Search Optimization Algorithm

Directory of Open Access Journals (Sweden)

Kerim Guney

2015-01-01

Full Text Available An evolutionary method based on backtracking search optimization algorithm (BSA is proposed for linear antenna array pattern synthesis with prescribed nulls at interference directions. Pattern nulling is obtained by controlling only the amplitude, position, and phase of the antenna array elements. BSA is an innovative metaheuristic technique based on an iterative process. Various numerical examples of linear array patterns with the prescribed single, multiple, and wide nulls are given to illustrate the performance and flexibility of BSA. The results obtained by BSA are compared with the results of the following seventeen algorithms: particle swarm optimization (PSO, genetic algorithm (GA, modified touring ant colony algorithm (MTACO, quadratic programming method (QPM, bacterial foraging algorithm (BFA, bees algorithm (BA, clonal selection algorithm (CLONALG, plant growth simulation algorithm (PGSA, tabu search algorithm (TSA, memetic algorithm (MA, nondominated sorting GA-2 (NSGA-2, multiobjective differential evolution (MODE, decomposition with differential evolution (MOEA/D-DE, comprehensive learning PSO (CLPSO, harmony search algorithm (HSA, seeker optimization algorithm (SOA, and mean variance mapping optimization (MVMO. The simulation results show that the linear antenna array synthesis using BSA provides low side-lobe levels and deep null levels.

Ant colony algorithm implementation in electron and photon Monte Carlo transport: application to the commissioning of radiosurgery photon beams.

Science.gov (United States)

García-Pareja, S; Galán, P; Manzano, F; Brualla, L; Lallena, A M

2010-07-01

In this work, the authors describe an approach which has been developed to drive the application of different variance-reduction techniques to the Monte Carlo simulation of photon and electron transport in clinical accelerators. The new approach considers the following techniques: Russian roulette, splitting, a modified version of the directional bremsstrahlung splitting, and the azimuthal particle redistribution. Their application is controlled by an ant colony algorithm based on an importance map. The procedure has been applied to radiosurgery beams. Specifically, the authors have calculated depth-dose profiles, off-axis ratios, and output factors, quantities usually considered in the commissioning of these beams. The agreement between Monte Carlo results and the corresponding measurements is within approximately 3%/0.3 mm for the central axis percentage depth dose and the dose profiles. The importance map generated in the calculation can be used to discuss simulation details in the different parts of the geometry in a simple way. The simulation CPU times are comparable to those needed within other approaches common in this field. The new approach is competitive with those previously used in this kind of problems (PSF generation or source models) and has some practical advantages that make it to be a good tool to simulate the radiation transport in problems where the quantities of interest are difficult to obtain because of low statistics.

Ant colony algorithm implementation in electron and photon Monte Carlo transport: Application to the commissioning of radiosurgery photon beams

Energy Technology Data Exchange (ETDEWEB)

Garcia-Pareja, S.; Galan, P.; Manzano, F.; Brualla, L.; Lallena, A. M. [Servicio de Radiofisica Hospitalaria, Hospital Regional Universitario ' ' Carlos Haya' ' , Avda. Carlos Haya s/n, E-29010 Malaga (Spain); Unidad de Radiofisica Hospitalaria, Hospital Xanit Internacional, Avda. de los Argonautas s/n, E-29630 Benalmadena (Malaga) (Spain); NCTeam, Strahlenklinik, Universitaetsklinikum Essen, Hufelandstr. 55, D-45122 Essen (Germany); Departamento de Fisica Atomica, Molecular y Nuclear, Universidad de Granada, E-18071 Granada (Spain)

2010-07-15

Purpose: In this work, the authors describe an approach which has been developed to drive the application of different variance-reduction techniques to the Monte Carlo simulation of photon and electron transport in clinical accelerators. Methods: The new approach considers the following techniques: Russian roulette, splitting, a modified version of the directional bremsstrahlung splitting, and the azimuthal particle redistribution. Their application is controlled by an ant colony algorithm based on an importance map. Results: The procedure has been applied to radiosurgery beams. Specifically, the authors have calculated depth-dose profiles, off-axis ratios, and output factors, quantities usually considered in the commissioning of these beams. The agreement between Monte Carlo results and the corresponding measurements is within {approx}3%/0.3 mm for the central axis percentage depth dose and the dose profiles. The importance map generated in the calculation can be used to discuss simulation details in the different parts of the geometry in a simple way. The simulation CPU times are comparable to those needed within other approaches common in this field. Conclusions: The new approach is competitive with those previously used in this kind of problems (PSF generation or source models) and has some practical advantages that make it to be a good tool to simulate the radiation transport in problems where the quantities of interest are difficult to obtain because of low statistics.

Ant colony algorithm implementation in electron and photon Monte Carlo transport: Application to the commissioning of radiosurgery photon beams

International Nuclear Information System (INIS)

Garcia-Pareja, S.; Galan, P.; Manzano, F.; Brualla, L.; Lallena, A. M.

2010-01-01

Purpose: In this work, the authors describe an approach which has been developed to drive the application of different variance-reduction techniques to the Monte Carlo simulation of photon and electron transport in clinical accelerators. Methods: The new approach considers the following techniques: Russian roulette, splitting, a modified version of the directional bremsstrahlung splitting, and the azimuthal particle redistribution. Their application is controlled by an ant colony algorithm based on an importance map. Results: The procedure has been applied to radiosurgery beams. Specifically, the authors have calculated depth-dose profiles, off-axis ratios, and output factors, quantities usually considered in the commissioning of these beams. The agreement between Monte Carlo results and the corresponding measurements is within ∼3%/0.3 mm for the central axis percentage depth dose and the dose profiles. The importance map generated in the calculation can be used to discuss simulation details in the different parts of the geometry in a simple way. The simulation CPU times are comparable to those needed within other approaches common in this field. Conclusions: The new approach is competitive with those previously used in this kind of problems (PSF generation or source models) and has some practical advantages that make it to be a good tool to simulate the radiation transport in problems where the quantities of interest are difficult to obtain because of low statistics.

A scalable method for parallelizing sampling-based motion planning algorithms

KAUST Repository

Jacobs, Sam Ade; Manavi, Kasra; Burgos, Juan; Denny, Jory; Thomas, Shawna; Amato, Nancy M.

2012-01-01

This paper describes a scalable method for parallelizing sampling-based motion planning algorithms. It subdivides configuration space (C-space) into (possibly overlapping) regions and independently, in parallel, uses standard (sequential) sampling-based planners to construct roadmaps in each region. Next, in parallel, regional roadmaps in adjacent regions are connected to form a global roadmap. By subdividing the space and restricting the locality of connection attempts, we reduce the work and inter-processor communication associated with nearest neighbor calculation, a critical bottleneck for scalability in existing parallel motion planning methods. We show that our method is general enough to handle a variety of planning schemes, including the widely used Probabilistic Roadmap (PRM) and Rapidly-exploring Random Trees (RRT) algorithms. We compare our approach to two other existing parallel algorithms and demonstrate that our approach achieves better and more scalable performance. Our approach achieves almost linear scalability on a 2400 core LINUX cluster and on a 153,216 core Cray XE6 petascale machine. © 2012 IEEE.

A scalable method for parallelizing sampling-based motion planning algorithms

KAUST Repository

Jacobs, Sam Ade

2012-05-01

This paper describes a scalable method for parallelizing sampling-based motion planning algorithms. It subdivides configuration space (C-space) into (possibly overlapping) regions and independently, in parallel, uses standard (sequential) sampling-based planners to construct roadmaps in each region. Next, in parallel, regional roadmaps in adjacent regions are connected to form a global roadmap. By subdividing the space and restricting the locality of connection attempts, we reduce the work and inter-processor communication associated with nearest neighbor calculation, a critical bottleneck for scalability in existing parallel motion planning methods. We show that our method is general enough to handle a variety of planning schemes, including the widely used Probabilistic Roadmap (PRM) and Rapidly-exploring Random Trees (RRT) algorithms. We compare our approach to two other existing parallel algorithms and demonstrate that our approach achieves better and more scalable performance. Our approach achieves almost linear scalability on a 2400 core LINUX cluster and on a 153,216 core Cray XE6 petascale machine. © 2012 IEEE.

ROBUST-HYBRID GENETIC ALGORITHM FOR A FLOW-SHOP SCHEDULING PROBLEM (A Case Study at PT FSCM Manufacturing Indonesia

Directory of Open Access Journals (Sweden)

Johan Soewanda

2007-01-01

Full Text Available This paper discusses the application of Robust Hybrid Genetic Algorithm to solve a flow-shop scheduling problem. The proposed algorithm attempted to reach minimum makespan. PT. FSCM Manufacturing Indonesia Plant 4's case was used as a test case to evaluate the performance of the proposed algorithm. The proposed algorithm was compared to Ant Colony, Genetic-Tabu, Hybrid Genetic Algorithm, and the company's algorithm. We found that Robust Hybrid Genetic produces statistically better result than the company's, but the same as Ant Colony, Genetic-Tabu, and Hybrid Genetic. In addition, Robust Hybrid Genetic Algorithm required less computational time than Hybrid Genetic Algorithm

An integrated ant colony optimization approach to compare strategies of clearing market in electricity markets. Agent-based simulation

International Nuclear Information System (INIS)

Azadeh, A.; Maleki-Shoja, B.; Skandari, M.R.

2010-01-01

In this paper, an innovative model of agent based simulation, based on Ant Colony Optimization (ACO) algorithm is proposed in order to compare three available strategies of clearing wholesale electricity markets, i.e. uniform, pay-as-bid, and generalized Vickrey rules. The supply side actors of the power market are modeled as adaptive agents who learn how to bid strategically to optimize their profit through indirect interaction with other actors of the market. The proposed model is proper for bidding functions with high number of dimensions and enables modelers to avoid curse of dimensionality as dimension grows. Test systems are then used to study the behavior of each pricing rule under different degrees of competition and heterogeneity. Finally, the pricing rules are comprehensively compared using different economic criteria such as average cleared price, efficiency of allocation, and price volatility. Also, principle component analysis (PCA) is used to rank and select the best price rule. To the knowledge of the authors, this is the first study that uses ACO for assessing strategies of wholesale electricity market. (author)

Research on bulk-cargo-port berth assignment based on priority of resource allocation

Directory of Open Access Journals (Sweden)

Chunfang Guo

2013-03-01

Full Text Available Purpose: The purpose of this paper is to propose a Priority of Resource Allocation model about how to utilize the resources of the port efficiently, through the improvement of traditional ant colony algorithm, the ship-berth matching relation constraint matrix formed by ontology reasoning. Design/methodology/approach: Through questionnaires?Explore factor analysis (EFA and principal component analysis, the authors extract the importance of the goods, the importance of customers, and type of trade as the main factors of the ship operating priority. Then the authors combine berth assignment problem with the improved ant colony algorithm, and use the model to improve ship scheduling quality. Finally, the authors verify the model with physical data in a bulk-cargo-port in China. Findings: Test by the real data of bulk cargo port, it show that ships’ resource using priority and the length of waiting time are consistent; it indicates that the priority of resource allocation play a prominent role in improving ship scheduling quality. Research limitations: The questionnaires is limited in only one port group, more  related Influence factors should be considered to extend the conclusion. Practical implications: The Priority of Resource Allocation model in this paper can be used to improve the efficiency of the dynamic berth assignment. Originality: This paper makes the time of ship in port minimized as the optimization of key indicators and establishes a dynamic berth assignment model based on improved ant colony algorithm and the ontology reasoning model.

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

International Nuclear Information System (INIS)

Fiorina, E.; Valzano, S.; Arteche Diaz, R.; Bosco, P.; Gargano, G.; Megna, R.; Oppedisano, C.; Massafra, A.

2011-01-01

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.

Exploring Algorithms for Stellar Light Curves With TESS

Science.gov (United States)

Buzasi, Derek

2018-01-01

The Kepler and K2 missions have produced tens of thousands of stellar light curves, which have been used to measure rotation periods, characterize photometric activity levels, and explore phenomena such as differential rotation. The quasi-periodic nature of rotational light curves, combined with the potential presence of additional periodicities not due to rotation, complicates the analysis of these time series and makes characterization of uncertainties difficult. A variety of algorithms have been used for the extraction of rotational signals, including autocorrelation functions, discrete Fourier transforms, Lomb-Scargle periodograms, wavelet transforms, and the Hilbert-Huang transform. In addition, in the case of K2 a number of different pipelines have been used to produce initial detrended light curves from the raw image frames.In the near future, TESS photometry, particularly that deriving from the full-frame images, will dramatically further expand the number of such light curves, but details of the pipeline to be used to produce photometry from the FFIs remain under development. K2 data offers us an opportunity to explore the utility of different reduction and analysis tool combinations applied to these astrophysically important tasks. In this work, we apply a wide range of algorithms to light curves produced by a number of popular K2 pipeline products to better understand the advantages and limitations of each approach and provide guidance for the most reliable and most efficient analysis of TESS stellar data.

Flexible Job Shop Scheduling Problem Using an Improved Ant Colony Optimization

Directory of Open Access Journals (Sweden)

Lei Wang

2017-01-01

Full Text Available As an extension of the classical job shop scheduling problem, the flexible job shop scheduling problem (FJSP plays an important role in real production systems. In FJSP, an operation is allowed to be processed on more than one alternative machine. It has been proven to be a strongly NP-hard problem. Ant colony optimization (ACO has been proven to be an efficient approach for dealing with FJSP. However, the basic ACO has two main disadvantages including low computational efficiency and local optimum. In order to overcome these two disadvantages, an improved ant colony optimization (IACO is proposed to optimize the makespan for FJSP. The following aspects are done on our improved ant colony optimization algorithm: select machine rule problems, initialize uniform distributed mechanism for ants, change pheromone’s guiding mechanism, select node method, and update pheromone’s mechanism. An actual production instance and two sets of well-known benchmark instances are tested and comparisons with some other approaches verify the effectiveness of the proposed IACO. The results reveal that our proposed IACO can provide better solution in a reasonable computational time.

Carbohydrate supply limits invasion of natural communities by Argentine ants.

Science.gov (United States)

Rowles, Alexei D; Silverman, Jules

2009-08-01

The ability of species to invade new habitats is often limited by various biotic and physical factors or interactions between the two. Invasive ants, frequently associated with human activities, flourish in disturbed urban and agricultural environments. However, their ability to invade and establish in natural habitats is more variable. This is particularly so for the invasive Argentine ant (Linepithema humile). While biotic resistance and low soil moisture limits their invasion of natural habitats in some instances, the effect of food availability has been poorly explored. We conducted field experiments to determine if resource availability limits the spread and persistence of Argentine ants in remnant natural forest in North Carolina. Replicated transects paired with and without sucrose solution feeding stations were run from invaded urban edges into forest remnants and compared over time using baits and direct counts at feeding stations. Repeated under different timing regimes in 2006 and 2007, access to sucrose increased local Argentine ant abundances (1.6-2.5 fold) and facilitated their progression into the forest up to 73 +/- 21% of 50-m transects. Resource removal caused an expected decrease in Argentine ant densities in 2006, in conjunction with their retreat to the urban/forest boundary. However, in 2007, Argentine ant numbers unexpectedly continued to increase in the absence of sugar stations, possibly through access to alternative resources or conditions not available the previous year such as honeydew-excreting Hemiptera. Our results showed that supplementing carbohydrate supply facilitates invasion of natural habitat by Argentine ants. This is particularly evident where Argentine ants continued to thrive following sugar station removal.

Modified Bat Algorithm Based on Lévy Flight and Opposition Based Learning

Directory of Open Access Journals (Sweden)

Xian Shan

2016-01-01

Full Text Available Bat Algorithm (BA is a swarm intelligence algorithm which has been intensively applied to solve academic and real life optimization problems. However, due to the lack of good balance between exploration and exploitation, BA sometimes fails at finding global optimum and is easily trapped into local optima. In order to overcome the premature problem and improve the local searching ability of Bat Algorithm for optimization problems, we propose an improved BA called OBMLBA. In the proposed algorithm, a modified search equation with more useful information from the search experiences is introduced to generate a candidate solution, and Lévy Flight random walk is incorporated with BA in order to avoid being trapped into local optima. Furthermore, the concept of opposition based learning (OBL is embedded to BA to enhance the diversity and convergence capability. To evaluate the performance of the proposed approach, 16 benchmark functions have been employed. The results obtained by the experiments demonstrate the effectiveness and efficiency of OBMLBA for global optimization problems. Comparisons with some other BA variants and other state-of-the-art algorithms have shown the proposed approach significantly improves the performance of BA. Performances of the proposed algorithm on large scale optimization problems and real world optimization problems are not discussed in the paper, and it will be studied in the future work.

Microorganisms transported by ants induce changes in floral nectar composition of an ant-pollinated plant.

Science.gov (United States)

de Vega, Clara; Herrera, Carlos M

2013-04-01

Interactions between plants and ants abound in nature and have significant consequences for ecosystem functioning. Recently, it has been suggested that nectar-foraging ants transport microorganisms to flowers; more specifically, they transport yeasts, which can potentially consume sugars and alter nectar composition. Therefore, ants could indirectly change nectar sugar profile, an important floral feature involved in the plant-pollinator mutualism. But this novel role for ants has never been tested. We here investigate the effects of nectarivorous ants and their associated yeasts on the floral nectar sugar composition of an ant-pollinated plant. Differences in the nectar sugar composition of ant-excluded and ant-visited flowers were examined in 278 samples by using high-performance liquid-chromatography. The importance of the genetic identity and density of ant-transported basidiomycetous and ascomycetous yeasts on the variation of nectar traits was also evaluated. Ant visitation had significant effects on nectar sugar composition. The nectar of ant-visited flowers contained significantly more fructose, more glucose, and less sucrose than the nectar of ant-excluded flowers, but these effects were context dependent. Nectar changes were correlated with the density of yeast cells in nectar. The magnitude of the effects of ant-transported ascomycetes was much higher than that of basiodiomycetes. Ants and their associated yeasts induce changes in nectar sugar traits, reducing the chemical control of the plant over this important floral trait. The potential relevance of this new role for ants as indirect nectar modifiers is a rich topic for future research into the ecology of ant-flower interactions.

An efficient, large-scale, non-lattice-detection algorithm for exhaustive structural auditing of biomedical ontologies.

Science.gov (United States)

Zhang, Guo-Qiang; Xing, Guangming; Cui, Licong

2018-04-01

One of the basic challenges in developing structural methods for systematic audition on the quality of biomedical ontologies is the computational cost usually involved in exhaustive sub-graph analysis. We introduce ANT-LCA, a new algorithm for computing all non-trivial lowest common ancestors (LCA) of each pair of concepts in the hierarchical order induced by an ontology. The computation of LCA is a fundamental step for non-lattice approach for ontology quality assurance. Distinct from existing approaches, ANT-LCA only computes LCAs for non-trivial pairs, those having at least one common ancestor. To skip all trivial pairs that may be of no practical interest, ANT-LCA employs a simple but innovative algorithmic strategy combining topological order and dynamic programming to keep track of non-trivial pairs. We provide correctness proofs and demonstrate a substantial reduction in computational time for two largest biomedical ontologies: SNOMED CT and Gene Ontology (GO). ANT-LCA achieved an average computation time of 30 and 3 sec per version for SNOMED CT and GO, respectively, about 2 orders of magnitude faster than the best known approaches. Our algorithm overcomes a fundamental computational barrier in sub-graph based structural analysis of large ontological systems. It enables the implementation of a new breed of structural auditing methods that not only identifies potential problematic areas, but also automatically suggests changes to fix the issues. Such structural auditing methods can lead to more effective tools supporting ontology quality assurance work. Copyright © 2018 Elsevier Inc. All rights reserved.

Competitive Swarm Optimizer Based Gateway Deployment Algorithm in Cyber-Physical Systems.

Science.gov (United States)

Huang, Shuqiang; Tao, Ming

2017-01-22

Wireless sensor network topology optimization is a highly important issue, and topology control through node selection can improve the efficiency of data forwarding, while saving energy and prolonging lifetime of the network. To address the problem of connecting a wireless sensor network to the Internet in cyber-physical systems, here we propose a geometric gateway deployment based on a competitive swarm optimizer algorithm. The particle swarm optimization (PSO) algorithm has a continuous search feature in the solution space, which makes it suitable for finding the geometric center of gateway deployment; however, its search mechanism is limited to the individual optimum (pbest) and the population optimum (gbest); thus, it easily falls into local optima. In order to improve the particle search mechanism and enhance the search efficiency of the algorithm, we introduce a new competitive swarm optimizer (CSO) algorithm. The CSO search algorithm is based on an inter-particle competition mechanism and can effectively avoid trapping of the population falling into a local optimum. With the improvement of an adaptive opposition-based search and its ability to dynamically parameter adjustments, this algorithm can maintain the diversity of the entire swarm to solve geometric K -center gateway deployment problems. The simulation results show that this CSO algorithm has a good global explorative ability as well as convergence speed and can improve the network quality of service (QoS) level of cyber-physical systems by obtaining a minimum network coverage radius. We also find that the CSO algorithm is more stable, robust and effective in solving the problem of geometric gateway deployment as compared to the PSO or Kmedoids algorithms.

Competitive Swarm Optimizer Based Gateway Deployment Algorithm in Cyber-Physical Systems

Directory of Open Access Journals (Sweden)

Shuqiang Huang

2017-01-01

Full Text Available Wireless sensor network topology optimization is a highly important issue, and topology control through node selection can improve the efficiency of data forwarding, while saving energy and prolonging lifetime of the network. To address the problem of connecting a wireless sensor network to the Internet in cyber-physical systems, here we propose a geometric gateway deployment based on a competitive swarm optimizer algorithm. The particle swarm optimization (PSO algorithm has a continuous search feature in the solution space, which makes it suitable for finding the geometric center of gateway deployment; however, its search mechanism is limited to the individual optimum (pbest and the population optimum (gbest; thus, it easily falls into local optima. In order to improve the particle search mechanism and enhance the search efficiency of the algorithm, we introduce a new competitive swarm optimizer (CSO algorithm. The CSO search algorithm is based on an inter-particle competition mechanism and can effectively avoid trapping of the population falling into a local optimum. With the improvement of an adaptive opposition-based search and its ability to dynamically parameter adjustments, this algorithm can maintain the diversity of the entire swarm to solve geometric K-center gateway deployment problems. The simulation results show that this CSO algorithm has a good global explorative ability as well as convergence speed and can improve the network quality of service (QoS level of cyber-physical systems by obtaining a minimum network coverage radius. We also find that the CSO algorithm is more stable, robust and effective in solving the problem of geometric gateway deployment as compared to the PSO or Kmedoids algorithms.

Competitive Swarm Optimizer Based Gateway Deployment Algorithm in Cyber-Physical Systems

Science.gov (United States)

Huang, Shuqiang; Tao, Ming

2017-01-01

Wireless sensor network topology optimization is a highly important issue, and topology control through node selection can improve the efficiency of data forwarding, while saving energy and prolonging lifetime of the network. To address the problem of connecting a wireless sensor network to the Internet in cyber-physical systems, here we propose a geometric gateway deployment based on a competitive swarm optimizer algorithm. The particle swarm optimization (PSO) algorithm has a continuous search feature in the solution space, which makes it suitable for finding the geometric center of gateway deployment; however, its search mechanism is limited to the individual optimum (pbest) and the population optimum (gbest); thus, it easily falls into local optima. In order to improve the particle search mechanism and enhance the search efficiency of the algorithm, we introduce a new competitive swarm optimizer (CSO) algorithm. The CSO search algorithm is based on an inter-particle competition mechanism and can effectively avoid trapping of the population falling into a local optimum. With the improvement of an adaptive opposition-based search and its ability to dynamically parameter adjustments, this algorithm can maintain the diversity of the entire swarm to solve geometric K-center gateway deployment problems. The simulation results show that this CSO algorithm has a good global explorative ability as well as convergence speed and can improve the network quality of service (QoS) level of cyber-physical systems by obtaining a minimum network coverage radius. We also find that the CSO algorithm is more stable, robust and effective in solving the problem of geometric gateway deployment as compared to the PSO or Kmedoids algorithms. PMID:28117735

Short-Term Fuzzy Load Forecasting Model Using Genetic–Fuzzy and Ant Colony–Fuzzy Knowledge Base Optimization

Directory of Open Access Journals (Sweden)

Murat Luy

2018-05-01

Full Text Available The estimation of hourly electricity load consumption is highly important for planning short-term supply–demand equilibrium in sources and facilities. Studies of short-term load forecasting in the literature are categorized into two groups: classical conventional and artificial intelligence-based methods. Artificial intelligence-based models, especially when using fuzzy logic techniques, have more accurate load estimations when datasets include high uncertainty. However, as the knowledge base—which is defined by expert insights and decisions—gets larger, the load forecasting performance decreases. This study handles the problem that is caused by the growing knowledge base, and improves the load forecasting performance of fuzzy models through nature-inspired methods. The proposed models have been optimized by using ant colony optimization and genetic algorithm (GA techniques. The training and testing processes of the proposed systems were performed on historical hourly load consumption and temperature data collected between 2011 and 2014. The results show that the proposed models can sufficiently improve the performance of hourly short-term load forecasting. The mean absolute percentage error (MAPE of the monthly minimum in the forecasting model, in terms of the forecasting accuracy, is 3.9% (February 2014. The results show that the proposed methods make it possible to work with large-scale rule bases in a more flexible estimation environment.

Using Load Balancing to Scalably Parallelize Sampling-Based Motion Planning Algorithms

KAUST Repository

Fidel, Adam; Jacobs, Sam Ade; Sharma, Shishir; Amato, Nancy M.; Rauchwerger, Lawrence

2014-01-01

Motion planning, which is the problem of computing feasible paths in an environment for a movable object, has applications in many domains ranging from robotics, to intelligent CAD, to protein folding. The best methods for solving this PSPACE-hard problem are so-called sampling-based planners. Recent work introduced uniform spatial subdivision techniques for parallelizing sampling-based motion planning algorithms that scaled well. However, such methods are prone to load imbalance, as planning time depends on region characteristics and, for most problems, the heterogeneity of the sub problems increases as the number of processors increases. In this work, we introduce two techniques to address load imbalance in the parallelization of sampling-based motion planning algorithms: an adaptive work stealing approach and bulk-synchronous redistribution. We show that applying these techniques to representatives of the two major classes of parallel sampling-based motion planning algorithms, probabilistic roadmaps and rapidly-exploring random trees, results in a more scalable and load-balanced computation on more than 3,000 cores. © 2014 IEEE.

Using Load Balancing to Scalably Parallelize Sampling-Based Motion Planning Algorithms

KAUST Repository

Fidel, Adam

2014-05-01

Motion planning, which is the problem of computing feasible paths in an environment for a movable object, has applications in many domains ranging from robotics, to intelligent CAD, to protein folding. The best methods for solving this PSPACE-hard problem are so-called sampling-based planners. Recent work introduced uniform spatial subdivision techniques for parallelizing sampling-based motion planning algorithms that scaled well. However, such methods are prone to load imbalance, as planning time depends on region characteristics and, for most problems, the heterogeneity of the sub problems increases as the number of processors increases. In this work, we introduce two techniques to address load imbalance in the parallelization of sampling-based motion planning algorithms: an adaptive work stealing approach and bulk-synchronous redistribution. We show that applying these techniques to representatives of the two major classes of parallel sampling-based motion planning algorithms, probabilistic roadmaps and rapidly-exploring random trees, results in a more scalable and load-balanced computation on more than 3,000 cores. © 2014 IEEE.

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.

Improved discrete swarm intelligence algorithms for endmember extraction from hyperspectral remote sensing images

Science.gov (United States)

Su, Yuanchao; Sun, Xu; Gao, Lianru; Li, Jun; Zhang, Bing

2016-10-01

Endmember extraction is a key step in hyperspectral unmixing. A new endmember extraction framework is proposed for hyperspectral endmember extraction. The proposed approach is based on the swarm intelligence (SI) algorithm, where discretization is used to solve the SI algorithm because pixels in a hyperspectral image are naturally defined within a discrete space. Moreover, a "distance" factor is introduced into the objective function to limit the endmember numbers which is generally limited in real scenarios, while traditional SI algorithms likely produce superabundant spectral signatures, which generally belong to the same classes. Three endmember extraction methods are proposed based on the artificial bee colony, ant colony optimization, and particle swarm optimization algorithms. Experiments with both simulated and real hyperspectral images indicate that the proposed framework can improve the accuracy of endmember extraction.

Exploring hadronic tau identification with DC1 datat samples a track based approach

CERN Document Server

Richter-Was, Elzbieta; Tarrade, F

2004-01-01

In this note we discuss the identification of hadronic $\\tau$s. We propose an algorithm, tauID, which starts from a reconstructed, relatively high pT track and then collects calorimetric energy deposition in a fixed cone seeded by the track eta and phi at the vertex. With the proposed algorithm we explore exclusive features of the hadronic $\\tau$ decays and we indicate also the possibility of using an energy-flow based approach for defining the energy scale of the reconstructed tau-candidates. The results presented here are limited to the barrel region (|eta| < 1.5) and are based on the DC1 events simulated without pile-up and electronic noise. We compare the performances of the proposed algorithm and of the base-line tauRec algorithm and draw some conclusions for further studies.

Identification of related multilingual documents using ant clustering algorithms Identificación de documentos multilingües relacionados mediante algoritmos de clustering de hormigas

Directory of Open Access Journals (Sweden)

Ángel Cobo

2011-12-01

Full Text Available This paper presents a document representation strategy and a bio-inspired algorithm to cluster multilingual collections of documents in the field of economics and business. The proposed approach allows the user to identify groups of related economics documents written in Spanish and English using techniques inspired on clustering and sorting behaviours observed in some types of ants. In order to obtain a language independent vector representation of each document two multilingual resources are used: an economic glossary and a thesaurus. Each document is represented using four feature vectors: words, proper names, economic terms in the glossary and thesaurus descriptors. The proper name identification, word extraction and lemmatization are performed using specific tools. The tf-idf scheme is used to measure the importance of each feature in the document, and a convex linear combination of angular separations between feature vectors is used as similarity measure of documents. The paper shows experimental results of the application of the proposed algorithm in a Spanish-English corpus of research papers in economics and management areas. The results demonstrate the usefulness and effectiveness of the ant clustering algorithm and the proposed representation scheme.Este artículo presenta una estrategia de representación documental y un algoritmo bioinspirado para realizar procesos de agrupamiento en colecciones multilingües de documentos en las áreas de la economía y la empresa. El enfoque propuesto permite al usuario identificar grupos de documentos económicos relacionados escritos en español o inglés usando técnicas inspiradas en comportamientos de organización y agrupamiento de objetos observados en algunos tipos de hormigas. Para conseguir una representación vectorial de cada documento independiente del idioma, se han utilizado dos recursos lingüísticos: un glosario económico y un tesauro. Cada documento es representado usando

The interplay between scent trails and group-mass recruitment systems in ants.

Science.gov (United States)

Planqué, Robert; van den Berg, Jan Bouwe; Franks, Nigel R

2013-10-01

Large ant colonies invariably use effective scent trails to guide copious ant numbers to food sources. The success of mass recruitment hinges on the involvement of many colony members to lay powerful trails. However, many ant colonies start off as single queens. How do these same colonies forage efficiently when small, thereby overcoming the hurdles to grow large? In this paper, we study the case of combined group and mass recruitment displayed by some ant species. Using mathematical models, we explore to what extent early group recruitment may aid deployment of scent trails, making such trails available at much smaller colony sizes. We show that a competition between group and mass recruitment may cause oscillatory behaviour mediated by scent trails. This results in a further reduction of colony size to establish trails successfully.

Improved Ant Colony Optimization for Seafood Product Delivery Routing Problem

Directory of Open Access Journals (Sweden)

Baozhen Yao

2014-02-01

Full Text Available This paper deals with a real-life vehicle delivery routing problem, which is a seafood product delivery routing problem. Considering the features of the seafood product delivery routing problem, this paper formulated this problem as a multi-depot open vehicle routing problem. Since the multi-depot open vehicle routing problem is a very complex problem, a method is used to reduce the complexity of the problem by changing the multi-depot open vehicle routing problem into an open vehicle routing problem with a dummy central depot in this paper. Then, ant colony optimization is used to solve the problem. To improve the performance of the algorithm, crossover operation and some adaptive strategies are used. Finally, the computational results for the benchmark problems of the multi-depot vehicle routing problem indicate that the proposed ant colony optimization is an effective method to solve the multi-depot vehicle routing problem. Furthermore, the computation results of the seafood product delivery problem from Dalian, China also suggest that the proposed ant colony optimization is feasible to solve the seafood product delivery routing problem.

Covariance-Based Measurement Selection Criterion for Gaussian-Based Algorithms

Directory of Open Access Journals (Sweden)

Fernando A. Auat Cheein

2013-01-01

Full Text Available Process modeling by means of Gaussian-based algorithms often suffers from redundant information which usually increases the estimation computational complexity without significantly improving the estimation performance. In this article, a non-arbitrary measurement selection criterion for Gaussian-based algorithms is proposed. The measurement selection criterion is based on the determination of the most significant measurement from both an estimation convergence perspective and the covariance matrix associated with the measurement. The selection criterion is independent from the nature of the measured variable. This criterion is used in conjunction with three Gaussian-based algorithms: the EIF (Extended Information Filter, the EKF (Extended Kalman Filter and the UKF (Unscented Kalman Filter. Nevertheless, the measurement selection criterion shown herein can also be applied to other Gaussian-based algorithms. Although this work is focused on environment modeling, the results shown herein can be applied to other Gaussian-based algorithm implementations. Mathematical descriptions and implementation results that validate the proposal are also included in this work.

The Study on Food Sensory Evaluation based on Particle Swarm Optimization Algorithm

OpenAIRE

Hairong Wang; Huijuan Xu

2015-01-01

In this study, it explores the procedures and methods of the system for establishing food sensory evaluation based on particle swarm optimization algorithm, by means of explaining the interpretation of sensory evaluation and sensory analysis, combined with the applying situation of sensory evaluation in food industry.

Risk Assessment for Bridges Safety Management during Operation Based on Fuzzy Clustering Algorithm

Directory of Open Access Journals (Sweden)

Xia Hanyu

2016-01-01

Full Text Available In recent years, large span and large sea-crossing bridges are built, bridges accidents caused by improper operational management occur frequently. In order to explore the better methods for risk assessment of the bridges operation departments, the method based on fuzzy clustering algorithm is selected. Then, the implementation steps of fuzzy clustering algorithm are described, the risk evaluation system is built, and Taizhou Bridge is selected as an example, the quantitation of risk factors is described. After that, the clustering algorithm based on fuzzy equivalence is calculated on MATLAB 2010a. In the last, Taizhou Bridge operation management departments are classified and sorted according to the degree of risk, and the safety situation of operation departments is analyzed.

GIS-BASED ROUTE FINDING USING ANT COLONY OPTIMIZATION AND URBAN TRAFFIC DATA FROM DIFFERENT SOURCES

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M. Davoodi

2015-12-01

Full Text Available 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.

Protection of Vochysia elliptica (Vochysiaceae by a nectar-thieving ant

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G. Q. ROMERO

Full Text Available Vochysia elliptica (Vochysiaceae is a shrubby plant, which does not have EFNs. Camponotus ants thieve nectar, and can decrease plant fitness by making flowers less attractive to pollinators. However, ants remove herbivores, wich can be beneficial. Results show that plants from which ants were excluded had lower rates of termite (simulated herbivore removal than did plants visited by ants. Plants accessible to ants showed higher rates of termite removal in the base of leaves and in the inflorescence, than in the tip of leaves. This occurs because ants must pass through the principal axis to reach the inflorescence. Conclusive results of this cost/benefit analysis of the Camponotus sp. presence for V. elliptica can be obtained, with experimental manipulations.

Harnessing ant defence at fruits reduces bruchid seed predation in a symbiotic ant-plant mutualism.

Science.gov (United States)

Pringle, Elizabeth G

2014-06-22

In horizontally transmitted mutualisms, mutualists disperse separately and reassemble in each generation with partners genetically unrelated to those in the previous generation. Because of this, there should be no selection on either partner to enhance the other's reproductive output directly. In symbiotic ant-plant mutualisms, myrmecophytic plants host defensive ant colonies, and ants defend the plants from herbivores. Plants and ants disperse separately, and, although ant defence can indirectly increase plant reproduction by reducing folivory, it is unclear whether ants can also directly increase plant reproduction by defending seeds. The neotropical tree Cordia alliodora hosts colonies of Azteca pittieri ants. The trees produce domatia where ants nest at stem nodes and also at the node between the peduncle and the rachides of the infloresence. Unlike the stem domatia, these reproductive domatia senesce after the tree fruits each year. In this study, I show that the tree's resident ant colony moves into these ephemeral reproductive domatia, where they tend honeydew-producing scale insects and patrol the nearby developing fruits. The presence of ants significantly reduced pre-dispersal seed predation by Amblycerus bruchid beetles, thereby directly increasing plant reproductive output.

Normalization based K means Clustering Algorithm

OpenAIRE

Virmani, Deepali; Taneja, Shweta; Malhotra, Geetika

2015-01-01

K-means is an effective clustering technique used to separate similar data into groups based on initial centroids of clusters. In this paper, Normalization based K-means clustering algorithm(N-K means) is proposed. Proposed N-K means clustering algorithm applies normalization prior to clustering on the available data as well as the proposed approach calculates initial centroids based on weights. Experimental results prove the betterment of proposed N-K means clustering algorithm over existing...

3rd International Conference on Harmony Search Algorithm

CERN Document Server

2017-01-01

This book presents state-of-the-art technical contributions based around one of the most successful evolutionary optimization algorithms published to date: Harmony Search. Contributions span from novel technical derivations of this algorithm to applications in the broad fields of civil engineering, energy, transportation & mobility and health, among many others and focus not only on its cross-domain applicability, but also on its core evolutionary operators, including elements inspired from other meta-heuristics. The global scientific community is witnessing an upsurge in groundbreaking, new advances in all areas of computational intelligence, with a particular flurry of research focusing on evolutionary computation and bio-inspired optimization. Observed processes in nature and sociology have provided the basis for innovative algorithmic developments aimed at leveraging the inherent capability to adapt characterized by various animals, including ants, fireflies, wolves and humans. However, it is the beha...

"Ant-egg" cataract revisited

DEFF Research Database (Denmark)

Clemmensen, Kåre; Enghild, Jan J; Ivarsen, Anders

2017-01-01

-ray scans and electron microscopy. The purpose of this study was to further characterize "ant-egg" cataract using modern technology and display the history of the "ant-eggs" after cataract extraction. METHODS: "Ant-eggs" were examined using Heidelberg SPECTRALIS Optical Coherence Tomography (OCT...

Comparing different methods to assess weaver ant abundance in plantation trees

DEFF Research Database (Denmark)

Wargui, Rosine; Offenberg, Joachim; Sinzogan, Antonio

2015-01-01

Weaver ants (Oecophylla spp.) are widely used as effective biological control agents. In order to optimize their use, ant abundance needs to be tracked. As several methods have been used to estimate ant abundance on plantation trees, abundances are not comparable between studies and no guideline...... is available on which method to apply in a particular study. This study compared four existing methods: three methods based on the number of ant trails on the main branches of a tree (called the Peng 1, Peng 2 and Offenberg index) and one method based on the number of ant nests per tree. Branch indices did...... not produce equal scores and cannot be compared directly. The Peng 1 index was the fastest to assess, but showed only limited seasonal fluctuations when ant abundance was high, because it approached its upper limit. The Peng 2 and Offenberg indices were lower and not close to the upper limit and therefore...

Polarized light use in the nocturnal bull ant, Myrmecia midas.

Science.gov (United States)

Freas, Cody A; Narendra, Ajay; Lemesle, Corentin; Cheng, Ken

2017-08-01

Solitary foraging ants have a navigational toolkit, which includes the use of both terrestrial and celestial visual cues, allowing individuals to successfully pilot between food sources and their nest. One such celestial cue is the polarization pattern in the overhead sky. Here, we explore the use of polarized light during outbound and inbound journeys and with different home vectors in the nocturnal bull ant, Myrmecia midas . We tested foragers on both portions of the foraging trip by rotating the overhead polarization pattern by ±45°. Both outbound and inbound foragers responded to the polarized light change, but the extent to which they responded to the rotation varied. Outbound ants, both close to and further from the nest, compensated for the change in the overhead e-vector by about half of the manipulation, suggesting that outbound ants choose a compromise heading between the celestial and terrestrial compass cues. However, ants returning home compensated for the change in the e-vector by about half of the manipulation when the remaining home vector was short (1-2 m) and by more than half of the manipulation when the remaining vector was long (more than 4 m). We report these findings and discuss why weighting on polarization cues change in different contexts.

How ants drop out: ant abundance on tropical mountains.

Science.gov (United States)

Longino, John T; Branstetter, Michael G; Colwell, Robert K

2014-01-01

In tropical wet forests, ants are a large proportion of the animal biomass, but the factors determining abundance are not well understood. We characterized ant abundance in the litter layer of 41 mature wet forest sites spread throughout Central America (Chiapas, Guatemala, Honduras, Nicaragua, and Costa Rica) and examined the impact of elevation (as a proxy for temperature) and community species richness. Sites were intentionally chosen to minimize variation in precipitation and seasonality. From sea level to 1500 m ant abundance very gradually declined, community richness declined more rapidly than abundance, and the local frequency of the locally most common species increased. These results suggest that within this elevational zone, density compensation is acting, maintaining high ant abundance as richness declines. In contrast, in sites above 1500 m, ant abundance dropped abruptly to much lower levels. Among these high montane sites, community richness explained much more of the variation in abundance than elevation, and there was no evidence of density compensation. The relative stability of abundance below 1500 m may be caused by opposing effects of temperature on productivity and metabolism. Lower temperatures may decrease productivity and thus the amount of food available for consumers, but slower metabolisms of consumers may allow maintenance of higher biomass at lower resource supply rates. Ant communities at these lower elevations may be highly interactive, the result of continuous habitat presence over geological time. High montane sites may be ephemeral in geological time, resulting in non-interactive communities dominated by historical and stochastic processes. Abundance in these sites may be determined by the number of species that manage to colonize and/or avoid extinction on mountaintops.

How ants drop out: ant abundance on tropical mountains.

Directory of Open Access Journals (Sweden)

John T Longino

Full Text Available In tropical wet forests, ants are a large proportion of the animal biomass, but the factors determining abundance are not well understood. We characterized ant abundance in the litter layer of 41 mature wet forest sites spread throughout Central America (Chiapas, Guatemala, Honduras, Nicaragua, and Costa Rica and examined the impact of elevation (as a proxy for temperature and community species richness. Sites were intentionally chosen to minimize variation in precipitation and seasonality. From sea level to 1500 m ant abundance very gradually declined, community richness declined more rapidly than abundance, and the local frequency of the locally most common species increased. These results suggest that within this elevational zone, density compensation is acting, maintaining high ant abundance as richness declines. In contrast, in sites above 1500 m, ant abundance dropped abruptly to much lower levels. Among these high montane sites, community richness explained much more of the variation in abundance than elevation, and there was no evidence of density compensation. The relative stability of abundance below 1500 m may be caused by opposing effects of temperature on productivity and metabolism. Lower temperatures may decrease productivity and thus the amount of food available for consumers, but slower metabolisms of consumers may allow maintenance of higher biomass at lower resource supply rates. Ant communities at these lower elevations may be highly interactive, the result of continuous habitat presence over geological time. High montane sites may be ephemeral in geological time, resulting in non-interactive communities dominated by historical and stochastic processes. Abundance in these sites may be determined by the number of species that manage to colonize and/or avoid extinction on mountaintops.

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®.

Nectar Theft and Floral Ant-Repellence: A Link between Nectar Volume and Ant-Repellent Traits?

Science.gov (United States)

Ballantyne, Gavin; Willmer, Pat

2012-01-01

As flower visitors, ants rarely benefit a plant. They are poor pollinators, and can also disrupt pollination by deterring other flower visitors, or by stealing nectar. Some plant species therefore possess floral ant-repelling traits. But why do particular species have such traits when others do not? In a dry forest in Costa Rica, of 49 plant species around a third were ant-repellent at very close proximity to a common generalist ant species, usually via repellent pollen. Repellence was positively correlated with the presence of large nectar volumes. Repellent traits affected ant species differently, some influencing the behaviour of just a few species and others producing more generalised ant-repellence. Our results suggest that ant-repellent floral traits may often not be pleiotropic, but instead could have been selected for as a defence against ant thieves in plant species that invest in large volumes of nectar. This conclusion highlights to the importance of research into the cost of nectar production in future studies into ant-flower interactions. PMID:22952793

Fault Diagnosis of Plunger Pump in Truck Crane Based on Relevance Vector Machine with Particle Swarm Optimization Algorithm

Directory of Open Access Journals (Sweden)

Wenliao Du

2013-01-01

Full Text Available Promptly and accurately dealing with the equipment breakdown is very important in terms of enhancing reliability and decreasing downtime. A novel fault diagnosis method PSO-RVM based on relevance vector machines (RVM with particle swarm optimization (PSO algorithm for plunger pump in truck crane is proposed. The particle swarm optimization algorithm is utilized to determine the kernel width parameter of the kernel function in RVM, and the five two-class RVMs with binary tree architecture are trained to recognize the condition of mechanism. The proposed method is employed in the diagnosis of plunger pump in truck crane. The six states, including normal state, bearing inner race fault, bearing roller fault, plunger wear fault, thrust plate wear fault, and swash plate wear fault, are used to test the classification performance of the proposed PSO-RVM model, which compared with the classical models, such as back-propagation artificial neural network (BP-ANN, ant colony optimization artificial neural network (ANT-ANN, RVM, and support vectors, machines with particle swarm optimization (PSO-SVM, respectively. The experimental results show that the PSO-RVM is superior to the first three classical models, and has a comparative performance to the PSO-SVM, the corresponding diagnostic accuracy achieving as high as 99.17% and 99.58%, respectively. But the number of relevance vectors is far fewer than that of support vector, and the former is about 1/12–1/3 of the latter, which indicates that the proposed PSO-RVM model is more suitable for applications that require low complexity and real-time monitoring.

Ant colony optimization techniques for the hamiltonian p-median problem

Directory of Open Access Journals (Sweden)

M. Zohrehbandian

2010-12-01

Full Text Available Location-Routing problems involve locating a number of facilitiesamong candidate sites and establishing delivery routes to a set of users in such a way that the total system cost is minimized. A special case of these problems is Hamiltonian p-Median problem (HpMP. This research applies the metaheuristic method of ant colony optimization (ACO to solve the HpMP. Modifications are made to the ACO algorithm used to solve the traditional vehicle routing problem (VRP in order to allow the search of the optimal solution of the HpMP. Regarding this metaheuristic algorithm a computational experiment is reported as well.

AntBot: Anti-pollution peer-to-peer botnets

Energy Technology Data Exchange (ETDEWEB)

Yan, Guanhua [Los Alamos National Laboratory; Eidenbenz, Stephan [Los Alamos National Laboratory; Ha, Duc T [UNIV. AT BUFFALO

2009-01-01

Botnets, which are responsible for many email sparnming and DDoS (Distributed Denial of Service) attacks in the current Internet, have emerged as one of most severe cyber-threats in recent years. To evade detection and improve resistance against countermeasures, botnets have evolved from the first generation that relies on IRC chat channels to deliver commands to the current generation that uses highly resilient P2P (Peer-to-Peer) protocols to spread their C&C (Command and Control) information. It is, however, revealed that P2P botnets, although relieved from the single point of failure that IRC botnets suffer, can be easily disrupted using pollution-based mitigation schemes [15]. In this paper, we play the devil's advocate and propose a new type of hypothetical botnets called AntBot, which aim to propagate their C&C information to individual bots even though there exists an adversary that persistently pollutes keys used by seized bots to search the command information. The key idea of AntBot is a tree-like structure that bots use to deliver the command so that captured bots reveal only limited information. To evaluate effectiveness of AntBot against pollution-based mitigation in a virtual environment, we develop a distributed P2P botnet simulator. Using extensive experiments, we demonstrate that AntBot operates resiliently against pollution-based mitigation. We further present a few potential defense schemes that could effectively disrupt AntBot operations.

Current issues in the evolutionary ecology of ant-plant symbioses.

Science.gov (United States)

Mayer, Veronika E; Frederickson, Megan E; McKey, Doyle; Blatrix, Rumsaïs

2014-05-01

Ant-plant symbioses involve plants that provide hollow structures specialized for housing ants and often food to ants. In return, the inhabiting ants protect plants against herbivores and sometimes provide them with nutrients. Here, we review recent advances in ant-plant symbioses, focusing on three areas. First, the nutritional ecology of plant-ants, which is based not only on plant-derived food rewards, but also on inputs from other symbiotic partners, in particular fungi and possibly bacteria. Food and protection are the most important 'currencies' exchanged between partners and they drive the nature and evolution of the relationships. Secondly, studies of conflict and cooperation in ant-plant symbioses have contributed key insights into the evolution and maintenance of mutualism, particularly how partner-mediated feedbacks affect the specificity and stability of mutualisms. There is little evidence that mutualistic ants or plants are under selection to cheat, but the costs and benefits of ant-plant interactions do vary with environmental factors, making them vulnerable to natural or anthropogenic environmental change. Thus, thirdly, ant-plant symbioses should be considered good models for investigating the effects of global change on the outcome of mutualistic interactions. © 2014 The Authors. New Phytologist © 2014 New Phytologist Trust.

Insecticide transfer efficiency and lethal load in Argentine ants

Energy Technology Data Exchange (ETDEWEB)

Hooper-Bui, L.M. [Department of Environmental Science, Louisiana State University, Baton Rouge, LA 70803 (United States); Department of Entomology, University of California, Riverside, CA 92521 (United States); Kwok, E.S.C. [Department of Cell Biology and Neuroscience, University of California, Riverside, CA 92521 (United States); Buchholz, B.A., E-mail: buchholz2@llnl.gov [Center for Accelerator Mass Spectrometry, Lawrence Livermore National Laboratory, Livermore, CA 94551 (United States); Department of Environmental Toxicology, University of California, Davis, CA 95616 (United States); Rust, M.K. [Department of Entomology, University of California, Riverside, CA 92521 (United States); Eastmond, D.A. [Department of Cell Biology and Neuroscience, University of California, Riverside, CA 92521 (United States); Vogel, J.S. [Center for Accelerator Mass Spectrometry, Lawrence Livermore National Laboratory, Livermore, CA 94551 (United States)

2015-10-15

Trophallaxis between individual worker ants and the toxicant load in dead and live Argentine ants (Linepithema humile) in colonies exposed to fipronil and hydramethylnon experimental baits were examined using accelerator mass spectrometry (AMS). About 50% of the content of the crop containing trace levels of {sup 14}C-sucrose, {sup 14}C-hydramethylnon, and {sup 14}C-fipronil was shared between single donor and recipient ants. Dead workers and queens contained significantly more hydramethylnon (122.7 and 22.4 amol/μg ant, respectively) than did live workers and queens (96.3 and 10.4 amol/μg ant, respectively). Dead workers had significantly more fipronil (420.3 amol/μg ant) than did live workers (208.5 amol/μg ant), but dead and live queens had equal fipronil levels (59.5 and 54.3 amol/μg ant, respectively). The distribution of fipronil differed within the bodies of dead and live queens; the highest amounts of fipronil were recovered in the thorax of dead queens whereas live queens had the highest levels in the head. Resurgence of polygynous ant colonies treated with hydramethylnon baits may be explained by queen survival resulting from sublethal doses due to a slowing of trophallaxis throughout the colony. Bait strategies and dose levels for controlling insect pests need to be based on the specific toxicant properties and trophic strategies for targeting the entire colony.

Insecticide transfer efficiency and lethal load in Argentine ants

International Nuclear Information System (INIS)

Hooper-Bui, L.M.; Kwok, E.S.C.; Buchholz, B.A.; Rust, M.K.; Eastmond, D.A.; Vogel, J.S.

2015-01-01

Trophallaxis between individual worker ants and the toxicant load in dead and live Argentine ants (Linepithema humile) in colonies exposed to fipronil and hydramethylnon experimental baits were examined using accelerator mass spectrometry (AMS). About 50% of the content of the crop containing trace levels of 14 C-sucrose, 14 C-hydramethylnon, and 14 C-fipronil was shared between single donor and recipient ants. Dead workers and queens contained significantly more hydramethylnon (122.7 and 22.4 amol/μg ant, respectively) than did live workers and queens (96.3 and 10.4 amol/μg ant, respectively). Dead workers had significantly more fipronil (420.3 amol/μg ant) than did live workers (208.5 amol/μg ant), but dead and live queens had equal fipronil levels (59.5 and 54.3 amol/μg ant, respectively). The distribution of fipronil differed within the bodies of dead and live queens; the highest amounts of fipronil were recovered in the thorax of dead queens whereas live queens had the highest levels in the head. Resurgence of polygynous ant colonies treated with hydramethylnon baits may be explained by queen survival resulting from sublethal doses due to a slowing of trophallaxis throughout the colony. Bait strategies and dose levels for controlling insect pests need to be based on the specific toxicant properties and trophic strategies for targeting the entire colony.

Exotic ants (Hymenoptera, Formicidae) of Ohio

OpenAIRE

Ivanov,Kal

2016-01-01

The worldwide transfer of plants and animals outside their native ranges is an ever increasing problem for global biodiversity. Ants are no exception and many species have been transported to new locations often with profound negative impacts on local biota. The current study is based on data gathered since the publication of the “Ants of Ohio” in 2005. Here I expand on our knowledge of Ohio’s myrmecofauna by contributing new records, new distributional information and natural history notes. ...

KANTS: a stigmergic ant algorithm for cluster analysis and swarm art.

Science.gov (United States)

Fernandes, Carlos M; Mora, Antonio M; Merelo, Juan J; Rosa, Agostinho C

2014-06-01

KANTS is a swarm intelligence clustering algorithm inspired by the behavior of social insects. It uses stigmergy as a strategy for clustering large datasets and, as a result, displays a typical behavior of complex systems: self-organization and global patterns emerging from the local interaction of simple units. This paper introduces a simplified version of KANTS and describes recent experiments with the algorithm in the context of a contemporary artistic and scientific trend called swarm art, a type of generative art in which swarm intelligence systems are used to create artwork or ornamental objects. KANTS is used here for generating color drawings from the input data that represent real-world phenomena, such as electroencephalogram sleep data. However, the main proposal of this paper is an art project based on well-known abstract paintings, from which the chromatic values are extracted and used as input. Colors and shapes are therefore reorganized by KANTS, which generates its own interpretation of the original artworks. The project won the 2012 Evolutionary Art, Design, and Creativity Competition.

Testing a polarimetric cloud imager aboard research vessel Polarstern: comparison of color-based and polarimetric cloud detection algorithms.

Science.gov (United States)

Barta, András; Horváth, Gábor; Horváth, Ákos; Egri, Ádám; Blahó, Miklós; Barta, Pál; Bumke, Karl; Macke, Andreas

2015-02-10

Cloud cover estimation is an important part of routine meteorological observations. Cloudiness measurements are used in climate model evaluation, nowcasting solar radiation, parameterizing the fluctuations of sea surface insolation, and building energy transfer models of the atmosphere. Currently, the most widespread ground-based method to measure cloudiness is based on analyzing the unpolarized intensity and color distribution of the sky obtained by digital cameras. As a new approach, we propose that cloud detection can be aided by the additional use of skylight polarization measured by 180° field-of-view imaging polarimetry. In the fall of 2010, we tested such a novel polarimetric cloud detector aboard the research vessel Polarstern during expedition ANT-XXVII/1. One of our goals was to test the durability of the measurement hardware under the extreme conditions of a trans-Atlantic cruise. Here, we describe the instrument and compare the results of several different cloud detection algorithms, some conventional and some newly developed. We also discuss the weaknesses of our design and its possible improvements. The comparison with cloud detection algorithms developed for traditional nonpolarimetric full-sky imagers allowed us to evaluate the added value of polarimetric quantities. We found that (1) neural-network-based algorithms perform the best among the investigated schemes and (2) global information (the mean and variance of intensity), nonoptical information (e.g., sun-view geometry), and polarimetric information (e.g., the degree of polarization) improve the accuracy of cloud detection, albeit slightly.

New fossil ants in French Cretaceous amber (Hymenoptera: Formicidae)

Science.gov (United States)

Perrichot, Vincent; Nel, André; Néraudeau, Didier; Lacau, Sébastien; Guyot, Thierry

2008-02-01

Recent studies on the ant phylogeny are mainly based on the molecular analyses of extant subfamilies and do not include the extinct, only Cretaceous subfamily Sphecomyrminae. However, the latter is of major importance for ant relationships, as it is considered the most basal subfamily. Therefore, each new discovery of a Mesozoic ant is of high interest for improving our understanding of their early history and basal relationships. In this paper, a new sphecomyrmine ant, allied to the Burmese amber genus Haidomyrmex, is described from mid-Cretaceous amber of France as Haidomyrmodes mammuthus gen. and sp. n. The diagnosis of the tribe Haidomyrmecini is emended based on the new type material, which includes a gyne (alate female) and two incomplete workers. The genus Sphecomyrmodes, hitherto known by a single species from Burmese amber, is also reported and a new species described as S. occidentalis sp. n. after two workers remarkably preserved in a single piece of Early Cenomanian French amber. The new fossils provide additional information on early ant diversity and relationships and demonstrate that the monophyly of the Sphecomyrminae, as currently defined, is still weakly supported.

An Improved Hierarchical Genetic Algorithm for Sheet Cutting Scheduling with Process Constraints

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Yunqing Rao

2013-01-01

Full Text Available For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony—hierarchical genetic algorithm is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem.

An improved hierarchical genetic algorithm for sheet cutting scheduling with process constraints.

Science.gov (United States)

Rao, Yunqing; Qi, Dezhong; Li, Jinling

2013-01-01

For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony--hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem.

Wavelet-LMS algorithm-based echo cancellers

Science.gov (United States)

Seetharaman, Lalith K.; Rao, Sathyanarayana S.

2002-12-01

This paper presents Echo Cancellers based on the Wavelet-LMS Algorithm. The performance of the Least Mean Square Algorithm in Wavelet transform domain is observed and its application in Echo cancellation is analyzed. The Widrow-Hoff Least Mean Square Algorithm is most widely used algorithm for Adaptive filters that function as Echo Cancellers. The present day communication signals are widely non-stationary in nature and some errors crop up when Least Mean Square Algorithm is used for the Echo Cancellers handling such signals. The analysis of non-stationary signals often involves a compromise between how well transitions or discontinuities can be located. The multi-scale or multi-resolution of signal analysis, which is the essence of wavelet transform, makes Wavelets popular in non-stationary signal analysis. In this paper, we present a Wavelet-LMS algorithm wherein the wavelet coefficients of a signal are modified adaptively using the Least Mean Square Algorithm and then reconstructed to give an Echo-free signal. The Echo Canceller based on this Algorithm is found to have a better convergence and a comparatively lesser MSE (Mean Square error).

A multi-objective improved teaching-learning based optimization algorithm for unconstrained and constrained optimization problems

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R. Venkata Rao

2014-01-01

Full Text Available The present work proposes a multi-objective improved teaching-learning based optimization (MO-ITLBO algorithm for unconstrained and constrained multi-objective function optimization. The MO-ITLBO algorithm is the improved version of basic teaching-learning based optimization (TLBO algorithm adapted for multi-objective problems. The basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self-motivated learning. The MO-ITLBO algorithm uses a grid-based approach to adaptively assess the non-dominated solutions (i.e. Pareto front maintained in an external archive. The performance of the MO-ITLBO algorithm is assessed by implementing it on unconstrained and constrained test problems proposed for the Congress on Evolutionary Computation 2009 (CEC 2009 competition. The performance assessment is done by using the inverted generational distance (IGD measure. The IGD measures obtained by using the MO-ITLBO algorithm are compared with the IGD measures of the other state-of-the-art algorithms available in the literature. Finally, Lexicographic ordering is used to assess the overall performance of competitive algorithms. Results have shown that the proposed MO-ITLBO algorithm has obtained the 1st rank in the optimization of unconstrained test functions and the 3rd rank in the optimization of constrained test functions.

New methods to minimize the preventive maintenance cost of series-parallel systems using ant colony optimization

International Nuclear Information System (INIS)

Samrout, M.; Yalaoui, F.; Cha-hat telet, E.; Chebbo, N.

2005-01-01

This article is based on a previous study made by Bris, Chatelet and Yalaoui [Bris R, Chatelet E, Yalaoui F. New method to minimise the preventive maintenance cost of series-parallel systems. Reliab Eng Syst Saf 2003;82:247-55]. They use genetic algorithm to minimize preventive maintenance cost problem for the series-parallel systems. We propose to improve their results developing a new method based on another technique, the Ant Colony Optimization (ACO). The resolution consists in determining the solution vector of system component inspection periods, T P . Those calculations were applied within the programming tool Matlab. Thus, highly interesting results and improvements of previous studies were obtained

Histrionicotoxin alkaloids finally detected in an ant

DEFF Research Database (Denmark)

Jones, Tappey H.; Adams, Rachelle Martha Marie; Spande, Thomas F.

2012-01-01

Workers of the ant Carebarella bicolor collected in Panama were found to have two major poison-frog alkaloids, cis- and trans-fused decahydroquinolines (DHQs) of the 269AB type, four minor 269AB isomers, two minor 269B isomers, and three isomers of DHQ 271D. For the first time in an ant, however......) sp., were found to have a very similar DHQ complex but failed to show HTXs. Several new DHQ alkaloids of MW 271 (named in the frog as 271G) are reported from the above ants that have both m/z 202 and 204 as major fragment ions, unlike the spectrum seen for the poison-frog alkaloid 271D, which has...... only an m/z 204 base peak. Found also for the first time in skin extracts from the comparison frog Oophaga granulifera of Costa Rica is a trace DHQ of MW 273. It is coded as 273F in the frog; a different isomer is found in the ant....

An Energy-Efficient Spectrum-Aware Reinforcement Learning-Based Clustering Algorithm for Cognitive Radio Sensor Networks.

Science.gov (United States)

Mustapha, Ibrahim; Mohd Ali, Borhanuddin; Rasid, Mohd Fadlee A; Sali, Aduwati; Mohamad, Hafizal

2015-08-13

It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach.

PELE:  Protein Energy Landscape Exploration. A Novel Monte Carlo Based Technique.

Science.gov (United States)

Borrelli, Kenneth W; Vitalis, Andreas; Alcantara, Raul; Guallar, Victor

2005-11-01

Combining protein structure prediction algorithms and Metropolis Monte Carlo techniques, we provide a novel method to explore all-atom energy landscapes. The core of the technique is based on a steered localized perturbation followed by side-chain sampling as well as minimization cycles. The algorithm and its application to ligand diffusion are presented here. Ligand exit pathways are successfully modeled for different systems containing ligands of various sizes:  carbon monoxide in myoglobin, camphor in cytochrome P450cam, and palmitic acid in the intestinal fatty-acid-binding protein. These initial applications reveal the potential of this new technique in mapping millisecond-time-scale processes. The computational cost associated with the exploration is significantly less than that of conventional MD simulations.

The max–min ant system and tabu search for pressurized water reactor loading pattern design

International Nuclear Information System (INIS)

Lin, Chaung; Chen, Ying-Hsiu

2014-01-01

Highlights: • An automatic loading pattern design tool for a pressurized water reactor is developed. • The design method consists of max–min ant system and tabu search. • The heuristic rules are developed to generate the candidates for tabu search. • The initial solution of tabu search is provided by max–min ant system. • The new algorithm shows very satisfactory results compared to the old one. - Abstract: An automatic loading pattern (LP) design tool for a pressurized water reactor is developed. The design procedure consists of two steps: first, a LP is generated using max–min ant system (MMAS) and then tabu search (TS) is adopted to search the satisfactory LP. The MMAS is previously developed and the TS process is newly-developed. The heuristic rules are implemented to generate the candidate LP in TS process. The heuristic rules are comprised of two kinds of action, i.e., a single swap in the location of two fuel assemblies and rotation of fuel assembly. Since developed TS process is a local search algorithm, it is efficient for the minor change of LP. It means that a proper initial LP should be provided by the first step, i.e., by MMAS. The design requirements such as hot channel factor, the hot zero power moderator temperature coefficient, and cycle length are formulated in the objective function. The results show that the developed tool can obtain the satisfactory LP and dramatically reduce the computation time compared with previous tool using ant system alone

Invasion risk of the yellow crazy ant (Anoplolepis gracilipes under the Representative Concentration Pathways 8.5 climate change scenario in South Korea

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Jae-Min Jung

2017-12-01

Full Text Available The yellow crazy ant (Anoplolepis gracilipes has destroyed local ecosystems in numerous countries, and their population sizes and distribution are likely to increase under global warming. To evaluate the risk of invasion by yellow crazy ant in South Korea, this study identified their potential habitats and predicted their future global distribution by modeling various climate change scenarios using CLIMEX software. Our modeling predicted that future climate conditions in South Korea will be favorable for the yellow crazy ant, and they could invade by the mid-21st century. We highlight the use of predictive algorithms to establish geographical areas with a high risk of yellow crazy ant invasion under Representative Concentration Pathways (RCP 8.5 climate scenarios. Keywords: Anoplolepis gracilipes, climate change scenario, CLIMEX, invasive species, yellow crazy ant

A preliminary checklist of the ants (Hymenoptera: Formicidae) of ...

African Journals Online (AJOL)

A preliminary species checklist of the ants (Hymenoptera: Formicidae) of. Kakamega Forest, Western Kenya, is presented. The species list is based on specimens sampled from 1999 until 2009, which are deposited in the ant collection of the Zoological Research Museum Koenig, Bonn, Germany, and the Natural History ...

Plant lock and ant key: pairwise coevolution of an exclusion filter in an ant-plant mutualism.

Science.gov (United States)

Brouat, C; Garcia, N; Andary, C; McKey, D

2001-10-22

Although observations suggest pairwise coevolution in specific ant-plant symbioses, coevolutionary processes have rarely been demonstrated. We report on, what is to the authors' knowledge, the strongest evidence yet for reciprocal adaptation of morphological characters in a species-specific ant-plant mutualism. The plant character is the prostoma, which is a small unlignified organ at the apex of the domatia in which symbiotic ants excavate an entrance hole. Each myrmecophyte in the genus Leonardoxa has evolved a prostoma with a different shape. By performing precise measurements on the prostomata of three related myrmecophytes, on their specific associated ants and on the entrance holes excavated by symbiotic ants at the prostomata, we showed that correspondence of the plant and ant traits forms a morphological and behavioural filter. We have strong evidence for coevolution between the dimensions and shape of the symbiotic ants and the prostoma in one of the three ant-Leonardoxa associations.

Beyond ANT

DEFF Research Database (Denmark)

Jansen, Till

2017-01-01

Actor-Network-Theory (ANT) offers an ‘infra-language’ of the social that allows one to trace social relations very dynamically, while at the same time dissolving human agency, thus providing a flat and de-centred way into sociology. However, ANT struggles with its theoretical design that may lead...... us to reduce agency to causation and to conceptualize actor-networks as homogeneous ontologies of force. This article proposes to regard ANT’s inability to conceptualize reflexivity and the interrelatedness of different ontologies as the fundamental problem of the theory. Drawing on Günther......, it offers an ‘infra-language’ of reflexive relations while maintaining ANT’s de-centred approach. This would enable us to conceptualize actor-networks as non-homogeneous, dynamic and connecting different societal rationales while maintaining the main strengths of ANT....

Dynamics of an ant-plant-pollinator model

Science.gov (United States)

Wang, Yuanshi; DeAngelis, Donald L.; Nathaniel Holland, J.

2015-03-01

In this paper, we consider plant-pollinator-ant systems in which plant-pollinator interaction and plant-ant interaction are both mutualistic, but there also exists interference of pollinators by ants. The plant-pollinator interaction can be described by a Beddington-DeAngelis formula, so we extend the formula to characterize plant-pollinator mutualisms, including the interference by ants, and form a plant-pollinator-ant model. Using dynamical systems theory, we show uniform persistence of the model. Moreover, we demonstrate conditions under which boundary equilibria are globally asymptotically stable. The dynamics exhibit mechanisms by which the three species could coexist when ants interfere with pollinators. We define a threshold in ant interference. When ant interference is strong, it can drive plant-pollinator mutualisms to extinction. Furthermore, if the ants depend on pollination mutualism for their persistence, then sufficiently strong ant interference could lead to their own extinction as well. Yet, when ant interference is weak, plant-ant and plant-pollinator mutualisms can promote the persistence of one another.

Disentangling the diversity of arboreal ant communities in tropical forest trees.

Science.gov (United States)

Klimes, Petr; Fibich, Pavel; Idigel, Cliffson; Rimandai, Maling

2015-01-01

Tropical canopies are known for their high abundance and diversity of ants. However, the factors which enable coexistence of so many species in trees, and in particular, the role of foragers in determining local diversity, are not well understood. We censused nesting and foraging arboreal ant communities in two 0.32 ha plots of primary and secondary lowland rainforest in New Guinea and explored their species diversity and composition. Null models were used to test if the records of species foraging (but not nesting) in a tree were dependent on the spatial distribution of nests in surrounding trees. In total, 102 ant species from 389 trees occurred in the primary plot compared with only 50 species from 295 trees in the secondary forest plot. However, there was only a small difference in mean ant richness per tree between primary and secondary forest (3.8 and 3.3 sp. respectively) and considerably lower richness per tree was found only when nests were considered (1.5 sp. in both forests). About half of foraging individuals collected in a tree belonged to species which were not nesting in that tree. Null models showed that the ants foraging but not nesting in a tree are more likely to nest in nearby trees than would be expected at random. The effects of both forest stage and tree size traits were similar regardless of whether only foragers, only nests, or both datasets combined were considered. However, relative abundance distributions of species differed between foraging and nesting communities. The primary forest plot was dominated by native ant species, whereas invasive species were common in secondary forest. This study demonstrates the high contribution of foragers to arboreal ant diversity, indicating an important role of connectivity between trees, and also highlights the importance of primary vegetation for the conservation of native ant communities.

Hybrid employment recommendation algorithm based on Spark

Science.gov (United States)

Li, Zuoquan; Lin, Yubei; Zhang, Xingming

2017-08-01

Aiming at the real-time application of collaborative filtering employment recommendation algorithm (CF), a clustering collaborative filtering recommendation algorithm (CCF) is developed, which applies hierarchical clustering to CF and narrows the query range of neighbour items. In addition, to solve the cold-start problem of content-based recommendation algorithm (CB), a content-based algorithm with users’ information (CBUI) is introduced for job recommendation. Furthermore, a hybrid recommendation algorithm (HRA) which combines CCF and CBUI algorithms is proposed, and implemented on Spark platform. The experimental results show that HRA can overcome the problems of cold start and data sparsity, and achieve good recommendation accuracy and scalability for employment recommendation.

Using Ants as bioindicators: Multiscale Issues in Ant Community Ecology

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Alan Andersen

1997-06-01

Full Text Available Ecological patterns and processes are characteristically scale dependent, and research findings often cannot be translated easily from one scale to another. Conservation biology is challenged by a lack of congruence between the spatial scales of ecological research (typically involving small plots and land management (typically involving whole landscapes. Here, I discuss spatial scaling issues as they relate to an understanding of ant communities and, consequently, their use as bioindicators in land management. Our perceptions of fundamental patterns and processes in ant communities depend on scale: taxa that are behaviorally dominant at one scale are not necessarily so at others, functional groups recognized at one scale are often inappropriate for others, and the role of competition in community structure depends on the scale of analysis. Patterns of species richness and composition, and the ability of total richness to be estimated by surrogates, are all also scale dependent. Ant community ecology has a tradition of detailed studies in small plots, but the use of ants as bioindicators requires a predictive understanding of community structure and dynamics at a range of spatial scales. Such an appreciation of ant communities and their most effective use as bioindicators is best served by studies integrating results from plot-scale research with the broad-scale paradigms of biogeography, systematics, and evolutionary biology.

Ecosystem services delivered by weaver ants

DEFF Research Database (Denmark)

Offenberg, Joachim

Weaver ants (Oecopgylla spp.) are increasingly being utilized as efficient biocontrol agents in a number of tropical tree crops, as they prey on pest insects and increase yields. However, recent studies and a review of the literature reveal that a number of other services may derive from the pres......Weaver ants (Oecopgylla spp.) are increasingly being utilized as efficient biocontrol agents in a number of tropical tree crops, as they prey on pest insects and increase yields. However, recent studies and a review of the literature reveal that a number of other services may derive from...... the presence of these ants. First of all, the chemical footprint left by the high density of ants in managed host trees may results in additional benefits. (i) Ant deposits may lead to improved fruit quality, e.g. increased sugar content, (ii) ant deposits may deter important pests (chemical deterrence) from...... crops, and lastly, (iii) ant waste products deposited ias anal spots contain urea that may be taken up by plant leaves and in this way fertilize ant-plants. On top of chemical services, weaver ants have been shown to reduce plant disease incidence via competitive exclusion of other ant species because...

Sampling-Based Motion Planning Algorithms for Replanning and Spatial Load Balancing

Energy Technology Data Exchange (ETDEWEB)

Boardman, Beth Leigh [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2017-10-12

The common theme of this dissertation is sampling-based motion planning with the two key contributions being in the area of replanning and spatial load balancing for robotic systems. Here, we begin by recalling two sampling-based motion planners: the asymptotically optimal rapidly-exploring random tree (RRT*), and the asymptotically optimal probabilistic roadmap (PRM*). We also provide a brief background on collision cones and the Distributed Reactive Collision Avoidance (DRCA) algorithm. The next four chapters detail novel contributions for motion replanning in environments with unexpected static obstacles, for multi-agent collision avoidance, and spatial load balancing. First, we show improved performance of the RRT* when using the proposed Grandparent-Connection (GP) or Focused-Refinement (FR) algorithms. Next, the Goal Tree algorithm for replanning with unexpected static obstacles is detailed and proven to be asymptotically optimal. A multi-agent collision avoidance problem in obstacle environments is approached via the RRT*, leading to the novel Sampling-Based Collision Avoidance (SBCA) algorithm. The SBCA algorithm is proven to guarantee collision free trajectories for all of the agents, even when subject to uncertainties in the knowledge of the other agents’ positions and velocities. Given that a solution exists, we prove that livelocks and deadlock will lead to the cost to the goal being decreased. We introduce a new deconfliction maneuver that decreases the cost-to-come at each step. This new maneuver removes the possibility of livelocks and allows a result to be formed that proves convergence to the goal configurations. Finally, we present a limited range Graph-based Spatial Load Balancing (GSLB) algorithm which fairly divides a non-convex space among multiple agents that are subject to differential constraints and have a limited travel distance. The GSLB is proven to converge to a solution when maximizing the area covered by the agents. The analysis

Ant-lepidopteran associations along African forest edges

Science.gov (United States)

Dejean, Alain; Azémar, Frédéric; Libert, Michel; Compin, Arthur; Hérault, Bruno; Orivel, Jérôme; Bouyer, Thierry; Corbara, Bruno

2017-02-01

Working along forest edges, we aimed to determine how some caterpillars can co-exist with territorially dominant arboreal ants (TDAAs) in tropical Africa. We recorded caterpillars from 22 lepidopteran species living in the presence of five TDAA species. Among the defoliator and/or nectarivorous caterpillars that live on tree foliage, the Pyralidae and Nymphalidae use their silk to protect themselves from ant attacks. The Notodontidae and lycaenid Polyommatinae and Theclinae live in direct contact with ants; the Theclinae even reward ants with abundant secretions from their Newcomer gland. Lichen feeders (lycaenid; Poritiinae), protected by long bristles, also live among ants. Some lycaenid Miletinae caterpillars feed on ant-attended membracids, including in the shelters where the ants attend them; Lachnocnema caterpillars use their forelegs to obtain trophallaxis from their host ants. Caterpillars from other species live inside weaver ant nests. Those of the genus Euliphyra (Miletinae) feed on ant prey and brood and can obtain trophallaxis, while those from an Eberidae species only prey on host ant eggs. Eublemma albifascia (Erebidae) caterpillars use their thoracic legs to obtain trophallaxis and trophic eggs from ants. Through transfer bioassays of last instars, we noted that herbivorous caterpillars living in contact with ants were always accepted by alien conspecific ants; this is likely due to an intrinsic appeasing odor. Yet, caterpillars living in ant shelters or ant nests probably acquire cues from their host colonies because they were considered aliens and killed. We conclude that co-evolution with ants occurred similarly in the Heterocera and Rhopalocera.

The metapleural gland of ants

DEFF Research Database (Denmark)

Yek, Sze Huei; Mueller, Ulrich G

2011-01-01

The metapleural gland (MG) is a complex glandular structure unique to ants, suggesting a critical role in their origin and ecological success. We synthesize the current understanding of the adaptive function, morphology, evolutionary history, and chemical properties of the MG. Two functions......-compressible invagination of the integument and the secretion is thought to ooze out passively through the non-closable opening of the MG or is groomed off by the legs and applied to target surfaces. MG loss has occurred repeatedly among the ants, particularly in the subfamilies Formicinae and Myrmicinae, and the MG...... is more commonly absent in males than in workers. MG chemistry has been characterized mostly in derived ant lineages with unique biologies (e.g. leafcutter ants, fire ants), currently precluding any inferences about MG chemistry at the origin of the ants. A synthetic approach integrating functional...

Bioinspired computation in combinatorial optimization: algorithms and their computational complexity

DEFF Research Database (Denmark)

Neumann, Frank; Witt, Carsten

2012-01-01

Bioinspired computation methods, such as evolutionary algorithms and ant colony optimization, are being applied successfully to complex engineering and combinatorial optimization problems, and it is very important that we understand the computational complexity of these algorithms. This tutorials...... problems. Classical single objective optimization is examined first. They then investigate the computational complexity of bioinspired computation applied to multiobjective variants of the considered combinatorial optimization problems, and in particular they show how multiobjective optimization can help...... to speed up bioinspired computation for single-objective optimization problems. The tutorial is based on a book written by the authors with the same title. Further information about the book can be found at www.bioinspiredcomputation.com....

Ant mosaics in Bornean primary rain forest high canopy depend on spatial scale, time of day, and sampling method

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Kalsum M. Yusah

2018-01-01

Full Text Available Background Competitive interactions in biological communities can be thought of as giving rise to “assembly rules” that dictate the species that are able to co-exist. Ant communities in tropical canopies often display a particular pattern, an “ant mosaic”, in which competition between dominant ant species results in a patchwork of mutually exclusive territories. Although ant mosaics have been well-documented in plantation landscapes, their presence in pristine tropical forests remained contentious until recently. Here we assess presence of ant mosaics in a hitherto under-investigated forest stratum, the emergent trees of the high canopy in primary tropical rain forest, and explore how the strength of any ant mosaics is affected by spatial scale, time of day, and sampling method. Methods To test whether these factors might impact the detection of ant mosaics in pristine habitats, we sampled ant communities from emergent trees, which rise above the highest canopy layers in lowland dipterocarp rain forests in North Borneo (38.8–60.2 m, using both baiting and insecticide fogging. Critically, we restricted sampling to only the canopy of each focal tree. For baiting, we carried out sampling during both the day and the night. We used null models of species co-occurrence to assess patterns of segregation at within-tree and between-tree scales. Results The numerically dominant ant species on the emergent trees sampled formed a diverse community, with differences in the identity of dominant species between times of day and sampling methods. Between trees, we found patterns of ant species segregation consistent with the existence of ant mosaics using both methods. Within trees, fogged ants were segregated, while baited ants were segregated only at night. Discussion We conclude that ant mosaics are present within the emergent trees of the high canopy of tropical rain forest in Malaysian Borneo, and that sampling technique, spatial scale, and time

Scope of Various Random Number Generators in ant System Approach for TSP

Science.gov (United States)

Sen, S. K.; Shaykhian, Gholam Ali

2007-01-01

Experimented on heuristic, based on an ant system approach for traveling salesman problem, are several quasi- and pseudo-random number generators. This experiment is to explore if any particular generator is most desirable. Such an experiment on large samples has the potential to rank the performance of the generators for the foregoing heuristic. This is mainly to seek an answer to the controversial issue "which generator is the best in terms of quality of the result (accuracy) as well as cost of producing the result (time/computational complexity) in a probabilistic/statistical sense."

Stigmergic construction and topochemical information shape ant nest architecture.

Science.gov (United States)

Khuong, Anaïs; Gautrais, Jacques; Perna, Andrea; Sbaï, Chaker; Combe, Maud; Kuntz, Pascale; Jost, Christian; Theraulaz, Guy

2016-02-02

The nests of social insects are not only impressive because of their sheer complexity but also because they are built from individuals whose work is not centrally coordinated. A key question is how groups of insects coordinate their building actions. Here, we use a combination of experimental and modeling approaches to investigate nest construction in the ant Lasius niger. We quantify the construction dynamics and the 3D structures built by ants. Then, we characterize individual behaviors and the interactions of ants with the structures they build. We show that two main interactions are involved in the coordination of building actions: (i) a stigmergic-based interaction that controls the amplification of depositions at some locations and is attributable to a pheromone added by ants to the building material; and (ii) a template-based interaction in which ants use their body size as a cue to control the height at which they start to build a roof from existing pillars. We then develop a 3D stochastic model based on these individual behaviors to analyze the effect of pheromone presence and strength on construction dynamics. We show that the model can quantitatively reproduce key features of construction dynamics, including a large-scale pattern of regularly spaced pillars, the formation and merging of caps over the pillars, and the remodeling of built structures. Finally, our model suggests that the lifetime of the pheromone is a highly influential parameter that controls the growth and form of nest architecture.

The application of ant colony optimization in the solution of 3D traveling salesman problem on a sphere

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Hüseyin Eldem

2017-08-01

Full Text Available Traveling Salesman Problem (TSP is a problem in combinatorial optimization that should be solved by a salesperson who has to travel all cities at the minimum cost (minimum route and return to the starting city (node. Todays, to resolve the minimum cost of this problem, many optimization algorithms have been used. The major ones are these metaheuristic algorithms. In this study, one of the metaheuristic methods, Ant Colony Optimization (ACO method (Max-Min Ant System – MMAS, was used to solve the Non-Euclidean TSP, which consisted of sets of different count points coincidentally located on the surface of a sphere. In this study seven point sets were used which have different point count. The performance of the MMAS method solving Non-Euclidean TSP problem was demonstrated by different experiments. Also, the results produced by ACO are compared with Discrete Cuckoo Search Algorithm (DCS and Genetic Algorithm (GA that are in the literature. The experiments for TSP on a sphere, show that ACO’s average results were better than the GA’s average results and also best results of ACO successful than the DCS.

Simplifying Multiproject Scheduling Problem Based on Design Structure Matrix and Its Solution by an Improved aiNet Algorithm

Directory of Open Access Journals (Sweden)

Chunhua Ju

2012-01-01

Full Text Available Managing multiple project is a complex task involving the unrelenting pressures of time and cost. Many studies have proposed various tools and techniques for single-project scheduling; however, the literature further considering multimode or multiproject issues occurring in the real world is rather scarce. In this paper, design structure matrix (DSM and an improved artificial immune network algorithm (aiNet are developed to solve a multi-mode resource-constrained scheduling problem. Firstly, the DSM is used to simplify the mathematic model of multi-project scheduling problem. Subsequently, aiNet algorithm comprised of clonal selection, negative selection, and network suppression is adopted to realize the local searching and global searching, which will assure that it has a powerful searching ability and also avoids the possible combinatorial explosion. Finally, the approach is tested on a set of randomly cases generated from ProGen. The computational results validate the effectiveness of the proposed algorithm comparing with other famous metaheuristic algorithms such as genetic algorithm (GA, simulated annealing algorithm (SA, and ant colony optimization (ACO.

Foliar uptake of nitrogen from ant fecal droplets: an overlooked service to ant plants

DEFF Research Database (Denmark)

Pinkalski, Christian Alexander Stidsen; Jensen, Karl-Martin Vagn; Damgaard, Christian Frølund

2018-01-01

and subsequently deposited fecal droplets on the seedlings, coffee leaves showed increased levels of 15N and total N compared to control plants without ants. This was evident for both exposed leaves and leaves covered in plastic bags (i.e. not directly exposed to ants). Thus, N from ant excretions was absorbed...

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.

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

Science.gov (United States)

O'Boyle, Noel M; Palmer, David S; Nigsch, Florian; Mitchell, John Bo

2008-10-29

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. 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 degrees 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, epsilon of 0.21) and an RMSE of 45.1 degrees C and R2 of 0.54. This model outperforms a kNN model (RMSE of 48.3 degrees C, R2 of 0.47) for the same data and has similar performance to a Random Forest model (RMSE of 44.5 degrees 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. 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.

Study on ant colony optimization for fuel loading pattern problem

International Nuclear Information System (INIS)

Kishi, Hironori; Kitada, Takanori

2013-01-01

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

Ant Colony Optimization for Markowitz Mean-Variance Portfolio Model

Science.gov (United States)

Deng, Guang-Feng; Lin, Woo-Tsong

This work presents Ant Colony Optimization (ACO), which was initially developed to be a meta-heuristic for combinatorial optimization, for solving the cardinality constraints Markowitz mean-variance portfolio model (nonlinear mixed quadratic programming problem). To our knowledge, an efficient algorithmic solution for this problem has not been proposed until now. Using heuristic algorithms in this case is imperative. Numerical solutions are obtained for five analyses of weekly price data for the following indices for the period March, 1992 to September, 1997: Hang Seng 31 in Hong Kong, DAX 100 in Germany, FTSE 100 in UK, S&P 100 in USA and Nikkei 225 in Japan. The test results indicate that the ACO is much more robust and effective than Particle swarm optimization (PSO), especially for low-risk investment portfolios.

Uni-directional trail sharing by two species of ants a Monte Carlo study

International Nuclear Information System (INIS)

Kunduraci, T; Kayacan, O

2015-01-01

We study insect traffic, specifically ant traffic on a uni-directional trail which is shared by two species of ants, one of which is ‘good’ at smelling and the other ‘poor’. The two distinct species of ants are placed mixed on the same trail and individuals of both are permitted to make a U-turn when they encounter another ant in front of them. The theoretical scheme for the ant traffic is based on an asymmetric simple exclusion model. The ant traffic on the uni-directional trail is studied as a function of the number of ‘good-smelling’ ants and the evaporation probability of pheromones by keeping the number of ‘poor-smelling ants’ constant during Monte Carlo simulations. (paper)

Voltage Profile Enhancement and Reduction of Real Power loss by Hybrid Biogeography Based Artificial Bee Colony algorithm

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K. Lenin

2014-04-01

Full Text Available This paper presents Hybrid Biogeography algorithm for solving the multi-objective reactive power dispatch problem in a power system. Real Power Loss minimization and maximization of voltage stability margin are taken as the objectives. Artificial bee colony optimization (ABC is quick and forceful algorithm for global optimization. Biogeography-Based Optimization (BBO is a new-fangled biogeography inspired algorithm. It mainly utilizes the biogeography-based relocation operator to share the information among solutions. In this work, a hybrid algorithm with BBO and ABC is projected, and named as HBBABC (Hybrid Biogeography based Artificial Bee Colony Optimization, for the universal numerical optimization problem. HBBABC merge the searching behavior of ABC with that of BBO. Both the algorithms have different solution probing tendency like ABC have good exploration probing tendency while BBO have good exploitation probing tendency.  HBBABC used to solve the reactive power dispatch problem and the proposed technique has been tested in standard IEEE30 bus test system.

Species diversity and distribution patterns of the ants of Amazonian Ecuador.

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Kari T Ryder Wilkie

2010-10-01

Full Text Available Ants are among the most diverse, abundant and ecologically significant organisms on earth. Although their species richness appears to be greatest in the New World tropics, global patterns of ant diversity and distribution are not well understood. We comprehensively surveyed ant diversity in a lowland primary rainforest in Western Amazonia, Ecuador using canopy fogging, pitfall traps, baits, hand collecting, mini-Winkler devices and subterranean probes to sample ants. A total of 489 ant species comprising 64 genera in nine subfamilies were identified from samples collected in only 0.16 square kilometers. The most species-rich genera were Camponotus, Pheidole, Pseudomyrmex, Pachycondyla, Brachymyrmex, and Crematogaster. Camponotus and Pseudomyrmex were most diverse in the canopy, while Pheidole was most diverse on the ground. The three most abundant ground-dwelling ant genera were Pheidole, Solenopsis and Pyramica. Crematogaster carinata was the most abundant ant species in the canopy; Wasmannia auropunctata was most abundant on the ground, and the army ant Labidus coecus was the most abundant subterranean species. Ant species composition among strata was significantly different: 80% of species were found in only one stratum, 17% in two strata, and 3% in all three strata. Elevation and the number of logs and twigs available as nest sites were significant predictors of ground-dwelling ant species richness. Canopy species richness was not correlated with any ecological variable measured. Subterranean species richness was negatively correlated with depth in the soil. When ant species were categorized using a functional group matrix based on diet, nest-site preference and foraging ecology, the greatest diversity was found in Omnivorous Canopy Nesters. Our study indicates ant species richness is exceptionally high at Tiputini. We project 647-736 ant species in this global hotspot of biodiversity. Considering the relatively small area surveyed, this

Odorous house ants (Tapinoma sessile as back-seat drivers of localized ant decline in urban habitats.

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Adam Salyer

Full Text Available Invasive species and habitat disturbance threaten biodiversity worldwide by modifying ecosystem performance and displacing native organisms. Similar homogenization impacts manifest locally when urbanization forces native species to relocate or reinvade perpetually altered habitat. This study investigated correlations between ant richness and abundance in response to urbanization and the nearby presence of invasive ant species, odorous house ants (Tapinoma sessile, within its native region. Surveying localized ant composition within natural, semi-natural, and urban habitat supported efforts to determine whether T. sessile appear to be primary (drivers threats as instigators or secondary (passengers threats as inheritors of indigenous ant decline. Sampling 180 sites, evenly split between all habitats with and without T. sessile present, yielded 45 total species. Although urbanization and T. sessile presence factors were significantly linked to ant decline, their interaction correlated to the greatest reduction of total ant richness (74% and abundance (81%. Total richness appeared to decrease from 27 species to 18 when natural habitat is urbanized and from 18 species to 7 with T. sessile present in urban plots. Odorous house ant presence minimally influenced ant communities within natural and semi-natural habitat, highlighting the importance of habitat alteration and T. sessile presence interactions. Results suggest urbanization releases T. sessile from unknown constraints by decreasing ant richness and competition. Within urban environment, T. sessile are pre-adapted to quickly exploit new resources and grow to supercolony strength wherein T. sessile drive adjacent biodiversity loss. Odorous house ants act as passengers and drivers of ecological change throughout different phases of urban 'invasion'. This progression through surviving habitat alteration, exploiting new resources, thriving, and further reducing interspecific competition supports a

antaRNA: ant colony-based RNA sequence design.

Science.gov (United States)

Kleinkauf, Robert; Mann, Martin; Backofen, Rolf

2015-10-01

RNA sequence design is studied at least as long as the classical folding problem. Although for the latter the functional fold of an RNA molecule is to be found ,: inverse folding tries to identify RNA sequences that fold into a function-specific target structure. In combination with RNA-based biotechnology and synthetic biology ,: reliable RNA sequence design becomes a crucial step to generate novel biochemical components. In this article ,: the computational tool antaRNA is presented. It is capable of compiling RNA sequences for a given structure that comply in addition with an adjustable full range objective GC-content distribution ,: specific sequence constraints and additional fuzzy structure constraints. antaRNA applies ant colony optimization meta-heuristics and its superior performance is shown on a biological datasets. http://www.bioinf.uni-freiburg.de/Software/antaRNA CONTACT: backofen@informatik.uni-freiburg.de Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

Microsatellite primers for fungus-growing ants

DEFF Research Database (Denmark)

Villesen, Palle; Gertsch, P J; Boomsma, JJ

2002-01-01

We isolated five polymorphic microsatellite loci from a library of two thousand recombinant clones of two fungus-growing ant species, Cyphomyrmex longiscapus and Trachymyrmex cf. zeteki. Amplification and heterozygosity were tested in five species of higher attine ants using both the newly...... developed primers and earlier published primers that were developed for fungus-growing ants. A total of 20 variable microsatellite loci, developed for six different species of fungus-growing ants, are now available for studying the population genetics and colony kin-structure of these ants....

Microsatellite Primers for Fungus-Growing Ants

DEFF Research Database (Denmark)

Villesen Fredsted, Palle; Gertsch, Pia J.; Boomsma, Jacobus Jan (Koos)

2002-01-01

We isolated five polymorphic microsatellite loci from a library of two thousand recombinant clones of two fungus-growing ant species, Cyphomyrmex longiscapus and Trachymyrmex cf. zeteki. Amplification and heterozygosity were tested in five species of higher attine ants using both the newly...... developed primers and earlier published primers that were developed for fungus-growing ants. A total of 20 variable microsatellite loci, developed for six different species of fungus-growing ants, are now available for studying the population genetics and colony kin-structure of these ants....

Robust MST-Based Clustering Algorithm.

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Liu, Qidong; Zhang, Ruisheng; Zhao, Zhili; Wang, Zhenghai; Jiao, Mengyao; Wang, Guangjing

2018-06-01

Minimax similarity stresses the connectedness of points via mediating elements rather than favoring high mutual similarity. The grouping principle yields superior clustering results when mining arbitrarily-shaped clusters in data. However, it is not robust against noises and outliers in the data. There are two main problems with the grouping principle: first, a single object that is far away from all other objects defines a separate cluster, and second, two connected clusters would be regarded as two parts of one cluster. In order to solve such problems, we propose robust minimum spanning tree (MST)-based clustering algorithm in this letter. First, we separate the connected objects by applying a density-based coarsening phase, resulting in a low-rank matrix in which the element denotes the supernode by combining a set of nodes. Then a greedy method is presented to partition those supernodes through working on the low-rank matrix. Instead of removing the longest edges from MST, our algorithm groups the data set based on the minimax similarity. Finally, the assignment of all data points can be achieved through their corresponding supernodes. Experimental results on many synthetic and real-world data sets show that our algorithm consistently outperforms compared clustering algorithms.

A curious robot: An explorative-exploitive inference algorithm

DEFF Research Database (Denmark)

Pedersen, Kim Steenstrup; Johansen, Peter

2007-01-01

We propose a sequential learning algorithm with a focus on robot control. It is initialised by a teacher who directs the robot through a series of example solutions of a problem. Left alone, the control chooses its next action by prediction based on a variable order Markov chain model selected to...

Competitive assembly of South Pacific invasive ant communities

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Sarty Megan

2009-01-01

Full Text Available Abstract Background The relative importance of chance and determinism in structuring ecological communities has been debated for nearly a century. Evidence for determinism or assembly rules is often evaluated with null models that randomize the occurrence of species in particular locales. However, analyses of the presence or absence of species ignores the potential influence of species abundances, which have long been considered of major importance on community structure. Here, we test for community assembly rules in ant communities on small islands of the Tokelau archipelago using both presence-absence and abundance data. We conducted three sets of analyses on two spatial scales using three years of sampling data from 39 plots on 11 islands. Results First, traditional null model tests showed support for negative species co-occurrence patterns among plots within islands, but not among islands. A plausible explanation for this result is that analyses at larger spatial scales merge heterogeneous habitats that have considerable effects on species occurrences. Second, analyses of ant abundances showed that samples with high ant abundances had fewer species than expected by chance, both within and among islands. One ant species, the invasive yellow crazy ant Anoplolepis gracilipes, appeared to have a particularly strong effect on community structure correlated with its abundance. Third, abundances of most ant species were inversely correlated with the abundances of all other ants at both spatial scales. This result is consistent with competition theory, which predicts species distributions are affected by diffuse competition with suites of co-occurring species. Conclusion Our results support a pluralistic explanation for ant species abundances and assembly. Both stochastic and deterministic processes interact to determine ant community assembly, though abundance patterns clearly drive the deterministic patterns in this community. These deterministic

Trail Pheromone Disruption of Argentine Ant Trail Formation and Foraging

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Suckling, D.M.; Peck, R.W.; Stringer, L.D.; Snook, K.; Banko, P.C.

2010-01-01

Trail pheromone disruption of invasive ants is a novel tactic that builds on the development of pheromone-based pest management in other insects. Argentine ant trail pheromone, (Z)-9-hexadecenal, was formulated as a micro-encapsulated sprayable particle and applied against Argentine ant populations in 400 m2 field plots in Hawai'i Volcanoes National Park. A widely dispersed point source strategy for trail pheromone disruption was used. Traffic rates of ants in bioassays of treated filter paper, protected from rainfall and sunlight, indicated the presence of behaviorally significant quantities of pheromone being released from the formulation for up to 59 days. The proportion of plots, under trade wind conditions (2-3 m s-1), with visible trails was reduced for up to 14 days following treatment, and the number of foraging ants at randomly placed tuna-bait cards was similarly reduced. The success of these trail pheromone disruption trials in a natural ecosystem highlights the potential of this method for control of invasive ant species in this and other environments. ?? Springer Science+Business Media, LLC 2010.

Water stress strengthens mutualism among ants, trees, and scale insects.

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Elizabeth G Pringle

2013-11-01

Full Text Available Abiotic environmental variables strongly affect the outcomes of species interactions. For example, mutualistic interactions between species are often stronger when resources are limited. The effect might be indirect: water stress on plants can lead to carbon stress, which could alter carbon-mediated plant mutualisms. In mutualistic ant-plant symbioses, plants host ant colonies that defend them against herbivores. Here we show that the partners' investments in a widespread ant-plant symbiosis increase with water stress across 26 sites along a Mesoamerican precipitation gradient. At lower precipitation levels, Cordia alliodora trees invest more carbon in Azteca ants via phloem-feeding scale insects that provide the ants with sugars, and the ants provide better defense of the carbon-producing leaves. Under water stress, the trees have smaller carbon pools. A model of the carbon trade-offs for the mutualistic partners shows that the observed strategies can arise from the carbon costs of rare but extreme events of herbivory in the rainy season. Thus, water limitation, together with the risk of herbivory, increases the strength of a carbon-based mutualism.

Water stress strengthens mutualism among ants, trees, and scale insects.

Science.gov (United States)

Pringle, Elizabeth G; Akçay, Erol; Raab, Ted K; Dirzo, Rodolfo; Gordon, Deborah M

2013-11-01

Abiotic environmental variables strongly affect the outcomes of species interactions. For example, mutualistic interactions between species are often stronger when resources are limited. The effect might be indirect: water stress on plants can lead to carbon stress, which could alter carbon-mediated plant mutualisms. In mutualistic ant-plant symbioses, plants host ant colonies that defend them against herbivores. Here we show that the partners' investments in a widespread ant-plant symbiosis increase with water stress across 26 sites along a Mesoamerican precipitation gradient. At lower precipitation levels, Cordia alliodora trees invest more carbon in Azteca ants via phloem-feeding scale insects that provide the ants with sugars, and the ants provide better defense of the carbon-producing leaves. Under water stress, the trees have smaller carbon pools. A model of the carbon trade-offs for the mutualistic partners shows that the observed strategies can arise from the carbon costs of rare but extreme events of herbivory in the rainy season. Thus, water limitation, together with the risk of herbivory, increases the strength of a carbon-based mutualism.

DATA MINING UNTUK KLASIFIKASI PELANGGAN DENGAN ANT COLONY OPTIMIZATION

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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

Kombinasi Firefly Algorithm-Tabu Search untuk Penyelesaian Traveling Salesman Problem

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Riyan Naufal Hay's

2017-07-01

Full Text Available Traveling Salesman Problem (TSP adalah masalah optimasi kombinatorial klasik dan memiliki peran dalam perencanaan, penjadwalan, dan pencarian pada bidang rekayasa dan pengetahuan (Dong, 2012. TSP juga merupakan objek yang baik untuk menguji kinerja metode optimasi, beberapa metode seperti Cooperative Genetic Ant System (CGAS (Dong, 2012, Parallelized Genetic Ant Colony System (PGAS Particle Swarm Optimization and Ant Colony Optimization Algorithms (PSO–ACO (Elloumi, 2014, dan Ant Colony Hyper-Heuristics (ACO HH (Aziz, 2015 telah dikembangkan untuk memecahkan TSP. Sehingga, pada penelitian ini diimplementasikan kombinasi metode baru untuk meningkatkan akurasi penyelesaian TSP. Firefly Algorithm (FA merupakan salah satu algoritma yang dapat digunakan untuk memecahkan masalah optimasi kombinatorial (Layeb, 2014. FA merupakan algoritma yang berpotensi kuat dalam memecahkan kasus optimasi dibanding algoritma yang ada termasuk Particle Swarm Optimization (Yang, 2010. Namun, FA memiliki kekurangan dalam memecahkan masalah optimasi dengan skala besar (Baykasoğlu dan Ozsoy, 2014. Tabu Search (TS merupakan metode optimasi yang terbukti efektif untuk memecahkan masalah optimasi dengan skala besar (Pedro, 2013. Pada penelitian ini, TS akan diterapkan pada FA (FATS untuk memecahkan kasus TSP. Hasil FATS akan dibandingkan terhadap penelitian sebelumnya yaitu ACOHH. Perbandingan hasil menunjukan peningkatan akurasi sebesar 0.89% pada dataset Oliver30, 0.14% dataset Eil51, 3.81% dataset Eil76 dan 1.27% dataset KroA100.

Cuticular hydrocarbons correlate with queen reproductive status in native and invasive Argentine ants (Linepithema humile, Mayr)

Science.gov (United States)

Diaz, Mireia; Lenoir, Alain; Ivon Paris, Carolina; Boulay, Raphaël; Gómez, Crisanto

2018-01-01

In insect societies, chemical communication plays an important role in colony reproduction and individual social status. Many studies have indicated that cuticular hydrocarbons (CHCs) are the main chemical compounds encoding reproductive status. However, these studies have largely focused on queenless or monogynous species whose workers are capable of egg laying and have mainly explored the mechanisms underlying queen-worker or worker-worker reproductive conflicts. Less is known about what occurs in highly polygynous ant species with permanently sterile workers. Here, we used the Argentine ant as a model to examine the role of CHCs in communicating reproductive information in such insect societies. The Argentine ant is unicolonial, highly polygynous, and polydomous. We identified several CHCs whose presence and levels were correlated with queen age, reproductive status, and fertility. Our results also provide new insights into queen executions in the Argentine ant, a distinctive feature displayed by this species in its introduced range. Each spring, just before new sexuals appear, workers eliminate up to 90% of the mated queens in their colonies. We discovered that queens that survived execution had different CHC profiles from queens present before and during execution. More specifically, levels of some CHCs were higher in the survivors, suggesting that workers could eliminate queens based on their chemical profiles. In addition, queen CHC profiles differed based on season and species range (native vs. introduced). Overall, the results of this study provide new evidence that CHCs serve as queen signals and do more than just regulate worker reproduction. PMID:29470506

HSTLBO: A hybrid algorithm based on Harmony Search and Teaching-Learning-Based Optimization for complex high-dimensional optimization problems.

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Shouheng Tuo

Full Text Available Harmony Search (HS and Teaching-Learning-Based Optimization (TLBO as new swarm intelligent optimization algorithms have received much attention in recent years. Both of them have shown outstanding performance for solving NP-Hard optimization problems. However, they also suffer dramatic performance degradation for some complex high-dimensional optimization problems. Through a lot of experiments, we find that the HS and TLBO have strong complementarity each other. The HS has strong global exploration power but low convergence speed. Reversely, the TLBO has much fast convergence speed but it is easily trapped into local search. In this work, we propose a hybrid search algorithm named HSTLBO that merges the two algorithms together for synergistically solving complex optimization problems using a self-adaptive selection strategy. In the HSTLBO, both HS and TLBO are modified with the aim of balancing the global exploration and exploitation abilities, where the HS aims mainly to explore the unknown regions and the TLBO aims to rapidly exploit high-precision solutions in the known regions. Our experimental results demonstrate better performance and faster speed than five state-of-the-art HS variants and show better exploration power than five good TLBO variants with similar run time, which illustrates that our method is promising in solving complex high-dimensional optimization problems. The experiment on portfolio optimization problems also demonstrate that the HSTLBO is effective in solving complex read-world application.

The Müller-Lyer illusion in ant foraging.

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Tomoko Sakiyama

Full Text Available The Müller-Lyer illusion is a classical geometric illusion in which the apparent (perceived length of a line depends on whether the line terminates in an arrow tail or arrowhead. This effect may be caused by economic compensation for the gap between the physical stimulus and visual fields. Here, we show that the Müller-Lyer illusion can also be produced by the foraging patterns of garden ants (Lasius niger and that the pattern obtained can be explained by a simple, asynchronously updated foraging ant model. Our results suggest that the geometric illusion may be a byproduct of the foraging process, in which local interactions underlying efficient exploitation can also give rise to global exploration, and that visual information processing in human could implement similar modulation between local efficient processing and widespread computation.

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

Beneficial Effects of Ants and Spiders on the Reproductive Value of Eriotheca gracilipes (Malvaceae) in a Tropical Savanna.

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Stefani, Vanessa; Pires, Tayna Lopes; Torezan-Silingardi, Helena Maura; Del-Claro, Kleber

2015-01-01

Predators affect plant fitness when they forage on them and reduce the action of herbivores. Our study evaluates the complementary effects of spiders and ants that visit the extrafloral nectaries of Eriotheca gracilipes (Malvaceae) on the production of fruits and viable seeds of these savanna trees. Four experimental groups were established: control group - with free access of spiders and ants; exclusion group - spiders and ants excluded; ant group - absence of spiders; and spider group - absence of ants. The presence of ants reduced the spider richness; however, the presence of spiders did not affect the ant richness. A significantly higher number of fruits per buds were found in the presence of spiders alone or spiders and ants together (control group) compared with the absence of both predators (exclusion group). The number of seeds per fruits and seed viability were higher in the control group. This is the first study showing that spiders and ants may exert a positive and complementary effect on the reproductive value of an extrafloral nectaried plant. Mostly the impact of ants and/or spiders on herbivores is considered, whereas our study reinforces the importance of evaluating the effect of multiple predators simultaneously, exploring how the interactions among predators with distinct skills may affect the herbivores and the plants on which they forage.

Partial incompatibility between ants and symbiotic fungi in two sympatric species of Acromyrmex leaf-cutting ants.

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Bot, A N; Rehner, S A; Boomsma, J J

2001-10-01

We investigate the nature and duration of incompatibility between certain combinations of Acromyrmex leaf-cutting ants and symbiotic fungi, taken from sympatric colonies of the same or a related species. Ant-fungus incompatibility appeared to be largely independent of the ant species involved, but could be explained partly by genetic differences among the fungus cultivars. Following current theoretical considerations, we develop a hypothesis, originally proposed by S. A. Frank, that the observed incompatibilities are ultimately due to competitive interactions between genetically different fungal lineages, and we predict that the ants should have evolved mechanisms to prevent such competition between cultivars within a single garden. This requires that the ants are able to recognize unfamiliar fungi, and we show that this is indeed the case. Amplified fragment length polymorphism genotyping further shows that the two sympatric Acromyrmex species share each other's major lineages of cultivar, confirming that horizontal transfer does occasionally take place. We argue and provide some evidence that chemical substances produced by the fungus garden may mediate recognition of alien fungi by the ants. We show that incompatibility between ants and transplanted, genetically different cultivars is indeed due to active killing of the novel cultivar by the ants. This incompatibility disappears when ants are force-fed the novel cultivar for about a week, a result that is consistent with our hypothesis of recognition induced by the resident fungus and eventual replacement of incompatibility compounds during force-feeding.

Sick ants become unsociable

DEFF Research Database (Denmark)

Bos, Nicky Peter Maria; Lefevre, T.; Jensen, A.B.

2012-01-01

Parasites represent a severe threat to social insects, which form high-density colonies of related individuals, and selection should favour host traits that reduce infection risk. Here, using a carpenter ant (Camponotus aethiops) and a generalist insect pathogenic fungus (Metarhizium brunneum), we...... show that infected ants radically change their behaviour over time to reduce the risk of colony infection. Infected individuals (i) performed less social interactions than their uninfected counterparts, (ii) did not interact with brood anymore and (iii) spent most of their time outside the nest from...... day 3 post-infection until death. Furthermore, infected ants displayed an increased aggressiveness towards non-nestmates. Finally, infected ants did not alter their cuticular chemical profile, suggesting that infected individuals do not signal their physiological status to nestmates. Our results...

Interactive effects of soil-dwelling ants, ant mounds and simulated grazing on local plant community composition

NARCIS (Netherlands)

Veen, G.F.; Olff, H.

2011-01-01

Interactions between aboveground vertebrate herbivores and subterranean yellow meadow ants (Lasius flavus) can drive plant community patterns in grassland ecosystems. Here, we study the relative importance of the presence of ants (L. flavus) and ant mounds under different simulated grazing regimes

Association between Pseudonocardia symbionts and Atta leaf-cutting ants suggested by improved isolation methods

DEFF Research Database (Denmark)

Marsh, Sarah E.; Poulsen, Michael; Gorosito, Norma B.

2013-01-01

Fungus-growing ants associate with multiple symbiotic microbes, including Actinobacteria for production of antibiotics. The best studied of these bacteria are within the genus Pseudonocardia, which in most fungus-growing ants are conspicuously visible on the external cuticle of workers. However......, given that fungus-growing ants in the genus Atta do not carry visible Actinobacteria on their cuticle, it is unclear if this genus engages in the symbiosis with Pseudonocardia. Here we explore whether improving culturing techniques can allow for successful isolation of Pseudonocardia from Atta...

Roadside Survey of Ants on Oahu, Hawaii

Science.gov (United States)

Tong, Reina L.; Grace, J. Kenneth; Krushelnycky, Paul D.

2018-01-01

Hawaii is home to over 60 ant species, including five of the six most damaging invasive ants. Although there have been many surveys of ants in Hawaii, the last island-wide hand-collection survey of ants on Oahu was conducted in 1988–1994. In 2012, a timed hand-collection of ants was made at 44 sites in a systematic, roadside survey throughout Oahu. Ants were identified and species distribution in relation to elevation, precipitation and soil type was analyzed. To assess possible convenience sampling bias, 15 additional sites were sampled further from roads to compare with the samples near roads. Twenty-four species of ants were found and mapped; Pheidole megacephala (F.), Ochetellus glaber (Mayr), and Technomyrmex difficilis Forel were the most frequently encountered ants. For six ant species, a logistic regression was performed with elevation, average annual precipitation, and soil order as explanatory variables. O. glaber was found in areas with lower precipitation around Oahu. Paratrechina longicornis (Latrielle) and Tetramorium simillimum (Smith, F.) were found more often in lower elevations and in areas with the Mollisol soil order. Elevation, precipitation, and soil type were not significant sources of variation for P. megacephala, Plagiolepis alluaudi Emery, and T. difficilis. P. megacephala was associated with fewer mean numbers of ants where it occurred. Ant assemblages near and far from roads did not significantly differ. Many species of ants remain established on Oahu, and recent invaders are spreading throughout the island. Mapping ant distributions contributes to continued documentation and understanding of these pests. PMID:29439503

The effectiveness of weaver ant (Oecophylla smaragdina) biocontrol in Southeast Asian citrus and mango

DEFF Research Database (Denmark)

Offenberg, Joachim; Cuc, Nguyen Thi Thu; Wiwatwitaya, Decha

2013-01-01

Oecophylla ants may protect tropical plantation crops against pests. Cost-benefit studies comparing ant-based protection with conventional methods are needed to assess whether it is economically viable. Here we contrast profits in ant and chemically protected plots in a Thai and a Vietnamese citrus...

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.

Transfer function fitting using a continuous Ant Colony Optimization (ACO algorithm

Directory of Open Access Journals (Sweden)

A. Reineix

2015-03-01

Full Text Available An original approach is proposed in order to achieve the  fitting of ultra-wideband complex frequency functions, such  as the complex impedances, by using the so-called ACO  (Ant Colony Optimization methods. First, we present the  optimization principle of ACO, which originally was  dedicated to the combinatorial problems. Further on, the  extension to the continuous and mixed problems is  explained in more details. The interest in this approach is  proved by its ability to define practical constraints and  objectives, such as minimizing the number of filters used in  the model with respect to a fixed relative error. Finally, the  establishment of the model for the first and second order  filter types illustrates the power of the method and its  interest for the time-domain electromagnetic computation.

The evolution of genome size in ants

Directory of Open Access Journals (Sweden)

Spagna Joseph C

2008-02-01

Full Text Available Abstract Background Despite the economic and ecological importance of ants, genomic tools for this family (Formicidae remain woefully scarce. Knowledge of genome size, for example, is a useful and necessary prerequisite for the development of many genomic resources, yet it has been reported for only one ant species (Solenopsis invicta, and the two published estimates for this species differ by 146.7 Mb (0.15 pg. Results Here, we report the genome size for 40 species of ants distributed across 10 of the 20 currently recognized subfamilies, thus making Formicidae the 4th most surveyed insect family and elevating the Hymenoptera to the 5th most surveyed insect order. Our analysis spans much of the ant phylogeny, from the less derived Amblyoponinae and Ponerinae to the more derived Myrmicinae, Formicinae and Dolichoderinae. We include a number of interesting and important taxa, including the invasive Argentine ant (Linepithema humile, Neotropical army ants (genera Eciton and Labidus, trapjaw ants (Odontomachus, fungus-growing ants (Apterostigma, Atta and Sericomyrmex, harvester ants (Messor, Pheidole and Pogonomyrmex, carpenter ants (Camponotus, a fire ant (Solenopsis, and a bulldog ant (Myrmecia. Our results show that ants possess small genomes relative to most other insects, yet genome size varies three-fold across this insect family. Moreover, our data suggest that two whole-genome duplications may have occurred in the ancestors of the modern Ectatomma and Apterostigma. Although some previous studies of other taxa have revealed a relationship between genome size and body size, our phylogenetically-controlled analysis of this correlation did not reveal a significant relationship. Conclusion This is the first analysis of genome size in ants (Formicidae and the first across multiple species of social insects. We show that genome size is a variable trait that can evolve gradually over long time spans, as well as rapidly, through processes that may

The GSAM software: A global search algorithm of minima exploration for the investigation of low lying isomers of clusters

Energy Technology Data Exchange (ETDEWEB)

Marchal, Rémi; Carbonnière, Philippe; Pouchan, Claude [Université de Pau et des Pays de l' Adour, IPREM/ECP, UMR CNRS 5254 (France)

2015-01-22

The study of atomic clusters has become an increasingly active area of research in the recent years because of the fundamental interest in studying a completely new area that can bridge the gap between atomic and solid state physics. Due to their specific properties, such compounds are of great interest in the field of nanotechnology [1,2]. Here, we would present our GSAM algorithm based on a DFT exploration of the PES to find the low lying isomers of such compounds. This algorithm includes the generation of an intial set of structure from which the most relevant are selected. Moreover, an optimization process, called raking optimization, able to discard step by step all the non physically reasonnable configurations have been implemented to reduce the computational cost of this algorithm. Structural properties of Ga{sub n}Asm clusters will be presented as an illustration of the method.

An Improved Hierarchical Genetic Algorithm for Sheet Cutting Scheduling with Process Constraints

OpenAIRE

Yunqing Rao; Dezhong Qi; Jinling Li

2013-01-01

For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony—hierarchical genetic algorithm) is developed for better ...

Seizure detection algorithms based on EMG signals

DEFF Research Database (Denmark)

Conradsen, Isa

Background: the currently used non-invasive seizure detection methods are not reliable. Muscle fibers are directly connected to the nerves, whereby electric signals are generated during activity. Therefore, an alarm system on electromyography (EMG) signals is a theoretical possibility. Objective...... on the amplitude of the signal. The other algorithm was based on information of the signal in the frequency domain, and it focused on synchronisation of the electrical activity in a single muscle during the seizure. Results: The amplitude-based algorithm reliably detected seizures in 2 of the patients, while...... the frequency-based algorithm was efficient for detecting the seizures in the third patient. Conclusion: Our results suggest that EMG signals could be used to develop an automatic seizuredetection system. However, different patients might require different types of algorithms /approaches....

The interactions of ants with their biotic environment.

Science.gov (United States)

Chomicki, Guillaume; Renner, Susanne S

2017-03-15

This s pecial feature results from the symposium 'Ants 2016: ant interactions with their biotic environments' held in Munich in May 2016 and deals with the interactions between ants and other insects, plants, microbes and fungi, studied at micro- and macroevolutionary levels with a wide range of approaches, from field ecology to next-generation sequencing, chemical ecology and molecular genetics. In this paper, we review key aspects of these biotic interactions to provide background information for the papers of this s pecial feature After listing the major types of biotic interactions that ants engage in, we present a brief overview of ant/ant communication, ant/plant interactions, ant/fungus symbioses, and recent insights about ants and their endosymbionts. Using a large molecular clock-dated Formicidae phylogeny, we map the evolutionary origins of different ant clades' interactions with plants, fungi and hemiptera. Ants' biotic interactions provide ideal systems to address fundamental ecological and evolutionary questions about mutualism, coevolution, adaptation and animal communication. © 2017 The Author(s).

Chemically armed mercenary ants protect fungus-farming societies

DEFF Research Database (Denmark)

Adams, Rachelle Martha Marie; Liberti, Joanito; Illum, Anders A.

2013-01-01

guest ants are sufficient to kill raiders that invariably exterminate host nests without a cohabiting guest ant colony. We also show that the odor of guest ants discourages raider scouts from recruiting nestmates to host colonies. Our results imply that Sericomyrmex fungus-growers obtain a net benefit......The ants are extraordinary in having evolved many lineages that exploit closely related ant societies as social parasites, but social parasitism by distantly related ants is rare. Here we document the interaction dynamics among a Sericomyrmex fungus-growing ant host, a permanently associated...... parasitic guest ant of the genus Megalomyrmex, and a raiding agro-predator of the genus Gnamptogenys. We show experimentally that the guest ants protect their host colonies against agro-predator raids using alkaloid venom that is much more potent than the biting defenses of the host ants. Relatively few...

A cellular automata model for ant trails

Indian Academy of Sciences (India)

In this study, the unidirectional ant traffic flow with U-turn in an ant trail was inves- tigated using ... the literature, it was considered in the model that (i) ant colony consists of two kinds of ants, good- ... ponents without a central controller [8].

Adaptive Radiation in Socially Advanced Stem-Group Ants from the Cretaceous.

Science.gov (United States)

Barden, Phillip; Grimaldi, David A

2016-02-22

Across terrestrial ecosystems, modern ants are ubiquitous. As many as 94 out of every 100 individual arthropods in rainforests are ants, and they constitute up to 15% of animal biomass in the Amazon. Moreover, ants are pervasive agents of natural selection as over 10,000 arthropod species are specialized inquilines or myrmecomorphs living among ants or defending themselves through mimicry. Such impact is traditionally explained by sociality: ants are the first major group of ground-dwelling predatory insects to become eusocial, increasing efficiency of tasks and establishing competitive superiority over solitary species. A wealth of specimens from rich deposits of 99 million-year-old Burmese amber resolves ambiguity regarding sociality and diversity in the earliest ants. The stem-group genus Gerontoformica maintained distinct reproductive castes including morphotypes unknown in solitary aculeate (stinging) wasps, providing insight into early behavior. We present rare aggregations of workers, indicating group recruitment as well as an instance of interspecific combat; such aggression is a social feature of modern ants. Two species and an unusual new genus are described, further expanding the remarkable diversity of early ants. Stem-group ants are recovered as a paraphyletic assemblage at the base of modern lineages varying greatly in size, form, and mouthpart structure, interpreted here as an adaptive radiation. Though Cretaceous stem-group ants were eusocial and adaptively diverse, we hypothesize that their extinction resulted from the rise of competitively superior crown-group taxa that today form massive colonies, consistent with Wilson and Hölldobler's concept of "dynastic succession." Copyright © 2016 Elsevier Ltd. All rights reserved.

Verification-Based Interval-Passing Algorithm for Compressed Sensing

OpenAIRE

Wu, Xiaofu; Yang, Zhen

2013-01-01

We propose a verification-based Interval-Passing (IP) algorithm for iteratively reconstruction of nonnegative sparse signals using parity check matrices of low-density parity check (LDPC) codes as measurement matrices. The proposed algorithm can be considered as an improved IP algorithm by further incorporation of the mechanism of verification algorithm. It is proved that the proposed algorithm performs always better than either the IP algorithm or the verification algorithm. Simulation resul...

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....

Ant-plants and fungi: a new threeway symbiosis.

Science.gov (United States)

Defossez, Emmanuel; Selosse, Marc-André; Dubois, Marie-Pierre; Mondolot, Laurence; Faccio, Antonella; Djieto-Lordon, Champlain; McKey, Doyle; Blatrix, Rumsaïs

2009-06-01

Symbioses between plants and fungi, fungi and ants, and ants and plants all play important roles in ecosystems. Symbioses involving all three partners appear to be rare. Here, we describe a novel tripartite symbiosis in which ants and a fungus inhabit domatia of an ant-plant, and present evidence that such interactions are widespread. We investigated 139 individuals of the African ant-plant Leonardoxa africana for occurrence of fungus. Behaviour of mutualist ants toward the fungus within domatia was observed using a video camera fitted with an endoscope. Fungi were identified by sequencing a fragment of their ribosomal DNA. Fungi were always present in domatia occupied by mutualist ants but never in domatia occupied by opportunistic or parasitic ants. Ants appear to favour the propagation, removal and maintenance of the fungus. Similar fungi were associated with other ant-plants in Cameroon. All belong to the ascomycete order Chaetothyriales; those from L. africana formed a monophyletic clade. These new plant-ant-fungus associations seem to be specific, as demonstrated within Leonardoxa and as suggested by fungal phyletic identities. Such tripartite associations are widespread in African ant-plants but have long been overlooked. Taking fungal partners into account will greatly enhance our understanding of symbiotic ant-plant mutualisms.

Ant brood function as life preservers during floods.

Directory of Open Access Journals (Sweden)

Jessica Purcell

Full Text Available Social organisms can surmount many ecological challenges by working collectively. An impressive example of such collective behavior occurs when ants physically link together into floating 'rafts' to escape from flooded habitat. However, raft formation may represent a social dilemma, with some positions posing greater individual risks than others. Here, we investigate the position and function of different colony members, and the costs and benefits of this functional geometry in rafts of the floodplain-dwelling ant Formica selysi. By causing groups of ants to raft in the laboratory, we observe that workers are distributed throughout the raft, queens are always in the center, and 100% of brood items are placed on the base. Through a series of experiments, we show that workers and brood are extremely resistant to submersion. Both workers and brood exhibit high survival rates after they have rafted, suggesting that occupying the base of the raft is not as costly as expected. The placement of all brood on the base of one cohesive raft confers several benefits: it preserves colony integrity, takes advantage of brood buoyancy, and increases the proportion of workers that immediately recover after rafting.

Preliminary survey of ants at a reserve area of Prince of Songkla University, Songkhla Province, Southern Thailand

Directory of Open Access Journals (Sweden)

Niranee Binnima

2005-01-01

Full Text Available Prince of Songkla University is the first university established in the southern part of Thailand. A reserve area is planned at Ko Hong Hill near the university. The flora of this area has been previously explored but a few fauna species have been studied. Although ants are one of dominant groups in this forest, there is no record of their diversity. Thus, the aim of this study is to determine the ant diversity in terms of species composition. Three sampling methods, pitfall trap (PF, hand collecting (HC and leaf litter sifting (LL were applied to collection of ants along 3 line transects each of 90 meter in length and 500 meter apart during April 2001. Six subfamilies (Formicinae, Myrmicinae, Dorylinae, Ponerinae, Dolichoderinae and Pseudomyrmecinae of ants, comprising 44 species, were found. The results also showed that HC was the most sufficient method resulting in the highest number of ant species, while the combination of two methods (HC and LL yielded the highest number of ant species.

Gradient Evolution-based Support Vector Machine Algorithm for Classification

Science.gov (United States)

Zulvia, Ferani E.; Kuo, R. J.

2018-03-01

This paper proposes a classification algorithm based on a support vector machine (SVM) and gradient evolution (GE) algorithms. SVM algorithm has been widely used in classification. However, its result is significantly influenced by the parameters. Therefore, this paper aims to propose an improvement of SVM algorithm which can find the best SVMs’ parameters automatically. The proposed algorithm employs a GE algorithm to automatically determine the SVMs’ parameters. The GE algorithm takes a role as a global optimizer in finding the best parameter which will be used by SVM algorithm. The proposed GE-SVM algorithm is verified using some benchmark datasets and compared with other metaheuristic-based SVM algorithms. The experimental results show that the proposed GE-SVM algorithm obtains better results than other algorithms tested in this paper.

A Trust-region-based Sequential Quadratic Programming Algorithm

DEFF Research Database (Denmark)

Henriksen, Lars Christian; Poulsen, Niels Kjølstad

This technical note documents the trust-region-based sequential quadratic programming algorithm used in other works by the authors. The algorithm seeks to minimize a convex nonlinear cost function subject to linear inequalty constraints and nonlinear equality constraints.......This technical note documents the trust-region-based sequential quadratic programming algorithm used in other works by the authors. The algorithm seeks to minimize a convex nonlinear cost function subject to linear inequalty constraints and nonlinear equality constraints....

Population Development of Several Species of Ants on the Cocoa Trees in South Sulawesi

Directory of Open Access Journals (Sweden)

Fatahuddin Fatahuddin

2010-08-01

Full Text Available Several species of ants with different behavior have been found in cocoa plantations and their behavior is important to be considered because it might be correlated with the degree of protection of cocoa plant from cocoa pests. The aim of this research is to manipulate and to develop ants population in environment, so they are able to establish permanently in cocoa trees. This research was conducted in Papakaju Regions Luwu Regency in Juli to November 2009. In this study, 10 cocoa trees with ants were sampled (each species of ant in 10 cocoa trees. A control of 10 tree samples without ant was also taken. In order to assess the abundance of ant population, it was grouped based on scoring, which score 1 for less than 20 ants, score 2 for 21–50 ants, score 3 for 51–200 ants, score 4 for 201–1000 ants, and score 5 for more than 1000 per tree. The results indicated that average of population score of the three ants species reached the highest population for the Oecophylla. smaragdina with average score 4.85 (>1000 ants, Dolichoderus thoracicus, with average score 3.90 (> 200 ants and Crematogaster. difformis with average score 3.10 (>200 ants. This research indicated that three species of ants, Oecophylla smaragdina (weaver ant, Dolichoderus thoracicus (cocoa black ant and Crematogaster difformis (cracking ant. in farmer cocoa plantations in South Sulawesi giving better performance against major pests of cocoa in particular cocoa pod borer (CPB. Key words: Ant Population, Oecophylla smaragdina, Dolichoderus thoracicus, Crematogaster difformis, artificial nest, cocoa.

Natural selection drives the evolution of ant life cycles.

Science.gov (United States)

Wilson, Edward O; Nowak, Martin A

2014-09-02

The genetic origin of advanced social organization has long been one of the outstanding problems of evolutionary biology. Here we present an analysis of the major steps in ant evolution, based for the first time, to our knowledge, on combined recent advances in paleontology, phylogeny, and the study of contemporary life histories. We provide evidence of the causal forces of natural selection shaping several key phenomena: (i) the relative lateness and rarity in geological time of the emergence of eusociality in ants and other animal phylads; (ii) the prevalence of monogamy at the time of evolutionary origin; and (iii) the female-biased sex allocation observed in many ant species. We argue that a clear understanding of the evolution of social insects can emerge if, in addition to relatedness-based arguments, we take into account key factors of natural history and study how natural selection acts on alleles that modify social behavior.

Food source quality and ant dominance hierarchy influence the outcomes of ant-plant interactions in an arid environment

Science.gov (United States)

Flores-Flores, Rocío Vianey; Aguirre, Armando; Anjos, Diego V.; Neves, Frederico S.; Campos, Ricardo I.; Dáttilo, Wesley

2018-02-01

In this study, we conducted a series of experiments in a population of Vachellia constricta (Fabaceae) in the arid Tehuacan-Cuicatláan valley, Mexico, in order to evaluate if the food source quality and ant dominance hierarchy influence the outcomes of ant-plant interactions. Using an experiment with artificial nectaries, we observed that ants foraging on food sources with higher concentration of sugar are quicker in finding and attacking potential herbivorous insects. More specifically, we found that the same ant species may increase their defence effectiveness according to the quality of food available. These findings indicate that ant effectiveness in plant protection is context-dependent and may vary according to specific individual characteristics of plants. In addition, we showed that competitively superior ant species tend to dominate plants in periods with high nectar activity, emphasizing the role of the dominance hierarchy structuring ant-plant interactions. However, when high sugar food sources were experimentally available ad libitum, the nocturnal and competitively superior ant species, Camponotus atriceps, did not dominate the artificial nectaries during the day possibly due to limitation of its thermal tolerance. Therefore, temporal niche partitioning may be allowing the coexistence of two dominant ant species (Camponotus rubritorax during the day and C. atriceps at night) on V. constricta. Our findings indicate that the quality of the food source, and temporal shifts in ant dominance are key factors which structure the biotic plant defences in an arid environment.

SUPER-SAPSO: A New SA-Based PSO Algorithm

NARCIS (Netherlands)

Bahrepour, M.; Mahdipour, E.; Cheloi, R.; Yaghoobi, M.

2008-01-01

Swarm Optimisation (PSO) has been received increasing attention due to its simplicity and reasonable convergence speed surpassing genetic algorithm in some circumstances. In order to improve convergence speed or to augment the exploration area within the solution space to find a better optimum

Balancing emergency message dissemination and network lifetime in wireless body area network using ant colony optimization and Bayesian game formulation

Directory of Open Access Journals (Sweden)

R. Latha

Full Text Available Nowadays, Wireless Body Area Network (WBAN is emerging very fast and so many new methods and algorithms are coming up for finding the optimal path for disseminating emergency messages. Ant Colony Optimization (ACO is one of the cultural algorithms for solving many hard problems such as Travelling Salesman Problem (TSP. ACO is a natural behaviour of ants, which work stochastically with the help of pheromone trails deposited in the shortest route to find their food. This optimization procedure involves adapting, positive feedback and inherent parallelism. Each ant will deposit certain amount of pheromone in the tour construction it makes searching for food. This type of communication is known as stigmetric communication. In addition, if a dense WBAN environment prevails, such as hospital, i.e. in the environment of overlapping WBAN, game formulation was introduced for analyzing the mixed strategy behaviour of WBAN. In this paper, the ant colony optimization approach to the travelling salesman problem was applied to the WBAN to determine the shortest route for sending emergency message to the doctor via sensor nodes; and also a static Bayesian game formulation with mixed strategy was analysed to enhance the network lifetime. Whenever the patient needs any critical care or any other medical issue arises, emergency messages will be created by the WBAN and sent to the doctor's destination. All the modes of communication were realized in a simulation environment using OMNet++. The authors investigated a balanced model of emergency message dissemination and network lifetime in WBAN using ACO and Bayesian game formulation. Keywords: Wireless body area network, Ant colony optimization, Bayesian game model, Sensor network, Message latency, Network lifetime

Persistence of pollination mutualisms in the presence of ants.

Science.gov (United States)

Wang, Yuanshi; Wang, Shikun

2015-01-01

This paper considers plant-pollinator-ant systems in which the plant-pollinator interaction is mutualistic but ants have both positive and negative effects on plants. The ants also interfere with pollinators by preventing them from accessing plants. While a Beddington-DeAngelis (BD) formula can describe the plant-pollinator interaction, the formula is extended in this paper to characterize the pollination mutualism under the ant interference. Then, a plant-pollinator-ant system with the extended BD functional response is discussed, and global dynamics of the model demonstrate the mechanisms by which pollination mutualism can persist in the presence of ants. When the ant interference is strong, it can result in extinction of pollinators. Moreover, if the ants depend on pollination mutualism for survival, the strong interference could drive pollinators into extinction, which consequently lead to extinction of the ants themselves. When the ant interference is weak, a cooperation between plant-ant and plant-pollinator mutualisms could occur, which promotes survival of both ants and pollinators, especially in the case that ants (respectively, pollinators) cannot survive in the absence of pollinators (respectively, ants). Even when the level of ant interference remains invariant, varying ants' negative effect on plants can result in survival/extinction of both ants and pollinators. Therefore, our results provide an explanation for the persistence of pollination mutualism when there exist ants.

Social context predicts recognition systems in ant queens

DEFF Research Database (Denmark)

Dreier, Stéphanie Agnès Jeanine; d'Ettorre, Patrizia

2009-01-01

Recognition of group-members is a key feature of sociality. Ants use chemical communication to discriminate nestmates from intruders, enhancing kin cooperation and preventing parasitism. The recognition code is embedded in their cuticular chemical profile, which typically varies between colonies....... We predicted that ants might be capable of accurate recognition in unusual situations when few individuals interact repeatedly, as new colonies started by two to three queens. Individual recognition would be favoured by selection when queens establish dominance hierarchies, because repeated fights...... for dominance are costly; but it would not evolve in absence of hierarchies. We previously showed that Pachycondyla co-founding queens, which form dominance hierarchies, have accurate individual recognition based on chemical cues. Here, we used the ant Lasius niger to test the null hypothesis that individual...

ENHANCED HYBRID PSO – ACO ALGORITHM FOR GRID SCHEDULING

Directory of Open Access Journals (Sweden)

P. Mathiyalagan

2010-07-01

Full Text Available Grid computing is a high performance computing environment to solve larger scale computational demands. Grid computing contains resource management, task scheduling, security problems, information management and so on. Task scheduling is a fundamental issue in achieving high performance in grid computing systems. A computational GRID is typically heterogeneous in the sense that it combines clusters of varying sizes, and different clusters typically contains processing elements with different level of performance. In this, heuristic approaches based on particle swarm optimization and ant colony optimization algorithms are adopted for solving task scheduling problems in grid environment. Particle Swarm Optimization (PSO is one of the latest evolutionary optimization techniques by nature. It has the better ability of global searching and has been successfully applied to many areas such as, neural network training etc. Due to the linear decreasing of inertia weight in PSO the convergence rate becomes faster, which leads to the minimal makespan time when used for scheduling. To make the convergence rate faster, the PSO algorithm is improved by modifying the inertia parameter, such that it produces better performance and gives an optimized result. The ACO algorithm is improved by modifying the pheromone updating rule. ACO algorithm is hybridized with PSO algorithm for efficient result and better convergence in PSO algorithm.

Spider diversity in coffee agroecosystems: the influence of agricultural intensification and aggressive ants.

Science.gov (United States)

Marín, Linda; Perfecto, Ivette

2013-04-01

Spiders are a very diverse group of invertebrate predators found in agroecosystems and natural systems. However, spider distribution, abundance, and eventually their ecological function in ecosystems can be influenced by abiotic and biotic factors such as agricultural intensification and dominant ants. Here we explore the influence of both agricultural intensification and the dominant arboreal ant Azteca instabilis on the spider community in coffee agroecosystems in southern Mexico. To measure the influence of the arboreal ant Azteca instabilis (F. Smith) on the spider community inhabiting the coffee layer of coffee agroecosystems, spiders were collected from coffee plants that were and were not patrolled by the ant in sites differing in agricultural intensification. For 2008, generalized linear mixed models showed that spider diversity was affected positively by agricultural intensification but not by the ant. However, results suggested that some spider species were associated with A. instabilis. Therefore, in 2009 we concentrated our research on the effect of A. instabilis on spider diversity and composition. For 2009, generalized linear mixed models show that spider richness and abundance per plant were significantly higher in the presence of A. instabilis. In addition, analyses of visual counts of insects and sticky traps data show that more resources were present in plants patrolled by the ant. The positive effect of A. instabilis on spiders seems to be caused by at least two mechanisms: high abundance of insects and protection against predators.

Riding with the ants

NARCIS (Netherlands)

Duarte, A. P. M.; Attili-Angelis, D.; Baron, N. C.; Groenewald, Johannes Z.; Crous, Pedro W.; Pagnocca, F. C.

Isolates of Teratosphaeriaceae have frequently been found in the integument of attine ants, proving to be common and diverse in this microenvironment. The LSU phylogeny of the ant-isolated strains studied revealed that they cluster in two main lineages. The first was associated with the genus

Design of SVC Controller Based on Improved Biogeography-Based Optimization Algorithm

Directory of Open Access Journals (Sweden)

Feifei Dong

2014-01-01

Full Text Available Considering that common subsynchronous resonance controllers cannot adapt to the characteristics of the time-varying and nonlinear behavior of a power system, the cosine migration model, the improved migration operator, and the mutative scale of chaos and Cauchy mutation strategy are introduced into an improved biogeography-based optimization (IBBO algorithm in order to design an optimal subsynchronous damping controller based on the mechanism of suppressing SSR by static var compensator (SVC. The effectiveness of the improved controller is verified by eigenvalue analysis and electromagnetic simulations. The simulation results of Jinjie plant indicate that the subsynchronous damping controller optimized by the IBBO algorithm can remarkably improve the damping of torsional modes and thus effectively depress SSR, and ensure the safety and stability of units and power grid operation. Moreover, the IBBO algorithm has the merits of a faster searching speed and higher searching accuracy in seeking the optimal control parameters over traditional algorithms, such as BBO algorithm, PSO algorithm, and GA algorithm.