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Multiple Ant Colony Optimizations for Stereo Matching
XiaoNian Wang,Ping Jiang
International Journal of Image Processing , 2009,
Abstract: The stereo matching problem, which obtains the correspondence between leftand right images, can be cast as a search problem. The matching of allcandidates in the same line forms a 2D optimization task, which is an NP-hardproblem in nature. Two characteristics are often utilized to enhance theperformance of stereo matching, i.e. concurrent optimization of several scanlinesand correlations among adjacent scan-lines. Such correlations areconsidered to be posterior, which require several trails for their discovery. In thispaper, a Multiple Ant Colony based approach is proposed for stereo matchingbecause of the Ant Colony optimization’s inherent capability of relation discoverythrough parallel searching. The Multiple Ant Colony Optimization (MACO) isefficient to solve large scale problems. For stereo matching, it evaluates subsolutionsand propagates the discovered information by pheromone, taking intoaccount the ordering and uniqueness constraints of candidates in images. Theproposed algorithm is proved to be able to find the optimal matched pairstheoretically and verified by experiments.
Mechanisms of social regulation change across colony development in an ant
Dani Moore, Jürgen Liebig
BMC Evolutionary Biology , 2010, DOI: 10.1186/1471-2148-10-328
Abstract: We investigated policing behaviour across colony growth in the ant Camponotus floridanus. In large colonies of this species, worker reproduction is policed by the destruction of worker-laid eggs. We found workers from incipient colonies do not exhibit policing behaviour, and instead tolerate all conspecific eggs. The change in policing behaviour is consistent with changes in egg surface hydrocarbons, which provide the informational basis for policing; eggs laid by queens from incipient colonies lack the characteristic hydrocarbons on the surface of eggs laid by queens from large colonies, making them chemically indistinguishable from worker-laid eggs. We also tested the response to fertility information in the context of queen tolerance. Workers from incipient colonies attacked foreign queens from large colonies; whereas workers from large colonies tolerated such queens. Workers from both incipient and large colonies attacked foreign queens from incipient colonies.Our results provide novel insights into the regulation of worker reproduction in social insects at both the proximate and ultimate levels. At the proximate level, our results show that mechanisms of social regulation, such as the response to fertility signals, change dramatically over a colony's life cycle. At the ultimate level, our results emphasize the importance of factors besides relatedness in predicting the level of conflict within a colony. Our results also suggest policing may not be an important regulatory force at every stage of colony development. Changes relating to the life cycle of the colony are sufficient to account for major differences in social regulation in an insect colony. Mechanisms of conflict mediation observed in one phase of a social group's development cannot be generalized to all stages.Kin selection theory can explain the evolution of cooperation within groups of related individuals, but unless group members are clones, there is also potential for conflict [1]. Because relate
Finding a Maximum Clique using Ant Colony Optimization and Particle Swarm Optimization in Social Networks  [PDF]
Mohammad Soleimani-Pouri,Alireza Rezvanian,Mohammad Reza Meybodi
Computer Science , 2013, DOI: 10.1109/ASONAM.2012.20
Abstract: Interaction between users in online social networks plays a key role in social network analysis. One on important types of social group is full connected relation between some users, which known as clique structure. Therefore finding a maximum clique is essential for some analysis. In this paper, we proposed a new method using ant colony optimization algorithm and particle swarm optimization algorithm. In the proposed method, in order to attain better results, it is improved process of pheromone update by particle swarm optimization. Simulation results on popular standard social network benchmarks in comparison standard ant colony optimization algorithm are shown a relative enhancement of proposed algorithm.
Friends and Foes from an Ant Brain's Point of View – Neuronal Correlates of Colony Odors in a Social Insect  [PDF]
Andreas Simon Brandstaetter, Wolfgang R?ssler, Christoph Johannes Kleineidam
PLOS ONE , 2011, DOI: 10.1371/journal.pone.0021383
Abstract: Background Successful cooperation depends on reliable identification of friends and foes. Social insects discriminate colony members (nestmates/friends) from foreign workers (non-nestmates/foes) by colony-specific, multi-component colony odors. Traditionally, complex processing in the brain has been regarded as crucial for colony recognition. Odor information is represented as spatial patterns of activity and processed in the primary olfactory neuropile, the antennal lobe (AL) of insects, which is analogous to the vertebrate olfactory bulb. Correlative evidence indicates that the spatial activity patterns reflect odor-quality, i.e., how an odor is perceived. For colony odors, alternatively, a sensory filter in the peripheral nervous system was suggested, causing specific anosmia to nestmate colony odors. Here, we investigate neuronal correlates of colony odors in the brain of a social insect to directly test whether they are anosmic to nestmate colony odors and whether spatial activity patterns in the AL can predict how odor qualities like “friend” and “foe” are attributed to colony odors. Methodology/Principal Findings Using ant dummies that mimic natural conditions, we presented colony odors and investigated their neuronal representation in the ant Camponotus floridanus. Nestmate and non-nestmate colony odors elicited neuronal activity: In the periphery, we recorded sensory responses of olfactory receptor neurons (electroantennography), and in the brain, we measured colony odor specific spatial activity patterns in the AL (calcium imaging). Surprisingly, upon repeated stimulation with the same colony odor, spatial activity patterns were variable, and as variable as activity patterns elicited by different colony odors. Conclusions Ants are not anosmic to nestmate colony odors. However, spatial activity patterns in the AL alone do not provide sufficient information for colony odor discrimination and this finding challenges the current notion of how odor quality is coded. Our result illustrates the enormous challenge for the nervous system to classify multi-component odors and indicates that other neuronal parameters, e.g., precise timing of neuronal activity, are likely necessary for attribution of odor quality to multi-component odors.
Ant Colony Optimization for Multiple Knapsack Problems with Controlled Starts  [PDF]
Fidanova S.,Atanassov K.,Marinov P.,Parvathi R.
Bioautomation , 2009,
Abstract: Ant Colony Optimization is a stochastic search method that mimics the social behaviour of real ant colonies, which manage to establish the shortest routes to feeding sources and backwards. Such algorithms have been developed to reach near-optimum solutions of large-scale optimization problems, for which traditional mathematical techniques may fail. In this paper, a generalized net model of the process of ant colony optimization is constructed and on each iteration intuitionistic fuzzy estimations of the ants' start nodes are made. Several start strategies are developed and combined. This new technique is tested on Multiple Knapsack Problem, which is a real world problem. Benchmark comparisons among the strategies are presented in terms of quality of the results. Based on this comparison analysis, the performance of the algorithm is discussed along with some guidelines for determining the best strategy. The study presents ideas that should be beneficial to both practitioners and researchers involved in solving optimization problems.
An Effective Clustering Algorithm With Ant Colony  [cached]
Xiao-yong Liu,Hui Fu
Journal of Computers , 2010, DOI: 10.4304/jcp.5.4.598-605
Abstract: This paper proposes a new clustering algorithm based on ant colony to solve the unsupervised clustering problem. Ant colony optimization (ACO) is a population-based meta-heuristic that can be used to find approximate solutions to difficult combinatorial optimization problems. Clustering Analysis, which is an important method in data mining, classifies a set of observations into two or more mutually exclusive unknown groups. This paper presents an effective clustering algorithm with ant colony which is based on stochastic best solution kept--ESacc. The algorithm is based on Sacc algorithm that was proposed by P.S.Shelokar. It’s mainly virtue that best values iteratively are kept stochastically. Moreover, the new algorithm using Jaccard index to identify the optimal cluster number. The results of several times experiments in three datasets show that the new algorithm-ESacc is less in running time, is better in clustering effect and more stable than Sacc. Experimental results validate the novel algorithm’s efficiency. In addition, Three indices of clustering validity analysis are selected and used to evaluate the clustering solutions of ESacc and Sacc.
The Regulation of Ant Colony Foraging Activity without Spatial Information  [PDF]
Balaji Prabhakar,Katherine N. Dektar,Deborah M. Gordon
PLOS Computational Biology , 2012, DOI: 10.1371/journal.pcbi.1002670
Abstract: 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.
Sociogenomics of Cooperation and Conflict during Colony Founding in the Fire Ant Solenopsis invicta  [PDF]
Fabio Manfredini ,Oksana Riba-Grognuz,Yannick Wurm,Laurent Keller,DeWayne Shoemaker,Christina M. Grozinger
PLOS Genetics , 2013, DOI: 10.1371/journal.pgen.1003633
Abstract: One of the fundamental questions in biology is how cooperative and altruistic behaviors evolved. The majority of studies seeking to identify the genes regulating these behaviors have been performed in systems where behavioral and physiological differences are relatively fixed, such as in the honey bee. During colony founding in the monogyne (one queen per colony) social form of the fire ant Solenopsis invicta, newly-mated queens may start new colonies either individually (haplometrosis) or in groups (pleometrosis). However, only one queen (the “winner”) in pleometrotic associations survives and takes the lead of the young colony while the others (the “losers”) are executed. Thus, colony founding in fire ants provides an excellent system in which to examine the genes underpinning cooperative behavior and how the social environment shapes the expression of these genes. We developed a new whole genome microarray platform for S. invicta to characterize the gene expression patterns associated with colony founding behavior. First, we compared haplometrotic queens, pleometrotic winners and pleometrotic losers. Second, we manipulated pleometrotic couples in order to switch or maintain the social ranks of the two cofoundresses. Haplometrotic and pleometrotic queens differed in the expression of genes involved in stress response, aging, immunity, reproduction and lipid biosynthesis. Smaller sets of genes were differentially expressed between winners and losers. In the second experiment, switching social rank had a much greater impact on gene expression patterns than the initial/final rank. Expression differences for several candidate genes involved in key biological processes were confirmed using qRT-PCR. Our findings indicate that, in S. invicta, social environment plays a major role in the determination of the patterns of gene expression, while the queen's physiological state is secondary. These results highlight the powerful influence of social environment on regulation of the genomic state, physiology and ultimately, social behavior of animals.
Experiment Study of Entropy Convergence of Ant Colony Optimization  [PDF]
Chao-Yang Pang,Chong-Bao Wang,Ben-Qiong Hu
Computer Science , 2009,
Abstract: Ant colony optimization (ACO) has been applied to the field of combinatorial optimization widely. But the study of convergence theory of ACO is rare under general condition. In this paper, the authors try to find the evidence to prove that entropy is related to the convergence of ACO, especially to the estimation of the minimum iteration number of convergence. Entropy is a new view point possibly to studying the ACO convergence under general condition. Key Words: Ant Colony Optimization, Convergence of ACO, Entropy
A Novel Ant Colony Genetic Hybrid Algorithm  [cached]
Shang Gao,Zaiyue Zhang,Cungen Cao
Journal of Software , 2010, DOI: 10.4304/jsw.5.11.1179-1186
Abstract: By use of the properties of ant colony algorithm and genetic algorithm, a novel ant colony genetic hybrid algorithm, whose framework of hybrid algorithm is genetic algorithm, is proposed to solve the traveling salesman problems. The selection operator is an artificial version of natural selection, and chromosomes with better length of tour have higher probabilities of being selected in the next generation. Based on the properties of pheromone in ant colony algorithm the ant colony crossover operation is given. Four mutation strategies are put forward using the characteristic of traveling salesman problems. The hybrid algorithm with 2-opt local search can effectively find better minimum beyond premature convergence. Ants choose several tours based on trail, and these tours will replace the worse solution. Compare with the simulated annealing algorithm, the standard genetic algorithm and the standard ant colony algorithm, all the 4 hybrid algorithms are proved effective. Especially the hybrid algorithm with strategy D is a simple and effective better algorithm than others.
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