Showing papers on "Ant colony published in 2014"

A comprehensive survey: artificial bee colony (ABC) algorithm and applications

Abstract: Swarm intelligence (SI) is briefly defined as the collective behaviour of decentralized and self-organized swarms. The well known examples for these swarms are bird flocks, fish schools and the colony of social insects such as termites, ants and bees. In 1990s, especially two approaches based on ant colony and on fish schooling/bird flocking introduced have highly attracted the interest of researchers. Although the self-organization features are required by SI are strongly and clearly seen in honey bee colonies, unfortunately the researchers have recently started to be interested in the behaviour of these swarm systems to describe new intelligent approaches, especially from the beginning of 2000s. During a decade, several algorithms have been developed depending on different intelligent behaviours of honey bee swarms. Among those, artificial bee colony (ABC) is the one which has been most widely studied on and applied to solve the real world problems, so far. Day by day the number of researchers being interested in ABC algorithm increases rapidly. This work presents a comprehensive survey of the advances with ABC and its applications. It is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.

An ant colony algorithm for the multi-compartment vehicle routing problem

Abstract: We demonstrate the use of Ant Colony System (ACS) to solve the capacitated vehicle routing problem associated with collection of recycling waste from households, treated as nodes in a spatial network. For networks where the nodes are concentrated in separate clusters, the use of k-means clustering can greatly improve the efficiency of the solution. The ACS algorithm is extended to model the use of multi-compartment vehicles with kerbside sorting of waste into separate compartments for glass, paper, etc. The algorithm produces high-quality solutions for two-compartment test problems.

The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music

Abstract: This comprehensive book gives an up-to-date survey of the relevant bioinspired computing research fields such as evolutionary computation, artificial life, swarm intelligence and ant colony algorithms and examines applications in art, music and design. The editors and contributors are researchers and artists with deep experience of the related science, tools and applications, and the book includes overviews of historical developments and future perspectives.

Simultaneous balancing and sequencing of mixed-model parallel two-sided assembly lines

Abstract: Growing interests from customers in customised products and increasing competitions among peers necessitate companies to configure their manufacturing systems more effectively than ever before. We propose a new assembly line system configuration for companies that need intelligent solutions to satisfy customised demands on time with existing resources. A mixed-model parallel two-sided assembly line system is introduced based on the parallel two-sided assembly line system previously proposed in the literature. The mixed-model parallel two-sided assembly line balancing problem is illustrated with examples from the perspective of simultaneous balancing and sequencing. An agent-based ant colony optimisation algorithm is proposed to solve the problem. This algorithm is the first attempt in the literature to solve an assembly line balancing problem with an agent-based ant colony optimisation approach. The algorithm is illustrated with an example and its operational procedures and principles are explained and di...

Evolution of thorax architecture in ant castes highlights trade-off between flight and ground behaviors

Abstract: The size and shape of an animal, known as its morphology, often reflect the actions it can perform. A grasshopper’s long legs, for example, are well suited to hopping, whilst the streamlined body of a dolphin helps swimming through water. These specialized features result from the interplay between morphology and behavior during evolution. A change in morphology can make new behaviors possible, which can then expose the animal to new environments and selective pressures that, in turn, can lead to further changes in morphology. The interplay between morphology and behavior is particularly interesting in social insects such as ants. Queens and workers within an ant colony have a similar set of genes, but they have dramatically different morphologies and very different roles within the colony. Queens are responsible for reproduction, and are larger and have wings, which allow them to fly and establish a new colony away from where they were born. Workers are smaller and lack wings, and they devote themselves to building the nest, feeding the young larvae and protecting the colony. This marked morphological divergence, unique to ants, has fascinated researchers for more than a century. However, most studies have focused on the presence or absence of wings and have overlooked the interactions between morphology and the actions performed on the ground. Like all insects, an ant’s body is divided into three parts: the head, the thorax (to which the legs and wings are attached), and the abdomen. Now, Keller et al. have examined the shape of the thorax in many species of ants and found that workers are not just smaller wingless versions of queens: rather, the architecture of their thorax is unique among species of flying insects. The front end of the worker thorax is greatly enlarged and is filled by strong neck muscles that power the head and its jaws, and allow workers to hunt and carry prey many times their own weight. Keller et al. also identified two distinct types of queens and went on to show that these two shapes evolved in association with the two types of strategy that lone queens use to found new colonies. In species where queens convert their own wing muscles into the food for the first generation of workers, the wing muscles are much enlarged and the neck segment is extremely reduced. In species where queens hunt to feed the new colony, the wing and neck muscles are more balanced in size. As such, for those ant species where very little is known about how new colonies are founded, Keller et al. show that we can use the shape of the queen’s thorax to help predict this behavior. Taken together, the results of Keller et al. show that female ants invest in the relative size of the different segments of the thorax in a way that reflects their behavior as adults. These adaptations partly explain why ants have been so extraordinarily successful in nature, and underscore the importance of carefully analyzing an organism’s form to fully understand its biology.

Adapt-Traf: An adaptive multiagent road traffic management system based on hybrid ant-hierarchical fuzzy model

Abstract: Usually, road networks are characterized by their great dynamics including different entities in interactions. This leads to more complex road traffic management. This paper proposes an adaptive multiagent system based on the ant colony behavior and the hierarchical fuzzy model. This system allows adjusting efficiently the road traffic according to the real-time changes in road networks by the integration of an adaptive vehicle route guidance system. The proposed system is implemented and simulated under a multiagent platform in order to discuss the improvement of the global road traffic quality in terms of time, fluidity and adaptivity.

Rule-Based Cooperative Continuous Ant Colony Optimization to Improve the Accuracy of Fuzzy System Design

Abstract: This paper proposes a cooperative continuous ant colony optimization (CCACO) algorithm and applies it to address the accuracy-oriented fuzzy systems (FSs) design problems. All of the free parameters in a zero- or first-order Takagi-Sugeno-Kang (TSK) FS are optimized through CCACO. The CCACO algorithm performs optimization through multiple ant colonies, where each ant colony is only responsible for optimizing the free parameters in a single fuzzy rule. The ant colonies cooperate to design a complete FS, with a complete parameter solution vector (encoding a complete FS) that is formed by selecting a subsolution component (encoding a single fuzzy rule) from each colony. Subsolutions in each ant colony are evolved independently using a new continuous ant colony optimization algorithm. In the CCACO, solutions are updated via the techniques of pheromone-based tournament ant path selection, ant wandering operation, and best-ant-attraction refinement. The performance of the CCACO is verified through applications to fuzzy controller and predictor design problems. Comparisons with other population-based optimization algorithms verify the superiority of the CCACO.

Ant dominance hierarchy determines the nested pattern in ant–plant networks

Abstract: Extrafloral nectar (EFN) is a predictable and renewable resource for many ant colonies, and different ant species compete strongly to obtain and monopolize this highly nutritious food resource. Despite the importance of competition in structuring patterns of ant–plant interactions, this biological mechanism has been largely ignored in studies involving ant–plant networks. In this study we investigate the role of ant dominance hierarchy in structuring an ecological network involving ants and EFN-bearing plants in a tropical coastal environment in Mexico. We show that within a nested ant–plant network, ant species found in the central core of highly interacting species were competitively superior, showing massive recruitment and resource domination, compared with peripheral species with fewer interactions. Moreover, we also observed that both central and peripheral ant species have the ability to quickly find the food resource. However, after 2 h of observation, central ant species are more frequently collected on the food resource when compared with peripheral species. We hypothesize that the existence of a central core of competitive ant species may indicate that most plant species found within ant–plant networks could be better protected against herbivory by these dominant ant species. In short, our results highlight the importance of competition and monopolization in the resource use by ants in the maintenance of the nested pattern in ant–plant mutualistic networks. © 2014 The Linnean Society of London, Biological Journal of the Linnean Society, 2014, 113, 405–414.

Myrmica ants host highly diverse parasitic communities: from social parasites to microbes

Abstract: Myrmica ants have been model species for studies in a variety of disciplines, including insect physiology, chemical communication, ant social dynamics, ant population, community ecology, and ant interactions with other organisms. Species belonging to the genus Myrmica can be found in virtually every habitat within the temperate regions of the northern hemisphere and their biology and systematics have been thoroughly studied. These ants serve as hosts to highly diverse parasitic organisms from socially parasitic butterfly caterpillars to microbes, and many Myrmica species even evolved into parasitizing species of their own genus. These parasites have various impacts both on the individuals and on the social structure of their hosts, ranging from morphological malformations to reduction in colony fitness. A comprehensive review of the parasitic organisms supported by Myrmica and the effects of these organisms on individuals and on whole ant colonies has not yet been compiled. Here, we provide a review of the interactions of these organisms with Myrmica ants by discussing host and parasite functional, behavioral or physiological adaptations. In addition, for all “symbiont groups” of Myrmica ants described in this paper, we examine the present limitations of the knowledge at present of their impact on individuals and host colony fitness. In conclusion, we argue that Myrmica ants serve as remarkable resource for the evolution of a wide variety of associated organisms.

Pheromone communication in ants: a detailed analysis of concentration-dependent decisions in three species

Abstract: The collective behavior of ants and the emergence of self-organizing patterns in ant colonies have been explained with various theoretical approaches based on models of trail following behavior elicited by pheromones. Although existing models can explain collective behavior of ants, there is little empirical evidence on how ants precisely respond to various pheromone concentrations. Thus, important knowledge is lacking about how much realistic description of ant behavior can be provided by the models and their underlying mathematical functions. To fill in this gap, we conducted experiments with three different ant species to explore their responses to varying concentrations of pheromones that elicit ants’ trail following behavior. We found that ants’ decision making processes in trail following are best explained by psychophysical theory (PT), which describes the relationship between physical stimuli, sensory perception and decision making in humans, other primates, birds and insects. Furthermore, the theory provides clear definitions of biological parameters, such as detection- and discrimination thresholds. The species studied were distinctively different in the shape and parameters of their psychometric functions, which we attribute to specific adaptions to their environment. The observed differences are discussed in relation to their natural trail following behaviors. Our study opens a new perspective of understanding and explaining important aspects of collective ant behavior using a well-established theory of perception.

An Ant Colony Based Load Balancing Strategy in Cloud Computing

Abstract: Cloud computing thrives a new supplement of consumption and delivery model for internet based services and protocol. It provides large scale computing infrastructure defined on usage and also provides infrastructure services in a very flexible manner which may scales up and down according to user demand. To meet the QoS and satisfy the end users demands for resources in time is one of the main goals for cloud service provider. For this reason selecting a proper node that can complete end users task with QoS is really challenging job. Thus in Cloud distributing dynamic workload across multiple nodes in a distributed environment evenly, is called load balancing. Load balancing can be an optimization problem and should be adapting its strategy to the changing needs. This paper proposes a novel ant colony based algorithm to balance the load by searching under loaded node. Proposed load balancing strategy has been simulated using the CloudAnalyst. Experimental result for a typical sample application outperformed the traditional approaches like First Come First Serve (FCFS), local search algorithm like Stochastic Hill Climbing (SHC),another soft computing approach Genetic Algorithm (GA) and some existing Ant Colony Based strategy.

Ant colony optimisation for vehicle traffic systems: applications and challenges

Abstract: Ant-based algorithms simulate the cooperative behaviour of real ants in finding food resources. A significant number of studies have focused on the self-organised behaviour of ants in the natural environment to develop effective systems for dynamic problems. Ant-based systems have special properties such as scalability, adaptability, and dynamicity, which are the main requirements for solving vehicle traffic congestion problem. Thus, ant-based algorithms are now being adopted by vehicle traffic systems VTSs to guide vehicles to less congested paths. However, literature shows that comprehensive reviews are lacking in this field. The main contribution of this paper is the review and classification of the most relevant systems based on novel taxonomy. A survey that includes statistical analyses on ant-based VTS was conducted to identify the limitations and evaluation process of VTS. This paper concludes by proposing a general framework in applying ant colony optimisation to VTS.

How collective comparisons emerge without individual comparisons of the options

Abstract: Collective decisions in animal groups emerge from the actions of individuals who are unlikely to have global information. Comparative assessment of options can be valuable in decision-making. Ant colonies are excellent collective decision-makers, for example when selecting a new nest-site. Here, we test the dependency of this cooperative process on comparisons conducted by individual ants. We presented ant colonies with a choice between new nests: one good and one poor. Using individually radio-tagged ants and an automated system of doors, we manipulated individual-level access to information: ants visiting the good nest were barred from visiting the poor one and vice versa. Thus, no ant could individually compare the available options. Despite this, colonies still emigrated quickly and accurately when comparisons were prevented. Individual-level rules facilitated this behavioural robustness: ants allowed to experience only the poor nest subsequently searched more. Intriguingly, some ants appeared particularly discriminating across emigrations under both treatments, suggesting they had stable, high nest acceptance thresholds. Overall, our results show how a colony of ants, as a cognitive entity, can compare two options that are not both accessible by any individual ant. Our findings illustrate a collective decision process that is robust to differences in individual access to information.

Efficiency and robustness of ant colony transportation networks

Abstract: Efficient and robust transportation networks are key to the effectiveness of many natural systems. In polydomous ant colonies, which consist of two or more spatially separated but socially connected nests, resources must be transported between nests. In this study, we analyse the network structure of the inter-nest trails formed by natural polydomous ant colonies. In contrast to previous laboratory studies, the natural colonies in our study do not form minimum spanning tree networks. Instead the networks contain extra connections, suggesting that in natural colonies, robustness may be an important factor in network construction. Spatial analysis shows that nests are randomly distributed within the colony boundary and we find nests are most likely to connect to their nearest neighbours. However, the network structure is not entirely determined by spatial associations. By showing that the networks do not minimise total trail length and are not determined only by spatial associations, the results suggest that the inter-nest networks produced by ant colonies are influenced by previously unconsidered factors. We show that the transportation networks of polydomous ant colonies balance trail costs with the construction of networks that enable efficient transportation of resources. These networks therefore provide excellent examples of effective biological transport networks which may provide insight into the design and management of transportation systems.

Integrated Ant Colony and Tabu Search approach for time dependent vehicle routing problems with simultaneous pickup and delivery

Abstract: Today manufacturers have become much more concerned with the coordination of both manufacturing (of new products) and recycling (of reusable resources) operations. This requires simultaneous scheduling of both forward and reverse flows of goods over a supply chain network. This paper studies time dependent vehicle routing problems with simultaneous pickup and delivery (TD-VRPSPD). We formulate this problem as a mixed integer programming model, where the time step function is used to calculate the travel time. To efficiently solve this complex problem, we develop a hybrid algorithm that integrates both Ant Colony System (ACS) and Tabu Search (TS) algorithms. Our algorithm uses the pheromones, travel time and vehicle residual loading capacity as a factor structure according to the characteristics of TD-VRPSPD. In our computational experiments, 56 groups of benchmark instances are used to evaluate the performance of our hybrid algorithm. In addition, we compare the performance of our hybrid algorithm with those of individual ACS and TS algorithms. The computational results suggest that our hybrid algorithm outperform stand-alone ACS and the TS algorithms.

Resource redistribution in polydomous ant nest networks: local or global?

Abstract: An important problem facing organisms in a heterogeneous environment is how to redistribute resources to where they are required. This is particularly complex in social insect societies as resources have to be moved both from the environment into the nest and between individuals within the nest. Polydomous ant colonies are split between multiple spatially separated, but socially connected, nests. Whether, and how, resources are redistributed between nests in polydomous colonies is unknown. We analyzed the nest networks of the facultatively polydomous wood ant Formica lugubris. Our results indicate that resource redistribution in polydomous F. lugubris colonies is organized at the local level between neighboring nests and not at the colony level. We found that internest trails connecting nests that differed more in their amount of foraging were stronger than trails between nests with more equal foraging activity. This indicates that resources are being exchanged directly from nests with a foraging excess to nests that require resources. In contrast, we found no significant relationships between nest properties, such as size and amount of foraging, and network measures such as centrality and connectedness. This indicates an absence of a colony-level resource exchange. This is a clear example of a complex behavior emerging as a result of local interactions between parts of a system.

Variation in Butterfly Larval Acoustics as a Strategy to Infiltrate and Exploit Host Ant Colony Resources

Abstract: About 10,000 arthropods live as ants' social parasites and have evolved a number of mechanisms allowing them to penetrate and survive inside the ant nests. Many of them can intercept and manipulate their host communication systems. This is particularly important for butterflies of the genus Maculinea, which spend the majority of their lifecycle inside Myrmica ant nests. Once in the colony, caterpillars of Maculinea “predatory species” directly feed on the ant larvae, while those of “cuckoo species” are fed primarily by attendance workers, by trophallaxis. It has been shown that Maculinea cuckoo larvae are able to reach a higher social status within the colony's hierarchy by mimicking the acoustic signals of their host queen ants. In this research we tested if, when and how myrmecophilous butterflies may change sound emissions depending on their integration level and on stages of their life cycle. We studied how a Maculinea predatory species (M. teleius) can acoustically interact with their host ants and highlighted differences with respect to a cuckoo species (M. alcon). We recorded sounds emitted by Maculinea larvae as well as by their Myrmica hosts, and performed playback experiments to assess the parasites' capacity to interfere with the host acoustic communication system. We found that, although varying between and within butterfly species, the larval acoustic emissions are more similar to queens' than to workers' stridulations. Nevertheless playback experiments showed that ant workers responded most strongly to the sounds emitted by the integrated (i.e. post-adoption) larvae of the cuckoo species, as well as by those of predatory species recorded before any contact with the host ants (i.e. in pre-adoption), thereby revealing the role of acoustic signals both in parasite integration and in adoption rituals. We discuss our findings in the broader context of parasite adaptations, comparing effects of acoustical and chemical mimicry.

Bio-inspired Computing

Abstract: Living biological systems, even though made of very simple parts, can reach a great complexity as a whole. The ant colonies are a representative example: despite the fact that every single ant seems to behave independently, the colony as a whole is highly ordered in a remarkable efficient way. The foraging behavioral studies of ants colonies had revealed that they can travel between their nests and food sources in a highly efficient way. Based on these kind of observations, the ant system introduced in Dorigo’s PhD Thesis [72] was one of the first bio-inspired models for solving optimization problems.

Task Allocation in Ant Colonies

Abstract: In this paper we propose a mathematical model for studying the phenomenon of division of labor in ant colonies. Inside this model we investigate how simple task allocation mechanisms can be used to achieve an optimal division of labor.

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

Abstract: Climate change may affect ecosystems and biodiversity through the impacts of rising temperature on species’ body size. In terms of physiology and genetics, the colony is the unit of selection for ants so colony size can be considered the body size of a colony. For polydomous ant species, a colony is spread across several nests. This study aims to clarify how climate change may influence an ecologically significant ant species group by investigating thermal effects on wood ant colony size. The strong link between canopy cover and the local temperatures of wood ant’s nesting location provides a feasible approach for our study. Our results showed that nests were larger in shadier areas where the thermal environment was colder and more stable compared to open areas. Colonies (sum of nests in a polydomous colony) also tended to be larger in shadier areas than in open areas. In addition to temperature, our results supported that food resource availability may be an additional factor mediating the relationship between canopy cover and nest size. The effects of canopy cover on total colony size may act at the nest level because of the positive relationship between total colony size and mean nest size, rather than at the colony level due to lack of link between canopy cover and number of nests per colony. Causal relationships between the environment and the life-history characteristics may suggest possible future impacts of climate change on these species.

Task Allocation in Ant Colonies

Abstract: In this paper we propose a mathematical model for studying the phenomenon of division of labor in ant colonies Inside this model we investigate how simple task allocation mechanisms can be used to achieve an optimal division of labor

A multiple ant colony system for a vehicle routing problem with time windows and uncertain travel times

Abstract: In this paper, we study the capacitated vehicle routing problem with time window constraints, under travel time uncertainty. The uncertainty here represents the perturbation on the data caused by the effects of the unpredictable events in the reality, like traffic jams, road constructions, etc. To be able to near-optimally solve the large-instances of this problem without encountering memory errors or without taking too much time, we propose a heuristic approach based on ant colony optimization, which generates multiple solutions at the end of its execution, each solution with a different protection against the uncertainty. The trade-off between robustness and cheapness shown by these generated multiple solutions are

Balancing transfer lines using Benders decomposition and ant colony optimisation techniques

Abstract: In this paper, we investigate a transfer line balancing problem in order to find the line configuration that minimises the non-productive time. The problem is defined at an auto manufacturing company where the cylinder head is manufactured. Technological restrictions among design features and manufacturing operations are taken into consideration. The problem is represented by an integer programming model that assigns design features and cutting tools to machining stations, and specifies the number of machines and production sequence in each station. Three algorithms are developed to efficiently solve the problem under study. The first algorithm uses Benders decomposition approach that decomposes the proposed model into an assignment problem and a sequencing problem. The second algorithm is a hybrid algorithm that mixes Benders decomposition approach with the ant colony optimisation technique. The third algorithm solves the problem using two nested ant colonies. Using 15 different problem dimensions, we co...

Data Clustering Using Cuckoo Search Algorithm (CSA)

Abstract: Cluster Analysis is a popular data analysis in data mining technique. Clusters play a vital role for users to organize, summarize and navigate the data effectively. Swarm Intelligence (SI) is a relatively new subfield of artificial intelligence which studies the emergent collective intelligence of groups of simple agents. It is based on social behavior that can be observed in nature, such as ant colonies, flocks of birds, fish schools and bee hives. SI technique is integrated with clustering algorithms. This paper proposes new approaches for using Cuckoo Search Algorithm (CSA) to cluster data. It is shown how CSA can be used to find the optimally clustering N object into K clusters. The CSA is tested on various data sets, and its performance is compared with those of K-Means, Fuzzy C-Means, Fuzzy PSO and Genetic K-Means clustering. The simulation results show that the new method carries out better results than the K-Means, Fuzzy C-Means, Fuzzy PSO and Genetic K-Means.

Adaptive ant colony optimization algorithm

Abstract: An adaptive ant colony algorithm is proposed to overcome the premature convergence problem in the conventional ant colony algorithm. The adaptive ant colony is composed of three groups of ants: ordinary ants, abnormal ants and random ants. Each ordinary ant searches the path with the high concentration pheromone at the high probability, each abnormal ant searches the path with the high concentration pheromone at the low probability, and each random ant randomly searches the path regardless of the pheromone concentration. Three groups of ants provide a good initial state of pheromone trails together. As the optimization calculation goes on, the number of the abnormal ants and the random ants decreases gradually. In the late optimization stage, all of ants transform to the ordinary ants, which can rapidly concentrate to the optimal paths. Simulation results show that the algorithm has a good optimization performance, and can resolve traveling salesman problem effectively.