Symmetry breaking in mass-recruiting ants: extent of foraging biases depends on resource quality

The communication involved in the foraging behaviour of social insects is integral to their success. Many ant species use trail pheromones to make decisions about where to forage. The strong positive feedback caused by the trail pheromone is thought to create a decision between two or more options. When the two options are of identical quality, this is known as symmetry breaking, and is important because it helps colonies to monopolise food sources in a competitive environment. Symmetry breaking is thought to increase with the quantity of pheromone deposited by ants, but empirical studies exploring the factors affecting symmetry breaking are limited. Here, we tested if (i) greater disparity between two food sources increased the degree to which a higher quality food source is favoured and (ii) if the quality of identical food sources would affect the degree of symmetry breaking that occurs. Using the mass-recruiting Pharaoh ant, Monomorium pharaonis, we carried out binary choice tests to investigate how food quality affects the choice and distribution of colony foraging decisions. We found that colonies could coordinate foraging to exploit food sources of greater quality, and a greater contrast in quality between the food sources created a stronger collective decision. Contrary to prediction, we found that symmetry breaking decreased as the quality of two identical food sources increased. We discuss how stochastic effects might lead to relatively strong differences in the amount of pheromone on alternative routes when food source quality is low. Significance statement: Pheromones used by social insects should guide a colony via positive feedback to distribute colony members at resources in the most adaptive way given the current environment. This study shows that when food resources are of equal quality, Pharaoh ant foragers distribute themselves more evenly if the two food sources are both of high quality compared to if both are of low quality. The results highlight the way in which individual ants can modulate their response to pheromone trails which may lead colonies to exploiting resources more evenly when in a resource rich environment

Using swarm intelligence for distributed job scheduling on the grid

With the rapid growth of data and computational needs, distributed systems and computational Grids are gaining more and more attention. Grids are playing an important and growing role in today networks. The huge amount of computations a Grid can fulfill in a specific time cannot be done by the best super computers. However, Grid performance can still be improved by making sure all the resources available in the Grid are utilized by a good load balancing algorithm. The purpose of such algorithms is to make sure all nodes are equally involved in Grid computations. This research proposes two new distributed swarm intelligence inspired load balancing algorithms. One is based on ant colony optimization and is called AntZ, the other one is based on particle swarm optimization and is called ParticleZ. Distributed load balancing does not incorporate a single point of failure in the system. In the AntZ algorithm, an ant is invoked in response to submitting a job to the Grid and this ant surfs the network to find the best resource to deliver the job to. In the ParticleZ algorithm, each node plays a role as a particle and moves toward other particles by sharing its workload among them. We will be simulating our proposed approaches using a Grid simulation toolkit (GridSim) dedicated to Grid simulations. The performance of the algorithms will be evaluated using several performance criteria (e.g. makespan and load balancing level). A comparison of our proposed approaches with a classical approach called State Broadcast Algorithm and two random approaches will also be provided. Experimental results show the proposed algorithms (AntZ and ParticleZ) can perform very well in a Grid environment. In particular, the use of particle swarm optimization, which has not been addressed in the literature, can yield better performance results in many scenarios than the ant colony approach

Synergy between biology and systems resilience

Resilient systems have the ability to endure and successfully recover from disturbances by identifying problems and mobilizing the available resources to cope with the disturbance. Resiliency lets a system recover from disruptions, variations, and a degradation of expected working conditions. Biological systems are resilient. Immune systems are highly adaptive and scalable, with the ability to cope with multiple data sources, fuse information together, makes decisions, have multiple interacting agents, operate in a distributed manner over a multiple scales, and have a memory structure to facilitate learning. Ecosystems are resilient since they have the capacity to absorb disturbance and are able to tolerate the disturbances. Ants build colonies that are dispersed, modular, fine grained, and standardized in design, yet they manage to forage intelligently for food and also organize collective defenses by the property of resilience. Are there any rules that we can identify to explain the resilience in these systems? The answer is yes. In insect colonies, rules determine the division of labor and how individual insects act towards each other and respond to different environmental possibilities. It is possible to group these rules based on attributes. These attributes are distributability, redundancy, adaptability, flexibility, interoperability, and diversity. It is also possible to incorporate these rules into engineering systems in their design to make them resilient. It is also possible to develop a qualitative model to generate resilience heuristics for engineering system based on a given attribute. The rules seen in nature and those of an engineering system are integrated to incorporate the desired characteristics for system resilience. The qualitative model for systems resilience will be able to generate system resilience heuristics. This model is simple and it can be applied to any system by using attribute based heuristics that are domain dependent. It also provides basic foundation for building computational models for designing resilient system architectures. This model was tested on recent catastrophes like the Mumbai terror attack and hurricane Katrina. With the disturbances surrounding the current world this resilience model based on heuristics will help a system to deal with crisis and still function in the best way possible by depending mainly on internal variables within the system --Abstract, page iii

Swarm intelligence: novel tools for optimization, feature extraction, and multi-agent system modeling

Abstract Animal swarms in nature are able to adapt to dynamic changes in their envi-ronment, and through cooperation they can solve problems that are crucial for their survival. Only by means of local interactions with other members of the swarm and with the environment, they can achieve a common goal more efficiently than it would be done by a single individual. This problem-solving behavior that results from the multiplicity of such interactions is referred to as Swarm Intelligence. The mathematical models of swarming behavior in nature were initially proposed to solve optimization problems. Nevertheless, this decentralized approach can be a valuable tool for a variety of applications, where emerging global patterns represent a solution to the task at hand. Methods for the solution of difficult computational problems based on Swarm Intelligence have been experimentally demonstrated and reported in the literature. However, a general framework that would facilitate their design does not exist yet. In this dissertation, a new general design methodology for Swarm Intelligence tools is proposed. By defining a discrete space in which the members of the swarm can move, and by modifying the rules of local interactions and setting the adequate objective function for solutions evaluation, the proposed methodology is tested in various domains. The dissertation presents a set of case studies, and focuses on two general approaches. One approach is to apply Swarm Intelligence as a tool for optimization and feature extraction, and the other approach is to model multi-agent systems such that they resemble swarms of animals in nature providing them with the ability to autonomously perform a task at hand. Artificial swarms are designed to be autonomous, scalable, robust, and adaptive to the changes in their environment. In this work, the methods that exploit one or more of these features are presented. First, the proposed methodology is validated in a real-world scenario seen as a combinatorial optimization problem. Then a set of novel tools for feature extraction, more precisely the adaptive edge detection and the broken-edge linking in digital images is proposed. A novel data clustering algorithm is also proposed and applied to image segmentation. Finally, a scalable algorithm based on the proposed methodology is developed for distributed task allocation in multi-agent systems, and applied to a swarm of robots. The newly proposed general methodology provides a guideline for future developers of the Swarm Intelligence tools. Los enjambres de animales en la naturaleza son capaces de adaptarse a cambios dinamicos en su entorno y, por medio de la cooperación, pueden resolver problemas ´ cruciales para su supervivencia. Unicamente por medio de interacciones locales con otros miembros del enjambre y con el entorno, pueden lograr un objetivo común de forma más eficiente que lo haría un solo individuo. Este comportamiento problema-resolutivo que es resultado de la multiplicidad de interacciones se denomina Inteligencia de Enjambre. Los modelos matemáticos de comportamiento de enjambres en entornos naturales fueron propuestos inicialmente para resolver problemas de optimización. Sin embargo, esta aproximación descentralizada puede ser una herramienta valiosa en una variedad de aplicaciones donde patrones globales emergentes representan una solución de las tareas actuales. Aunque en la literatura se muestra la utilidad de los métodos de Inteligencia de Enjambre, no existe un entorno de trabajo que facilite su diseño. En esta memoria de tesis proponemos una nueva metodologia general de diseño para herramientas de Inteligencia de Enjambre. Desarrollamos herramientas noveles que representan ejem-plos ilustrativos de su implementación. Probamos la metodología propuesta en varios dominios definiendo un espacio discreto en el que los miembros del enjambre pueden moverse, modificando las reglas de las interacciones locales y fijando la función objetivo adecuada para evaluar las soluciones. La memoria de tesis presenta un conjunto de casos de estudio y se centra en dos aproximaciones generales. Una aproximación es aplicar Inteligencia de Enjambre como herramienta de optimización y extracción de características mientras que la otra es modelar sistemas multi-agente de tal manera que se asemejen a enjambres de animales en la naturaleza a los que se les confiere la habilidad de ejecutar autónomamente la tarea. Los enjambres artificiales están diseñados para ser autónomos, escalables, robustos y adaptables a los cambios en su entorno. En este trabajo, presentamos métodos que explotan una o más de estas características. Primero, validamos la metodología propuesta en un escenario del mundo real visto como un problema de optimización combinatoria. Después, proponemos un conjunto de herramientas noveles para ex-tracción de características, en concreto la detección adaptativa de bordes y el enlazado de bordes rotos en imágenes digitales, y el agrupamiento de datos para segmentación de imágenes. Finalmente, proponemos un algoritmo escalable para la asignación distribuida de tareas en sistemas multi-agente aplicada a enjambres de robots. La metodología general recién propuesta ofrece una guía para futuros desarrolladores deherramientas de Inteligencia de Enjambre

Adaptive and learning-based formation control of swarm robots

Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation

The Application of Ant Colony Optimization

The application of advanced analytics in science and technology is rapidly expanding, and developing optimization technics is critical to this expansion. Instead of relying on dated procedures, researchers can reap greater rewards by utilizing cutting-edge optimization techniques like population-based metaheuristic models, which can quickly generate a solution with acceptable quality. Ant Colony Optimization (ACO) is one the most critical and widely used models among heuristics and meta-heuristics. This book discusses ACO applications in Hybrid Electric Vehicles (HEVs), multi-robot systems, wireless multi-hop networks, and preventive, predictive maintenance

Neuroecology of social organization in the Australasian weaver ant, Oecophylla smaragdina

The social brain hypothesis predicts that larger group size and greater social complexity select for increased brain size. In ants, social complexity is associated with large colony size, emergent collective action, and division of labor among workers. The great diversity of social organization in ants offers numerous systems to test social brain theory and examine the neurobiology of social behavior. My studies focused on the Australasian weaver ant, Oecophylla smaragdina, a polymorphic species, as a model of advanced social organization. I critically analyzed how biogenic amines modulate social behavior in ants and examined their role in worker subcaste-related territorial aggression. Major workers that naturally engage in territorial defense showed higher levels of brain octopamine in comparison to more docile, smaller minor workers, whose social role is nursing. Through pharmacological manipulations of octopaminergic action in both subcastes, octopamine was found to be both necessary and sufficient for aggression, suggesting subcaste-related task specialization results from neuromodulation. Additionally, I tested social brain theory by contrasting the neurobiological correlates of social organization in a phylogenetically closely related ant species, Formica subsericea, which is more basic in social structure. Specifically, I compared brain neuroanatomy and neurometabolism in respect to the neuroecology and degree of social complexity of O. smaragdina major and minor workers and F. subsericea monomorphic workers. Increased brain production costs were found in both O. smaragdina subcastes, and the collective action of O. smaragdina majors appeared to compensate for these elevated costs through decreased ATP usage, measured from cytochrome oxidase activity, an endogenous marker of neurometabolism. Macroscopic and cellular neuroanatomical analyses of brain development showed that higher-order sensory processing regions in workers of O. smaragdina, but not F. subsericea, had age-related synaptic reorganization and increased volume. Supporting the social brain hypothesis, ecological and social challenges associated with large colony size were found to contribute to increased brain size. I conclude that division of labor and collective action, among other components of social complexity, may drive the evolution of brain structure and function in compensatory ways by generating anatomically and metabolically plastic mosaic brains that adaptively reflect cognitive demands of worker task specialization and colony-level social organization

The multiple pheromone Ant clustering algorithm

Ant Colony Optimisation algorithms mimic the way ants use pheromones for marking paths to important locations. Pheromone traces are followed and reinforced by other ants, but also evaporate over time. As a consequence, optimal paths attract more pheromone, whilst the less useful paths fade away. In the Multiple Pheromone Ant Clustering Algorithm (MPACA), ants detect features of objects represented as nodes within graph space. Each node has one or more ants assigned to each feature. Ants attempt to locate nodes with matching feature values, depositing pheromone traces on the way. This use of multiple pheromone values is a key innovation. Ants record other ant encounters, keeping a record of the features and colony membership of ants. The recorded values determine when ants should combine their features to look for conjunctions and whether they should merge into colonies. This ability to detect and deposit pheromone representative of feature combinations, and the resulting colony formation, renders the algorithm a powerful clustering tool. The MPACA operates as follows: (i) initially each node has ants assigned to each feature; (ii) ants roam the graph space searching for nodes with matching features; (iii) when departing matching nodes, ants deposit pheromones to inform other ants that the path goes to a node with the associated feature values; (iv) ant feature encounters are counted each time an ant arrives at a node; (v) if the feature encounters exceed a threshold value, feature combination occurs; (vi) a similar mechanism is used for colony merging. The model varies from traditional ACO in that: (i) a modified pheromone-driven movement mechanism is used; (ii) ants learn feature combinations and deposit multiple pheromone scents accordingly; (iii) ants merge into colonies, the basis of cluster formation. The MPACA is evaluated over synthetic and real-world datasets and its performance compares favourably with alternative approaches

Space-Time Continuous Models of Swarm Robotic Systems: Supporting Global-to-Local Programming

A generic model in as far as possible mathematical closed-form was developed that predicts the behavior of large self-organizing robot groups (robot swarms) based on their control algorithm. In addition, an extensive subsumption of the relatively young and distinctive interdisciplinary research field of swarm robotics is emphasized. The connection to many related fields is highlighted and the concepts and methods borrowed from these fields are described shortly

The tabu ant colony optimizer and its application in an energy market

A new ant colony optimizer, the \u27tabu ant colony optimizer\u27 (TabuACO) is introduced, tested, and applied to a contemporary problem. The TabuACO uses both attractive and repulsive pheromones to speed convergence to a solution. The dual pheromone TabuACO is benchmarked against several other solvers using the traveling salesman problem (TSP), the quadratic assignment problem (QAP), and the Steiner tree problem. In tree-shaped puzzles, the dual pheromone TabuACO was able to demonstrate a significant improvement in performance over a conventional ACO. As the amount of connectedness in the network increased, the dual pheromone TabuACO offered less improvement in performance over the conventional ACO until it was applied to fully-interconnected mesh-shaped puzzles, where it offered no improvement. The TabuACO is then applied to implement a transactive energy market and tested with published circuit models from IEEE and EPRI. In the IEEE feeder model, the application was able to limit the sale of power through an overloaded transformer and compensate by bringing downstream power online to relieve it. In the EPRI feeder model, rapid voltage changes due to clouds passing over PV arrays caused the PV contribution to outstrip the ability of the substation to compensate. The TabuACO application was able to find a manageable limit to the photovoltaic energy that could be contributed on a cloudy day --Abstract, page iii