Understanding the Power of Stigmergy of Anonymous Agents in Discrete Environments

International audienceCommunication by stigmergy consists, for agents/robots devoid of other dedicated communication devices, in exchanging information by observing each other's movements, similar to how honeybees use a dance to inform each other on the location of food sources. Stigmergy, while a popular technique in soft computing (e.g., swarm intelligence and swarm robotics), has received little attention from a computational viewpoint, with only one study proposing a method in a continuous environment. An important question is whether there are limits intrinsic to the environment on the feasibility of stigmergy. While it is not the case in a continuous environment, we show that the answer is quite different when the environment is discrete. This paper considers stigmergy in graphs and identifies classes of graphs in which robots can communicate by stigmergy. We provide two algorithms with different tradeoffs. One algorithm achieves faster stigmergy when the density of robots is low enough to let robots move independently. This algorithm works when the graph contains some particular pairwise-disjoint subgraphs. The second algorithm, while slower solves the problem under an extremely high density of robots assuming that the graph admits some large cycle. Both algorithms are described in a general way, for any graph that admits the desired properties and with identified nodes. We show how the latter assumption can be removed in more specific topologies. Indeed, we consider stigmergy in the grid which offers additional orientation information not available in a general graphs, allowing us to relax some of the assumptions. Given an $N\times M$ anonymous grid, we show that the first algorithm requires $O(\mathcal{M})$ steps to achieve communication by stigmergy, where $\mathcal{M}$ is the maximum length of a communication message, but it works only if the number of robots is less than $\left\lfloor\frac{N\cdot M}{9}\right\rfloor$. The second algorithm, which requires $O(k^2)$ steps, where $k$ is the number of robots, on the other hand, works for up to $N\cdot M-5$ robots. In both cases, we consider very weak assumptions on the robots capabilities: i.e., we assume that the robots are anonymous, asynchronous, uniform, and execute deterministic algorithms

Performance Comparison of Parallel Bees Algorithm on Rosenbrock Function

The optimization algorithms that imitate nature have acquired much attention principally mechanisms for solving the difficult issues for example the travelling salesman problem (TSP) which is containing routing and scheduling of the tasks. This thesis presents the parallel Bees Algorithm as a new approach for optimizing the last results for the Bees Algorithm. Bees Algorithm is one of the optimization algorithms inspired from the natural foraging ways of the honey bees of finding the best solution. It is a series of activities based on the searching algorithm in order to access the best solutions. It is an iteration algorithm; therefore, it is suffering from slow convergence. The other downside of the Bee Algorithm is that it has needless computation. This means that it spends a long time for the bees algorithm converge the optimum solution. In this study, the parallel bees algorithm technique is proposed for overcoming of this issue. Due to that, this would lead to reduce the required time to get a solution with faster results accuracy than original Bees Algorithm

Deaf, Dumb, and Chatting Robots, Enabling Distributed Computation and Fault-Tolerance Among Stigmergic Robot

We investigate ways for the exchange of information (explicit communication) among deaf and dumb mobile robots scattered in the plane. We introduce the use of movement-signals (analogously to flight signals and bees waggle) as a mean to transfer messages, enabling the use of distributed algorithms among the robots. We propose one-to-one deterministic movement protocols that implement explicit communication. We first present protocols for synchronous robots. We begin with a very simple coding protocol for two robots. Based on on this protocol, we provide one-to-one communication for any system of n \geq 2 robots equipped with observable IDs that agree on a common direction (sense of direction). We then propose two solutions enabling one-to-one communication among anonymous robots. Since the robots are devoid of observable IDs, both protocols build recognition mechanisms using the (weak) capabilities offered to the robots. The first protocol assumes that the robots agree on a common direction and a common handedness (chirality), while the second protocol assumes chirality only. Next, we show how the movements of robots can provide implicit acknowledgments in asynchronous systems. We use this result to design asynchronous one-to-one communication with two robots only. Finally, we combine this solution with the schemes developed in synchronous settings to fit the general case of asynchronous one-to-one communication among any number of robots. Our protocols enable the use of distributing algorithms based on message exchanges among swarms of Stigmergic robots. Furthermore, they provides robots equipped with means of communication to overcome faults of their communication device

Hybrid Swarm Intelligence Energy Efficient Clustered Routing Algorithm for Wireless Sensor Networks

Currently, wireless sensor networks (WSNs) are used in many applications, namely, environment monitoring, disaster management, industrial automation, and medical electronics. Sensor nodes carry many limitations like low battery life, small memory space, and limited computing capability. To create a wireless sensor network more energy efficient, swarm intelligence technique has been applied to resolve many optimization issues in WSNs. In many existing clustering techniques an artificial bee colony (ABC) algorithm is utilized to collect information from the field periodically. Nevertheless, in the event based applications, an ant colony optimization (ACO) is a good solution to enhance the network lifespan. In this paper, we combine both algorithms (i.e., ABC and ACO) and propose a new hybrid ABCACO algorithm to solve a Nondeterministic Polynomial (NP) hard and finite problem of WSNs. ABCACO algorithm is divided into three main parts: (i) selection of optimal number of subregions and further subregion parts, (ii) cluster head selection using ABC algorithm, and (iii) efficient data transmission using ACO algorithm. We use a hierarchical clustering technique for data transmission; the data is transmitted from member nodes to the subcluster heads and then from subcluster heads to the elected cluster heads based on some threshold value. Cluster heads use an ACO algorithm to discover the best route for data transmission to the base station (BS). The proposed approach is very useful in designing the framework for forest fire detection and monitoring. The simulation results show that the ABCACO algorithm enhances the stability period by 60% and also improves the goodput by 31% against LEACH and WSNCABC, respectively

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

Bees Algorithm: Theory, improvements and applications

In this thesis, a new population-based search algorithm called the Bees Algorithm (BA) is presented. The algorithm mimics the food foraging behaviour of swarms of honey bees. In its basic version, the algorithm performs a kind of neighbourhood search combined with random search and can be used for both combinatorial and functional optimisation. In the context of this thesis both domains are considered. Following a description of the algorithm, the thesis gives the results obtained for a number of complex problems demonstrating the efficiency and robustness of the new algorithm. Enhancements of the Bees Algorithm are also presented. Several additional features are considered to improve the efficiency of the algorithm. Dynamic recruitment, proportional shrinking and site abandonment strategies are presented. An additional feature is an evaluation of several different functions and of the performance of the algorithm compared with some other well-known algorithms, including genetic algorithms and simulated annealing. The Bees Algorithm can be applied to many complex optimisations problems including multi-layer perceptrons, neural networks training for statistical process control and the identification of wood defects in wood veneer sheets. Also, the algorithm can be used to design 2D electronic recursive filters, to show its potential in electronics applications. A new structure is proposed so that the algorithm can work in combinatorial domains. In addition, several applications are presented to show the robustness of the algorithm in various conditions. Also, some minor modifications are proposed for representations of the problems since it was originally developed for continuous domains. In the final part, a new algorithm is introduced as a successor to the original algorithm. A new neighbourhood structure called Gaussian patch is proposed to reduce the complexity of the algorithm as well as increasing its efficiency. The performance of the algorithm is tested by use on several multi-model complex optimisation problems and this is compared to the performance of some well-known algorithms

Understanding the Power of Stigmergy of Anonymous Agents in Discrete Environments

Communication by stigmergy consists, for agents/robots devoid of other dedicated communication devices, in exchanging information by observing each other's movements, similar to how honeybees use a dance to inform each other on the location of food sources. Stigmergy, while a popular technique in soft computing (e.g., swarm intelligence and swarm robotics), has received little attention from a computational viewpoint, with only one study proposing a method in a continuous environment. An important question is whether there are limits intrinsic to the environment on the feasibility of stigmergy. While it is not the case in a continuous environment, we show that the answer is quite different when the environment is discrete. This paper considers stigmergy in graphs and identifies classes of graphs in which robots can communicate by stigmergy. We provide two algorithms with different tradeoffs. One algorithm achieves faster stigmergy when the density of robots is low enough to let robots move independently. This algorithm works when the graph contains some particular pairwise-disjoint subgraphs. The second algorithm, while slower solves the problem under an extremely high density of robots assuming that the graph admits some large cycle. Both algorithms are described in a general way, for any graph that admits the desired properties and with identified nodes. We show how the latter assumption can be removed in more specific topologies. Indeed, we consider stigmergy in the grid which offers additional orientation information not available in a general graphs, allowing us to relax some of the assumptions. Given an $N\times M$ anonymous grid, we show that the first algorithm requires $O(\mathcal{M})$ steps to achieve communication by stigmergy, where $\mathcal{M}$ is the maximum length of a communication message, but it works only if the number of robots is less than $\left\lfloor\frac{N\cdot M}{9}\right\rfloor$. The second algorithm, which requires $O(k^2)$ steps, where $k$ is the number of robots, on the other hand, works for up to $N\cdot M-5$ robots. In both cases, we consider very weak assumptions on the robots capabilities: i.e., we assume that the robots are anonymous, asynchronous, uniform, and execute deterministic algorithms

Bio-inspired multi-agent systems for reconfigurable manufacturing systems

The current market’s demand for customization and responsiveness is a major challenge for producing intelligent, adaptive manufacturing systems. The Multi-Agent System (MAS) paradigm offers an alternative way to design this kind of system based on decentralized control using distributed, autonomous agents, thus replacing the traditional centralized control approach. The MAS solutions provide modularity, flexibility and robustness, thus addressing the responsiveness property, but usually do not consider true adaptation and re-configuration. Understanding how, in nature, complex things are performed in a simple and effective way allows us to mimic nature’s insights and develop powerful adaptive systems that able to evolve, thus dealing with the current challenges imposed on manufactur- ing systems. The paper provides an overview of some of the principles found in nature and biology and analyses the effectiveness of bio-inspired methods, which are used to enhance multi-agent systems to solve complex engineering problems, especially in the manufacturing field. An industrial automation case study is used to illustrate a bio-inspired method based on potential fields to dynamically route pallets