Swarm Intelligence

Introduction and Applications

herausgegeben von: Christian Blum, Daniel Merkle

Verlag: Springer Berlin Heidelberg

Buchreihe : Natural Computing Series

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

Swarm intelligence is a modern arti?cial intelligence discipline that is c- cerned with the design of multiagent systems with applications, e.g., in - timization and in robotics. The design paradigm for these systems is fun- mentally di?erent from more traditional approaches. Instead of a sophisticated controller that governs the global behavior of the system, the swarm intelligence principle is based on many unsophisticated entities that cooperate in order to exhibit a desired behavior. Inspiration for the design of these systems is taken from the collective behavior of social insects such as ants, termites, bees, and wasps, as well as from the behavior of otheranimalsocietiessuchas?ocksofbirdsorschoolsof?sh.Coloniesofsocial insects have mesmerized researchers for many years. However, the principles that govern their behavior remained unknown for a long time. Even though the single members of these societies are unsophisticated individuals, they are able to achieve complex tasks in cooperation. Coordinated behavior emerges from relatively simple actions or interactions between the individuals.

Inhaltsverzeichnis

Frontmatter

Introduction

Frontmatter

Biological Foundations of Swarm Intelligence

Madeleine Beekman, Gregory A. Sword, Stephen J. Simpson

Swarm Intelligence in Optimization

Christian Blum, Xiaodong Li

Swarm Robotics

Abstract

Swarm robotics is a novel approach to the coordination of large numbers of robots and has emerged as the application of swarm intelligence to multi-robot systems. Different from other swarm intelligence studies, swarm robotics puts emphases on the physical embodiment of individuals and realistic interactions among the individuals and between the individuals and the environment. In this chapter, we present a brief review of this new approach. We first present its definition, discuss the main motivations behind the approach, as well as its distinguishing characteristics and major coordination mechanisms. Then we present a brief review of swarm robotics research along four axes; namely design, modelling and analysis, robots and problems.

Erol Şahin, Sertan Girgin, Levent Bayindir, Ali Emre Turgut

Routing Protocols for Next-Generation Networks Inspired by Collective Behaviors of Insect Societies: An Overview

Summary

In this chapter we discuss the properties and review the main instances of network routing algorithms whose bottom-up design has been inspired by collective behaviors of social insects such as ants and bees. This class of bio-inspired routing algorithms includes a relatively large number of algorithms mostly developed during the last ten years. The characteristics inherited by the biological systems of inspiration almost naturally empower these algorithms with characteristics such as autonomy, self-organization, adaptivity, robustness, and scalability, which are all desirable if not necessary properties to deal with the challenges of current and next-generation networks. In the chapter we consider different classes of wired and wireless networks, and for each class we briefly discuss the characteristics of the main ant- and bee-colony-inspired algorithms which can be found in literature. We point out their distinctive features and discuss their general pros and cons in relationship to the state of the art.

Muddassar Farooq, Gianni A. Di Caro

Applications

Frontmatter

Evolution, Self-organization and Swarm Robotics

Summary

The activities of social insects are often based on a self-organising process, that is, “a process in which pattern at the global level of a system emerges solely from numerous interactions among the lower-level components of the system”(see Camazine-EtAl:01, p. 8). In a self-organising system such as an ant colony, there is neither a leader that drives the activities of the group, nor are the individual ants informed about a global recipe or blueprint to be executed. On the contrary, each single ant acts autonomously following simple rules and locally interacting with the other ants. As a consequence of the numerous interactions among individuals, a coherent behaviour can be observed at the colony level.

A similar organisational structure is definitely beneficial for a swarm of autonomous robots. In fact, a coherent group behaviour can be obtained providing each robot with simple individual rules. Moreover, the features that characterise a self-organising system—such as decentralisation, flexibility and robustness—are highly desirable also for a swarm of autonomous robots. The main problem that has to be faced in the design of a self-organising robotic system is the definition of the individual rules that lead to the desired collective behaviour. The solution we propose to this design problem relies on artificial evolution as the main tool for the synthesis of self-organising behaviours. In this chapter, we provide an overview of successful applications of evolutionary techniques to the evolution of self-organising behaviours for a group of simulated autonomous robots. The obtained results show that the methodology is viable, and that it produces behaviours that are efficient, scalable and robust enough to be tested in reality on a physical robotic platform.

Vito Trianni, Stefano Nolfi, Marco Dorigo

Particle Swarms for Dynamic Optimization Problems

Tim Blackwell, Jürgen Branke, Xiaodong Li

An Agent-Based Approach to Self-organized Production

Abstract

The chapter describes the modeling of a material handling system with the production of individual units in a scheduled order. The units represent the agents in the model and are transported in the system which is abstracted as a directed graph. Since the hindrances of units on their path to the destination can lead to inefficiencies in the production, the blockages of units are to be reduced. Therefore, the units operate in the system by means of local interactions in the conveying elements and indirect interactions based on a measure of possible hindrances. If most of the units behave cooperatively (“socially”), the blockings in the system are reduced.

A simulation based on the model shows the collective behavior of the units in the system. The transport processes in the simulation can be compared with the processes in a real plant, which draws conclusions about the consequences of production based on superordinate planning.

Thomas Seidel, Jeanette Hartwig, Richard L. Sanders, Dirk Helbing

Organic Computing and Swarm Intelligence

Abstract

The relations between swarm intelligence and organic computing are discussed in this chapter. The aim of organic computing is to design and study computing systems that consist of many autonomous components and show forms of collective behavior. Such organic computing systems (OC systems) should possess self-x properties (e.g., self-healing, self-managing, self-optimizing), have a decentralized control, and be adaptive to changing requirements of their user. Examples of OC systems are described in this chapter and two case studies are presented that show in detail that OC systems share important properties with social insect colonies and how methods of swarm intelligence can be used to solve problems in organic computing.

Daniel Merkle, Martin Middendorf, Alexander Scheidler

Titel: Swarm Intelligence
herausgegeben von: Christian Blum
Daniel Merkle
Verlag: Springer Berlin Heidelberg
Electronic ISBN: 978-3-540-74089-6
Print ISBN: 978-3-540-74088-9
DOI: https://doi.org/10.1007/978-3-540-74089-6