Showing papers on "Swarm intelligence published in 1999"

Swarm intelligence: from natural to artificial systems

Abstract: 1. Introduction 2. Ant Foraging Behavior, Combinatorial Optimization, and Routing in Communications Networks 3. Division of Labor and Task Allocation 4. Cemetery Organization, Brood Sorting, Data Analysis, and Graph Partitioning 5. Self-Organization and Templates: Application to Data Analysis and Graph Partitioning 6. Nest Building and Self-Assembling 7. Cooperative Transport by Insects and Robots 8. Epilogue

Ant algorithms for discrete optimization

Abstract: This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic biological findings on real ants are reviewed and their artificial counterparts as well as the ACO metaheuristic are defined. In the second part of the article a number of applications of ACO algorithms to combinatorial optimization and routing in communications networks are described. We conclude with a discussion of related work and of some of the most important aspects of the ACO metaheuristic.

The ant colony optimization meta-heuristic

Abstract: This chapter contains sections titled: Combinatorial Optimization, The ACO Metaheuristic, How Do I Apply ACO?, Other Metaheuristics, Bibliographical Remarks, Things to Remember, Thought and Computer Exercises

From Natural to Artificial Swarm Intelligence

Abstract: From the Publisher: This book provides a detailed look at models of social insect behavior and how to apply these models in the design of complex systems. The book shows how these models replace an emphasis on control, preprogramming, and centralization with designs featuring autonomy, emergence, and distributed functioning. These designs are proving immensely flexible and robust, able to adapt quickly to changing environments and to continue functioning even when individual elements fail. In particular, these designs are an exciting approach to the tremendous growth of complexity in software and information. Swarm Intelligence draws on up-to-date research from biology, neuroscience, artificial intelligence, robotics, operations research, and computer graphics, and each chapter is organized around a particular biological example, which is then used to develop an algorithm, a multiagent system, or a group of robots. The book will be an invaluable resource for a broad range of disciplines.

The particle swarm: social adaptation in information-processing systems

Abstract: A clutch mechanism of the rocker-shift type has a carrier member interposed between and affixed to one of a rotatable driving and a rotatable driven member. The carrier member has circumferentially spaced rocker arms, having normally self-disengaging clutch portions, that are adapted to be rocked radially, on at least one end thereof, by means of a shift yoke and collar, for selective rocking engagement into coupling engagement with adjacent corresponding normally self-disengaging clutch portions on the other of the driving and driven members. The collar has locked neutral and locked engaged positions on the carrier, with the improvement comprising means for axially shifting the collar to an unlocked engaged position intermediate the locked neutral and locked engaged positions and yieldingly maintaining the collar in this unlocked engaged position at a predetermined torque level, with the collar being axially shifted back into locked neutral position as result of the separation of the self-disengaging clutch portions when the predetermined torque level is exceeded by the torque transfer between the driving and driven members.

Foraging behaviour of multi-robot system and emergence of swarm intelligence

Abstract: We focus on the quantitative aspect of the multi-robot system and discuss the emergence of swarm intelligence. Emergence of swarm intelligence is investigated through the foraging task. As the robots assumed in the paper are very simple in order to discuss their behavior analytically, there are few parameters to characterize their behavior such as interaction duration. The behavior of the system is investigated by computer simulation and mathematical model. We choose two types of foraging task: one is carrying pucks to the home and the other is gathering pucks to an unfixed point. Efficiency of the swarm depends on the interaction duration between each robot, and we find out that there is an optimum interaction duration in each task. We also find out that the system shows a kind of pattern formation under certain conditions.