Showing papers on "Swarm intelligence published in 2001"

Swarm intelligence. A whole new way to think about business.

Abstract: What do ants and bees have to do with business? A great deal, it turns out. Individually, social insects are only minimally intelligent, and their work together is largely self-organized and unsupervised. Yet collectively they're capable of finding highly efficient solutions to difficult problems and can adapt automatically to changing environments. Over the past 20 years, the authors and other researchers have developed rigorous mathematical models to describe this phenomenon, which has been dubbed "swarm intelligence," and they are now applying them to business. Their research has already helped several companies develop more efficient ways to schedule factory equipment, divide tasks among workers, organize people, and even plot strategy. Emulating the way ants find the shortest path to a new food supply, for example, has led researchers at Hewlett-Packard to develop software programs that can find the most efficient way to route phone traffic over a telecommunications network. South-west Airlines has used a similar model to efficiently route cargo. To allocate labor, honeybees appear to follow one simple but powerful rule--they seem to specialize in a particular activity unless they perceive an important need to perform another function. Using that model, researchers at Northwestern University have devised a system for painting trucks that can automatically adapt to changing conditions. In the future, the authors speculate, a company might structure its entire business using the principles of swarm intelligence. The result, they believe, would be the ultimate self-organizing enterprise--one that could adapt quickly and instinctively to fast-changing markets.

Swarm intelligence for routing in communication networks

Abstract: Swarm intelligence, as demonstrated by natural biological swarms, exhibits numerous powerful features that are desirable in many engineering systems, such as communication networks. In addition, new paradigms for designing autonomous and scalable systems may result from analytically understanding and extending the design principles and operations inherent in intelligent biological swarms. A key element of future design paradigms will be emergent intelligence - simple local interactions of autonomous swarm members, with simple primitives, giving rise to complex and intelligent global behavior. Communication network management is becoming increasingly difficult due to the increasing network size, rapidly changing topology, and complexity. A new class of algorithms, inspired by swarm intelligence, is currently being developed that can potentially solve numerous problems of such networks. These algorithms rely on the interaction of a multitude of simultaneously interacting agents. A survey of such algorithms and their performance is presented here.

Swarm robotic odor localization

Abstract: This paper presents an investigation of odor localization by groups of autonomous mobile robots using principles of swarm intelligence. We describe a distributed algorithm by which groups of agents can solve the full odor localization task more efficiently than a single agent. We then demonstrate that a group of real robots under fully distributed control can successfully traverse a real odor plume. Finally, we show that an embodied simulator can faithfully reproduce the real robots experiments and thus can be a useful tool for off-line study and optimization of odor localization in the real world.

A Monogenous MBO Approach to Satisfiability

Abstract: The marriage in honey–bees optimization (MBO) algorithm was recently proposed and showed good results for combinatorial optimization problems. Contrary to most of the swarm intelligence algorithms (such as Ant Colony Optimization), MBO uses self-organization to mix different heuristics. This paper presents a variation of the MBO algorithm where the colony contains a single queen with multiple workers. The model is used to solve a special class of the propositional satisfiability problems (SAT) known as 3-SAT, where each constraint contains exactly three variables. The objective of this paper is to detail the algorithm and analyze its behavior on 3-SAT using three parameters gleaned from biological concepts: the queen’s spermatheca size, the colony size, and the amount of time devoted for brood-care. The MBO algorithm outperformed well known heuristics for SAT.

Improving the Particle Swarm Optimizer by Function “Stretching”

Abstract: In this paper a new technique, named Function “Stretching”, for the alleviation of the local minima problem is proposed. The main feature of this technique is the usage of a two—stage transformation of the objective function to eliminate local minima, while preserving the global ones. Experiments indicate that combined with the Particle Swarm Optimizer method, the new algorithm is capable of escaping from local minima and effectively locate the global ones. Our experience is that the modified algorithm behaves predictably and reliably and the results were quite satisfactory. The function “Stretching” technique provides stable convergence and thus a better probability of success to the method with which it is combined.

A clustering algorithm based on swarm intelligence

Abstract: This paper focuses on swarm intelligence based clustering algorithm. A clustering algorithm based on swarm intelligence is systematically proposed. It derived from a basic model interpreting ant colony organization of cemeteries. Some important concepts, such as swarm similarity, swarm similarity coefficient and probability conversion function are also proposed. A simplified probability conversion function is given for simplifying adaptation of parameters, meanwhile the importance of swarm similarity coefficient for the algorithm is analyzed. Experimental results show the good performance of the clustering algorithm.

Adaptive routing in wireless communication networks using swarm intelligence

Abstract: In this paper we focus on the network routing problem, and survey swarm intelligent approaches for its efficient solution, after a brief overview of power-aware routing schemes, which are important in the network examples outlined above

An Experimental Study of the Simple Ant Colony Optimization Algorithm

Abstract: Ant Colony Optimization (ACO) is a recently proposed metaheuristic inspired by the foraging behavior of ant colonies. Although it has been experimentally shown to be highly effective on a number of static and dynamic discrete optimization problems, only limited knowledge is available to explain why the metaheuristic is so successful. In this paper we propose a simple framework that allows the investigation of some basic properties of ACO and we report about some experiments and what we learned from them. Key-Words: Ant Colony Optimization, Simple-ACO algorithm, shortest path problem

An Ant Colony Algorithm Based Partition Algorithm for TSP

Abstract: Optimization algorithms inspired by models of co-operative food retrieval in ants have been unexpectedly successful and become known in recent years as Ant Colony Optimization (ACO).As a novel computational approach, swarm intelligence systems such as ant system have become a hot research domain. This paper proposes a meeting algorithm and a partition algorithm for TSP based on typical ant algorithms. The meeting algorithm improves the ant touring quality, it provides good initial touring results for local optimization on the condition of low iteration times. Combining with a simple parallelization strategy--the partition algorithm, this paper gets some good experiment results on Traveling Salesman Problems.The main idea of meeting algorithm is that there are two ants in a touring instead of one ant in typical ant algorithms. The two ants start a touring from a same city, and choose cities from different directions. By sharing a same tabu list, they meet at the middle of a touring. Experiment results show the two-ant touring is slightly shorter than the one-ant touring. Based on the shorter touring by two ants on the condition of low iteration times, a parallelization strategy is developed. It is the partition algorithm. The whole path is partitioned into several segments, then adapted meeting algorithm is again applied on the segments. Three TSP instances are used in this paper. They are ST70(70 cities), KroB150 (from TSPLIB) and CHC144(Chinese 144 cities). Comparing the best results available, 678.59( from the best path provided by TSPLIB), 26130 (from TSPLIB) and 30354.3 (by GA), some experiment results on the synthesized algorithm of meeting algorithm and partition algorithm are rather better. They are 677.1096, 26127.35 and 30354.3.

Swarm Intelligence

Abstract: Swarm Intelligence is the principal peer reviewed publication dedicated to reporting research and new developments in this multidisciplinary field. The journal publishes original research articles and occasional reviews on theoretical, experimental, and practical aspects of swarm intelligence. It offers readers reports on advances in the understanding and utilization of systems that are based on the principles of swarm intelligence. Emphasis is given to such topics as the modeling and analysis of collective biological systems; application of biological swarm intelligence models to real-world problems; and theoretical and empirical research in ant colony optimization, particle swarm optimization, swarm robotics, and other swarm intelligence algorithms. Articles often combine experimental and theoretical work.

Swarm intelligence for hybrid cost dispatch problem

Abstract: The paper presents a modified particle swarm optimizer (PSO) to solve the economic power dispatch problem with piecewise quadratic cost function. Practically, operating conditions of many generating units require that the cost function be segmented as piecewise quadratic functions instead of using one convex function for each generator. The proposed technique is applied to a case study of multiple intersecting cost functions for each unit. Unlike the hierarchical method, the proposed algorithm finds combination of power generation that minimizes the total cost function while exactly satisfying the total demand.

Collective Complexity out of Individual Simplicity

Abstract: The concept of Swarm Intelligence (SI) was first introduced by Gerardo Beni, Suzanne Hackwood, and Jing Wang in 1989 when they were investigating the properties of simulated, self-organizing agents in the framework of cellular robotic systems [1]. Eric Bonabeau, Marco Dorigo, and Guy Theraulaz extend the restrictive context of this early work to include “any attempt to design algorithms or distributed problem-solving devices inspired by the collective behavior of social insect colonies,” such as ants, termites, bees, wasps, “and other animal societies.” The abilities of such systems appear to transcend the abilities of the constituent individuals. In most biological cases studied so far, robust and capable high-level group behavior has been found to be mediated by nothing more than a small set of simple low-level interactions between individuals, and between individuals and the environment. The SI approach, therefore, emphasizes parallelism, distributedness, and exploitation of direct (agent-to-agent) or indirect (via the environment) local interactions among relatively simple agents.

An Annealing Approach to the Mating-Flight Trajectories in the Marriage in Honey Bees Optimization Algorithm

Abstract: Marriage in Honey Bees Optimization (MBO) is a new swarm intelligence technique inspired by the marriage process of honey bees. It has been shown to be very effective in solving a special group of propositional satisfiability problems called 3-SAT. In the current MBO implementation, the acceptance of a drone for mating is determined probabilistically using a variation of the annealing function. However, the algorithm does not exactly implement an annealing approach as it follows a pure exploration strategy. Currently, all state transitions made during the queens’ mating-flight are generated independent of the queens’ fitness, are always accepted as long as they are created, and used to spawn the drones. The objective of this paper is to investigate a more conventional annealing approach for the mating-flight process to balance search exploration with search intensification. It is proposed that the trajectories of the queens’ mating-flight in the search space are accepted according to a probabilistic function of the queens’ fitness. This modified MBO algorithm is tested using a group of randomly generated hard 3-SAT problems to compare its behavior and efficiency against the original implementation. We found that the proposed annealing function improved one of the MBO implementations and MBO outperformed all the standalone SAT heuristics including WalkSAT.

Performance measures of an ant based decentralised routing scheme for circuit switching communication networks

Abstract: Swarm intelligence is a new challenging branch of artificial life which takes advantage of the collective behaviour of animals with limited intellectual faculties (insects, flocking birds, schools of fish) to solve algorithmically complex problems. Recently a new routing method based on the way that ants are communicating with each other has been applied to solve routing problems in telecommunication networks. This paper examines the behavior of an ant based decentralised router using an adequate set of commonly acceptable and some newly introduced metrics.

Collective Complexity out of Individual Simplicity. Invited book review on""Swarm Intelligence: From Natural to Artificial Systems"", by E. Bonabeau, M. Dorigo, and G. Theraulaz

Abstract: Review of: Swarm Intelligence: From Natural to Artificial Systems by Eric Bonabeau, Marco Dorigo, and Guy Theraulaz, Oxford University Press, 1999, 307 pp.

Book Review: Collective Complexity out of Individual Simplicity

Abstract: The concept of Swarm Intelligence (SI) was first introduced by Gerardo Beni, Suzanne Hackwood, and Jing Wang in 1989 when they were investigating the properties of simulated, self-organizing agents in the framework of cellular robotic systems [1]. Eric Bonabeau, Marco Dorigo, and Guy Theraulaz extend the restrictive context of this early work to include “any attempt to design algorithms or distributed problem-solving devices inspired by the collective behavior of social insect colonies,” such as ants, termites, bees, wasps, “and other animal societies.” The abilities of such systems appear to transcend the abilities of the constituent individuals. In most biological cases studied so far, robust and capable high-level group behavior has been found to be mediated by nothing more than a small set of simple low-level interactions between individuals, and between individuals and the environment. The SI approach, therefore, emphasizes parallelism, distributedness, and exploitation of direct (agent-to-agent) or indirect (via the environment) local interactions among relatively simple agents. The title, Swarm Intelligence: From Natural to Artificial Systems, summarizes the content and the structure of the book well: Each of the central chapters starts by presenting experimental results of one or several biological studies (foraging, division of labor, clustering and sorting, nest building, cooperative transportation), then describes a model for explaining these results, and moves on to discuss engineering outcomes in the form of algorithms or collective robotic systems that have or could have been inspired by the biological examples. One of the strengths of this monograph is undoubtedly this extensive use of models as a quantitative and abstract interface for the implementation of natural principles in artificial systems. Without an adequate level of description provided by models, it would be difficult, if not impossible, to understand the collective behavior of natural systems or to explore parametric ranges, which can be of interest for engineering purposes, though not necessarily useful in nature. While the biological examples and their related modeling are well structured, the book tends to be less systematic in the myriad of engineering case studies that are associated with them. The global picture is far from being exhaustive and unitary: The book suffers from the current youth and rapidly developing nature of the SI field. The field currently lacks mature and sound methodologies to transfer biological mechanisms into useful engineering algorithms or to choose an adequate level of description for modeling. Bio-inspiration is a process fully dominated by the intuition and imagination of a few researchers. A full body of theory for designing and describing such distributed