An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.

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Ant system: optimization by a colony of cooperating agents

Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as ant colony optimization Ant colony optimization (ACO) takes inspiration from the foraging behavior of some ant species These ants deposit pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony Ant colony optimization exploits a similar mechanism for solving optimization problems From the early nineties, when the first ant colony optimization algorithm was proposed, ACO attracted the attention of increasing numbers of researchers and many successful applications are now available Moreover, a substantial corpus of theoretical results is becoming available that provides useful guidelines to researchers and practitioners in further applications of ACO The goal of this article is to introduce ant colony optimization and to survey its most notable applications

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Ant Colony Optimization

The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behavior of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization.

/pdf/metaheuristics-in-combinatorial-optimization-overview-and-menr0q3bxw.pdf

Metaheuristics in combinatorial optimization: Overview and conceptual comparison

Since the subject of traffic dynamics has captured the interest of physicists, many surprising effects have been revealed and explained. Some of the questions now understood are the following: Why are vehicles sometimes stopped by ``phantom traffic jams'' even though drivers all like to drive fast? What are the mechanisms behind stop-and-go traffic? Why are there several different kinds of congestion, and how are they related? Why do most traffic jams occur considerably before the road capacity is reached? Can a temporary reduction in the volume of traffic cause a lasting traffic jam? Under which conditions can speed limits speed up traffic? Why do pedestrians moving in opposite directions normally organize into lanes, while similar systems ``freeze by heating''? All of these questions have been answered by applying and extending methods from statistical physics and nonlinear dynamics to self-driven many-particle systems. This article considers the empirical data and then reviews the main approaches to modeling pedestrian and vehicle traffic. These include microscopic (particle-based), mesoscopic (gas-kinetic), and macroscopic (fluid-dynamic) models. Attention is also paid to the formulation of a micro-macro link, to aspects of universality, and to other unifying concepts, such as a general modeling framework for self-driven many-particle systems, including spin systems. While the primary focus is upon vehicle and pedestrian traffic, applications to biological or socio-economic systems such as bacterial colonies, flocks of birds, panics, and stock market dynamics are touched upon as well.

Traffic and related self-driven many-particle systems

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.

http://www.iro.umontreal.ca/~dift6751/paper_aco.pdf

Ant algorithms for discrete optimization

Workers of the Argentine ant, Iridomyrmex humilis,start to explore a chemically unmarked territory randomly. As the exploratory front advances, other explorers are recruited and a trail extends from it to the nest. Whereas recruitment trails are generally constructed between two points, these exploratory trails have no fixed destination, and strongly resemble the foraging patterns of army ants. A minimal model shows how the exploratory pattern may be generated by the individual workers' simple trail-laying and -following behavior, illustrating how complex collective structures in insect colonies may be based on self-organization.

http://www.intelligentappliances.org/files/2012presentations/The%20self-organizing%20exploratory%20pattern%20of%20the%20argentine%20ant.pdf

The self-organizing exploratory pattern of the argentine ant

A series of experiments shows how the andLasius niger uses its trail recruitment system to select between two food sources Simultaneously presented with to 1M sucrose solution it concentrates on one of them When offered a 1M solution together with a 01M solution it selects the richer source, unless the trait to the 01M source had become well-developed before the 1M source was introduced In the same situation, however, the group/mass recruiting antTetramorium caespitum uses its more individual transmission of information to switch to the 1M source A mathematical model describes these processes and its dynamics reflect the experimental results

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Collective decision making through food recruitment

Some 12,000 ant species are known by now, with colony sizes ranging from a few individuals to 20,000,000 individuals. What constraints does this vast range of colony sizes place on the systems of organisation that they use? Alternatively, how does this range of colony sizes reflect the different systems of organisation used? We shall examine these questions in relation to ant foraging strategy, which as well as being the most visible aspect of their activity illustrates most clearly the roles and limits of communication in their collective behavior. This paper aims to verify a prediction of the following hypothesis (Pasteels et al. 1985; Deneubourg et al. 1986). In theory, the organization of a small insect society can rely on most individuals at any moment \"knowing\", principally by learning, what it must do, where it must go, etc., and the workers’ behavior has a strong determinist component. In a large insect society organization by individual learning is harder to achieve (Deneubourg et al. 1987). The workers’ behavior is necessarily more random and their coordination becomes a major problem. To cope with this, a completely different organisational system is added to that already in place. This supplementary system is based on the complex collective structures, patterns and decisions that spontaneously emerge from simple autocatalytic interactions between numerous individuals and with the environment, mediated by essentially chemical communication (see, e.g., Pasteels et al. 1987; Goss and Deneubourg 1989; Beckers et al. in press; Deneubourg et al, 1989, in press; Goss et al. 1990). The prediction that follows from this hypothesis is that the larger the colony size, the less foraging is individually based and the more

/pdf/colony-size-communication-and-ant-foraging-strategy-23z94y3v6z.pdf

Colony size, communication and ant foraging strategy

Le modele propose et les simulations de Monte-Carlo montrent que le type caracteristique d'essaimage provient des interactions entre de nombreux individus, chacun avec un comportement de marquage de piste et de suivi de piste

/pdf/the-blind-leading-the-blind-modeling-chemically-mediated-4wzot7kqob.pdf

Jacques Pasteels

Papers

The self-organizing exploratory pattern of the argentine ant

Collective decision making through food recruitment

Colony size, communication and ant foraging strategy

The blind leading the blind: Modeling chemically mediated army ant raid patterns

Self-organization mechanisms in ant societies. I: Trail recruitment to newly discovered food sources