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.

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The allocation of discrete, complementm'y resources is a fundamental probleln in econolnics and of direct interest to e-colnlnerce applications. Combinatorial auctions account for complementarities by optimizing over offers expressed in terms of bundles. Progressive versions of combinatorial auctions alleviate the burden on bidders of expressing offers for all bundles of interest by providing interiln feedback based on partial sets of bids. Feedback in terms of hypothetical prices is particularly useful, as it directs bidders to,yard those bundles potentially yielding the greatest surplus. For a general class of discrete resource allocation problelns vith free disposal, we establish by construction the existence of competitive equilibriuln prices on bundles that support the efficient allocation. We introduce AkBA, a falnily of progressive auctions that use these equilibriuln bundle prices. We exalnine a particular instance of the family, called A1BA, and present SOlne elnpirical data on its perforlnance.

The Quadratic Assignment Problem (QAP) is a well-known combinatorial optimization problem with a wide variety of practical applications. Although many heuristics and semi-enumerative procedures for QAP have been proposed, no dominant algorithm has emerged. In this paper, we describe a Genetic Algorithm (GA) approach to QAP. Genetic algorithms are a class of randomized parallel search heuristics which emulate biological natural selection on a population of feasible solutions. We present computational results which show that this GA approach finds solutions competitive with those of the best previously-known heuristics, and argue that genetic algorithms provide a particularly robust method for QAP and its more complex extensions. 5 A Genetic Approach to the Quadratic Assignment Problem David M. Tate and Alice E. Smith Department of Industrial Engineering 1048 Benedum Hall University of Pittsburgh Pittsburgh, PA 15261 412-624-9837 412-624-9831 (Fax) 1. Introduction The Quadrat...

Recent developments in computer networks have driven the cost of distributing information virtually to zero, creating extraordinary opportunities for sharing product evaluations. We present pricing and subsidy mechanisms that operate through a computerized market and induce the efficient provision of evaluations. The mechanisms overcome three major challenges: first, evaluations, which are public goods, are likely to be underprovided; second, an inefficient ordering of evaluators may arise; third, the optimal quantity of evaluations depends on what is learned from the initial evaluations. Keywords: evaluations, information sharing, product quality, computer network, market (JEL D70, D83, H41, L15) 2 Subjective evaluations by others are a valuable tool for consumers who are choosing which products to buy or how to spend their time. For example, we read magazines devoted to product evaluation before purchasing cars and appliances. We ask our friends and read reviews by professional cr...

In this paper, a fitness landscape analysis for several instances of the quadratic assignment problem (QAP) is performed and the results are used to classify problem instances according to their hardness for local search heuristics and meta-heuristics based on local search. The local properties of the tness landscape are studied by performing an autocorrelation analysis, while the global structure is investigated by employing a fitness distance correlation analysis. It is shown that epistasis, as expressed by the dominance of the flow and distance matrices of a QAP instance, the landscape ruggedness in terms of the correlation length of a landscape, and the correlation between fitness and distance of local optima in the landscape together are useful for predicting the performance of memetic algorithms - evolutionary algorithms incorporating local search - to a certain extent. Thus, based on these properties a favorable choice of recombination and/or mutation operators can be found.

Dissatisfaction with previous assignment mechanisms and the desire to raise revenue induced Congress to grant the FCC authority to auction radio licenses. The debate over an appropriate auction design produced a variety of innovative proposals. One point that could not be unambiguously resolved [in the PCS debate] was whether the auction rules should permit combinatorial bids (i.e., bids on packages of licenses). In the end, the FCC chose not to do so because of perceived complexities with permitting such bids and concerns over the performance properties of a combinatorial auction. This paper presents an analysis of the proper role of combinatorial auctions as a mechanism for assigning spectrum licenses. In general, synergies across license valuations complicate the auction design process. Theory suggests that a simple (i.e., non-combinatorial) auction will have difficulty in assigning licenses efficiently in such an environment. This difficulty increases with increases in fitting complexity. In some environments, bidding may become mutually destructive. Experiments indicate that a combinatorial auction is superior to a simple auction in terms of economic efficiency and revenue generation in bidding environments with a low amount of fitting complexity. Concerns that a combinatorial auction will cause a threshold problem are not borne out when bidders for small packages can communicate.

This paper introduces two new techniques for solving the Quadratic Assignment Problem. The first is a heuristic technique, defined in accordance to the Ant System metaphor, and includes as a distinctive feature the use of a new lower bound at each constructive step. The second is a branch and bound exact approach, containing some elements introduced in the Ant algorithm. Computational results prove the effectiveness of both approaches.

. A simple mechanism is presented, based on ant-like agents, for routing and load balancing in telecommunications networks, following the initial works of Appleby and Stewart (1994) and Schoonderwoerd et al. (1997). In the present work, agents are very similar to those proposed by Schoonderwoerd et al. (1997), but a r e supplemented with a simplified dynamic programming capability, initially experimented by Gurin (1997) with more complex agents, which is shown to significantly improve the network's relaxation and its response to perturbations. Topic area: Intelligent agents and network management 2 1. Introduction 1.1 Routing in telecommunications networks Routing is a mechanism that allows calls to be transmitted from a source to a destination through a sequence of intermediate switching stations or nodes, because not all points are directly connected: the cost of completely connecting a network becomes prohibitive for more than a few nodes. Routing selects routes that meet the o...

In this paper we present an extension of MAX --MIN Ant System applying it to Traveling Salesman Problems and Quadratic Assignment Problems. The extension involves the use of a modified choice rule and a hybrid scheme allowing ants to improve their solution by local search. The computational results show that this algorithm can be used to efficiently find near optimal solutions to hard combinatorial optimization problems and is one of the best methods for the solution of structured quadratic assignment problems. 1 Introduction Ant Colony Optimization (ACO) is a population based, cooperative search metaphor inspired by the foraging behavior of real ants. One of the basic ideas of ACO is to use the equivalent of the pheromone trail used by real ants as a medium for cooperation and communication among a colony of artificial ants. The seminal work on ACO is Ant System [8, 10] that was first proposed for solving the Traveling Salesman Problem (TSP). In Ant System, the ants are simple agent...