Ant algorithms are multi-agent systems in which the behavior of each single agent, called artificial ant or ant for short in the following, is inspired by the behavior of real ants. Ant algorithms are one of the most successful examples of swarm intelligent systems [3], and have been applied to many types of problems, ranging from the classical traveling salesman

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 behaviour 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.

This paper describes a racing procedure for finding, in a limited amount of time, a configuration of a metaheuristic that performs as good as possible on a given instance class of a combinatorial optimization problem. Taking inspiration from methods proposed in the machine learning literature for model selection through cross-validation, we propose a procedure that empirically evaluates a set of candidate configurations by discarding bad ones as soon as statistically sufficient evidence is gathered against them. We empirically evaluate our procedure using as an example the configuration of an ant colony optimization algorithm applied to the traveling salesman problem.

Abstract: The cross-entropy method is a recent versatile Monte Carlo technique. This article provides a brief introduction to the cross-entropy method and discusses how it can be used for rare-event probability estimation and for solving combinatorial, continuous, constrained and noisy optimization problems. A comprehensive list of references on cross-entropy methods and applications is included.

Abstract – This work proposes an algorithm for data mining called Ant-Miner (Ant Colony-based Data Miner). The goal of Ant-Miner is to extract classification rules from data. The algorithm is inspired by both research on the behavior of real ant colonies and some data mining concepts and principles. We compare the performance of Ant-Miner with CN2, a well-known data mining algorithm for classification, in six public domain data sets. The results provide evidence that: (a) Ant-Miner is competitive with CN2 with respect to predictive accuracy; and (b) The rule lists discovered by Ant-Miner are considerably simpler (smaller) than those discovered by CN2. Index Terms – Ant Colony Optimization, data mining, knowledge discovery, classification. I.

We present a novel, general, optimally fast, incremental way of searching for a universal algorithm that solves each task in a sequence of tasks. The Optimal Ordered Problem Solver (OOPS) continually organizes and exploits previously found solutions to earlier tasks, eciently searching not only the space of domain-specific algorithms, but also the space of search algorithms. Essentially we extend the principles of optimal nonincremental universal search to build an incremental universal learner that is able to improve itself through experience.

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Abstract- Recently, a number of algorithms inspired by the foraging behavior of ant colonies have been applied to the solution of difficult discrete optimization problems. In this paper we put these algorithms in a common framework by defining the Ant Colony Optimization (ACO) meta-heuristic. A couple of paradigmatic examples of applications of these novel meta-heuristic are given, as well as a brief overview of existing applications. 1

Abstract In this paper we introduce model-based search as a unifying framework accommodating some recently proposed heuristics for combinatorial optimization such as ant colony optimization, stochastic gradient ascent, cross-entropy and estimation of distribution methods. We discuss similarities as well as distinctive features of each method, propose some extensions and present a comparative experimental study of these algorithms. 1

Abstract. In this paper we present AntHocNet, a new algorithm for routing in mobile ad hoc networks. Due to the ever changing topology and limited bandwidth it is very hard to establish and maintain good routes in such networks. Especially reliability and efficiency are important concerns. AntHocNet is based on ideas from Ant Colony Optimization. It consists of both reactive and proactive components. In a reactive path setup phase, multiple paths are set up between the source and destination of a data session, and during the course of the communication session, ants proactively test existing paths and explore new ones. In simulation tests we show that AntHocNet can outperform AODV, one of the most important current state-of-the-art algorithms, both in terms of end-to-end delay and packet delivery ratio. 1