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

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

https://wwwmayr.informatik.tu-muenchen.de/lehre/1999SS/optprak/BC.04-DorDic-NIO99.pdf

The ant colony optimization meta-heuristic

This paper introduces AntNet, a novel approach to the adaptive learning of routing tables in communications networks. AntNet is a distributed, mobile agents based Monte Carlo system that was inspired by recent work on the ant colony metaphor for solving optimization problems. AntNet's agents concurrently explore the network and exchange collected information. The communication among the agents is indirect and asynchronous, mediated by the network itself. This form of communication is typical of social insects and is called stigmergy. We compare our algorithm with six state-of-the-art routing algorithms coming from the telecommunications and machine learning fields. The algorithms' performance is evaluated over a set of realistic testbeds. We run many experiments over real and artificial IP datagram networks with increasing number of nodes and under several paradigmatic spatial and temporal traffic distributions. Results are very encouraging. AntNet showed superior performance under all the experimental conditions with respect to its competitors. We analyze the main characteristics of the algorithm and try to explain the reasons for its superiority.

/pdf/antnet-distributed-stigmergetic-control-for-communications-1f5guxa4mo.pdf

AntNet: distributed stigmergetic control for communications networks

Cooperative multi-agent systems (MAS) are ones in which several agents attempt, through their interaction, to jointly solve tasks or to maximize utility. Due to the interactions among the agents, multi-agent problem complexity can rise rapidly with the number of agents or their behavioral sophistication. The challenge this presents to the task of programming solutions to MAS problems has spawned increasing interest in machine learning techniques to automate the search and optimization process. We provide a broad survey of the cooperative multi-agent learning literature. Previous surveys of this area have largely focused on issues common to specific subareas (for example, reinforcement learning, RL or robotics). In this survey we attempt to draw from multi-agent learning work in a spectrum of areas, including RL, evolutionary computation, game theory, complex systems, agent modeling, and robotics. We find that this broad view leads to a division of the work into two categories, each with its own special issues: applying a single learner to discover joint solutions to multi-agent problems (team learning), or using multiple simultaneous learners, often one per agent (concurrent learning). Additionally, we discuss direct and indirect communication in connection with learning, plus open issues in task decomposition, scalability, and adaptive dynamics. We conclude with a presentation of multi-agent learning problem domains, and a list of multi-agent learning resources.

/pdf/cooperative-multi-agent-learning-the-state-of-the-art-4jmhlfb1be.pdf

Cooperative Multi-Agent Learning: The State of the Art

In recent years, routing has been the most focused area in ad hoc networks research On-demand routing in particular, is widely developed in bandwidth constrained mobile wireless ad hoc networks because of its effectiveness and efficiency Most proposed on-demand routing protocols however, build and rely on a single route for each data session Whenever there is a link disconnection on the active route, the routing protocol must perform a route recovery process In QoS routing for wired networks, multiple path routing is popularly used Multiple routes are however, constructed using link-state or distance vector algorithms which are not well-suited for ad hoc networks We propose an on-demand routing scheme called split multipath routing (SMR) that establishes and utilizes multiple routes of maximally disjoint paths Providing multiple routes helps minimizing route recovery process and control message overhead Our protocol uses a per-packet allocation scheme to distribute data packets into multiple paths of active sessions This traffic distribution efficiently utilizes available network resources and prevents nodes of the route from being congested in heavily loaded traffic situations We evaluate the performance of our scheme using extensive simulation

Split multipath routing with maximally disjoint paths in ad hoc networks

We investigate two new distributed routing algorithms for data networks based on simple biological "ants" that explore the network and rapidly learn good routes, using a novel variation of reinforcement learning. These two algorithms are fully adaptive to topology changes and changes in link costs in the network, and have space and computational overheads that are competitive with traditional packet routing algorithms: although they can generate more routing traffic when the rate of failures in a network is low, they perform much better under higher failure rates. Both algorithms are more resilient than traditional algorithms, in the sense that random corruption of routing state has limited impact on the computation of paths. We present convergence theorems for both of our algorithms drawing on the theory of non-stationary and stationary discrete-time Markov chains over the reals. We present an extensive empirical evaluation of our algorithms on a simulator that is widely used in the computer networks community for validating and testing protocols. We present comparative results on data delivery performance, aggregate routing traffic (algorithm overhead), as well as the degree of resilience for our new algorithms and two traditional routing algorithms in current use. We also show that the performance of our algorithms scale well with increase in network size-using a realistic topology.

/pdf/ants-and-reinforcement-learning-a-case-study-in-routing-in-3g4kxzbhg1.pdf

Ants and reinforcement learning: a case study in routing in dynamic networks

This thesis develops new routing methods for large-scale, packet-switched data networks such as the Internet. The methods developed increase network performance by considering routing approaches that take advantage of more available network resources than do current methods. Two approaches are explored: dynamic metric and multipath routing. Dynamic metric routing provides paths that change dynamically in response to network traffic and congestion, thereby increasing network performance because data travel less congested paths. The second approach, multipath routing, provides multiple paths between nodes and allows nodes to use these paths to best increase their network performance. Nodes in this environment achieve increased performance through aggregating the resources of multiple paths. 
This thesis implements and analyzes algorithms for these two routing approaches. The first approach develops hybrid-Scout, a dynamic metric routing algorithm that calculates independent and selective dynamic metric paths. These two calculation properties are key to reducing routing costs and avoiding routing instabilities, two difficulties commonly experienced in traditional dynamic metric routing. For the second approach, multipath routing, this thesis develops a complete multipath network that includes the following components: routing algorithms that compute multiple paths, a multipath forwarding method to ensure that data travel their specified paths, and an end-host protocol that effectively uses multiple paths. 
Simulations of these two routing approaches and their components demonstrate significant improvement over traditional routing strategies. The hybrid-Scout algorithm requires 3–4 times to 1–2 orders of magnitude less routing cost compared to traditional dynamic metric routing algorithms while delivering comparable network performance. For multipath routing, nodes using the multipath protocol fully exploit the offered paths and increase performance linearly in the additional resources provided by the multipath network. The performance improvements validate the multipath routing algorithms and the effectiveness of the proposed end-host protocol. Furthermore, this new multipath forwarding method allows multipath networks to be supported at low routing costs. This thesis demonstrates that the proposed methods to implement dynamic metric and multipath routing are efficient and deliver significant performance improvements.

/pdf/new-approaches-to-routing-for-large-scale-data-networks-3fvquin2hn.pdf

New approaches to routing for large-scale data networks

We present a new routing algorithm to compute paths within a network using dynamic link metrics. Dynamic link metrics are cost metrics that depend on a link's dynamic state, e.g., the congestion on the link. Our algorithm is destination-initiated: a destination initiates a global path computation to itself using dynamic link metrics. All other destinations that do not initiate this dynamic metric computation use paths that are calculated and maintained by a traditional routing algorithm using static link metrics. Analysis of Internet packet traces show that a high percentage of network traffic is destined for a small number of networks. Because our algorithm is destination-initiated, it achieves maximum performance at minimum cost when it only computes dynamic metric paths to these selected "hot" destination networks. This selective approach to route recomputation reduces many of the problems (principally route oscillations) associated with calculating all routes simultaneously. We compare the routing efficiency and end to-end performance of our algorithm against those of traditional algorithms using dynamic link metrics. The results of our experiments show that our algorithm can provide higher network performance at a significantly lower routing cost under conditions that arise in real networks. The effectiveness of the algorithm stems from the independent, time-staggered recomputation of important paths using dynamic metrics, allowing for splits in congested traffic that cannot be made by traditional routing algorithms.

A new approach to routing with dynamic metrics

We present a simple and practical distributed routing algorithm based on backward learning. The algorithm periodically oods scout packets that explore paths to a destination in reverse. Scout packets are small and of xed size; therefore, they lend themselves to hop-by-hop piggy-backing on data packets, largely defraying their cost to the network. The correctness of the proposed algorithm is analytically veriied. Our algorithm also has loop-free multi-path routing capabilities, providing increased network utilization and route stability. The Scout algorithm requires very little state and computation in the routers, and can eeciently and gracefully handle high rates of change in the network's topology and link costs. An extensive simulation study shows that the proposed algorithm is competitive with link-state and distance vector algorithms, particularly in highly dynamic networks.

A Simple, Practical Distributed Multi-Path Routing Algorithm

We motivate and formally define dynamic multipath routing and present the problem of packet forwarding in the multipath routing context. We demonstrate that for multipath sets that are suffix matched, forwarding can be efficiently implemented with (1) a per packet overhead of a small, fixed-length path identifier, and (2) router space overhead linear in K, the number of alternate paths between a source and a destination. We derive multipath forwarding schemes for suffix matched path sets computed by both de-centralized (link-state) and distributed (distance-vector) routing algorithms. We also prove that (1) distributed multipath routing algorithms compute suffix matched multipath sets, and (2) for the criterion of ranked k-shortest paths, decentralized routing algorithms also yield suffix matched multipath sets.

An efficient multipath forwarding method

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