Mobile ad hoc networks are a class of highly dynamic networks. In previous work, we developed a new routing algorithm, called AntHocNet, for these challenging network environments. AntHocNet has been designed after the Ant Colony Optimization (ACO) framework, and its general architecture shares strong similarities with the architectures of typical ACO implementations for network routing. On the...

This chapter presents a rigorous Design of Experiments (DOE) approach for determining whether a problem characteristic affects the performance of a heuristic. Specifically, it reports a study on the effect of the cost matrix standard deviation of symmetric Travelling Salesman Problem (TSP) instances on the performance of Ant Colony Optimisation (ACO) heuristics. Results demonstrate that for a g...

This work presents a multiobjective algorithm for multicast traffic engineering. The proposed algorithm is a new version of MultiObjective Ant Colony System (MOACS), based on Ant Colony Optimization (ACO). The proposed MOACS simultaneously optimizes the maximum link utilization, the cost of the multicast tree, the averages delay and the maximum end-to-end delay. In this way, a set of optimal so...

This paper presents an application of the ant colony metaphor for continuous space optimization problems. The ant algortihm proposed works following the principle of the ant colony approach, i.e., a population of agents iteratively, cooperatively, and independently search for a solution. Each ant in the distributed algorithm applies a local search operator which explores the neighborhood region...

Ant colony optimization (ACO) has been proved to be one of the best performing algorithms for NP-hard problems as TSP. The volatility rate of pheromone trail is one of the main parameters in ACO algorithms. It is usually set experimentally in the literatures for the application of ACO. The present paper proposes an adaptive strategy for the volatility rate of pheromone trail according to the qu...

Configuring parameter settings for ant colony optimisation (ACO) based algorithms is a challenging and time consuming task, because it usually requires evaluating a large number of parameter combinations to find the most appropriate setting. In this study, a multilevel ParamILS (MParamILS) technique, that combines a graph coarsening method and the ParamILS framework, has been developed for conf...

0965-9978/$ see front matter 2013 Civil-Comp Ltd and Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.advengsoft.2013.02.002 ⇑ Corresponding author at: AMADE, Polytechnic School, Universitat de Girona, Campus Montilivi s/n, 17071 Girona, Spain. Tel.: +34 972418817. E-mail addresses: [email protected], [email protected] (T.A. Sebaey), [email protected] (C.S. Lopes), norb...

The use of Network Lifetime Maximization while disseminating queries to appropriate regions since data queries often include geographic attributes and apply Ant Colony Optimization clustering is proposed here. The protocol, uses energy aware and geographically-informed neighbor selection heuristics to route a packet towards the destination region to improve the link stability during data transf...

In this paper we describe the design of a fuzzy logic controller for the ball and beam system using a modified Ant Colony Optimization (ACO) method for optimizing the type of membership functions, the parameters of the membership functions and the fuzzy rules. This is achieved by applying a systematic and hierarchical optimization approach modifying the conventional ACO algorithm using an ant s...

This paper conducts experimental tests to study the stagnation behavior the Interacted Multiple Ant Colonies Optimization (IMACO) framework. The idea of different ant colonies use different types of problem dependent heuristics has been proposed as well. The performance of IMACO was demonstrated by comparing it with the Ant Colony System (ACS) the best performing ant algorithm. The computationa...