A critical analysis of parameter adaptation in ant colony optimization
Reads0
Chats0
TLDR
It is shown that the adaptation methods proposed so far do not improve, and often even worsen the performance when applied to high performing ant colony optimization algorithms for some classical combinatorial optimization problems.Abstract:
Applying parameter adaptation means operating on parameters of an algorithm while it is tackling an instance. For ant colony optimization, several parameter adaptation methods have been proposed. In the literature, these methods have been shown to improve the quality of the results achieved in some particular contexts. In particular, they proved to be successful when applied to novel ant colony optimization algorithms for tackling problems that are not a classical testbed for optimization algorithms. In this paper, we show that the adaptation methods proposed so far do not improve, and often even worsen the performance when applied to high performing ant colony optimization algorithms for some classical combinatorial optimization problems.read more
Citations
More filters
Journal ArticleDOI
The irace package: Iterated racing for automatic algorithm configuration
Manuel López-Ibáñez,Jérémie Dubois-Lacoste,Leslie Pérez Cáceres,Mauro Birattari,Thomas Stützle +4 more
TL;DR: The rationale underlying the iterated racing procedures in irace is described and a number of recent extensions are introduced, including a restart mechanism to avoid premature convergence, the use of truncated sampling distributions to handle correctly parameter bounds, and an elitist racing procedure for ensuring that the best configurations returned are also those evaluated in the highest number of training instances.
Journal ArticleDOI
Novel Ant Colony Optimization Methods for Simplifying Solution Construction in Vehicle Routing Problems
TL;DR: A novel ACO algorithm (called AMR) to solve the vehicle routing problem (VRP) that allows ants to go in and out the depots more than once until they have visited all customers, which simplifies the procedure of constructing feasible solutions.
Journal ArticleDOI
Inertia weight control strategies for particle swarm optimization: Too much momentum, not enough analysis
TL;DR: An overview of 18 inertia weight control strategies is provided, conditions required for the strategies to exhibit convergent behaviour are derived, and results of the empirical investigation show that none of the examined strategies even perform on par with a constant inertia weight.
Book ChapterDOI
Ant Colony Optimization: A Component-Wise Overview
Manuel López-Ibáñez,Thomas Stützle,Marco Dorigo,Rafael Martí,Panos M. Pardalos,Mauricio G. C. Resende +5 more
TL;DR: This chapter gives an overview of the history of ACO, explains in detail its algorithmic components and summarizes its key characteristics, and introduces a software framework that unifies the implementation of these ACO algorithms for two example problems, the traveling salesman problem and the quadratic assignment problem.
Journal ArticleDOI
Large neighborhood search for multi-trip vehicle routing
TL;DR: This work considers the multi-trip vehicle routing problem, in which each vehicle can perform several routes during the same working shift to serve a set of customers, and proposes two large neighborhood search heuristics to perform the comparison.
References
More filters
Book ChapterDOI
Individual Comparisons by Ranking Methods
TL;DR: The comparison of two treatments generally falls into one of the following two categories: (a) a number of replications for each of the two treatments, which are unpaired, or (b) we may have a series of paired comparisons, some of which may be positive and some negative as mentioned in this paper.
Journal ArticleDOI
Ant colony system: a cooperative learning approach to the traveling salesman problem
TL;DR: The results show that the ACS outperforms other nature-inspired algorithms such as simulated annealing and evolutionary computation, and it is concluded comparing ACS-3-opt, a version of the ACS augmented with a local search procedure, to some of the best performing algorithms for symmetric and asymmetric TSPs.
Book
Ant Colony Optimization
TL;DR: Ant colony optimization (ACO) is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals as discussed by the authors 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.
Journal ArticleDOI
MAX-MIN Ant system
Thomas Stützle,Holger H. Hoos +1 more
TL;DR: Computational results on the Traveling Salesman Problem and the Quadratic Assignment Problem show that MM AS is currently among the best performing algorithms for these problems.
Book
The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization
TL;DR: In this paper, Johnson and Papadimitriou proposed a performance guarantee for heuristics, based on the notion of well-solved special cases (P. Gilmore, et al.).