An ACO-Based Reactive Framework for Ant Colony Optimization: First Experiments on Constraint Satisfaction Problems

Khichane, Madjid; Albert, Patrick; Solnon, Christine

doi:10.1007/978-3-642-11169-3_9

Cited by 18 publications

(13 citation statements)

References 11 publications

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“…Parametersˇand are the earliest and most frequently modified parameters in the literature [90,93]. A significant effort has been done to define adaptive parameter choices, where the algorithm parameters are modified as a predefined function of the ACO search behavior [72,111], or to define self-adaptive parameter adaptation schemes, where the algorithm modifies the parameters itself during execution [69,87,110]. A recent paper [127] critically reviews the works that have applied parameter adaptation to ACO algorithms.…”

Section: Parameter Adaptationmentioning

confidence: 99%

Ant Colony Optimization: A Component-Wise Overview

López-Ibáñez¹,

Stützle²,

Dorigo³

2016

Handbook of Heuristics

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The indirect communication and foraging behavior of certain species of ants have inspired a number of optimization algorithms for NP-hard problems. These algorithms are nowadays collectively known as the ant colony optimization (ACO) metaheuristic. This chapter gives an overview of the history of ACO, explains in detail its algorithmic components, and summarizes its key characteristics. In addition, the chapter 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. By configuring the parameters of the framework, one can combine features from various ACO algorithms in novel ways. Examples on how to find a good configuration automatically are given in the chapter. The chapter closes with a review of combinations of ACO with other techniques and extensions of the ACO metaheuristic to other problem classes.

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Section: Parameter Adaptationmentioning

confidence: 99%

Ant Colony Optimization: A Component-Wise Overview

López-Ibáñez¹,

Stützle²,

Dorigo³

2016

Handbook of Heuristics

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“…Castillo (2013) [23] apresenta um controle fuzzy para os parâmetros de taxa de evaporação ρ. O controle utiliza uma medida de erro para Tutorial Sobre o Uso de Técnicas para Controle Online de Parâmetros em Algoritmos de Inteligência de Enxame e Computação Evolutiva ajustar os valores dos parâmetros (TC-CF:2). Khichane (2009) [66] propôs um controle para os parâmetros α e β, baseado em matriz de feromônio. A adaptação dos parâmetros ocorre em ciclos de iterações (TC-MF:2).…”

Section: Otimização Por Colônia De Formigasunclassified

Tutorial Sobre o Uso de Técnicas para Controle de Parâmetros em Algoritmos de Inteligência de Enxame e Computação Evolutiva

André

Parpinelli

2014

RITA

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Nature has always been a great source of inspiration for the development of computational approaches for optimization. Two major groups representing this class of biologically inspired algorithms are Swarm Intelligence and Evolutionary Computation. Such algorithms are called metaheuristics and are recognized to be efficient approaches for solving complex problems. Both Swarm Intelligence and Evolutionary Computation share common features such as the use of stochastic components during the optimization process and various parameters for configuration. The setup of parameters of an algorithm has an important role in defining its behavior, guiding the search and biasing the quality of the solutions found. However, adjusting the parameters is not a simple task, becoming an optimization problem within the problem being optimized. In addition, an appropriate setting for the parameters may change during the optimization process making this task even harder. There are two ways to adjust the parameters of an algorithm. The offline control that is performed before running the algorithm and parameter values remains fixed and the online control where parameter values may change during the optimization process. This article focuses on reviewing the online parameter control strategies applied in Evolutionary Computation and Swarm Intelligence. As a result, this review analyzes and points out the key techniques and algorithms used and suggests some directions for future research.

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“…Ces travaux ont été publiés dans [KAS09a,KAS09b] Organisation du mémoire Ce mémoire de thèse est organisé en deux grandes parties. La première partie est consacrée à l'introduction des notions de base des problèmes d'optimisation combinatoires sous contraintes et des méthodes de ré-solution les plus connues.…”

Section: Adaptation Dynamique Et Automatique Des Paramètres D'acounclassified

“…Dans [KAS09a,KAS09b], nous avons proposé une nouvelle stratégie adaptative qui permet à l'algorithme ACO de régler lui-même certains de ses paramètres pendant la résolution d'un problème.…”

Section: Adaptation Dynamique Et Automatique Des Paramètres D'acounclassified

Strong Combination of Ant Colony Optimization with Constraint Programming Optimization

Khichane

Albert²,

Solnon

2010

Lecture Notes in Computer Science

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An ACO-Based Reactive Framework for Ant Colony Optimization: First Experiments on Constraint Satisfaction Problems

Cited by 18 publications

References 11 publications

Ant Colony Optimization: A Component-Wise Overview

Ant Colony Optimization: A Component-Wise Overview

Tutorial Sobre o Uso de Técnicas para Controle de Parâmetros em Algoritmos de Inteligência de Enxame e Computação Evolutiva

Strong Combination of Ant Colony Optimization with Constraint Programming Optimization

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