Results 1  10
of
17
Searching for Maximum Cliques with Ant Colony Optimization
 In Applications of Evolutionary Computing, Proceedings of EvoWorkshops 2003: EvoCOP, EvoIASP, EvoSTim, volume 2611 of lncs
, 2003
"... In this paper, we investigate the capabilities of Ant Colony Optimization (ACO) for solving the maximum clique problem. We describe AntClique, an algorithm that successively generates maximal cliques through the repeated addition of vertices into partial cliques. ..."
Abstract

Cited by 18 (1 self)
 Add to MetaCart
In this paper, we investigate the capabilities of Ant Colony Optimization (ACO) for solving the maximum clique problem. We describe AntClique, an algorithm that successively generates maximal cliques through the repeated addition of vertices into partial cliques.
nd Semester 200102 5 Sci 10K,N • As the project is integrative, your grade would depend on coverage of topics, integration and correlation of the various topics and ideas, personal reflection and reaction to the material, originality and creativity of t
 HP Protein Folding Problem, Lecture
"... Abstract. The prediction of a protein’s structure from its aminoacid sequence is one of the most important problems in computational biology. In the current work, we focus on a widely studied abstraction of this problem, the 2dimensional hydrophobicpolar (2D HP) protein folding problem. We presen ..."
Abstract

Cited by 6 (2 self)
 Add to MetaCart
Abstract. The prediction of a protein’s structure from its aminoacid sequence is one of the most important problems in computational biology. In the current work, we focus on a widely studied abstraction of this problem, the 2dimensional hydrophobicpolar (2D HP) protein folding problem. We present an improved version of our recently proposed Ant Colony Optimisation (ACO) algorithm for this£¥ ¤hard combinatorial problem and demonstrate its ability to solve standard benchmark instances substantially better than the original algorithm; the performance of our new algorithm is comparable with stateoftheart Evolutionary and Monte Carlo algorithms for this problem. The improvements over our previous ACO algorithm include long range moves that allows us to perform modification of the protein at high densities, the use of improving ants, and selective local search. Overall, the results presented here establish our new ACO algorithm for 2D HP protein folding as a stateoftheart method for this highly relevant problem from bioinformatics. 1
Boosting ACO with a Preprocessing Step
 Applications of Evolutionary Computing, Proceedings of EvoWorkshops2002: EvoCOP, EvoIASP, EvoSTim
, 2002
"... When solving a combinatorial optimization problem with the Ant Colony Optimization (ACO) metaheuristic, one usually has to nd a compromise between guiding or diversifying the search. Indeed, ACO uses pheromone to attract ants. When increasing the sensibility of ants to pheromone, they converge ..."
Abstract

Cited by 5 (2 self)
 Add to MetaCart
When solving a combinatorial optimization problem with the Ant Colony Optimization (ACO) metaheuristic, one usually has to nd a compromise between guiding or diversifying the search. Indeed, ACO uses pheromone to attract ants. When increasing the sensibility of ants to pheromone, they converge quicker towards a solution but, as a counterpart, they usually nd worse solutions. In this paper, we rst study the inuence of ACO parameters on the exploratory ability of ants. We then study the evolution of the impact of pheromone during the solution process with respect to its cost's management. We nally propose to introduce a preprocessing step that actually favors a larger exploration of the search space at the beginning of the search at low cost. We illustrate our approach on AntSolver, an ACO algorithm that has been designed to solve Constraint Satisfaction Problems, and we show on random binary problems that it allows to nd better solutions more than twice quicker.
Search bias in constructive metaheuristics and implications for ant colony optimisation
 4th Int’l Workshop on Ant Colony Optimization and Swarm Intelligence, ANTS 2004, volume 3172 of Lecture Notes in Computer Science
, 2004
"... Constructive metaheuristics explore a tree of constructive decisions, the topology of which is determined by the way solutions are represented and constructed. Some solution representations allow particular solutions to be reached on a greater number of paths in this construction tree than other sol ..."
Abstract

Cited by 4 (3 self)
 Add to MetaCart
Constructive metaheuristics explore a tree of constructive decisions, the topology of which is determined by the way solutions are represented and constructed. Some solution representations allow particular solutions to be reached on a greater number of paths in this construction tree than other solutions, which can introduce a bias to the search. However, the ultimate determinant of search bias is the topology of the construction tree. This is particularly the case in problems where certain solution representations are infeasible. This paper presents an examination of the mechanisms that determine the topologies of construction trees and the implications for ant colony optimisation. The results provide insights into why certain assignment orders perform better in problems such as the quadratic and generalised assignment problems, in terms of both solution quality and avoiding infeasible solutions. Additionally, insight is gained into why certain pheromone representations are more effective than others on different problems. 1
An ACObased Reactive Framework for Ant Colony Optimization: First Experiments on Constraint Satisfaction Problems
"... Abstract. We introduce two reactive frameworks for dynamically adapting some parameters of an Ant Colony Optimization (ACO) algorithm. Both reactive frameworks use ACO to adapt parameters: pheromone trails are associated with parameter values; these pheromone trails represent the learnt desirability ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
Abstract. We introduce two reactive frameworks for dynamically adapting some parameters of an Ant Colony Optimization (ACO) algorithm. Both reactive frameworks use ACO to adapt parameters: pheromone trails are associated with parameter values; these pheromone trails represent the learnt desirability of using parameter values and are used to dynamically set parameters in a probabilistic way. The two frameworks differ in the granularity of parameter learning. We experimentally evaluate these two frameworks on an ACO algorithm for solving constraint satisfaction problems. 1
Local Search Methods
, 2006
"... Local search is one of the fundamental paradigms for solving computationally hard combinatorial problems, including the constraint satisfaction problem (CSP). It provides the basis for some of the most successful and versatile methods for solving the large and difficult problem instances encountered ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
Local search is one of the fundamental paradigms for solving computationally hard combinatorial problems, including the constraint satisfaction problem (CSP). It provides the basis for some of the most successful and versatile methods for solving the large and difficult problem instances encountered in many reallife applications. Despite impressive advances in systematic, complete search algorithms, local search methods in many cases represent the only feasible way for solving these large and complex instances. Local search algorithms are also naturally suited for dealing with the optimisation criteria arising in many practical applications. The basic idea underlying local search is to start with a randomly or heuristically generated candidate solution of a given problem instance, which may be infeasible, suboptimal or incomplete, and to iteratively improve this candidate solution by means of typically minor modifications. Different local search methods vary in the way in which improvements are achieved, and in particular, in the way in which situations are handled in which no direct improvement is possible. Most local search methods use randomisation to ensure that the search process does not
A Genetic Algorithm for Searching Spatial Configurations
 IEEE Transactions on Evolutionary Computation
"... Searching spatial configurations is a particular case of maximal constraint satisfaction problems, where constraints expressed by spatial and nonspatial properties guide the search process. In the spatial domain, binary spatial relations are typically used for specifying constraints while searchin ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
Searching spatial configurations is a particular case of maximal constraint satisfaction problems, where constraints expressed by spatial and nonspatial properties guide the search process. In the spatial domain, binary spatial relations are typically used for specifying constraints while searching spatial configurations. Searching configurations is particularly intractable when configurations are derived from a combination of objects, which involves a hard combinatorial problem. This paper presents a genetic algorithm that combines a direct and an indirect approach to treating binary constraints in genetic operators. A new genetic operator combines randomness and heuristics for guiding the reproduction of new individuals in a population. Individuals are composed of spatial objects whose relationships are indexed by a content measure. This paper describes the genetic algorithm and presents experimental results that compare the genetic versus a deterministic and a localsearch algorithm. These experiments show the convenience of using a genetic algorithm when the complexity of the queries and databases do no guarantee the tractability of a deterministic strategy. Index Terms: Evolutionary computation, genetic algorithm, geographic information systems, constraint satisfaction problems, information retrieval.
Integration of ACO in a Constraint Programming Language
"... Abstract. We propose to integrate ACO in a Constraint Programming (CP) language. Basically, we use the CP language to describe the problem to solve by means of constraints and we use the CP propagation engine to reduce the search space and check constraint satisfaction; however, the classical backtr ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
Abstract. We propose to integrate ACO in a Constraint Programming (CP) language. Basically, we use the CP language to describe the problem to solve by means of constraints and we use the CP propagation engine to reduce the search space and check constraint satisfaction; however, the classical backtrack search of CP is replaced by an ACO search. We report first experimental results on the car sequencing problem and compare different pheromone strategies for this problem. 1
A study of aco capabilities for solving the maximum clique problem
 Journal of Heuristics
, 2004
"... This paper investigates the capabilities of the Ant Colony Optimization (ACO) metaheuristic for solving the maximum clique problem, the goal of which is to find a largest set of pairwise adjacent vertices in a graph. We propose two ACO algorithms for this problem. Basically, these algorithms succes ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
This paper investigates the capabilities of the Ant Colony Optimization (ACO) metaheuristic for solving the maximum clique problem, the goal of which is to find a largest set of pairwise adjacent vertices in a graph. We propose two ACO algorithms for this problem. Basically, these algorithms successively generate maximal cliques through the repeated addition of vertices into partial cliques, and both of them use “pheromone trails ” as a greedy heuristic to choose, at each step, the next vertex to enter the clique. However, these two algorithms differ in the way pheromone trails are laid and exploited, i.e., on edges or on vertices of the graph. We illustrate and compare the behavior of the two proposed ACO algorithms on a representative benchmark instance and we study the impact of pheromone on the solution process. We consider two measures —the resampling and the dispersion ratio — for providing an insight into the two algorithms performances. We also study the benefit of integrating a local search procedure within the proposed ACO algorithms, and we show that this improves the solution process. Finally, we compare ACO performances with three representative heuristic approaches, showing that it obtains competitive results.
The DynCOAA Algorithm for Dynamic Constraint Optimization Problems
 WIRTSCHAFTSINFORMATIK
, 2006
"... research, manufacturing control and others can be transformed in constraint optimization problems (COPs). Moreover, most practical problems change constantly, requiring algorithms that can handle dynamic problems. When these problems are situated in a distributed setting, distributed algorithms are ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
research, manufacturing control and others can be transformed in constraint optimization problems (COPs). Moreover, most practical problems change constantly, requiring algorithms that can handle dynamic problems. When these problems are situated in a distributed setting, distributed algorithms are preferred or even necessary.