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111
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
 ACM COMPUTING SURVEYS
, 2003
"... The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important meta ..."
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Cited by 315 (17 self)
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The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behaviour of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization.
A Racing Algorithm for Configuring Metaheuristics
, 2002
"... This paper describes a racing procedure for finding, in a limited amount of time, a configuration of a metaheuristic that performs as good as possible on a given instance class of a combinatorial optimization problem. Taking inspiration from methods proposed in the machine learning literature ..."
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Cited by 164 (34 self)
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This paper describes a racing procedure for finding, in a limited amount of time, a configuration of a metaheuristic that performs as good as possible on a given instance class of a combinatorial optimization problem. Taking inspiration from methods proposed in the machine learning literature for model selection through crossvalidation, we propose a procedure that empirically evaluates a set of candidate configurations by discarding bad ones as soon as statistically sufficient evidence is gathered against them. We empirically evaluate our procedure using as an example the configuration of an ant colony optimization algorithm applied to the traveling salesman problem.
The hypercube framework for ant colony optimization
, 2004
"... Ant colony optimization is a metaheuristic approach belonging to the class of modelbased search algorithms. In this paper, we propose a new framework for implementing ant colony optimization algorithms called the hypercube framework for ant colony optimization. In contrast to the usual way of impl ..."
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Cited by 71 (22 self)
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Ant colony optimization is a metaheuristic approach belonging to the class of modelbased search algorithms. In this paper, we propose a new framework for implementing ant colony optimization algorithms called the hypercube framework for ant colony optimization. In contrast to the usual way of implementing ant colony optimization algorithms, this framework limits the pheromone values to the interval [0,1]. This is obtained by introducing changes in the pheromone value update rule. These changes can in general be applied to any pheromone value update rule used in ant colony optimization. We discuss the benefits coming with this new framework. The benefits are twofold. On the theoretical side, the new framework allows us to prove that in Ant System, the ancestor of all ant colony optimization algorithms, the average quality of the solutions produced increases in expectation over time when applied to unconstrained problems. On the practical side, the new framework automatically handles the scaling of the objective function values. We experimentally show that this leads on average to a more robust behavior of ant colony optimization algorithms.
Modelbased search for combinatorial optimization
, 2001
"... Abstract In this paper we introduce modelbased search as a unifying framework accommodating some recently proposed heuristics for combinatorial optimization such as ant colony optimization, stochastic gradient ascent, crossentropy and estimation of distribution methods. We discuss similarities as ..."
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Cited by 64 (12 self)
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Abstract In this paper we introduce modelbased search as a unifying framework accommodating some recently proposed heuristics for combinatorial optimization such as ant colony optimization, stochastic gradient ascent, crossentropy and estimation of distribution methods. We discuss similarities as well as distinctive features of each method, propose some extensions and present a comparative experimental study of these algorithms. 1
A Taxonomy and an Empirical Analysis of Multiple Objective Ant Colony Optimization Algorithms for the Bicriteria TSP
, 2004
"... The difficulty to solve multiple objective combinatorial optimization problems with traditional techniques has urged researchers to look for alternative, better performing approaches for them. Recently, several algorithms have been proposed which are based on the Ant Colony Optimization metaheuristi ..."
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Cited by 36 (0 self)
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The difficulty to solve multiple objective combinatorial optimization problems with traditional techniques has urged researchers to look for alternative, better performing approaches for them. Recently, several algorithms have been proposed which are based on the Ant Colony Optimization metaheuristic. In this contribution, the existing algorithms of this kind are reviewed and a proposal of a taxonomy for them is presented. In addition, a complete empirical analysis is developed by analyzing their performances on several instances of the bicriteria traveling salesman problem in comparison with two wellknown multiobjective genetic algorithms.
A short convergence proof for a class of Ant Colony Optimization algorithms
 IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
, 2002
"... In this paper, we prove some convergence properties for a class of ant colony optimization algorithms. In particular, we prove that for any small constant 0 and for a sufficiently large number of algorithm iterations, the probability of finding an optimal solution at least once is ( ) 1 and that th ..."
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Cited by 33 (1 self)
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In this paper, we prove some convergence properties for a class of ant colony optimization algorithms. In particular, we prove that for any small constant 0 and for a sufficiently large number of algorithm iterations, the probability of finding an optimal solution at least once is ( ) 1 and that this probability tends to 1 for. We also prove that, after an optimal solution has been found, it takes a finite number of iterations for the pheromone trails associated to the found optimal solution to grow higher than any other pheromone trail and that, for, any fixed ant will produce the optimal solution during the th iteration with probability 1 ^ ( min max), where min and max are the minimum and maximum values that can be taken by pheromone trails.
A Review on the Ant Colony Optimization Metaheuristic: Basis, Models and New Trends
 Mathware & Soft Computing
, 2002
"... Ant Colony Optimization (ACO) is a recent metaheuristic method that is inspired by the behavior of real ant colonies. In this paper, we review the underlying ideas of this approach that lead from the biological inspiration to the ACO metaheuristic, which gives a set of rules of how to apply ACO ..."
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Cited by 30 (2 self)
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Ant Colony Optimization (ACO) is a recent metaheuristic method that is inspired by the behavior of real ant colonies. In this paper, we review the underlying ideas of this approach that lead from the biological inspiration to the ACO metaheuristic, which gives a set of rules of how to apply ACO algorithms to challenging combinatorial problems. We present some of the algorithms that were developed under this framework, give an overview of current applications, and analyze the relationship between ACO and some of the best known metaheuristics. In addition, we describe recent theoretical developments in the eld and we conclude by showing several new trends and new research directions in this eld.
Ant Colony Optimisation and Local Search for Bin Packing and Cutting Stock Problems
 Journal of the Operational Research Society. (forthcoming
, 2003
"... The Bin Packing Problem and the Cutting Stock Problem are two related classes of NPhard combinatorial optimisation problems. Exact solution methods can only be used for very small instances, so for realworld problems we have to rely on heuristic methods. In recent years, researchers have started t ..."
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Cited by 16 (1 self)
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The Bin Packing Problem and the Cutting Stock Problem are two related classes of NPhard combinatorial optimisation problems. Exact solution methods can only be used for very small instances, so for realworld problems we have to rely on heuristic methods. In recent years, researchers have started to apply evolutionary approaches to these problems, including Genetic Algorithms and Evolutionary Programming. In the work presented here, we used an ant colony optimisation (ACO) approach to solve both Bin Packing and Cutting Stock Problems. We present a pure ACO approach, as well as an ACO approach augmented with a simple but very effective local search algorithm. It is shown that the pure ACO approach can outperform some existing solution methods, whereas the hybrid approach can compete with the best known solution methods. The local search algorithm is also run with random restarts and shown to perform significantly worse than when combined with ACO.
Ant Colony Optimization
 OPTIMIZATION TECHNIQUES IN ENGINEERING. SPRINGERVERLAG
, 2004
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Static Multiprocessor Scheduling with Ant Colony Optimisation & Local Search
 GENOME RESEARCH
, 2003
"... Efficient multiprocessor scheduling is essentially the problem of allocating a set of computational jobs to a set of processors to minimise the overall execution time. There are many variations of this problem, most of which are NPhard, so we must rely on heuristics to solve real world problem ins ..."
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Cited by 13 (0 self)
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Efficient multiprocessor scheduling is essentially the problem of allocating a set of computational jobs to a set of processors to minimise the overall execution time. There are many variations of this problem, most of which are NPhard, so we must rely on heuristics to solve real world problem instances. This dissertation describes several novel approaches using the ant colony optimisation (ACO) metaheuristic and local search techniques, including tabu search, to two important versions of the problem: the static scheduling of independent jobs onto homogeneous and heterogeneous processors.