Topic
Extremal optimization
About: Extremal optimization is a research topic. Over the lifetime, 1168 publications have been published within this topic receiving 104943 citations.
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TL;DR: 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.
41 citations
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07 Jul 2010
TL;DR: The experimental results show that the solution quality obtained by CGS-TSP is comparable with or better than that obtained by Ant Colony System and MAX-MIN Ant System.
Abstract: In this paper, we present Consultant-Guided Search (CGS), a new metaheuristic for combinatorial optimization problems, based on the direct exchange of information between individuals in a population. CGS is a swarm intelligence technique inspired by the way real people make decisions based on advice received from consultants. We exemplify the application of this metaheuristic to a specific class of problems by introducing the CGS-TSP algorithm, an instantiation of CGS for the Traveling Salesman Problem. To determine if our direct communication approach can compete with stigmergy-based methods, we compare the performance of CGS-TSP with that of Ant Colony Optimization algorithms. Our experimental results show that the solution quality obtained by CGS-TSP is comparable with or better than that obtained by Ant Colony System and MAX-MIN Ant System.
41 citations
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TL;DR: A procedure is presented which considerably improves the performance of local search based heuristic algorithms for combinatorial optimization problems by merging pairs of solutions: certain parts of either solution are transcribed by the related parts of the respective other solution.
Abstract: A procedure is presented which considerably improves the performance of local search based heuristic algorithms for combinatorial optimization problems. It increases the average `gain' of the individual local searches by merging pairs of solutions: certain parts of either solution are transcribed by the related parts of the respective other solution, corresponding to flipping clusters of a spin glass. This iterative partial transcription acts as a local search in the subspace spanned by the differing components of both solutions. Embedding it in the simple multi-start-local-search algorithm and in the thermal-cycling method, we demonstrate its effectiveness for several instances of the traveling salesman problem. The obtained results indicate that, for this task, such approaches are far superior to simulated annealing.
41 citations
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TL;DR: A mathematical formulation and a hybrid heuristic algorithm by combining ant colony optimization algorithm and dynamic programming technique to obtain high quality solutions for the covering salesman problem is proposed.
40 citations