C. Solnon. Ants can solve constraint satisfaction problems. IEEE Transactions on Evolutionary Computation, 6(4):347--357, August 2002. Home/Search Document Details and Download
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An Improved Ant Colony Optimisation Algorithm for the 2D HP.. - Shmygelska, Hoos (Correct)
.... points for the optimisation via local search) In general, we expect that the improvements introduced in this work for an ACO algorithm for the 2D HP Protein Folding Problem can be utilised for solving more traditional artificial intelligence problems (such as constraint satisfaction problems [17]) For example, the use of improving ants provides a general means of intensifying the search around high quality solutions, while long range moves in local search can be useful for escaping from local optima by considering higher order neighbourhoods relevant to the particular problem. There are ....
Solnon, C. Ants Can Solve Constraint Satisfaction Problems. IEEE Transactions on Evolut. Comput., 6 (4): 347-357, August 2002.
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Solnon, C.: Ants can solve constraint satisfaction problems. IEEE Transactions on Evolutionary Computation 6 (2002) 347--357
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C. Solnon. Ants can solve constraint satisfaction problems. IEEE Transactions on Evolutionary Computation, 6(4):347--357, 2002.
Boosting ACO with a Preprocessing Step - Solnon (2002) Self-citation (Solnon) (Correct)
....Ak ApplyLocalSearch(Ak ) UpdatePheromoneTrails( fA1 ; AnbAnts g) until cost(A i ) 0 for some i 2 f1: nbAntsg or max cycles reached We now describe the pheromone graph on which arti cial ants lay pheromone trails, and the di erent used functions. More details can be found in [8]. The pheromone graph associates a vertex with each variable value pair X i ; v such that X i 2 X and v 2 D(X i ) There is an edge between any pair of vertices corresponding to two di erent variables. The amount of pheromone laying on an edge ( X i ; v ; X j ; w ) is noted ( X i ; v ; X j ; ....
C. Solnon. Ants can solve Constraint Satisfaction Problems. Research report, 2001.
Searching for Maximum Cliques with Ant Colony Optimization - Fenet, Solnon Self-citation (Solnon) (Correct)
....will rst explore the integration within Ant Clique of some local search technics such as the one used by RLS. Actually, the best performing ACO algorithms for many combinatorial problems are hybrid algorithms that combine probabilistic solution construction by a colony of ants with local search [5, 9, 7]. Also, Ant Clique performances may be enhanced by introducing a local heuristic to guide ants. Indeed, as pointed out in Section 2, we already have experimented a rst heuristic, borrowed from [1] that has not given any improvement. However, we shall further investigate other heuristics for this ....
C. Solnon. Ants can solve constraint satisfaction problems. IEEE Transactions on Evolutionary Computation, 6(4):347-357, 2002.
Boosting ACO with a Preprocessing Step - Solnon (2002) Self-citation (Solnon) (Correct)
....techniques. Finally, pheromone trails are updated with respect to the di erent 4 local minima computed during the current cycle. We now brie y describe the pheromone graph on which arti cial ants lay pheromone trails, and the di erent functions used in Ant Solver. More details can be found in [Sol01]. The pheromone graph associates a vertex with each variable value pair X i ; v such that X i 2 X and v 2 D(X i ) There is an edge between any pair of vertices corresponding to two di erent variables. The amount of pheromone laying on an edge ( X i ; v ; X j ; w ) is noted ( X i ; v ; X j ; ....
C. Solnon. Ants can solve constraint satisfaction problems. Technical report, LISI, 2001.
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C. Solnon. Ants can solve constraint satisfaction problems. IEEE Transactions on Evolutionary Computation, 6(4):347--357, August 2002.
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C. Solnon. Ants can solve Constraint Satisfaction Problems. SubmittedtoIEEETransactionson Evolutionary Computation, feb 2001.
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