Titlebook: Ant Colony Optimization and Swarm Intelligence; 5th International Wo Marco Dorigo,Luca Maria Gambardella,Thomas Stützle Conference proceedi

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Incremental Local Search in Ant Colony Optimization: Why It Fails for the Quadratic Assignment Problt problem. Notwithstanding the promising results of incremental local search reported in the literature in a different context, the computational results of our new ACO algorithm are rather negative. We provide an empirical analysis that explains this failure.

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Eine blühende Psychiatrie in Gefahrrd deviations) found in the current PSO literature is that it is possible to evaluate the performance of an algorithm on different application scenarios at the same time. The RLDs reported in this paper show some of the strengths and weaknesses of the studied algorithms and suggest ways of improving their performance.

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https://doi.org/10.1007/978-3-8348-8180-9d an enhanced APS (eAPS), which uses a colony model with .. It allows a stronger exploitation of better solutions found and at the same time it can prevent premature stagnation of the search. Experimental results showed eAPS has higher performance than APS. It has also shown that the parameter settings for eAPS are more robust than for APS.