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Titlebook: Swarm Intelligence; 8th International Co Marco Dorigo,Mauro Birattari,Thomas Stützle Conference proceedings 2012 Springer-Verlag Berlin Hei

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11#
發(fā)表于 2025-3-23 10:57:18 | 只看該作者
Task Partitioning via Ant Colony Optimization for Distributed Assemblyteam of ants dividing a set of tasks, with pheromone marking connections between tasks guiding decisions on task allocation. We present simulation results for various 2-D and 3-D structures and discuss the advantages of the ACO formulation in the context of other existing approaches.
12#
發(fā)表于 2025-3-23 15:23:09 | 只看該作者
The Self-adaptive Comprehensive Learning Particle Swarm Optimizerhe search commences. Application of the Self-Adaptive Comprehensive Learning Particle Swarm Optimizer (SACLPSO) to 9 well known test functions show an improvement in performance on most of the functions compared to CLPSO and a tuned PSO.
13#
發(fā)表于 2025-3-23 18:08:24 | 只看該作者
14#
發(fā)表于 2025-3-23 22:54:32 | 只看該作者
A “Thermodynamic” Approach to Multi-robot Cooperative Localization with Noisy Sensorsnts and by presenting a novel analysis based on the .assumption. The results of this paper are more precise than what was previously reported. Nevertheless, when considering the limit of a large group of robots, and after a long “stabilization” time, the final results turn out to be identical.
15#
發(fā)表于 2025-3-24 05:56:48 | 只看該作者
16#
發(fā)表于 2025-3-24 08:19:06 | 只看該作者
17#
發(fā)表于 2025-3-24 12:33:00 | 只看該作者
18#
發(fā)表于 2025-3-24 15:27:23 | 只看該作者
19#
發(fā)表于 2025-3-24 19:03:32 | 只看該作者
20#
發(fā)表于 2025-3-25 01:21:55 | 只看該作者
AcoSeeD: An Ant Colony Optimization for Finding Optimal Spaced Seeds in Biological Sequence Searchte. Experimental results demonstrate that AcoSeeD brings a significant improvement of sensitivity while demanding the same computational time as other state-of-the-art methods. We also introduces an alternative way of using local search that exerts a fast approximation of the objective function in ACO.
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