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Titlebook: Advances in Swarm Intelligence; Third International Ying Tan,Yuhui Shi,Zhen Ji Conference proceedings 2012 Springer-Verlag Berlin Heidelbe

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51#
發(fā)表于 2025-3-30 10:25:16 | 只看該作者
52#
發(fā)表于 2025-3-30 15:29:06 | 只看該作者
53#
發(fā)表于 2025-3-30 19:23:00 | 只看該作者
54#
發(fā)表于 2025-3-30 23:04:17 | 只看該作者
Swarm Intelligence Supported e-Remanufacturingtion in the behavior of swarms of insects or other animals. After applied in other fields with success, SI started to gather the interest of researchers working in the field of remanufacturing. In this paper we provide a survey of SI methods that have been used in e-remanufacturing.
55#
發(fā)表于 2025-3-31 02:15:13 | 只看該作者
Quantum-Behaved Particle Swarm Optimization Algorithm Based on Border Mutation and Chaos for Vehiclees to enhance the optimization ability of the algorithm, avoid getting into local optimum and premature convergence. To thosecross-border particles,mutation strategy is used to increase the variety of swarm and strengthen the global search capability. This algorithm is applied to vehicle routing problem to achieve good results.
56#
發(fā)表于 2025-3-31 05:57:09 | 只看該作者
Training ANFIS Parameters with a Quantum-behaved Particle Swarm Optimization Algorithm) for training the parameters of an ANFIS. The ANFIS trained by the proposed method is applied to nonlinear system modeling and chaotic prediction. The simulation results show that the ANFIS-QPSO method performs much better than the original ANFIS and the ANFIS-PSO method.
57#
發(fā)表于 2025-3-31 11:14:50 | 只看該作者
58#
發(fā)表于 2025-3-31 13:43:16 | 只看該作者
A SI-Based Algorithm for Structural Damage Detectionl of 3-storey steel frame structure made in laboratory. Some illustrated results show that the proposed method is very suitable for the structural multi-damage identification, which also show that the SI-based algorithm for structural damage detection can provide an effective and robust tool in the SHM field.
59#
發(fā)表于 2025-3-31 17:37:59 | 只看該作者
60#
發(fā)表于 2025-4-1 00:11:41 | 只看該作者
Grey-Based Particle Swarm Optimization Algorithm may differ for different particles. The proposed PSO algorithm is applied to solve the optimization problems of twelve test functions for illustration. Simulation results are compared with the other three variants of PSO to demonstrate the search performance of the proposed algorithm.
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