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Titlebook: Computational Intelligence and Bioinformatics; International Confer De-Shuang Huang,Kang Li,George William Irwin Conference proceedings 200

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樓主: tornado
21#
發(fā)表于 2025-3-25 03:21:39 | 只看該作者
Ant Colony System for Optimizing Vehicle Routing Problem with Time Windows (VRPTW)nt colonies to successively achieve a multiple objective minimization. Experiments on a series of benchmark problems demonstrate the excellent performance of ACS when compared with other optimization methods.
22#
發(fā)表于 2025-3-25 08:41:41 | 只看該作者
A New Hybrid Algorithm of Particle Swarm Optimizationces the ability of getting rid of local optimum and improves the speed and precision of convergence. The testing results of several benchmark functions with different dimensions show that the proposed algorithm is superior to standard PSO and the other PSO algorithms.
23#
發(fā)表于 2025-3-25 11:43:03 | 只看該作者
24#
發(fā)表于 2025-3-25 17:11:03 | 只看該作者
25#
發(fā)表于 2025-3-25 21:10:00 | 只看該作者
26#
發(fā)表于 2025-3-26 01:36:03 | 只看該作者
An Improved Particle Swarm Optimization Algorithm with Disturbance Terming structure effectively mends the defects. The convergence of the improved algorithm is analyzed. Simulation results demonstrated that the improved algorithm have a better performance than the standard one.
27#
發(fā)表于 2025-3-26 05:32:12 | 只看該作者
28#
發(fā)表于 2025-3-26 09:37:51 | 只看該作者
Improving Quantum-Behaved Particle Swarm Optimization by Simulated Annealingloys both the ability to jump out of the local minima in Simulated Annealing and the capacity of searching the global optimum in QPSO algorithm. The experimental results show that the proposed hybrid algorithm increases the diversity of the population in the search process and improves its precision in the latter period of the search.
29#
發(fā)表于 2025-3-26 14:04:18 | 只看該作者
Predicted-Velocity Particle Swarm Optimization Using Game-Theoretic Approachme-theoretic approach for designing particle swarm optimization with a mixed strategy. The approach is applied to design a mixed strategy using velocity and position vectors. The experimental results show the mixed strategy can obtain the better performance than the best of pure strategy.
30#
發(fā)表于 2025-3-26 19:37:30 | 只看該作者
Collective Behavior of an Anisotropic Swarm Model Based on Unbounded Repulsion in Social Potential Fits anisotropy coefficient, and the collective behavior of mass individuals emerges from combination of the inter-individual interactions and the interaction of the individual with outer circumstances.
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