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Titlebook: Advances in Swarm Intelligence; 15th International C Ying Tan,Yuhui Shi Conference proceedings 2024 The Editor(s) (if applicable) and The A

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21#
發(fā)表于 2025-3-25 06:00:20 | 只看該作者
22#
發(fā)表于 2025-3-25 08:23:16 | 只看該作者
Gründungsintention von Akademikernoptima. 12 CEC2005 benchmark functions are selected for testing the performance of CSBOA, and the results of the simulation demonstrate that the CSBOA algorithm effectively accelerates the convergence speed, improves the convergence accuracy, and reduces the likelihood of falling into localized states.
23#
發(fā)表于 2025-3-25 12:14:12 | 只看該作者
Implikationen und Limitationen, The results of experimental comparative analysis on ten benchmark test functions demonstrate that the improved Kepler optimization algorithm based on a mixed strategy exhibits notable improvements in both convergence speed and solution accuracy.
24#
發(fā)表于 2025-3-25 18:25:21 | 只看該作者
25#
發(fā)表于 2025-3-25 22:23:07 | 只看該作者
A Tri-Swarm Particle Swarm Optimization Considering the Cooperation and the Fitness Valuest fitness value were respectively divided into ERS, EIS and CS. The results on seven unimodal benchmark functions demonstrated the superiority of the proposed variant compared with other five variants.
26#
發(fā)表于 2025-3-26 01:20:58 | 只看該作者
Circle Chaotic Search-Based Butterfly Optimization Algorithmoptima. 12 CEC2005 benchmark functions are selected for testing the performance of CSBOA, and the results of the simulation demonstrate that the CSBOA algorithm effectively accelerates the convergence speed, improves the convergence accuracy, and reduces the likelihood of falling into localized states.
27#
發(fā)表于 2025-3-26 05:48:02 | 只看該作者
Improved Kepler Optimization Algorithm Based on?Mixed Strategy The results of experimental comparative analysis on ten benchmark test functions demonstrate that the improved Kepler optimization algorithm based on a mixed strategy exhibits notable improvements in both convergence speed and solution accuracy.
28#
發(fā)表于 2025-3-26 08:36:38 | 只看該作者
29#
發(fā)表于 2025-3-26 14:42:36 | 只看該作者
30#
發(fā)表于 2025-3-26 20:14:32 | 只看該作者
Cooperative Search and Rescue Target Assignment Based on Improved Ant Colony Algorithmand makes full use of the global search ability of ant colony algorithm to explore the optimal solution. The simulation results show that this method can quickly and effectively provide the target assignment scheme of search and rescue resources, maximize the survival probability, and improve the efficiency of search and rescue at sea.
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