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Titlebook: Advances in Swarm Intelligence; 14th International C Ying Tan,Yuhui Shi,Wenjian Luo Conference proceedings 2023 The Editor(s) (if applicabl

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發(fā)表于 2025-3-30 10:37:09 | 只看該作者
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發(fā)表于 2025-3-30 14:23:19 | 只看該作者
https://doi.org/10.1007/978-981-19-4411-6ts are introduced to update the positions of particles in BPPSO, achieving complementary advantages of BFO and PPSO. Finally, BPPSO is validated using eight benchmark functions, demonstrating its fast convergence speed, high computational accuracy, and good stability, making it a powerful global optimization algorithm.
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發(fā)表于 2025-3-30 16:41:19 | 只看該作者
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發(fā)表于 2025-3-30 22:07:30 | 只看該作者
Swarm Intelligence Algorithms and Applications: An Experimental Surveyhavior of decentralized, self-organized systems composed of many simple agents that interact with each other and their environment. As the Swarm Intelligence (SI) field expands, more SI meta-heuristics are incorporated into developing new Artificial Intelligence (AI) tools. This study presents a com
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發(fā)表于 2025-3-31 01:23:13 | 只看該作者
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發(fā)表于 2025-3-31 11:13:16 | 只看該作者
Multi-Modal Honey Bee Foraging Optimizer for?Weapon Target Assignment Problem Defence or Military Operations research and the threat is continually increasing as technological advances increase and decision-making is becoming extremely intricate. As the problem falls into the category of NP-complete, heuristic algorithms compete over traditional methods and give near optimal
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