多智能体系统方法在萤火虫算法中的应用

Arian Yousefiankalareh, Taraneh Kamyab, Ali Mojarrad Ghahfarokhi, Fatemehalsadat Beheshtinejad, Hossein Mirzanejad, Shahaboddin Seddighi
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引用次数: 1

摘要

总的来说,利用集体智能是近年来研究人员感兴趣的话题之一,其目的是对生物的简单行为及其与环境和邻近生物的相互作用进行建模,以获得更复杂的行为。我们可以利用基于集体智慧的算法来解决复杂的问题,比如最优化问题。到目前为止,已经有各种各样的算法用于这一领域,萤火虫算法是其中的一种变体。在该算法中,每个成员都作为与自己有关的更好的响应。但该算法存在参数值不一致、局部搜索与全局搜索不平衡等缺点。一方面,多代理系统是包含代理集的软件系统。这些代理一起执行它们的任务来解决问题并达到预期的目的。在本文中,我们尝试利用多智能体系统,除了元启发式优化算法之外,提高萤火虫算法的性能,以更好地相互合作。实验结果表明,该算法具有良好的性能。
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Utilizing Multi-Agent Systems Approach in Firefly Algorithm
Generally, using collective intelligence is one of the interesting topics is researchers of recent years, which its purpose is modeling creatures’ simple behaviors and their interaction with the environment and neighbor creatures to obtain more complex behaviors. We could utilize algorithms based on collective intelligence to solve complicated problems like optimization problems. So far, various algorithms have been purposed in this field which firefly algorithm is a variant of these. In this algorithm, each member acts as a better response concerning itself. However, this algorithm has some drawbacks like the consistency of parameters value, lack of balance between local search and global search, and others. On one hand, multi-agent systems are software systems that contain sets of agents. These agents perform their tasks together to solve a problem and reach the desired purpose. In this paper, we have tried to utilize a multi-agent system, in addition to meta-heuristic optimization algorithms, to improve the performance of the firefly algorithm to better cooperate warms populations with each other. The results of the experiment show the acceptable performance of the proposed algorithm.
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