面向多峰优化问题的自主学习萤火虫算法
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1.南昌工程学院;2.华中科技大学

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TP18

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Firefly Algorithm Based on Self-learning for Multi-peak Optimization Problem
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Nanchang Institute of Technology

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    摘要:

    萤火虫算法在处理多峰优化问题时,易陷入局部最优。针对该问题,本文提出了一种自主学习萤火虫算法。算法将粒子按适应度划为自主学习粒子和普通粒子,自主学习粒子从种群中随机选择一个粒子并随机选择一个维度使用三种学习策略产生三个候选解,并在自身以及候选解中选择最好的解;普通粒子同时选择两个优于自身的粒子进行学习。自主学习粒子能够维持算法对多个极值空间的探索并提高算法优化精度;普通粒子以两个粒子的混合信息为指引,使算法跳出局部最优。此外,使用淘汰机制,让算法舍弃对劣质极值空间的维护,进而提高对优质极值空间的开发。实验结果表明,本文算法在处理多峰优化问题时具有高效的性能。

    Abstract:

    The firefly algorithm is prone to fall into local optimality when dealing with multi-peak optimization problems. To solve this problem, this paper proposes a firefly algorithm based on self-learning. According to the fitness, the particle is divided into self-learning particle and ordinary particle. The self-learning particles randomly selects a particle from the population and randomly selects a dimension to generate three candidate solutions by using three learning strategies, and the best solution among itself and the candidate solutions is obtained; Ordinary particles simultaneously choose two particles superior to themselves to learn. Self-learning particles can not only maintain the algorithm's exploration of multiple extremum Spaces, but also improve the algorithm's optimization accuracy. Guided by the mixed information of two particles, ordinary particles makes the algorithm jump out of the local optimal. In addition, the elimination mechanism is used to make the algorithm abandon the maintenance of low-quality extremum space, so as to improve the development of high-quality extremum space. Experimental results show that the proposed algorithm has high performance in multi-peak optimization problems.

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历史
  • 收稿日期:2020-12-25
  • 最后修改日期:2021-04-12
  • 录用日期:2021-04-21
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