计算机工程与应用 ›› 2016, Vol. 52 ›› Issue (21): 162-167.

• 模式识别与人工智能 • 上一篇    下一篇

基于萤火虫算法的自适应花授粉优化算法

卞京红1,贺兴时1,杨新社2   

  1. 1.西安工程大学 理学院,西安 710048
    2.密德萨斯大学 科学技术学院,英国 伦敦 NW4 4BT
  • 出版日期:2016-11-01 发布日期:2016-11-17

Hybrid algorithm of firefly algorithm and self-adaptive flower pollination algorithm

BIAN Jinghong1, HE Xingshi1, YANG Xinshe2   

  1. 1.College of Science, Xi’an Polytechnic University, Xi’an 710048, China
    2.School of Science and Technology, Middlesex University, London NW4 4BT, UK
  • Online:2016-11-01 Published:2016-11-17

摘要: 花授粉算法是一种新的启发式算法,由于存在易陷入局部最优且演化后期收敛速度慢等缺陷,导致算法的寻优能力受到限制。针对该算法存在的不足,在局部授粉过程中引入自适应的变异因子,并对花授粉算法中的转换概率进行自适应调整后,将其与萤火虫算法相结合,提出了一种基于萤火虫算法的改进花授粉算法;最后,通过经典的标准测试函数对新提出的算法与DE-FPA、PSO-FPA做比较实验。实验结果表明,改进后的算法比基本花授粉算法具有更高的收敛精度和稳定性。

关键词: 花授粉算法, 萤火虫算法, 最优解, 转换概率, 变异因子

Abstract: Flower pollination algorithm is a new metaheuristic algorithm for optimization, however, it can have some stagnation and thus a lower convergence rate under certain conditions, which can limit the search ability of the algorithm. For this reason and to improve the search efficiency, this paper proposes a self-adaptive mutation operator in the process of local pollination, self-adaptive adjustment of the switch probability, and hybridization of the flower pollination algorithm with the firefly algorithm, which leads to a new hybrid approach called self-adaptive flower pollination algorithm enhanced by the firefly algorithm. The proposed approach has been validated by benchmark functions and has been compared with other algorithms such as DE-FPA and PSO-FPA. Results indicate that the proposed hybrid algorithm has a higher rate of convergence and stability than other algorithms.

Key words: Flower Pollination Algorithm(FPA), Firefly Algorithm(FA), optimal solution, switch probability; , mutation operator