计算机工程

• 人工智能及识别技术 • 上一篇    下一篇

基于反向学习与动态记忆反馈的烟花算法

李席广,韩守飞,刘晓静,拱长青   

  1. (沈阳航空航天大学 计算机学院,沈阳 110136)
  • 收稿日期:2016-11-09 出版日期:2017-12-15 发布日期:2017-12-15
  • 作者简介:李席广(1979—),男,讲师、硕士,主研方向为智能优化算法、网络安全、云安全;韩守飞、刘晓静,硕士研究生;拱长青,教授、博士。
  • 基金项目:
    辽宁省教育厅科学基金(L2013064);中航工业技术创新基金(2013S60109R)。

Fireworks Algorithm Based on Reverse Learning and Dynamic Memory Feedback

LI Xiguang,HAN Shoufei,LIU Xiaojing,GONG Changqing   

  1. (College of Computer Science,Shenyang Aerospace University,Shenyang 110136,China)
  • Received:2016-11-09 Online:2017-12-15 Published:2017-12-15

摘要: 针对烟花算法收敛速度慢和求解精度不高的问题,通过引入反向学习策略和动态记忆反馈的机制,提出一种优化算法。采用反向学习策略生成初始种群以保证群体的多样性,在原算法的结构中增加反馈层用于记忆上一代最优烟花的位置信息,并从反馈层记忆的信息中提取烟花位置信息变化趋势特征,从而动态更新下一次迭代的烟花种群。在10个典型基准测试函数中的仿真结果表明,与烟花算法、标准粒子群优化算法和增强烟花算法相比,该算法在收敛速度、计算精度以及稳定性方面性能更优。

关键词: 烟花算法, 反馈层, 变化趋势, 反向学习, 基准函数

Abstract: Aiming at the slow convergence speed and solution accuracy of Fireworks Algorithm(FWA),by introducing putting reverse learning and dynamic memory feedback mechanism,this paper proposes an optimized algorithm.The proposed algorithm uses reverses learning strategy to generate initial population,so as to ensure the diversity of population.Then it adds feedback layer into the structure of FWA to memory location information for last iteration optimal fireworks and extracts the change of characteristics about position information from the feedback layer in the memory,so as to dynamic update the next iteration of fireworks populations.Simulation is carried out with 10 typical benchmark functions,and the results show that,compared with FWA,Standard Particle Swarm Optimization(SPSO) algorithm and Enhanced FWA(EFWA),the proposed algorithm has better performance in terms of convergence speed,accuracy and stability.

Key words: Fireworks Algorithm(FWA), feedback layer, change trend, reverse learning, benchmark function

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