面向多峰优化问题的自主学习萤火虫算法  

Firefly algorithm based on self-learning for multi-peak optimization problem

作　　者：赵嘉[1,2] 陈文平 肖人彬[3] 王晖[1,2] ZHAO Jia;CHEN Wen-ping;XIAO Ren-bin;WANG Hui(School of Information Engineering,Nanchang Institute of Technology,Nanchang 330099,China;Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing,Nanchang Institute of Technology,Nanchang 330099,China;School of Artificial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan 430074,China)

机构地区：[1]南昌工程学院信息工程学院,南昌330099 [2]南昌工程学院江西省水信息协同感知与智能处理重点实验室,南昌330099 [3]华中科技大学人工智能与自动化学院,武汉430074

出　　处：《控制与决策》2022年第8期1971-1980,共10页Control and Decision

基　　金：科技创新2030“新一代人工智能”重大项目(2018AAA0101200);国家自然科学基金项目(52069014,51669014);江西省杰出青年基金项目(2018ACB21029)。

摘　　要：萤火虫算法在处理多峰优化问题时易陷入局部最优,针对该问题提出一种自主学习萤火虫算法.该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3种学习策略产生3个候选解,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,所提出算法在处理多峰优化问题时具有高效的性能.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 particles are divided into self-learning particles and ordinary particles.The self-learning particles randomly select a particle from the population and randomly select 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 make 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.

关 键 词：萤火虫算法 多峰优化问题 自主学习 双样本学习 淘汰机制 局部最优 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]