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基于寄生行为的双种群萤火虫算法及其在柴油调合中的应用
孔祥东,钱锋
0
(华东理工大学化工过程先进控制和优化技术教育部重点实验室, 上海 200237)
摘要:
在实际的化工过程中会遇到许多非线性优化问题。常规群智能优化算法在解决这类问题时,常出现收敛精度差和容易陷入局部最优,本文针对此提出了一种基于寄生行为的双种群萤火虫算法(FAPB)。该算法将进化种群均分为两个种群,通过生物的寄生行为将两个种群联系起来,共享进化信息,提高了全局搜索能力;为防止算法陷入局部最优,引入基于自适应系数的高斯变异机制,提高了局部搜索能力。对4个经典测试函数进行仿真,结果表明:与标准FA算法、FALS算法、LDPSO算法比较,FAPB算法在收敛精度和全局搜索能力上都有较大提升。将该算法应用于柴油调合过程,结果验证了其在实际应用中的可行性。
关键词:  萤火虫算法  寄生行为  局部搜索  高斯变异  柴油调合
DOI:10.14135/j.cnki.1006-3080.2017.02.010
投稿时间:2016-09-09
基金项目:国家自然科学基金重点项目(61333010);国家自然科学基金面上项目(21376077,61403141);上海市“科技创新行动计划”研发平台建设项目(13DZ2295300)
A Double Population Firefly Algorithm Based on Parasitic Behavior and Its Application in Diesel Blending
KONG Xiang-dong,QIAN Feng
(Key Laboratory of Advanced Control and Optimization for Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China)
Abstract:
There exist many nonlinear optimization problems in the actual chemical process,for which the conventional swarm intelligence optimization algorithm easily falls into local optimum.This paper presents a parasitic behavior (FAPB) based double population firefly algorithm.By dividing into the evolutionary population into two ones linked together via the parasitic behavior of organisms,these population can share the information and improve the global searching ability.In order to prevent the algorithm from falling into local optimum,this paper further introduces Gauss mutation mechanism based on the adaptive coefficient to improve the local search ability.By simulations on four classical test functions,it is shown that,compared with the standard FA algorithm,FALS algorithm and LDPSO algorithm,the proposed FAPB algorithm can effectively improve the convergence accuracy and global search capability.Finally,the feasibility of the proposed algorithm is also demonstrated via diesel blending.
Key words:  firefly algorithm  parasitic behavior  local search  Gauss variation  diesel blending

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