《计算机应用研究》|Application Research of Computers

二进制中心引力优化算法及其在非线性0-1规划中的应用

Binary central force optimization algorithm and its application in nonlinear 0-1 programming

免费全文下载 (已被下载 次)  
获取PDF全文
作者 刘勇,马良
机构 上海理工大学 管理学院,上海 200093
统计 摘要被查看 次,已被下载
文章编号 1001-3695(2017)08-2372-04
DOI 10.3969/j.issn.1001-3695.2017.08.031
摘要 为求解非线性0-1规划问题,提出了一种二进制中心引力优化算法。根据引力计算加速度,利用加速度更新位置,采用转换函数实现连续的位置变量到离散的0-1变量的变换。采用典型的非线性0-1规划测试问题进行数值实验,并将算法与二进制粒子群优化算法和二进制引力搜索算法进行比较。实验结果表明,在解的稳定性和计算精度两个方面给出的算法具有显著优势,为非线性0-1规划问题的求解提供了新方法。
关键词 中心引力优化算法;转换函数;二进制;确定性
基金项目 国家自然科学基金资助项目(71401106)
国家教育部人文社会科学规划项目(16YJA630037)
上海市高原学科建设项目
上海高校青年教师培养计划资助项目(ZZSL15018)
上海理工大学国家级培育青年基金资助项目(16HJPY-QN15)
上海理工大学博士科研启动经费项目(1D-15-303-005)
本文URL http://www.arocmag.com/article/01-2017-08-031.html
英文标题 Binary central force optimization algorithm and its application in nonlinear 0-1 programming
作者英文名 Liu Yong, Ma Liang
机构英文名 SchoolofBusiness,UniversityofShanghaiforScience&Technology,Shanghai200093,China
英文摘要 To solve nonlinear 0-1 programming problems, this paper proposed a binary CFO (BCFO). It used the gravitational force to calculate the acceleration of the object, and employed the acceleration to update the position. To transform from the continuous position variables to discrete 0-1 variables, it adopted transfer functions, and used the benchmarks of nonlinear 0-1 programming problems to perform numerical experiments.It implemented performance comparison of BCFO, binary particle swarm optimization algorithm, and binary gravitational search algorithm using the set of benchmark test problems. The experimental results demonstrate that the proposed algorithm has significant advantages in the stability of solutions and computational accuracy. This paper provides a new method to solve the nonlinear 0-1 programming problems.
英文关键词 central force optimization algorithm; transfer functions; binary; deterministicness
参考文献 查看稿件参考文献
  [1] Formato R A. Central force optimization:a new metaheuristic with applications in applied electromagnetics[J] . Progress in Electromagnetics Research, 2007, 77:425-491.
[2] Formato R A. Central force optimization:a new gradient-like metaheuristic for multidimensional search and optimization[J] . International Journal of Bio-Inspired Computation, 2009, 1(4):217-238.
[3] Formato R A. Central force optimization with variable initial probes and adaptive decision space[J] . Applied Mathematics and Computation, 2011, 217(21):8866-8872. [4] 孟超, 刘三民, 孙知信. 中心引力算法收敛分析及在神经网络中的应用[J] . 软件学报, 2013, 24(10):2354-2365.
[5] Green R C, Wang L, Alam M. Training neural networks using central force optimization and particle swarm optimization:insights and comparisons[J] . Expert Systems with Applications, 2012, 39(1):555-563.
[6] Asi M J, Dib N I. Design of multilayer microwave broadband absorbers using central force optimization[J] . Progress in Electromagnetics Research B, 2010, 26(26):101-113.
[7] Mahmoud K R. Central force optimization:nelder-mead hybrid algorithm for rectangular microstrip antenna design[J] . Electromagne-tics, 2011, 31(8):578-592.
[8] Qubati G M, Formato R A, Dib N I. Antenna benchmark performance and array synthesis using central force optimisation[J] . IET Microwaves, Antennas & Propagation, 2010, 4(5):583-592.
[9] Haghighi A, Ramos H M. Detection of leakage freshwater and friction factor calibration in drinking networks using central force optimization[J] . Water Resources Management, 2012, 26(8):2347-2363.
[10] Roa O, Amaya I, Ramirez F, et al. Solution of nonlinear circuits with the central force optimization algorithm[C] //Proc of the 4th Colom-bian Workshop on Circuits and Systems. 2012:1-6.
[11] Chen Yongbo, Yu Jianqiao, Mei Yuesong, et al. Modified central force optimization (MCFO) algorithm for 3D UAV path planning[J] . Neurocomputing, 2016, 171(C):878-888.
[12] Li D, Wang J, Sun X L. Computing exact solution to nonlinear integer programming:convergent Lagrangian and objective level cut method[J] . Journal of Global Optimization, 2007, 39(1):127-154.
[13] 姜大立, 杜文, 朱松年. 一类二次0-1规划模型的遗传算法[J] . 系统工程, 1997, 15(4):21-25.
[14] 隋允康, 贾志超, 杜家政. 非线性0-1规划问题的连续化及其遗传算法[J] . 北京工业大学学报, 2008, 34(8):787-791.
[15] 李春梅, 马良. 非线性0-1规划问题的人工鱼群算法[J] . 计算机应用研究, 2011, 28(7) :2449-2451.
[16] Kennedy J, Eberhart R. A discrete binary version of the particle swarm algorithm[C] // Proc of IEEE International Conference on Computational Cybernetics and Simulation. 1997:4104-4108.
[17] Rashedi E, Nezamabadi-Pour H, Saryazdi S. BGSA:binary gravitatio-nal search algorithm[J] . Natural Computing, 2009, 9(3):727-745.
收稿日期 2016/5/17
修回日期 2016/7/9
页码 2372-2375
中图分类号 TP301
文献标志码 A