捕鱼策略与粒子群相结合的优化方法研究
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摘要
智能优化方法是计算机领域的前沿课题,也是当前计算机界学者研究的热点之一。因而开展捕鱼策略与粒子群相结合的优化方法研究,具有一定的应用背景和现实意义。
本文的主要内容概括如下:
(1)概述了智能优化方法的研究背景、目的和意义,介绍了采用捕鱼策略的优化方法和粒子群优化方法的原理、流程以及相关研究进展。
(2)在分析采用捕鱼策略的优化方法和粒子群优化方法均存在不足的基础上,提出将捕鱼策略与粒子群相结合的优化方法。该优化方法要求渔夫在打鱼活动中采用灵活机动的多点随机抛投鱼网策略,并且根据捕鱼环境的不同采取不同的探测策略。优化仿真结果表明,该方法具有收敛速度快、优化精度高、稳定性好的特点。
(3)提出将捕鱼策略与粒子群相结合的优化方法应用于约束优化问题。针对整数和实数混合型优化问题,使用上下界法解决整数范围寻优的问题。工程仿真实验结果表明,该优化方法具有较好的全局寻优能力和较快的收敛速度。
The intelligent optimization approach is a cutting-edge issue in the computer domain and it is also one of a researching focus for the computer scholars. So making a study on the optimization algorithm of which the particle swarm optimization (PSO) being combined into the optimization approach on fishing strategy (FSOA) has an application background and a practical significance.
The main contents of this dissertation are summarized as follows:
(1) The background、purpose and significance of the research on intelligent optimization approach are summarized. The principle and the process of FSOA and PSO are separately introduced and related research progresses are also introduced.
(2) An optimization algorithm of which PSO being combined into FSOA is presented (FPSOA), based on analyzing the shortcoming of FSOA and PSO. The algorithm is that every fisher makes use of an agile, mobile and multipoint random casting fishnet strategy in the fishing activity, and chooses different detection strategy according to different fishing circumstances. It shows, from the experimental and simulated results of typical functions’optimization, that the optimization approach has the performance of a rapid convergence rate, a high accurate numerical solution and a good stability.
(3) FPSOA is applied to the restrained optimization problem. For the optimization problems which mixed integer and real number, the upper and lower bound method is used to solve integer optimization problems. It shows, from the experimental and simulated results of restrained engineering functions’optimization, that the optimization approach has a better performance in global optimization and a better performance of convergence rate.
本文的主要内容概括如下:
(1)概述了智能优化方法的研究背景、目的和意义,介绍了采用捕鱼策略的优化方法和粒子群优化方法的原理、流程以及相关研究进展。
(2)在分析采用捕鱼策略的优化方法和粒子群优化方法均存在不足的基础上,提出将捕鱼策略与粒子群相结合的优化方法。该优化方法要求渔夫在打鱼活动中采用灵活机动的多点随机抛投鱼网策略,并且根据捕鱼环境的不同采取不同的探测策略。优化仿真结果表明,该方法具有收敛速度快、优化精度高、稳定性好的特点。
(3)提出将捕鱼策略与粒子群相结合的优化方法应用于约束优化问题。针对整数和实数混合型优化问题,使用上下界法解决整数范围寻优的问题。工程仿真实验结果表明,该优化方法具有较好的全局寻优能力和较快的收敛速度。
The intelligent optimization approach is a cutting-edge issue in the computer domain and it is also one of a researching focus for the computer scholars. So making a study on the optimization algorithm of which the particle swarm optimization (PSO) being combined into the optimization approach on fishing strategy (FSOA) has an application background and a practical significance.
The main contents of this dissertation are summarized as follows:
(1) The background、purpose and significance of the research on intelligent optimization approach are summarized. The principle and the process of FSOA and PSO are separately introduced and related research progresses are also introduced.
(2) An optimization algorithm of which PSO being combined into FSOA is presented (FPSOA), based on analyzing the shortcoming of FSOA and PSO. The algorithm is that every fisher makes use of an agile, mobile and multipoint random casting fishnet strategy in the fishing activity, and chooses different detection strategy according to different fishing circumstances. It shows, from the experimental and simulated results of typical functions’optimization, that the optimization approach has the performance of a rapid convergence rate, a high accurate numerical solution and a good stability.
(3) FPSOA is applied to the restrained optimization problem. For the optimization problems which mixed integer and real number, the upper and lower bound method is used to solve integer optimization problems. It shows, from the experimental and simulated results of restrained engineering functions’optimization, that the optimization approach has a better performance in global optimization and a better performance of convergence rate.
引文
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[3]Koza J R. Genetic Programming[M].Cambridge, MA: MIT Press, 1992.
[4]Fogel L J, Owens A J, Walsh M J. Artificial Intelligence through Simulated Evolution[M]. Chichester:Wiley, 1966.
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[6]Kennedy J, Eberhart R C, Shi Y. Swarm Intelligence[M]. San Francisco: Morgan Kaufman Publishers,2001.
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[10]李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式:鱼群算法[J].系统工程理论与实践,2002,22(11):32-38.
[11]B Theraulaz G, Bonabeau E, Deneubourg J L. Self-organization of hierarchies in animal societies:The case of the primitively eusocial wasp polistes dominulus christ[J]. Journal of Theoretical Biology, 1995, 174:313-323.
[12]Eusuffm M, Lansey K E. Optimization of water distribution network design using shuffled frog leaping algorithm[J]. Journal of Water Resources Planning and Management, 2003, 129(3): 210-225.
[13]陈建荣,王勇.采用捕鱼策略的优化方法[J].计算机工程与应用,2009,45(9):53-56.
[14]Shi Y, J, Eberhart R C. A modified particle swarm optimizer[A]. In: Proc. of the IEEE CEC[C]. 1998:69-73.
[15]Shi Y, J, Eberhart R C. Fuzzy adaptive particle swarm optimization[A]. In: Proc. of IEEE CEC[C]. 2001:101-106.
[16]S Clerc M, Kennedy J. The particle swarm: explosion, stability, and convergence in a multidimensional complex space[J]. IEEE Trans. Evolut. Comput. ,2002,6:58-73.
[17]He S,Wu Q H, Wen J Y, et al. A particle swarm optimizer with passive congregation[J]. BioSystems, 2004,78:135-147.
[18]Ratnaweera A, Halgamuge S K,Watson H C. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients[J]. IEEE Trans. on Evolu.Comput. ,2004,8:240-255.
[19]时招军,黄笑鹃,李其申.基于概率选择学习对象的粒子群优化算法[J].计算机工程, 2008,34(15):199-200.
[20]张建科,刘三阳,张晓清.改进的粒子群算法[J].计算机工程与设计, 2007,28(17):4215-4216.
[21]梁军,程灿.改进的粒子群优化算法[J].计算机工程与设计, 2008,29(11):2893-2896.
[22]赵全友,潘保昌,郑胜林.基于群评价的带变异粒子群算法[J].计算机工程与应用, 2009,45(12):57-59.
[23]王德强,罗琦,祁佳.一种改进的粒子群优化算法[J].计算机工程与应用, 2008,44(9):80-81.
[24]韦杏琼,周永权,黄华娟,罗德相.云自适应粒子群算法[J].计算机工程与应用, 2009,45(1):48-50.
[25]李旭芳,王士同.一种自适应粒子群算法[J].系统仿真学报,2009,21(9):2582-2585.
[26]黄辉先,陈资滨.一种改进的粒子群优化算法[J].系统仿真学报, 2007, 19(21): 4922-4925.
[27] Lφvbjerg M, Rasmussen T K, Krink T. Hybrid particle swarm optimizer with breeding and subpopulations[A]. In: Proc. of the GECCO[C].2001:469-476.
[28]Van den Bergh F, Engelbrecht A P. A cooperative approach to particle swarm optimization [J]. IEEE Trans. on Evolut. Comput. ,2004,8:225-239.
[29]许珂,刘栋.多粒子群协同进化算法[J].计算机工程与应用, 2009,45(3):51-54.
[30]李洪亮,侯朝桢,周绍生.一种高效的改进粒子群优化算法[J].计算机工程与应用, 2008,44(1):14-16.
[31]胡成玉,吴湘宁,王永骥.基于种群熵的多粒子群协同优化[J].计算机应用研究,2008,25(12):3593-3595.
[32]Kennedy J, Mendes R. Population structure and particle swarm performance[A]. In: Proc. of IEEE CEC[C]. 2002:1671-1676.
[33]姜磊,冯斌,孙俊.具有分工策略的量子粒子群优化算法[J].计算机工程与设计, 2007,28(22):5461-5463.
[34]高岳林,任子晖.带有变异算子的自适应粒子群优化算法[J].计算机工程与应用, 2007,43(25):43-47.
[35]吴茜,郑金华,宋武.改进的粒子群算法求解非线性约束优化问题[J].计算机工程与应用, 2007,43(24):61-64.
[36]田东平,徐成虎.改进的粒子群优化算法的研究和分析[J].计算机工程与应用, 2008,44(34):56-60.
[37]张文爱,刘丽芳,李孝荣.基于粒子进化的多粒子群优化算法[J].计算机工程与应用, 2008,44(7):51-53.
[38]权文,王晓丹,王坚,俞群.粒子群算法的新改进思想—递进式搜索[J].计算机工程与应用, 2009,45(2):44-47.
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