蚁群算法在磁测资料反演解释中的应用
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摘要
蚁群算法是模拟蚂蚁群体觅食行为的仿真优化算法,在旅行商等组合优化问题中展现出优异的性能,其在磁测资料反演解释中的应用却很少。基于磁测资料反演解释的特点,本文改善了目标函数值与信息素的映射机制,总结出节点划分策略的连续域多变量目标函数优化蚁群算法。并对磁测资料的模型参数反演进行理论模拟,最后应用于澳大利亚南部Iron Mount矿区低空航磁资料的条带状铁矿构建勘查,取得良好应用效果。
Simulating the behavior of ant colony searching for food,the Ant Colony Algorithm is an emulated and optimized algorithm,which demonstrates excellent performance in such combinatorial optimization problems as Traveling Salesman.However,it has not been widely applied to the inversion and interpretation of magnetic data.Based on the characteristics of magnetic data inversion and interpretation,this paper improves the mapping mechanism from objective function value to pheromone and summarizes Ant Colony Algorithm optimizing for continuous and multiple objective function using nodes partition strategy.Satisfactory results were achieved when Ant Colony Algorithm was simulated to inverse the parameters in the synthetic model experiment of magnetic data and was applied to prospect the banded iron formation according to low-altitude aeromagnetic data surveyed at Iron Mount mining area,southern Australia.
Simulating the behavior of ant colony searching for food,the Ant Colony Algorithm is an emulated and optimized algorithm,which demonstrates excellent performance in such combinatorial optimization problems as Traveling Salesman.However,it has not been widely applied to the inversion and interpretation of magnetic data.Based on the characteristics of magnetic data inversion and interpretation,this paper improves the mapping mechanism from objective function value to pheromone and summarizes Ant Colony Algorithm optimizing for continuous and multiple objective function using nodes partition strategy.Satisfactory results were achieved when Ant Colony Algorithm was simulated to inverse the parameters in the synthetic model experiment of magnetic data and was applied to prospect the banded iron formation according to low-altitude aeromagnetic data surveyed at Iron Mount mining area,southern Australia.
引文
[1]段海滨.蚁群算法原理及其应用[M].北京:科学出版社.2005.
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[5]Colorni A,Dorigo M,Maniezzo V.Distributed optimization by antcolonies[J].Proc.of the First European Conf.on Artificial Life.Paris,France:Elsevier Publishing,1991,134-142.
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[7]Dorigo M,Maniezzo V,Colorni A.Ant system:optimization by acolony of cooperating agents[J].IEEE Trans.On Systems,Man,and Cybernetics,1996,26(1):29-41.
[8]Dorigo M,GambardellaL M.Ant colony system:a cooperative learn-ing approach to the travelling salesman problem[J].IEEE Trans.on Evolutionary Computation,1997,1(1):53-66.
[9]高尚,钟娟,莫述军.连续优化问题的蚁群算法研究[J].微机发展,2003,13(1):21-22,69-70.
[10]温文波,杜维.蚁群算法概述[J].石油化工自动化,2002,1(19):19-22.
[11]马良,项培军.蚂蚁算法在组合优化中的应用[J].管理科学学报,2001,4(2):32-37.
[12]汪镭,吴启迪.蚁群算法在连续空间寻优问题求解中的应用[J].控制与决策,2003,18(1):45-48,57.
[13]黄捍东,张如伟,于茜.基于蚁群算法的层速度反演方法[J].石油地球物理勘探,2008,43(4):422-424.
[14]张双全,王尚旭,季敏,等.地震波阻抗反演的蚁群算法实现[J].石油物探,2005,44(6):551-554.
[15]吴祥松,朱合华,李晓军,等.多维连续参数的随机试验蚁群算法优化反演分析[J].岩土力学,2007,28(12):2589-2592,2597.
[2]李士勇,陈永强,李研.蚁群算法及其应用[M].哈尔滨:哈尔滨工业大学出版社.2004.
[3]吴启迪,汪镭.智能蚁群算法及其应用[M].上海:上海科技教育出版社.2004.
[4]姜长元.蚁群算法的理论及其应用[J].计算机时代,2004:1-4.
[5]Colorni A,Dorigo M,Maniezzo V.Distributed optimization by antcolonies[J].Proc.of the First European Conf.on Artificial Life.Paris,France:Elsevier Publishing,1991,134-142.
[6]Colorni A,DorigoM,Maniezzo V.An investigation of some proper-ties of an ant algorithm[J].Proc.of the Parallel Problem Solvingfrom Nature Conference(PPSN'92).Brussels,Belgium:ElsevierPublishing,1992,509-520.
[7]Dorigo M,Maniezzo V,Colorni A.Ant system:optimization by acolony of cooperating agents[J].IEEE Trans.On Systems,Man,and Cybernetics,1996,26(1):29-41.
[8]Dorigo M,GambardellaL M.Ant colony system:a cooperative learn-ing approach to the travelling salesman problem[J].IEEE Trans.on Evolutionary Computation,1997,1(1):53-66.
[9]高尚,钟娟,莫述军.连续优化问题的蚁群算法研究[J].微机发展,2003,13(1):21-22,69-70.
[10]温文波,杜维.蚁群算法概述[J].石油化工自动化,2002,1(19):19-22.
[11]马良,项培军.蚂蚁算法在组合优化中的应用[J].管理科学学报,2001,4(2):32-37.
[12]汪镭,吴启迪.蚁群算法在连续空间寻优问题求解中的应用[J].控制与决策,2003,18(1):45-48,57.
[13]黄捍东,张如伟,于茜.基于蚁群算法的层速度反演方法[J].石油地球物理勘探,2008,43(4):422-424.
[14]张双全,王尚旭,季敏,等.地震波阻抗反演的蚁群算法实现[J].石油物探,2005,44(6):551-554.
[15]吴祥松,朱合华,李晓军,等.多维连续参数的随机试验蚁群算法优化反演分析[J].岩土力学,2007,28(12):2589-2592,2597.
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