基于蚁群算法的层速度反演方法
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摘要
蚁群算法是近年优化领域中新出现的一种启发式仿生学算法,它采用分布式并行计算和正反馈机制,克服了传统优化算法的缺陷,能较好地解决实际运算中的寻优问题,因而引起人们的高度关注。本文介绍了蚁群算法用于层速度反演的方法和原理,从应用蚁群算法求解目标函数优化问题入手,且利用模型对该方法的反演结果进行检验,表明由蚁群反演算法求取的层速度与理论速度模型基本一致。
The ant colony algorithm has been newly-produced heuristic bionic algorithm in optimization region in the recent years, which uses distributed parallel computation and positive feedback mechanism, overcoming the shortcomings of traditional optimistic algorithm, can better solve the optimum seeking issue in practical operation, which is paid more attention to by the people. The paper introduced the method and principle using the ant colony algorithm for inversion of interval velocity. Starting from using ant colony algorithm to solve optimistic problem of objective function, and utilizing model to check the inversed results of the method, the paper showed that the interval velocity computed by ant colony inversion algorithm is basically coincident to theoretical model of velocity.
The ant colony algorithm has been newly-produced heuristic bionic algorithm in optimization region in the recent years, which uses distributed parallel computation and positive feedback mechanism, overcoming the shortcomings of traditional optimistic algorithm, can better solve the optimum seeking issue in practical operation, which is paid more attention to by the people. The paper introduced the method and principle using the ant colony algorithm for inversion of interval velocity. Starting from using ant colony algorithm to solve optimistic problem of objective function, and utilizing model to check the inversed results of the method, the paper showed that the interval velocity computed by ant colony inversion algorithm is basically coincident to theoretical model of velocity.
引文
[1]Coloni A,Dorigo Mand Maniezzo V.Distributed opti-mization by ant colonies.Proc of ECAL91-European Conference on Artificial Life,Paris,France,Elsevier,1991,134~142
[2]Coloni A,Dorigo Mand Maniezzo V.Aninvestigation of some properties of an ant algorithm.Proc of Par-allel Problem Solving from Nature(PPSN92),Brus-sels,Belgium,Elsevier,1992,509~520
[3]Coloni A,Dorigo M and Maniezzo V.Ant system:Opti mization by a colony of cooperatingagent.IEEE Trans on Systems,Man and Cybernetics-Part B:Cy-bernetics,1996,26(1):29~41
[4]Dorigo M,Gambardella L M.Ant colony system:A cooperative learning approach to the Traveling Sales-man Problem.IEEE Trans on Evolutionary Computa-tion.1(1):1996,53~66
[5]段海滨,王道波,于秀芬.蚁群算法的研究进展评述.自然科学史,2006,28(2):102~105
[6]段海滨,王道波,朱家强等.蚁群算法理论及应用研究的进展.控制与决策,2004,19(2):1327~1326,1340
[7]段海滨,王道波.一种快速全局优化的改进蚁群算法及仿真.信息与控制,2004,33(2):247~244
[8]高尚,钟捐,莫述军.连续优化问题的蚁群算法研究.微机进展,2003,13(1):21~22,69
[2]Coloni A,Dorigo Mand Maniezzo V.Aninvestigation of some properties of an ant algorithm.Proc of Par-allel Problem Solving from Nature(PPSN92),Brus-sels,Belgium,Elsevier,1992,509~520
[3]Coloni A,Dorigo M and Maniezzo V.Ant system:Opti mization by a colony of cooperatingagent.IEEE Trans on Systems,Man and Cybernetics-Part B:Cy-bernetics,1996,26(1):29~41
[4]Dorigo M,Gambardella L M.Ant colony system:A cooperative learning approach to the Traveling Sales-man Problem.IEEE Trans on Evolutionary Computa-tion.1(1):1996,53~66
[5]段海滨,王道波,于秀芬.蚁群算法的研究进展评述.自然科学史,2006,28(2):102~105
[6]段海滨,王道波,朱家强等.蚁群算法理论及应用研究的进展.控制与决策,2004,19(2):1327~1326,1340
[7]段海滨,王道波.一种快速全局优化的改进蚁群算法及仿真.信息与控制,2004,33(2):247~244
[8]高尚,钟捐,莫述军.连续优化问题的蚁群算法研究.微机进展,2003,13(1):21~22,69
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