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基于并行遗传算法的地震属性优化研究
A Study on Seismic Attribute Optimization Based on Parallel Genetic Algorithm
【摘要】 系统介绍了遗传算法各步的改进策略。首先,产生多样化的初始群体,引入五进制的编码策略提高搜索速度,然后采用无退还随机选择机制防止收敛早熟,采取两点交叉及多点变异方案以扩大模型空间的搜索范围和保持个体的多样性。另外,为了保证算法的收敛,使用了代间隙技术。最后,按照并行算法设计原则给出改进后的并行遗传算法的算法描述,并通过运用改进的并行遗传算法解决了非线性、多参数、多极值的地震属性优化的实际问题。
【Abstract】 The authors introduce the improvement strategies of each process stage systematically and comprehensively in genetic algorithm.Firstly,the authors create various initial colonies and introduce quinary coding strategy to improve searching speed.Then,they adopt random selection mechanism without reimbursement to prevent premature convergence.Two-point crossover and multipoint mutation are adopted to extend searching range of model space and to keep individual diversity.In addition,they use generation gap technique to ensure the convergence.Finally,the authors bring forward the description of the ameliorated parallel genetic algorithm according to PCAM principle.Thus seismic attribute optimization of nonlinear multi-parameter and multi-extremum is achieved obviously by running the mended parallel genetic algorithm.
【Key words】 nonlinear multi-parameter optimization; genetic algorithm; parallel computing;
- 【文献出处】 吉林大学学报(地球科学版) ,Journal of Jiling University (Earth Science Edition) , 编辑部邮箱 ,2005年05期
- 【分类号】P631.4;
- 【被引频次】9
- 【下载频次】292