几种优化算法在浅海匹配场反演中的性能比较  

Performance comparison of optimization algorithms in matched field inversion

作　　者：邹士新[1] 杨坤德[1] 马远良[1] 

机构地区：[1]西北工业大学航海学院,西安710072

出　　处：《声学技术》2005年第1期4-9,共6页Technical Acoustics

基　　金：国家自然科学基金(10304015)

摘　　要：在分析模拟退火算法、遗传算法、差异进化算法、下山单纯形差异进化算法的优化机理的基础上,定量比较了上述算法在浅海匹配场反演中的效率差异。模拟退火算法与遗传算法只使用目标函数值信息在参数空间搜索全局最优值,效率低且易受参数间耦合的影响。差异进化算法使用种群中个体间的距离与方位信息在参数空间中搜索全局最优值,优化效率随着优化过程的进行而下降。下山单纯形差异进化算法将下山单纯形算法融入差异进化算法,增强了差异进化算法的寻优能力,混合算法对目标函数梯度信息敏感的特性使得这一算法具有较强的解耦能力。浅海匹配场反演仿真算例从最优参数反演结果、最终目标函数值、反演时间等方面检验了上述算法的反演效率。Optimization efficiencies and mechanisms of simulated annealing, genetic algorithm, differential evolution and downhill simplex differential evolution are compared and analyzed. Simulated annealing and genetic algorithm use a direct random process to search the parameter space for an optimal solution. They include the ability to avoid local minima, but as no gradient information is used, searches are relatively inefficient. Differential evolution searches the parameter space by using distance and azimuth between individuals of a population, although initial searches are effective, the search speed decreases quickly because differential information between the individuals gradually vanishes. Local downhill simplex and global differential evolution methods are developed separately, and combined to produce a hybrid downhill simplex differential evolution algorithm. The hybrid algorithm is sensitive to gradients of the object function and search of the parameter space is effective. These algorithms are applied to matched field inversion with synthetic data. Optimal parameter values, final values of object function and inversion time are presented and compared.

关 键 词：匹配场反演 性能比较 浅海 优化算法 差异进化算法 模拟退火算法 目标函数值 遗传算法 单纯形算法 优化机理 定量比较 空间搜索 参数空间 方位信息 优化过程 优化效率 梯度信息 混合算法 参数反演 最优值 下山 能力 距离 

分 类 号：O242.23[理学—计算数学] P733.2[理学—数学]