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首页> 外文期刊>International Journal of Innovative Computing Information and Control >A MINIMUM-ELIMINATION-ESCAPE MEMETIC ALGORITHM FOR GLOBAL OPTIMIZATION: MEEM
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A MINIMUM-ELIMINATION-ESCAPE MEMETIC ALGORITHM FOR GLOBAL OPTIMIZATION: MEEM

机译：全局优化的最小消除偷偷摸摸的算法：MEEM

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摘要

Smoothing function method and filled function method are two of the most efficient methods for global optimization problems. The former can eliminate many local minima during the optimization process, but it often loses some useful information on looking for descent directions. The later can escape from local minima and find a better minimum, but it is usually parameter sensitive. To overcome these shortcomings, an auxiliary function is designed which integrates the advantages of both smoothing function and filled function; that is, it not only can eliminate many local minima and escape from local minima, but also cannot lose the useful information and is not parameter sensitive. By using such a function, many local minima will be eliminated and the algorithm successively goes from one local minimum to another better local minimum during optimization process, and finds the global minimum finally. To enhance the efficiency of the algorithm, a local search called square search is designed and integrated into the algorithm. Based on these techniques, a minimum-elimination-escape memetic algorithm called MEEM is proposed in this paper. The simulations are made on 30 standard benchmark problems and the performance of the proposed algorithm is compared with that of some well performed existing algorithms. The results indicate the performance of the proposed algorithm is more effective.
机译：平滑函数方法和填充函数方法是解决全局优化问题的两种最有效方法。前者可以在优化过程中消除许多局部最小值，但是在寻找下降方向时，它通常会丢失一些有用的信息。后者可以摆脱局部最小值并找到更好的最小值，但通常对参数敏感。为了克服这些缺点，设计了一种辅助功能，该功能兼顾了平滑功能和填充功能的优点。也就是说，它不仅可以消除许多局部极小值并从局部极小值中逃脱，而且不会丢失有用的信息并且对参数不敏感。通过使用这种功能，可以消除许多局部最小值，并且算法在优化过程中会从一个局部最小值到另一个更好的局部最小值，最后找到全局最小值。为了提高算法的效率，设计了一种称为平方搜索的局部搜索并将其集成到算法中。基于这些技术，提出了一种最小消除逃逸模因算法。针对30个标准基准问题进行了仿真，并将该算法的性能与某些性能良好的现有算法进行了比较。结果表明，所提算法的性能更为有效。

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