基于DE 和SA 的Memetic 高维全局优化算法

针对高维复杂多模态优化问题,传统的进化算法存在收敛速度慢,求解精度低等缺点,提出一种面向高维优化问题的Memetic全局优化算法。算法通过全局搜索和局部搜索结合的混合搜索策略,采用多模式并行差分进化算法进行全局搜索,基于高斯分布估计的模拟退火算法进行局部搜索。改进后的Memetic算法不仅继承了差分进化算法能发现全局最优解的优点,而且能大幅度提高搜索效率。最后,通过对4个高维多峰值Benchmark函数进行仿真实验,实验结果表明本文算法有效提高了算法的收敛速度和求解精度。

A Global Memetic Optimization Algorithm for Solving High-Dimensional Problems Based on Differential Evolution and Simulate Anneal

Aiming at high-dimensional multimodal optimization problems, traditional evolutionary algorithms have shortcomings, such as low convergence speed and solution precision. A global optimization algorithm based on Memetic algorithm using global search strategy and local search strategy is proposed to resolve the high-dimensional problem. The global search strategy is a multi-model parallel differential evolution. An improved Simulate Anneal Arithmetic is used for local search strategy. The improved Memetic algorithm inherits advantages of the differential evolution algorithm to discover the global optimal solution and overcomes the deficiencies of the differential evolution algorithm. Finally, four benchmark functions are used to test this algorithm. Experimental result illustrates that it has some advantages in convergence velocity, solution precision, and stabilization.