Hybrid Self Organizing Neurons and Evolutionary Algorithms for Global Optimization
Hybrid Self Organizing Neurons and Evolutionary Algorithms for Global Optimization
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用于全局优化的混合自组织神经元和进化算法
DOI：
10.1166/jctn.2012.2024
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发表时间：
2012
影响因子：
--
通讯作者：
Grosan C
中科院分区：
文献类型：
--
作者：
Grosan C
在这项工作中，一个新的算法的灵感来自自组织映射结合进化算法排队。图中的神经元不是自己进化的，而是在一组迭代过程中应用进化算法的结果。这个想法确实有助于提高自组织映射和进化算法的性能，同时单独考虑。在这项研究中进行的实验设想测试功能有一个单一的标准，但大量的维度。与其他四个著名的元优化算法（如差分进化，粒子群优化，模拟退火）的比较表明，所提出的方法的性能和效率。
In this work a new algorithm inspired by the self organizing maps combined with evolutionary algorithms is lined up. A neuron in the map is not evolving by itself but it is the result of the application of an evolutionary algorithm during a set of iterations. This idea really helps to increasing the performance of both self organizing maps and evolutionary algorithms while considered individually. The experiments performed in this research envisage test functions having a single criteria but a high number of dimensions. Comparisons with four other well known metaheuristics for optimization (such as differential evolution, particle swarm optimization, simulated annealing) show the performance and efficiency of the proposed approach.