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Lower-Energy Structure Optimization of (C60)N Clusters Using an Improved Genetic Algorithm
Guifang SHAO Wupeng HONG Tingna WANG Yuhua WEN
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2013/12/01
Online ISSN: 1745-1361
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Fundamentals of Information Systems
(C60)N clusters, genetic algorithm, population diversity, PR potential,
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An improved genetic algorithm is employed to optimize the structure of (C60)N (N≤25) fullerene clusters with the lowest energy. First, crossover with variable precision, realized by introducing the hamming distance, is developed to provide a faster search mechanism. Second, the bit string mutation and feedback mutation are incorporated to maintain the diversity in the population. The interaction between C60 molecules is described by the Pacheco and Ramalho potential derived from first-principles calculations. We compare the performance of the Improved GA (IGA) with that of the Standard GA (SGA). The numerical and graphical results verify that the proposed approach is faster and more robust than the SGA. The second finite differential of the total energy shows that the (C60)N clusters with N=7, 13, 22 are particularly stable. Performance with the lowest energy is achieved in this work.