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首页> 外文期刊>International journal of applied evolutionary computation >Efficient Mutation Strategies Embedded in Laplacian-Biogeography-Based Optimization Algorithm for Unconstrained Function Minimization
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Efficient Mutation Strategies Embedded in Laplacian-Biogeography-Based Optimization Algorithm for Unconstrained Function Minimization

机译：基于拉普拉斯生物地理学的优化算法中嵌入的有效变异策略，用于无约束函数最小化

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摘要

Biogeography-Based optimization (BBO) is a nature inspired optimization technique that has excellent exploitation ability but the exploration ability needs to be improved to make it more robust. With this objective in mind, Garg and Deep proposed Laplacian BBO (LX-BBO) based on the Laplace Crossover which is a Real Coded Genetic Crossover Operator. It was concluded that LX- BBO outperforms its competitors. A natural question is to incorporate real coded mutation strategies into LX-BBO in order to improve its diversity. Therefore, in this paper, the exploring ability of LX-BBO is further investigated by using six different types of mutation operators present in literature. Gaussian, Cauchy, Levy, Power, Polynomial and Random mutation are used to test which mutation works best for LX-BBO. The performance of all these versions of BBO are measured on the benchmark problem set proposed in CEC 2014. On the basis of the criteria lay down by CEC, analysis of numerical and graphical results and statistical tests it is concluded that LX-BBO works best with Random and Cauchy Mutation.
机译：基于生物地理的优化（BBO）是自然界启发的优化技术，具有出色的开发能力，但需要提高探索能力以使其更强大。考虑到这一目标，Garg和Deep提出了基于Laplace Crossover的Laplacian BBO（LX-BBO），后者是一个真正的编码遗传交叉算子。结论是，LX-BBO的表现优于竞争对手。一个自然的问题是将真正的编码突变策略纳入LX-BBO，以提高其多样性。因此，在本文中，通过使用文献中存在的六种不同类型的突变算子进一步研究了LX-BBO的探索能力。高斯，柯西，利维，幂，多项式和随机突变用于测试哪种突变最适合LX-BBO。所有这些版本的BBO的性能都是根据CEC 2014中提出的基准问题集进行衡量的。根据CEC制定的标准，对数字和图形结果的分析以及统计测试，可以得出结论，LX-BBO在以下情况下效果最佳。随机和柯西突变。

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