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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >An efficient modified harmony search algorithm with intersect mutation operator and cellular local search for continuous function optimization problems
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An efficient modified harmony search algorithm with intersect mutation operator and cellular local search for continuous function optimization problems

机译：带有相交突变算子和细胞局部搜索的有效改进的和声搜索算法，用于求解连续函数优化问题

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摘要

This paper proposes a modified harmony search (MHS) algorithm with an intersect mutation operator and cellular local search for continuous function optimization problems. Instead of focusing on the intelligent tuning of the parameters during the searching process, the MHS algorithm divides all harmonies in harmony memory into a better part and a worse part according to their fitness. The novel intersect mutation operation has been developed to generate new -harmony vectors. Furthermore, a cellular local search also has been developed in MHS, that helps to improve the optimization performance by exploring a huge search space in the early run phase to avoid premature, and exploiting a small region in the later run phase to refine the final solutions. To obtain better parameter settings for the proposed MHS algorithm, the impacts of the parameters are analyzed by an orthogonal test and a range analysis method. Finally, two sets of famous benchmark functions have been used to test and evaluate the performance of the proposed MHS algorithm. Functions in these benchmark sets have different characteristics so they can give a comprehensive evaluation on the performance of MHS. The experimental results show that the proposed algorithm not only performs better than those state-of-the-art HS variants but is also competitive with other famous meta-heuristic algorithms in terms of the solution accuracy and efficiency.
机译：针对连续函数优化问题，提出了一种具有相交变异算子和细胞局部搜索的改进的和谐搜索算法。 MHS算法没有专注于搜索过程中参数的智能调整，而是根据适合度将和声记忆中的所有和声分为好部分和坏部分。已经开发了新颖的相交突变操作以产生新的和谐载体。此外，在MHS中还开发了蜂窝本地搜索，通过在早期运行阶段探索巨大的搜索空间以避免过早出现，并在后期运行阶段利用小区域来完善最终解决方案，从而有助于提高优化性能。 。为了为提出的MHS算法获得更好的参数设置，通过正交测试和范围分析方法分析了参数的影响。最后，使用了两组著名的基准函数来测试和评估所提出的MHS算法的性能。这些基准集中的功能具有不同的特征，因此它们可以对MHS的性能进行全面评估。实验结果表明，该算法不仅性能优于最新的HS变体，而且在求解精度和效率方面也与其他著名的元启发式算法相竞争。

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