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利用多方向的多次脉冲发射来提高蝙蝠算法的收敛速度。

Improving Convergence Speed of Bat Algorithm Using Multiple Pulse Emissions along Multiple Directions.

机构信息

Department of Computer Science and Software Engineering, International Islamic University Islamabad, Islamabad 44000, Pakistan.

EIAS Data Science and Blockchain Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia.

出版信息

Sensors (Basel). 2022 Dec 5;22(23):9513. doi: 10.3390/s22239513.

Abstract

Metaheuristic algorithms are effectively used in searching some optical solution space. for optical solution. It is basically the type of local search generalization that can provide useful solutions for issues related to optimization. Several benefits are associated with this type of algorithms due to that such algorithms can be better to solve many issues in an effective way. To provide fast and accurate solutions to huge range of complex issues is one main benefit metaheuristic algorithms. Some metaheuristic algorithms are effectively used to classify the problems and BAT Algorithm (BA) is one of them is more popular in use to sort out issues related to optimization of theoretical and realistic. Sometimes BA fails to find global optima and gets stuck in local optima because of the absence of investigation and manipulation. We have improved the BA to boost its local searching ability and diminish the premature problem. An improved equation of search with more necessary information through the search is set for the generation of the solution. Test set of benchmark functions are utilized to verify the proposed method's performance. The results of simulation showed that proposed methods are best optimal solution as compare to others.

摘要

元启发式算法在搜索某些光学解决方案空间方面非常有效。对于光学解决方案。它基本上是一种局部搜索的泛化,可以为与优化相关的问题提供有用的解决方案。由于这种算法可以更好地有效地解决许多问题,因此与这种类型的算法相关联有几个好处。为范围广泛的复杂问题提供快速和准确的解决方案是元启发式算法的主要优势之一。一些元启发式算法可有效地用于对问题进行分类,而 BAT 算法(BA)就是其中之一,它在用于解决与理论和现实优化相关的问题方面更为流行。由于缺乏调查和操作,BA 有时无法找到全局最优解,而是陷入局部最优解。我们改进了 BA 以提高其局部搜索能力并减少过早问题。通过搜索设置了带有更多必要信息的改进搜索方程,以生成解决方案。基准函数的测试集用于验证所提出方法的性能。模拟结果表明,与其他方法相比,所提出的方法是最佳的最优解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dbe/9736267/d94c5dfe07c0/sensors-22-09513-g001.jpg

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