【源码】求解连续优化问题的球面演化算法仿真
近年来，越来越多的基于自然启发的元启发式算法被提出，搜索算子是其核心问题。
In these years, more and more nature-inspired meta-heuristic algorithms have been proposed; search operators have been their core problem.
在不同的算法中，搜索算子的共同特征或机制并没有用标准格式来表示。
The common characteristics or mechanism of search operators in different algorithms have not been represented by a standard format.
本文首先提出了搜索模式的概念和数学模型表示的搜索风格。
In this paper, we first propose the concept of a search pattern and a search style represented by a mathematical model.
其次，在传统超立方体搜索方式的启发下，提出了一种新的搜索方式，即球形搜索方式。
Second, we propose a new search style, namely a spherical search style, inspired by the traditional hypercube search style.
在此基础上，提出了一种基于搜索模式和球面搜索样式的球面演化算法。
Furthermore, a spherical evolution algorithm is proposed based on the search pattern and spherical search style.
最后，测试了CEC2017的30个基准函数和一个实际的优化问题。
At the end, 30 benchmark functions of CEC2017 and a real-world optimization problem are tested.
实验结果和分析表明，该方法始终优于目前的其他最先进算法。
Experimental results and analysis demonstrate that the proposed method consistently outperforms other state-of-the-art algorithms.
参考文献：
Deyu Tang, Spherical evolution for solving continuous optimization problems, Applied Soft Computing,2019, DOI:10.1016/j.asoc.2019.105499.
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