The Spiral Optimization Algorithm: Convergence Conditions and Settings
The Spiral Optimization Algorithm: Convergence Conditions and Settings
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DOI：
10.1109/tsmc.2017.2695577
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发表时间：
2020-01-01
影响因子：
8.7
通讯作者：
Yasuda, Keiichiro
中科院分区：
文献类型：
--
作者：
Tamura, Kenichi;Yasuda, Keiichiro
螺旋优化（SPO）算法是由Tamura和Yasuda提出的一种相对新颖和简单的搜索概念，其灵感来自自然螺旋现象。该算法不使用梯度，只使用由确定性螺旋模型生成的螺旋向量组成的螺旋轨迹来搜索连续空间。本文的主要目的是提出条件和设置，数学上确保SPO算法收敛到一个固定点。螺旋矢量的大小和方向以及初始搜索点的条件是基于直接搜索理论和最近的SPO算法理论。所提出的收敛性进行了数值验证，使用测试功能与不同的属性。
The spiral optimization (SPO) algorithm proposed by Tamura and Yasuda is a relatively novel and simple search concept inspired by natural spiral phenomena. This algorithm searches continuous space using no gradient and only spiral trajectories composed of spiral vectors generated by deterministic spiral models. The primary purpose of this paper is to propose conditions and settings that mathematically ensure the convergence of the SPO algorithm to a stationary point. The conditions relating to the sizes and directions of the spiral vectors and the initial search points are based on direct search theory and recent SPO algorithm theories. The presented convergence was numerically verified using test functions with different properties.