This paper proposes a modified optimization algorithm called polar coordinate salp swarm algorithm (PSSA). The main inspiration of PSSA is the aggregation chain and foraging trajectory of salp is spiral. Some curves are extremely complex when represented in Cartesian coordinate system, but if they are expressed in polar coordinates, it becomes very simple and easy to handle, and polar coordinates are widely used in scientific computing and engineering issues. It will be more intuitive and convenient if use polar coordinates to define the foraging and aggregation process of salps. At the same time, different from other algorithms proposed in the past, the PSSA directly initialize individuals in polar space instead of using mapping functions to convert to polar coordinates, change the position of particles by updating polar angles and polar diameters. This algorithm is tested on two complex polar coordinate equations, several curve approximation problems and engineering design problems using PSSA. The experimental results illustrated that the proposed PSSA algorithm is superior to the state-of-the-art metaheuristic algorithms in terms of the performance measures.

中文翻译：

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PSSA：极坐标Salp群算法求解曲线设计问题

提出了一种改进的优化算法，称为极坐标集合算法（PSSA）。PSSA的主要灵感是聚集链，而sal的觅食轨迹是螺旋的。当用笛卡尔坐标系表示某些曲线时，它们非常复杂，但是如果用极坐标表示，则它变得非常简单易用，极坐标在科学计算和工程问题中得到了广泛的应用。如果使用极坐标来定义种子的觅食和聚集过程，将会更加直观和方便。同时，与过去提出的其他算法不同，PSSA直接在极坐标空间中初始化个人，而不是使用映射函数转换为极坐标，通过更新极角和极直径来更改粒子的位置。使用PSSA在两个复杂的极坐标方程，几个曲线逼近问题和工程设计问题上对该算法进行了测试。实验结果表明，所提出的PSSA算法在性能指标方面优于最新的元启发式算法。