In this paper, a human-inspired optimization algorithm called stock exchange trading optimization (SETO) for solving numerical and engineering problems is introduced. The inspiration source of this optimizer is the behavior of traders and stock price changes in the stock market. Traders use various fundamental and technical analysis methods to gain maximum profit. SETO mathematically models the technical trading strategy of traders to perform optimization. It contains three main actuators including rising, falling, and exchange. These operators navigate the search agents toward the global optimum. The proposed algorithm is compared with seven popular meta-heuristic optimizers on forty single-objective unconstraint numerical functions and four engineering design problems. The statistical results obtained on test problems show that SETO is capable of providing competitive and promising performances compared with counterpart algorithms in solving optimization problems of different dimensions, especially 1000-dimension problems. Out of 40 numerical functions, the SETO algorithm has achieved the global optimum on 36 functions, and out of 4 engineering problems, it has obtained the best results on 3 problems.

中文翻译：

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证券交易所交易优化算法：一种受人类启发的全局优化方法。

在本文中，介绍了一种被称为证券交易优化 (SETO) 的受人类启发的优化算法，用于解决数值和工程问题。这个优化器的灵感来源是交易者的行为和股市中股价的变化。交易者使用各种基本和技术分析方法来获得最大利润。SETO 对交易者的技术交易策略进行数学建模以进行优化。它包含三个主要执行器，包括上升、下降和交换。这些运算符将搜索代理导航到全局最优。所提出的算法与七种流行的元启发式优化器在四十个单目标无约束数值函数和四个工程设计问题上进行了比较。在测试问题上获得的统计结果表明，与同类算法相比，SETO 在解决不同维度的优化问题，尤其是 1000 维问题时，能够提供具有竞争力和前景的性能。在40个数值函数中，SETO算法在36个函数上取得了全局最优，在4个工程问题中，在3个问题上取得了最好的结果。