求解最优化问题的切线搜索算法
The Tangent Search Algorithm for Solving Optimization Problems
摘要：
本文提出了一种新的基于种群的优化算法，称为切线搜索算法（TSA）来解决优化问题。TSA使用基于正切函数的数学模型来将给定的解决方案向更好的解决方案移动。切向飞行函数的优点是在开发和探索搜索之间取得平衡。此外，一个新的逃逸过程中使用，以避免陷入局部极小。此外，该算法还引入了自适应变步长，以提高算法的收敛能力。TSA的性能评估在三类测试：经典测试，CEC基准，和工程优化问题。此外，一些研究和指标已被用来观察所提出的TSA的行为。实验结果表明，TSA算法是能够提供非常有前途的和竞争力的结果在大多数基准函数由于更好的平衡之间的探索和利用的搜索空间。这种新的优化算法的主要特点是它的简单和效率，它只需要少量的用户定义的参数。
英文摘要：
This article proposes a new population-based optimization algorithm called the Tangent Search Algorithm (TSA) to solve optimization problems. The TSA uses a mathematical model based on the tangent function to move a given solution toward a better solution. The tangent flight function has the advantage to balance between the exploitation and the exploration search. Moreover, a novel escape procedure is used to avoid to be trapped in local minima. Besides, an adaptive variable step size is also integrated in this algorithm to enhance the convergence capacity. The performance of TSA is assessed in three classes of tests: classical tests, CEC benchmarks, and engineering optimization problems. Moreover, several studies and metrics have been used to observe the behavior of the proposed TSA. The experimental results show that TSA algorithm is capable to provide very promising and competitive results on most benchmark functions thanks to better balance between exploration and exploitation of the search space. The main characteristics of this new optimization algorithm is its simplicity and efficiency and it requires only a small number of user-defined parameters.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
