Pachung Probhat, Bansal Jagdish Chand
South Asian University, New Delhi, India.
MethodsX. 2022 Sep 3;9:101839. doi: 10.1016/j.mex.2022.101839. eCollection 2022.
The Tangent Search Algorithm (TSA) is a newly developed population-based meta-heuristic algorithm to solve complex optimization problems. It is based on the tangent function, which steers the given solution towards more promising regions of the search space. Though TSA has performed well for many optimization problems, the experimental analyses show that it suffers from the low exploration ability and slow convergence rate. This article proposes an improved TSA algorithm (iTSA). Using two concepts, 'Fitness Weighted Search Strategy' (FWSS) and 'Opposition Based learning' (OBL), iTSA is better in terms of exploration while maintaining the high convergence rate of TSA.•Fitness weighted search strategy (FWSS) is used to increase the exploration ability of TSA.•Opposition based learning (OBL) is used to increase the convergence speed of TSA.•Together, OBL and FWSS into iTSA outperformed the classical TSA and other considered state-of-the-art algorithms. The performance of the proposed iTSA is validated on two sets of test functions: CEC14 benchmark functions and a set of 21 well-known classical benchmark functions. The obtained results are compared with those obtained from the basic TSA and other considered state-of-the-art algorithms.
切线搜索算法（TSA）是一种新开发的基于种群的元启发式算法，用于解决复杂的优化问题。它基于正切函数，该函数将给定的解导向搜索空间中更有希望的区域。尽管TSA在许多优化问题上表现良好，但实验分析表明它存在探索能力低和收敛速度慢的问题。本文提出了一种改进的TSA算法（iTSA）。通过使用“适应度加权搜索策略”（FWSS）和“基于对立的学习”（OBL）这两个概念，iTSA在探索方面表现更好，同时保持了TSA的高收敛率。
适应度加权搜索策略（FWSS）用于提高TSA的探索能力。
基于对立的学习（OBL）用于提高TSA的收敛速度。
综合起来，将OBL和FWSS应用于iTSA的性能优于经典TSA和其他考虑的先进算法。所提出的iTSA的性能在两组测试函数上得到了验证：CEC14基准函数和一组21个著名的经典基准函数。将获得的结果与从基本TSA和其他考虑的先进算法获得的结果进行了比较。
