Abstract
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.