A new hybrid method based on Aquila optimizer and tangent search algorithm for global optimization

Since no single algorithm can provide the optimal solutions for all problems, new metaheuristic methods are always being proposed or developed by combining current algorithms or creating adaptable versions. Metaheuristic methods should have a balanced exploitation and exploration stages. One of these two talents may be sufficient in some metaheuristic methods, while the other may be insufficient. By integrating the strengths of the two algorithms and hybridizing them, a more efficient algorithm can be formed. In this paper, the Aquila optimizer-tangent search algorithm (AO-TSA) is proposed as a new hybrid approach that uses the intensification stage of the tangent search algorithm (TSA) instead of the limited exploration stage to improve the Aquila optimizer's exploitation capabilities (AO). In addition, the local minimum escape stage of TSA is applied in AO-TSA to avoid the local minimum stagnation problem. The performance of AO-TSA is compared with other current metaheuristic algorithms using a total of twenty-one benchmark functions consisting of six unimodal, six multimodal, six fixed-dimension multimodal, and three modern CEC 2019 benchmark functions according to different metrics. Furthermore, two real engineering design problems are also used for performance comparison. Sensitivity analysis and statistical test analysis are also performed. Experimental results show that hybrid AO-TSA gives promising results and seems an effective method for global solution search and optimization problems.