Novel nature-inspired meta-heuristic optimization algorithm based on hybrid dolphin and sparrow optimization

The increasing difficulty of actual-world optimization problems has prompted computer researchers to produce process improvement techniques regularly. Metaheuristic and evolutionary computation are popular in nature-inspired optimization methods. This paper introduces hybrid dolphin and sparrow optimization (DSO), which is a modification of a new metaphorical algorithm based on the natural behavior of sparrows and dolphins. Various adaptive and arbitrary variables are combined within this algorithm to indicate the exploitation and investigation of the exploration area in various discoveries of optimization. Multiple test strategies are used to calculate DSO performance. Initially, a collection of experiment events, including unimodal, multimodal, and composite functions, is applied to examine the exploitation, exploration, local optima avoidance, and convergence of DSO. Furthermore, unique metrics, such as the most suitable solution through optimization and search history, are applied to qualitatively and quantitatively examine and verify the achievement of DSO on turned 2D inspection functions. The effects of analysis functions and achievement metrics show that the proposed method can search various regions of a search space, provide local optima avoidance, converge toward the global optimum, and utilize encouraging areas of a search range while optimization proceeds efficiently. The DSO algorithm achieves a regular frame for an airfoil with a low drag, which explains that the methods are efficient in improving physical difficulties, including restrained plus unknown search spaces.