SSC: A hybrid nature-inspired meta-heuristic optimization algorithm for engineering applications

Chimp Optimization Algorithm (ChoA) is a recently developed meta-heuristic approach which is inspired by the individual intelligence and sexual motivation of chimps. It is designed for trapping the local optima to alleviate the slow convergence speed. In this paper, a hybrid algorithm is developed which is based on the sine-cosine functions and attacking strategy of Spotted Hyena Optimizer (SHO). This hybrid algorithm is termed as Sine-cosine and Spotted Hyena-based Chimp Optimization Algorithm (SSC). This algorithm is used to find the best optimal solutions of real-life complex problems. The sine-cosine and attacking strategy of SHO algorithm is responsible for better exploration and exploitation. These strategies are applied to update the equations of chimps during the searching process to overcome the drawbacks of the ChoA algorithm such as slow convergence and local minima. Experimental results based on IEEE CEC'17 and six real-life engineering problems such as welded beam design, tension/compression spring design, pressure vessel design, multiple disk clutch brake design, gear train design, and car side crashworthiness, demonstrate the robustness, effectiveness, efficiency, and convergence analysis of the proposed SSC algorithm in comparison with other competitor approaches. (C) 2021 Elsevier B.V. All rights reserved.