Niching Sparrow Search Algorithm for Solving Benchmark Problems, Speed Reducer Design, and Himmelblau’s Nonlinear Optimization Problem - Sidhu, Gagandeep Kaur; Kaur, Jatinder (Communications in Mathematics and Applications, 2024)
Metaheuristic algorithms are invented or modified in order to solve complex optimization problems at the global level. With the development of technology, almost every domain such as engineering, industrial, medical etcetera is facing the problem of optimization. In order to solve these problems, a number of algorithms have been discovered. One of the most recent optimization algorithms is Sparrow Search Algorithm (SSA) which is famous for its good optimal ability along with fast convergence, Although, the SSA has a lot of merits, it is still facing numerous drawbacks namely falling into the local optima, steady convergence, etc. Therefore, we have proposed Niching SSA (NSSA) by introducing the Niching technique in SSA for updating the position of followers and scouters. This NSSA has been tested on 18 benchmark functions, speed reducer design, and also on Himmelblau’s nonlinear optimization problem. In this work, we have examined NSSA from various aspects like optimal value, average mean for convergence accuracy, and the standard deviation for stability, and also have drawn the convergence curves through Matlab to check the convergence rate. Moreover, we have applied the Wilcoxon Signed rank test on NSSA. In all these aspects, computational results reveal that the performance of NSSA is superior with respect to SSA, GWO, PSO, and GSA.