Rubber bushing optimization by using a novel chaotic krill herd optimization algorithm

This study's primary purpose is to improve the original krill herd (KH) optimization algorithm by using chaos theory and propose a novel chaotic krill herd (CKH) optimization algorithm. Fourteen different chaotic map functions have been added to the several steps of the KH and CKH optimization algorithms already existing in the literature to improve their performances. Six different well-known benchmark functions have been used to test the performances of the developed algorithm. The proposed algorithm has better performance to reach the global optimum of the objective function which has many local minimums. The proposed algorithm improved the KH and CKH optimization algorithms' performances which already exist in the literature. Proposed novel CKH has been applied to rubber bushing stiffness optimization which is a real automotive industry problem. Obtained results have been compared with KH, CKH, genetic algorithm (GA), differential evaluation algorithm (DE) and particle swarm optimization (PSO). The proposed algorithm has better performance to reach the global optimum of the objective function. The performance and validity of the algorithm have been proved not only by using six different benchmark functions but also by using finite element analysis of rubber bushing. The study is also a unique optimization activity that uses the KH algorithm to optimize rubber bushing by using nonlinear finite element analysis.

Automated summary

This study introduces a novel chaotic krill herd (CKH) optimization algorithm by incorporating chaos theory, which demonstrates superior performance in optimizing rubber bushing stiffness.