An effective hybrid immune-hill climbing optimization approach for solving design and manufacturing optimization problems in industry

Abstract

The focus of this research is on a hybrid method combining immune algorithm with a hill climbing local search algorithm for solving complex real-world optimization problems. The objective is to contribute to the development of more efficient optimization approaches with the help of immune algorithm and hill climbing algorithm. The hybrid algorithm combines the exploration speed of immune algorithm with the powerful ability to avoid being trapped in local minimum of hill climbing. This hybridization results in a solution that leads to better parameter values. This research is the first application of immune algorithm to the optimization of machining parameters in turning and also shape design optimization problems in the literature. The results of single-objective benchmark problem, multi-objective disc-brake problem, an automobile shape design optimization problem taken from the literature and case studies for multi-pass turning operation have demonstrated the superiority of the proposed hybrid over the other techniques in terms of solution quality and convergence rates.