A Hybrid Algorithm Based on Differential Evolution, Artificial Immune System and Particle Swarm Algorithms for Selection of Optimal Machining Parameters in Milling Operations

This paper presents a combination of innovative methods for optimization using the integration of the evolutionary difference algorithm (DE) and the recipient's version of the immune system algorithm. In this paper, at the mutation stage, the particle swarm algorithm (PSO) was used to determine the value of the mutation coefficient using the immune system algorithm. Therefore, the algorithm modified its evolutionary method and searched for the optimal jump coefficient. The proposed model was tested on milling operations to determine its effect on the optimization of milling parameters. The results of the hybrid approach for the case study were compared with those of ant colony algorithm, body immunity, hybrid immune algorithm, genetic algorithm, HDRE approach, and the proposed values ​​of machining handbooks. According to this comparison, the development ratio between the proposed algorithm and other approaches were as follows: ant colony algorithm was 5.7%, immune algorithm was 4.5%, hybrid immune algorithm was 3%, genetic algorithm was 8.5%, HDRE approach was 2% and handbook recommendation was 300%.