The Harris hawks, grasshopper and multi-verse optimization algorithms for the selection of optimal machining parameters in manufacturing operations


YILDIZ A. R. , Yildiz B. S. , Sait S. M. , Li X.

MATERIALS TESTING, vol.61, no.8, pp.725-733, 2019 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 61 Issue: 8
  • Publication Date: 2019
  • Doi Number: 10.3139/120.111377
  • Title of Journal : MATERIALS TESTING
  • Page Numbers: pp.725-733
  • Keywords: Harris hawks optimization algorithm, grasshopper optimization algorithm, multi-verse optimization algorithm, manufacturing, grinding, design, STRUCTURAL DESIGN OPTIMIZATION, WATER CYCLE ALGORITHM, MULTIOBJECTIVE OPTIMIZATION, GRINDING PROCESS, DIFFERENTIAL EVOLUTION, GENETIC ALGORITHM, GRAVITATIONAL SEARCH, IMMUNE ALGORITHM, COLONY ALGORITHM, TOPOLOGY DESIGN

Abstract

In this research, the Harris hawks optimization algorithm (HHO), the grasshopper optimization algorithm (GOA) and the multi-verse optimization algorithm (MVO) have been used in solving manufacturing optimization problems. This paper is the first research study for the optimization of processing parameters for manufacturing processes using the HHO, the GOA, and the MVO in the literature, and in particular, for grinding operations. A well-known grinding optimization problem is solved to prove how effective the HHO, the GOA and the MVO are in solving manufacturing problems and to demonstrate superiority over other algorithms. The results of the HHO, the GOA and the MVO are compared with other methods such as the genetic algorithm, the ant colony algorithm, the scatter search, the differential evolution algorithm, the particle swarm optimization algorithm, simulated annealing, the artificial bee colony, harmony search, improved differential evolution, the hybrid particle swarm algorithm, teaching learning-based optimization algorithms, the cuckoo search, and the fractal search algorithm. The results show that the HHO, the GOA, and the MVO are efficient optimization approaches for obtaining optimal manufacturing variables in manufacturing operations.