Influence of Crossover Probability on Performance of Genetic Algorithm in Scheduling of Parallel Machines

Authors

  • B. V. Raghavendra Department of Mechanical Engineering, JSS Academy of Technical Education, Bengaluru – 560060, India.
  • Dayananda K. Pai Department of Aeronautical and Automobile Engineering, Manipal Institute of Technology, Manipal – 576104, India.

DOI:

https://doi.org/10.9734/bpi/taiert/v5/16084D

Keywords:

Genetic algorithm, crossover probability, bi-criteria objective, scheduling, parallel machines

Abstract

In this chapter, an investigation of the influence of Crossover Probability on Genetic Algorithm (GA) performance for the bi-criteria objective function to obtain the best solution in a reasonable time in scheduling of parallel machines is studied. A heuristic model for reducing the workload imbalance on the machines considering work-in-process material is developed. The simulation on a proposed genetic algorithm was carried out with a crossover probability of 0.4 to 0.95 (with a step of 0.05) and 0.97, and it was discovered that the results were converging for the crossover probability of 0.6 with a computing time of 3.41 seconds. The suggested algorithm assists the decision maker in analysing the objective function with the computational time.

Published

2023-01-05

How to Cite

B. V. Raghavendra, & Dayananda K. Pai. (2023). Influence of Crossover Probability on Performance of Genetic Algorithm in Scheduling of Parallel Machines. Techniques and Innovation in Engineering Research and Technology Vol. 5, 27–36. https://doi.org/10.9734/bpi/taiert/v5/16084D

Issue

Techniques and Innovation in Engineering Research and Technology Vol. 5

Section

Chapters