An improved whale optimization algorithm for job-shop scheduling based on quantum computing

Abstract

The traditional swarm intelligence algorithms are inefficient and difficult to converge to the optimal solution of the job-shop scheduling problem (JSP). In this paper, an improved whale optimization algorithm (IWOA) is proposed based on quantum computing to solve the discrete JSP. The algorithm was subjected to the analysis on computing complexity, the demonstration of global convergence, and simulation verification on a benchmark example of the JSP. Through the simulation, our algorithm achieved better minimum value, mean value and optimization success rate than traditional swarm intelligence algorithms. The results prove the convergence accuracy and global search ability of the IWOA.