A novel competitive co-evolutionary quantum genetic algorithm for stochastic job shop scheduling problem

In this paper, a novel competitive co-evolutionary quantum genetic algorithm (CCQGA) is proposed for a stochastic job shop scheduling problem (SJSSP) with the objective to minimize the expected value of makespan. Three new strategies named as competitive hunter, cooperative surviving and the big fish eating small fish are developed in population growth process. Based on improved co-evolution idea of multi-population and concepts of quantum theory, this algorithm could not only adjust population size dynamically to increase the diversity of genes and avoid premature convergence, but also accelerate the convergence speed with Q-bit representation and quantum rotation gate. FT benchmark-based problems where the processing times are subjected to independent normal distributions are solved effectively by CCQGA. The experiment results achieved by CCQGA are compared with quantum-inspired genetic algorithm (QGA) and standard genetic algorithm (GA), which shows that CCQGA has better feasibility and effectiveness.