Quantum Glowworm Swarm Algorithm and Its Application to No-wait Flowshop Scheduling

We propose a novel quantum glowworm swarm optimization algrorithm for minimizing total flow time in no-wait flowshop scheduling. First, we embed the quantum evolutionary mechanism into the glowworm swarm algorithm. Then, we design a fast local neighborhood search algorithm to search the partial neighborhood of each iteration and calculate a neighborhood solution with a target increment. This algorithm not only greatly improves the solution quality, but also increase the speed of convergence. Based on Taillard's benchmark simulation, the results show that the average relative percentage deviation from the best-known solution is reduced by more than 40%, compared with the optimal heuristic algorithm IHA and the swarm intelligence algorithms DGSO, GA-VNS, and DHS. These experimental results verify the superiority of the proposed algorithm in performing no-wait flowshop scheduling.