A quantum‐inspired cuckoo co‐evolutionary algorithm for no‐wait flow shop scheduling

Abstract

No-wait flow shop scheduling problems (NWFSPs) are widespread in practical applications. The authors propose a quantum-inspired cuckoo co-evolutionary algorithm for the NWFSP to minimize the makespan. There are three algorithm components: quantum solution construction, quantum population evolution, and an improved neighbourhood local search. They generate initial solutions, search solutions, and improve solution qualities, respectively. Parameters of the proposed algorithm are calibrated statistically. The proposal with calibrated parameters is compared with three existing algorithms on Reeves and Taillard benchmark instances with middle scales. Experimental results show that the proposal outperforms the compared algorithms. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Multi-Strategy Discrete Teaching–Learning-Based Optimization Algorithm to Solve No-Wait Flow-Shop-Scheduling Problem

“…Although the BRD values obtained in the Rec37, 39, 41 problem sets were not optimal, the algorithm still showed a better performance. It can also be seen that the average BRD of the MSDTLBO was significantly better than that of the other 30,2,36,24,42,31,27,40,41,4,35,6,10,25,20,7,12,47,19,48,15,14,44,13,18,23,39,26,17,43,1,21,33,38,37,8,28,45,32,49,9,0,5,29,11,34,3,16 comparison algorithms, except for HMM-FPA. Compared to the ARD, the MSDTLBO algorithm did not obtain optimal values on the Rec37 and 39 problem sets, while for the rest of the problem sets, its average ARD value was better than that of the other algorithms.…”

“…The completion time of job j on machine k is calculated using (2). Due to the no-waiting constraint, the necessary start-time delay between the two workpieces is calculated using (3).…”

“…Considering the publication-space limitations for this paper, only some examples of scheduling sequences are provided. For example, the sequence of Rec29 was {14, 28,24,25,1,6,10,3,22,5,9,11,29,0,21,7,8,19,15,16,13,23,12,4,26,27,2,20,17, 18}, with a completion time of 3291. The sequence of Rec33 was {46, 30,2,36,24,42,31,27,40,41,4,35,6,10,25,20,7,12,47,19,48,15,14,44,…”

“…By performing crossover on the personal historical best position of each particle, the population maintains diversity, and the technique is applicable in NWFSP. Zhu et al [2] proposed a new quantum-inspired cuckoo co-search algorithm, which generates initial solutions and searches for solutions through quantum solution formulation, quantum population evolution, and local neighborhood search to improve the quality of solutions. Zhao et al [3] proposed a jigsaw-puzzle-inspired heuristic and constructed a waiting-time matrix to solve the NWFSP.…”

An Evolutionary Algorithm: An Enhancement of Binary Tournament Selection for Fish Feed Formulation

“…In the late 1990s, there was a relatively unknown identifcation of EA [19,[50][51][52]. Tese were due to unavailable platforms of powerful computers at that time [19,53] and some weaknesses of early methodological [14,19].…”