A discrete whale optimization algorithm for the no-wait flow shop scheduling problem

Abstract

A discrete whale optimization algorithm (DWOA) is presented to solve the no-wait flow shop scheduling problem (NWFSSP) with the optimization objective makespan. An effective combination of nearest neighbor (NN) and standard deviation heuristics (SDH) is used to acquire initial solutions of the population. After that, three crossover operators, the two-point crossover (TPX), multiple-point crossover (MPX) and job-based crossover (JBX) operators, are designed to mimics the humpback whales hunting process. Moreover, the dynamic transform mechanism of search process is designed to better balance the exploration and exploitation ability of DWOA. In order to further improve the optimization effect of DWOA, the parallel neighborhood search (PNS) and the serial neighborhood search (SNS) are selected to execute the local search and promoting global search scheme, respectively. Finally, to test the performance of DWOA, extensive experiments are conducted on benchmarks designed by Reeves and Taillard, which contain parameters tuning, effectiveness of the improvement strategies in the DWOA and comparison with several existing algorithms. From the experimental results, it is validated that the effectiveness of the improved mechanisms and the performance of the DWOA which can find better makespan values compared with other algorithms for solving NWFSSP.

A State Transition Diagram and an Artificial Physarum polycephalum Colony Algorithm for the Flexible Job Shop Scheduling Problem with Transportation Constraints

“…It is very important and challenging to design efficient algorithms to address it in large-sized cases, such as simulated annealing (SA) [6] and fuzzy logic (FL) [11]. Among them, swarm intelligence (SI) algorithms have received great attention [23,26], i.e., genetic algorithms (GAs) [2,4,16], particle swarm optimiza-tion (PSO) [6,19], ant colony optimization (ACO) [8], deep learning (DL), artificial neural networks (ANNs) [12,27], artificial bee colony (ABC) [13], adaptive memetic algorithms (AMAs) [14], migrating birds optimization [17], grey wolf optimization (GWO) [20], quantum cat swarm optimization [22], artificial slime mold [28], artificial Physarum swarm [29], coronavirus herd immunity [30], artificial plant community [31,32], whale optimization [33], artificial algae [34], and the Jaya algorithm [35]. However, these swarm intelligence algorithms are also prone to fall into local optimization prematurely, and some scholars have tried to improve algorithm performance using hybrid algorithms [6,36].…”