Paper Info
Title
An efficient discrete invasive weed optimization for blocking flow-shop scheduling problem.
Abstract
This paper proposes a discrete invasive weed optimization (DIWO) to solve the blocking flow-shop scheduling problem (BFSP) with makespan criterion, which has important practical applications in modern industry. In the proposed DIWO, an effective heuristic and the random method are combined to generate an initial plant population with high quality and diversity. To keep the searching ability and efficiency, a random-insertion-based spatial dispersal is presented by means of the normal distribution. Moreover, a shuffle-based referenced local search is embedded to further enhance local exploitation ability. An improved competitive exclusion is developed to determine an offspring plant population with good quality and diversity. The parameters setting is investigated based on a design-of-experiment approach. The effectiveness and applicability of the proposed spatial dispersal and local search are confirmed through numerical comparisons. Finally, a comprehensive computational evaluation including several state-of-the-art algorithms, together with statistical analyses, show that the proposed DIWO algorithm produces better results than all compared algorithms by significant margin.
Year
DOI
Venue
2019
10.1016/j.engappai.2018.11.005
Engineering Applications of Artificial Intelligence
Keywords
Field
DocType
Flow-shop scheduling with blocking,Makespan,Invasive weed optimization,Spatial dispersal,Local search
Population,Heuristic,Weed,Mathematical optimization,Normal distribution,Job shop scheduling,Computer science,Flow shop scheduling,Artificial intelligence,Local search (optimization),Biological dispersal,Machine learning
Journal
Volume
ISSN
Citations 
78
0952-1976
1
PageRank 
References 
Authors
0.35
33
4
Name
Order
Citations
PageRank
Zhongshi Shao1927.91
De-Chang Pi217739.40
Weishi Shao3333.44
Peisen Yuan441.74