In order to complete the loading operation in a short time, the genetic algorithm was improved, and the improved mechanisms such as the total crossover of the population and the distributed dynamic penalty function method were proposed. The distribution of ballast water in barge stowage was optimized. The calculation of the ship’s floating state, stability, the check of the program strength, and the improvement of the genetic algorithm were discussed. By simplifying the model, all ballast water tanks were involved in the stowage, so as to better meet the limitation of tide level, tank capacity and strength. A more adaptable load optimization solution was obtained. The results show that the improved genetic algorithm has shorter time and higher efficiency than the basic loading scheme. The improved genetic algorithm was used to optimize the barge loading plan, which met the engineering requirements and shorten the working time of the barge. Therefore, the research on the improved genetic algorithm can effectively improve the quality and reliability of the project, which is beneficial to improve the scientific, safety and reliability of the barge loading operation. This program has important implications for marine engineering.
BharathiC., RekhaD. and VijayakumarV., Genetic algorithm based demand side management for smart grid, Wireless Personal Communications93(2) (2017), 481–502.
2.
NormanB.A. and BeanJ.C., A genetic algorithm methodology for complex scheduling problems, Naval Research Logistics46(2) (2015), 199–211.
3.
HartmannS., A competitive genetic algorithm for resource-constrained project scheduling, Naval Research Logistics45(7) (2015), 733–750.
4.
AickelinU. and DowslandK.A., Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem, Journal of Scheduling3(3) (2015), 139–153.
5.
DingD. and ChouM.C., Stowage planning for container ships: A heuristic algorithm to reduce the number of shifts, European Journal of Operational Research246(1) (2015), 242–249.
6.
ChavesA.A., LeduinoD.S.N.L., et al., Pareto clustering search applied for 3D container ship loading plan problem, Expert Systems with Applications4 (2016), 50–57.
7.
WuS., Nonlinear information data mining based on time series for fractional differential operators, Chaos29 (2019), 013114. DOI: 10.1063/1.5085430
8.
KeQ., WuS., WangM. and ZouY., Evaluation of developer efficiency based on improved DEA model, Wireless Personal Communications102(4) (2018), 3843–3849.
9.
WuS., WangM. and YuntaoZ., Sewage information monitoring system based on wireless sensor, Desalination and Water Treatment121 (2018), 73–83.
10.
TavakkolimoghaddamR., SafariJ. and SassaniF., Reliability optimization of series-parallel systems with a choice of redundancy strategies using a genetic algorithm, Reliability Engineering & System Safety93(4) (2017), 550–556.
11.
FengX., Butler-PurryK.L. and ZourntosT., Real-time electric load management for DC zonal all-electric ship power systems, Electric Power Systems Research154 (2018), 503–514.
12.
MoanT. and Ayala-UragaE., Reliability-based assessment of deteriorating ship structures operating in multiple sea loading climates, Reliability Engineering & System Safety93(3) (2017), 433–446.
13.
ChinH.M., SuC.L. and LiaoC.H., Estimating power pump loads and sizing generators for ship electrical load analysis, IEEE Transactions on Industry Applications52(6) (2016), 4619–4627.
14.
RauwolfG.A., Near-optimal low-thrust orbit transfers generated by a genetic algorithm, Journal of Spacecraft & Rockets33(6) (2015), 859–862.
15.
HaiL., WenS., HongY.Y., et al., Optimal sizing of hybrid PV/diesel/battery in ship power system, Applied Energy158 (2015), 26–34.
16.
KaiW., SunC. and YiJ., Research on adaptive guidance technology of UAV ship landing system based on net recovery, Procedia Engineering99 (2015), 1027–1034.
17.
KoD.E., ShinS.H. and ChoY.J., A study on the application of new lining material for ship propulsion shafting system, IOP Conference Series: Materials Science and Engineering269 (2017), 12–13.
18.
XinS., RuiX., WangG., et al., Vibration control of shipborn multiple launch rocket systems, Journal of Nanjing University of Science & Technology39(1) (2015), 1–6.
19.
GoerlandtF. and MontewkaJ., A framework for risk analysis of maritime transportation systems, A case study for oil spill from tankers in a ship–ship collision, Safety Science76 (2015), 42–66.
20.
IbrahimR.A., Overview of structural life assessment and reliability, part III: Impact, grounding, and reliability of ships under extreme loading, Journal of Ship Production & Design31(3) (2018), 137–169.
21.
DuttaA.K., ElhosenyM., DahiyaV. and ShankarK., An efficient hierarchical clustering protocol for multihop Internet of vehicles communication, Transactions on Emerging Telecommunications Technologies (2019). In Press. DOI: 10.1002/ett.3690
22.
ElhosenyM., ShankarK. and UthayakumarJ., Intelligent diagnostic prediction and classification system for chronic kidney disease, Nature Scientific Reports (2019). In Press. DOI: 10.1038/s41598-019-46074-2.
23.
ElhosenyM. and ShankarK., Reliable data transmission model for mobile ad hoc network using signcryption technique, IEEE Transactions on Reliability (2019), 1–10. In Press. DOI: 10.1109/TR.2019.2915800
24.
KrishnarajN., ElhosenyM., ThenmozhiM., SelimM.M. and ShankarK., Deep learning model for real-time image compression in Internet of Underwater Things (IoUT), Journal of Real-Time Image Processing (2019). In Press. DOI: 10.1007/s11554-019-00879-6.
25.
ElhosenyM. and ShankarK., Optimal bilateral filter and convolutional neural network based denoising method of medical image measurements, Measurement143 (2019), 125–135.
26.
Senthil RagavanV.K., ElhosenyM. and ShankarK., An enhanced whale optimization algorithm for vehicular communication networks, International Journal of Communication Systems (2019). DOI: 10.1002/dac.3953
27.
MuruganB.S., ElhosenyM., ShankarK. and UthayakumarJ., Region-based scalable smart system for anomaly detection in pedestrian walkways, Computers and Electrical Engineering75 (2019), 146–160.
28.
ShankarK., ElhosenyM., Eswaran PerumalM., IlayarajaK. and KumarS., An efficient image encryption scheme based on signcryption technique with adaptive elephant herding optimization, cybersecurity and secure information systems: Challenges and solutions in smart environments, Advanced Sciences and Technologies for Security Applications, ISBN: 978-3-030-16837-7 (2019), 31–42.
