Evolutionary algorithm and decisional DNA for multiple travelling salesman problem

Neurocomputing

Volumn 150, Issue Part A, 2015, Pages 50-57

  Wang, Peng ^a   Sanin, Cesar ^a   Szczerbicki, Edward ^b  

^a UNIVERSITY OF NEWCASTLE   (Australia)

^b GDANSK UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

Decisional DNA (DDNA); Evolutionary algorithm; Genetic algorithm; Heuristics; Optimization problem; Set of experience knowledge structure (SOEKS)

Indexed keywords

BIOINFORMATICS; EVOLUTIONARY ALGORITHMS; GENETIC ALGORITHMS; OPTIMAL SYSTEMS; PARETO PRINCIPLE; TRAVELING SALESMAN PROBLEM;

DECISIONAL DNA; HEURISTICS; MULTIPLE OPTIMIZATIONS; NON-DOMINATED SORTING GENETIC ALGORITHM - II; OPTIMIZATION PROBLEMS; PARETO OPTIMAL SOLUTIONS; SET OF EXPERIENCE KNOWLEDGE STRUCTURE; TRAVELLING SALESMAN PROBLEM;

PROBLEM SOLVING;

ARTICLE; DATA ANALYSIS; DECISIONAL DNA; EVOLUTIONARY ALGORITHM; GENETIC ALGORITHM; KNOWLEDGE BASE; SET OF EXPERIENCE KNOWLEDGE STRUCTURES; SMART EXPERIENCE BASED DATA ANALYSIS SYSTEM;

EID: 84912112925     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.01.075     Document Type: Article

References (24)

1. Ouertani M.-Z., Baïna S., Gzara L., Morel G. Traceability and management of dispersed product knowledge during design and manufacturing. Comput.-Aided Des. 2011, 43:546-562.
2. Davenport T.H., Prusak L. Working Knowledge 2000, Harvard Business School Press: Harvard College.
3. F. Leistner, Mastering Organisational Knowledge Flow, John Wiley&Sons, Hoboken, New Jersey.
4. Tiwari A., Vergidis K., Majeed B. Evolutionary multi-objective optimization of business processes. IEEE Congr. Evol. Comput. 2006, 1-6:3076-3082.
5. Sanin C., Szczerbicki E. Experience-based knowledge representation SOEKS. Cybern. Syst. Int. J. (2) 2009, 40:99-122.
6. Sanin C., Szczerbicki E. An OWL ontology of set of experience knowledge structure. J. Univ. Comput. Sci. 2007, 13:209-223.
7. Sanin C., Szczerbicki E. Towards the construction of decisional DNA: a set of experience knowledge structure Java Class within an Ontology System. Cybern. Syst. Int. J. 2007, 38:859-878.
8. Sanin C., Szczerbicki E. Dissimilar sets of experience knowledge structure: a negotiation process for decisional DNA. Cybern. Syst. Int. J. 2007, 38:455-474.
9. Sanin C., Mancilla-Amaya L., Szczerbicki E., CayfordHowell P. Application of a multi-domain knowledge structure: the decisional DNA. Intelligent Systems for Knowledge Management 2009, vol. 252:65-86. Springer, Berlin/Heidelberg.
10. Duong N.N.T., Jo T.H., G.S. Constructing and mining: a semantic-based academic social network. J. Intell. Fuzzy Syst. 2010, 21(3):197-207.
11. Pearl J. Heuristics: Intelligent Search Strategies for Computer Problem Solving 1984, Addison-Wesley Longman Publishing, London.
12. Coello C.A.C., Lamont G.B., Van Veldhuisen D.A. Evolutionary Algorithms for Solving Multi-objective Problems 2007, Springer, Berlin.
13. Erickson M., Mayer A., Horn J. Multi-objective optimal design of groundwater remediation systems: application of the niched Pareto genetic algorithm (NPGA). Adv. Water Res. 2002, 25:51-65.
14. C.M. Fonseca, P.J. Fleming, Multiobjective genetic algorithms, In: IEEE, Colloquium on Genetic Algorithms for Control Systems Engineering, 1993, pp. 6/1-6/5.
15. Banzhaf W., Reeves C.R. Foundations of Genetic Algorithms 5 1999, Morgan Kaufmann Publishers, San Francisco, Calif.
16. Deb K., Pratap A., Agarwal S., Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Congr. Evol. Comput. 2002, 6:182-197.
17. Deb K., Agrawal R.B. Simulated binary crossover for continuous search space. Complex Syst. 1994, 9:1-34.
18. Wang Peng, Sanin C., Szczerbicki E. Introducing the concept of Decisional DNA based web content mining. Cybern. Syst. Int. J. 2012, 43(2):136-142.
19. Wang Peng, Sanin C., Szczerbicki E. Prediction based on integration of decisional DNA and RELIEF-F algorithm. Cybern. Syst. Int. J. 2013, 44:173-183.
20. Hansen M. Use of substitute scalarizing functions to guide a local search based heuristic: the case of moTSP. J. Heuristics 2000, 6:419-431. (2000/08/01).
21. TSPLIB: A Library of Instances for the tsp and Other Related Problems. Available: (accessed July 13, 2013). http://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95/tsp/.
22. Arora, Sanjeev Nearly linear time approximation schemes for Euclidean TSP and other geometric problems, In: Foundations of Computer Science, 1997. Proceedings., 38th Annual Symposium on. IEEE, 1997.
23. Papadimitriou Christos H. The Euclidean travelling salesman problem is NP-complete. Theor. Comput. Sci. 1977, 4.3:237-244.
24. (accessed September 15, 2013). http://www.enseignement.polytechnique.fr/informatique/INF580/hw1/DOC.pdf.