Multi-objective optimisation of cancer chemotherapy using smart PSO with decomposition

IEEE SSCI 2011 - Symposium Series on Computational Intelligence - MCDM 2011: 2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making

Volumn , Issue , 2011, Pages 81-88

  Al Moubayed, Noura ^a   Petrovski, Andrei ^a   McCall, John ^a  

^a ROBERT GORDON UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ANTICANCER DRUG; CANCER CHEMOTHERAPY; CANCEROUS CELLS; CHEMOTHERAPEUTIC TREATMENTS; COMPUTATIONAL INTELLIGENCE TECHNIQUES; PARTICLE SWARM;

AGGLOMERATION; ALGORITHMS; ARTIFICIAL INTELLIGENCE; CHEMOTHERAPY; DECISION MAKING; DISEASES; PARTICLE SWARM OPTIMIZATION (PSO);

MULTIOBJECTIVE OPTIMIZATION;

EID: 79961149576     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SMDCM.2011.5949264     Document Type: Conference Paper

References (34)

1. Y. Liang, K.-S. Leung, and T. Mok. Evolutionary drug scheduling models with different toxicity metabolism in cancer chemotherapy. Applied Soft Computing, 1(8):140-49, 2008. (Pubitemid 47374551)
2. A. Petrovski. An Application of Genetic Algorithms to Chemotherapy Treatment. PhD thesis, Robert Gordon University, Aberdeen, United Kingdom, 1999.
3. A. Petrovski and J. McCall. Decision support system for cancer chemotherapy using genetic algorithms. In International Conference on Computational Intelligence for Modelling, Control and Automation, volume 1. IOS Press, 1999.
4. B. Sudha A. Petrovski and J. McCall. Optimising Cancer Chemotherapy Using Particle Swarm Optimisation and Genetic Algorithms, volume 3242 of Lecture Notes in Computer Science. Springer, 2004.
5. Z. Wang, G.L. Durst, R.C. Eberhart, D.B. Boyd, and Z. Ben Miled. Particle swarm optimization and neural network application for qsar. Parallel and Distributed Processing Symposium, International, 10:194, 2004.
6. B. Jaishia and W. Ren. Finite element model updating based on eigenvalue and strain. Mechanical Systems and Signal Processing, 21(5):2295-2317, 2007.
7. B. Awwad Shiekh Hasan, J.Q. Gan, and Q. Zhang. Multi-objective evolutionary methods for channel selection in brain-computer interfaces: some preliminary experimental results. In WCCI, pages 3339-3344, Barcelona, Spain, July 2010. IEEE.
8. Y. Li, D. Yao, J. Yao, and W. Chen. A particle swarm optimization algorithm for beam angle selection in intensity-modulated radiotherapy planning. Physics in Medicine and Biology, 1(50):3491-3514, 2005. (Pubitemid 41019093)
9. M. Reyes-Sierra and C.A.C. Coello. Multi-objective particle swarm optimizers: A survey of the state-of-the-art. International Journal of Computational Intelligence Research, 2(3):287-308, 2006.
10. A.M. Baltar and D.G. Fontane. A generalized multiobjective particle swarm optimization solver for spreadsheet models: application to water quality. In the twenty sixth annual American geophysical union hydrology days, 2006.
11. R. Hassan, B. Cohanim, O. de Weck, and G. Venter. A comparison of particle swarm optimization and the genetic algorithm. In Structural Dynamics and Materials, Texas, USA, 2005.
12. W. Peng and Q. Zhang. A decomposition-based multi-objective particle swarm optimization algorithm for continuous optimization problems. In IEEE International Conference on Granular Computing, Hangzhou, 2008.
13. N. Al Moubayed, A. Petrovski, and J. McCall. A novel smart particle swarm optimisation using decomposition. In Parallel Problem Solving from Nature - PPSN XI, Part II, volume 6239 of Lecture Notes in Computer Science, pages 1-10. Springer Berlin / Heidelberg, September 2010.
14. N. Al Moubayed, B. Awwad Shiekh Hasan, J. Q. Gan, A. Petrovski, and J. McCall. Binary-sdmopso and its application in channel selection for brain-computer interfaces. In 10th Annual Workshop on Computational Intelligence- UKCI, Colchester, UK, September 2010. IEEE.
15. A. Petrovski and J. McCall. Multi-objective optimisation of cancer chemotherapy using evolutionary algorithms. In The First International Conference on Evolutionary Multi-Criterion Optimisation, Zurich, Switzerland, 2001.
16. K. C. Tan, E. F. Khor, J. Cai, C. M. Heng, and T. H. Lee. Automating the drug scheduling of cancer chemotherapy via evolutionary computation. Artificial Intelligence in Medicine, 25:169-185, June 2002. (Pubitemid 34553373)
17. R. Allmendinger and J. Knowles. Evolutionary optimization on problems subject to changes of variables. In Parallel Problem Solving from Nature - PPSN XI, Part II, volume 6239 of Lecture Notes in Computer Science, pages 151-160. Springer Berlin / Heidelberg, September 2010.
18. S.-S. Feng and S. Chien. Chemotherapeutic engineering: Application and further development of chemical engineering principles for chemotherapy of cancer and other diseases. Chemical Engineering Science, 1(58):4087-4114, 2003. (Pubitemid 37164365)
19. J. Harrold and R. Parker. Clinically relevant cancer chemotherapy dose scheduling via mixed-integer optimization. Computers and Chemical Engineering, 1(33):2042-54, 2009.
20. T. Wheldon. Mathematical Models in Cancer Research. Taylor & Francis, January 1988.
21. R. Martin and K. L. Teo. Optimal Control of Drug Administration in Cancer Chemotherapy. World Scientific Pub Co Inc, 1994.
22. D. Dearnaly. Handbook of adult cancer chemotherapy schedules. The Medicine Group (Education) Ltd, Oxfordshire, 1995.
23. J. Cassidy and H. McLeod. Is it possible to design a logical development plan for an anti-cancer drug. Pharmaceutical Medicine, 9:95-103, 1995.
24. C.A. Coello Coello, G. B. Lamont, and D.A. van Veldhuizen. Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd ed. Springer, January 2007.
25. Q. Zhang and H. Li. Moea/d: A multi-objective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, 11(6):712-731, 2007. (Pubitemid 350187486)
26. A. Jaszkiewicz. On the performance of multiple-objective genetic local search on the 0/1 knapsack problem - a comparative experiment. IEEE Transactions on Evolutionary Computation, 6(4):402-412, 2002. (Pubitemid 35032741)
27. M. Reyes-Sierra and C.A.C. Coello. Improving PSO-based multiobjective optimization using crowding, mutation and ε-dominance, volume 3410 of Lecture Notes in Computer Science. Springer, 2005.
28. S. Mostaghim and J. Teich. Strategies for finding good local guides in multi-objective particle swarm optimization. In IEEE Swarm Intelligence Symposium SIS'03, pages 26-33, Indianapolis, USA, 2003.
29. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation, 6(2):181-197, April 2002.
30. T. El-Ghazali. Metaheuristics: from design to implementation. John Wiley & Sons, 2009.
31. J. Knowles and D. Corne. On metrics for comparing nondominated sets. E-Commerce Technology, IEEE International Conference on, 1:711-716, 2002.
32. M. Villasana, G. Ochoa, and S. Aguilar. Modeling and optimization of combined cytostatic and cytotoxic cancer chemotherapy. Artificial Intelligence in Medicine, 50(3):163-173, 2010.
33. J.J. Durillo, A.J. Nebro, F. Luna, B. Dorronsoro, and E. Alba. jMetal: A Java Framework for Developing Multi-Objective Optimization Metaheuristics. Technical Report ITI-2006-10, Departamento de Lenguajes y Ciencias de la Computación, University of Málaga, E.T.S.I. Informática, Campus de Teatinos, 2006.
34. J. J. Durillo, A. J. Nebro, and E. Alba. The jmetal framework for multiobjective optimization: Design and architecture. In IEEE Congress on Evolutionary Computation, pages 4138-4325, 2010.