Interactive multi-objective particle swarm optimization with heatmap-visualization-based user interface

Engineering Optimization

Volumn 42, Issue 2, 2010, Pages 119-139

  Hettenhausen, Jan ^a   Lewis, Andrew ^a   Mostaghim, Sanaz ^b  

^a GRIFFITH UNIVERSITY   (Australia)

^b UNIVERSITY OF KARLSRUHE   (Germany)

Author keywords

Heatmap visualization; Interactive multi objective particle swarm optimization; Interactive optimization; Multi objective optimization

Indexed keywords

DECISION MAKERS; DOMAIN SPECIFIC; DOMAIN-SPECIFIC KNOWLEDGE; HUMAN DECISIONS; HUMAN FATIGUE; INTERACTIVE OPTIMIZATION; MULTI OBJECTIVE PARTICLE SWARM OPTIMIZATION; NON-INTERACTIVE; OPTIMIZATION PROCESS; PARETO-FRONT; SIDE EFFECT; STANDARD TESTS;

GRAPHICAL USER INTERFACES; PARTICLE SWARM OPTIMIZATION (PSO); VISUALIZATION;

MULTIOBJECTIVE OPTIMIZATION;

EID: 77951129563     PISSN: 0305215X     EISSN: 10290273     Source Type: Journal    
DOI: 10.1080/03052150903042632     Document Type: Article

References (28)

1. Agrawal, S., et al., 2008. Interactive particle swarm: a Pareto-adaptive metaheuristic to multiobjective optimization. IEEE Transactions on Systems, Man and Cybernetics - Part A, 38 (2), 258-277.
2. Card, S.K., Mackinlay, J.D., and Shneiderman, B., 1999. Using vision to think. San Francisco, CA: Morgan Kaufmann.
3. Coello, C. and Lechuga, M., 2002. MOPSO: a proposal for multiple objective particle swarm optimization. In: Proceedings of the 2002 Congress on Evolutionary Computation (CEC'02), Honolulu, HI, 12-17 May 2002. Vol.2. Piscataway, NJ, IEEE Computer Society.
4. Deb, K., 1999. Multi-objective genetic algorithms: problem difficulties and construction of test problems. Evolutionary Computation, 7 (3), 205-230.
5. Deb, K., et al., 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6 (2), 182-197.
6. Engelbrecht, A., 2005. Fundamentals of computational swarm intelligence. Chichester, UK:Wiley.
7. Fieldsend, J. and Singh, S., 2002. A multi-objective algorithm based upon particle swarm optimization. In: Proceedings of the UK Workshop on Computational Intelligence, Birmingham, UK, 2-4 September 2002, 34-44.
8. Hayashida, N. and Takagi, H., 2000. Visualized IEC: interactive evolutionary computation with multidimensional data visualization. In: Proceedings of the IEEE International Conference on Industrial Electronics, Control and Instrumentation (IECON2000), Nagoya, Japan, 22-28 October 2000.Washington DC: IEEE Computer Society.
9. Hwang, C.L. and Masud, A.S.M., 1979. Multiple objective decision making: methods and applications. Lecture notes in economics and mathematical systems Vol. 164. Berlin: Springer-Verlag.
10. Ireland, D., et al., 2006. Hybrid particle guide selection methods in multi-objective particle swarm optimization. In: Proceedings of the Second IEEE International Conference on e-Science and Grid Computing (E-SCIENCE '06), Amsterdam, The Netherlands, 4-6 December 2006.Washington, DC: IEEE Computer Society, 116.
11. Kamalian, R., Takagi, H., and Agogino, A.M., 2004. Optimized design of MEMS by evolutionary multi-objective optimization with interactive evolutionary computation. In: Proceedings of the Genetic and Evolutionary Conference (GECCO-2004), Seattle,WA, 26-30 June 2004. Berlin: Springer-Verlag, 1030-1041.
12. Kamalian, R., et al., 2006. Reducing human fatigue in interactive evolutionary computation through fuzzy systems and machine learning systems. In: Proceedings of the IEEE International Conference on Fuzzy Systems, Vancouver, 16-21 July 2006.Washington, DC: IEEE Computer Society.
13. Kennedy, J. and Eberhart, R., 1995. Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks (ICNN'95), Perth, Australia, 27 November - 1 December 1995.Washington, DC: IEEE Computer Society, 4.
14. Knowles, J. and Corne, D., 2000. Approximating the nondominated front using the pareto archived evolution strategy. Evolutionary Computation, 8 (2), 149-172.
15. Máder, J., Abonyi, J., and Szeifert, F., 2005. Interactive particle swarm optimization. In: Proceedings of the 5th International Conference on Intelligent Systems Design and Applications (ISDA'05),Wroklaw, Poland, 8-10 September 2005, Indianapolis, IN, 314-319.
16. Moore, J. and Chapman, R., 1999. Application of particle swarm to multiobjective optimization. Technical report, Department of Computer Science and Software Engineering, Auburn University, Auburn, AL.
17. Mostaghim, S. and Teich, J., 2003. Strategies for finding good local guides in multi-objective particle swarm optimization. In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium (SIS'03), 21-23 September 2003, Washington, DC: IEEE Computer Society, 26-33.
18. Obayashi, S. and Sasaki, D., 2003. Visualization and data mining of Pareto solutions using self-organizing map. In: Proceedings of the Second International Conference on Evolutionary Multi-criterion Optimization, Faro Portugal, 8-11 April 2003. Berlin: Springer, 796-809. (Pubitemid 137634682)
19. Pryke, A., Mostaghim, S., and Nazemi, A., 2007. Heatmap visualization of population based multi objective algorithms. In: Proceedings of the 4th International Conference on Evolutionary Multi-criterion Optimization (EMO 2007), Matsushima, Japan, 5-8 March 2007. Berlin: Springer-Verlag.
20. Reyes-Sierra, M. and Coello, C., 2006. Multi-objective particle swarm optimizers: a survey of the state of the art. International Journal of Computational Intelligence Research, 2 (3), 287-308.
21. Shi, Y. and Eberhart, R., 1998. A modified particle swarm optimizer. In: Proceedings of the IEEE World Congress on Computational Intelligence - International Conference on Evolutionary Computation (ICEC'98), Anchorage, AK, 4-9 May 1998.Washington, DC: IEEE Computer Society, 69-73.
22. Takagi, H., 2000. Active user intervention in an EC search. In: Proceedings of the 5th Joint Conference on Information Sciences (JCIS2000), Atlantic City, NJ, 27 February - 3 March 2000, 995-998.
23. Takagi, H., 2001. Interactive evolutionary computation: fusion of the capabilities ofECoptimization and human evaluation. In: Proceedings of the Institute of Electronic and Electrical Engineers, 89 (9), 1275-1296.
24. Thomas, J. and Cook, K., eds., 2006. Illuminating the path: the research and development agenda for visual analytics. Richland,WA: National Visualization and Analytics Center.
25. Todd, D.S. and Sen, P., 1999. Directed multiple objective search of design spaces using genetic algorithms and neural networks. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'99),Vol. 2, Orlando, FL, 13-17 July. San Francisco, CA: Morgan Kaufman, 1738-1743.
26. Wickramasinghe, U.K. and Li, X., 2008. Integrating user preferences with particle swarms for multi-objective optimization. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation (GECCO '08), Atlanta, GA. NewYork: ACM, 745-752.
27. Zitzler, E., 1999. Evolutionary algorithms for multiobjective optimization: methods and applications. Thesis (PhD). ETH Zürich, Switzerland.
28. Zitzler, E. and Thiele, L., 1998. Multiobjective optimization using evolutionary algorithms - a comparative case study. In: Proceedings of the 5th International Conference on Parallel Problem Solving from Nature. Lecture notes in computer science Vol. 1498. London, UK: Springer-Verlag, 292-304.