Dynamic Multiobjective Optimization Problems: Test Cases, Approximation, and Applications
Parametric and dynamic multiobjective optimization problems for adaptive optimal control are carefully defined; some test problems are introduced for both continuous and discrete design spaces. A simple example of a dynamic multiobjective optimization problems arising from a dynamic control loop is given and an extension for dynamic situation of a previously proposed search direction based method is proposed and tested on the proposed test problems.
Unable to display preview. Download preview PDF.
- 1.Jessica M. Anderson, Tessa M. Sayers, and M. G. H. Bell. Optimization of a Fuzzy Logic Traffic Signal Controller by a Multiobjective Genetic Algorithm. In Proceedings of the Ninth International Conference on Road Transport Information and Control, pages 186–190, London, April 1998. IEE.Google Scholar
- 2.M. Annunziato. http://erg055.casaccia.enea.it/.
- 3.Zafer Bingul, Ali Sekmen, and Saleh Zein-Sabatto. Adaptive Genetic Algorithms Applied to Dynamic Multi-Objective Problems. In Cihan H. Dagli, Anna L. Buczak, Joydeep Ghosh, Mark Embrechts, Okan Ersoy, and Stephen Kercel, editors, Proceedings of the Artificial Neural Networks in Engineering Conference (ANNIE’2000), pages 273–278, New York, 2000. ASME Press.Google Scholar
- 5.J. Branke. Evolutionary approaches to dynamic optimization problems — A survey. Juergen Branke and Thomas Baeck editors: Evolutionary Algorithms for Dynamic Optimization Problems, 13:134–137, 1999.Google Scholar
- 6.C. O. Wilke C. Ronnewinkel and T. Martinetz. Genetic algorithms in time-dependent environments. In L. Kallel, B. Naudts, and A. Rogers, editors, Theoretical Aspects of Evolutionary Computing, pages 263–288, Berlin, 2000. Springer.Google Scholar
- 7.Carlos Manuel Mira de Fonseca. Multiobjective Genetic Algorithms with Applications to Control Engineering Problems. PhD thesis, Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK, September 1995.Google Scholar
- 9.Kalyanmoy Deb, Lothar Thiele, Marco Laumanns, and Eckart Zitzler. Scalable Test Problems for Evolutionary Multi-Objective Optimization. Technical Report 112, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Zurich, Switzerland, 2001.Google Scholar
- 10.M. Farina. A minimal cost hybrid strategy for pareto optimal front approximation. Evolutionary Optimization, 3(1):41–52, 2001.Google Scholar
- 11.J.J. Grefenstette. Genetic algorithms for changing environments. Proc. 2nd International Conference On Parallel problem Solving from Nature, Brussels, 1992.Google Scholar
- 12.J.J. Grefenstette. Evolvability in dynamic fitness landscapes: A genetic algorithm approach. Proc. Congress on Evolutionary Computation (CEC99) Washington DC IEEE press, pages 2031–2038, 1999.Google Scholar
- 13.P. Amato, M. Farina, G. Palma, and D. Porto. An alife-inspired evolutionary algorithm for adaptive control of time-varying systems. In Proceedings of the EUROGEN2001 Conference, Athens, Greece, September 19–21, 2001, pages 227–222. International Center for Numerical Methods in Engineering (CIMNE), Barcelona, Spain, March 2002.Google Scholar
- 14.F. Vavak, K. A. Jukes, and T. C. Fogarty. Performance of a genetic algorithm with variable local search range relative to frequency of the environmental changes. Genetic Programming 1998: Proceedings of the Third Annual Conference, 1998.Google Scholar
- 15.Kazuo Yamasaki. Dynamic Pareto Optimum GA against the changing environments. In 2001 Genetic and Evolutionary Computation Conference. Workshop Program, pages 47–50, San Francisco, California, July 2001.Google Scholar