A Genetic Algorithm for Mixed-Integer Optimisation in Power and Water System Design and Control
This chapter presents a dual mutation Genetic Algorithm (GA) which employs individual mutation schemes for different types of decision variables in optimal design and control problems. The GA is integrated with nuclear power station whole plant design and performance optimisation and optimal water system quality and quantity control problems. The objective of the nuclear power station design problem is to improve the overall performance of the thermal cycle of a nuclear power plant by optimising both station design and operation using integrated evolutionary search and conventional optimisation techniques. The objective of the water system control problem is to find the best trajectories of control variables through the controlled time period with minimum cost. The problems pursued are in the class of mixed-integer, nonlinear constrained optimisation problems. After an initial parametric study of various adaptive search and classical optimisation techniques to determine their relative potential within a search space characterised by heavy nonlinear constraints, a hybrid approach has been developed. This firstly utilises a genetic algorithm (GA) as a preprocessor to identify a feasible region within the search space before employing a dual-mutation GA strategy to search the space of mixed-integer variables. A linear programming optimisation routine then periodically searches from the best GA points with the design/control configuration fixed to return an optimal solution in terms of system performance.
KeywordsGenetic Algorithm Water Supply System Nuclear Power Station Search Path Boiler Feed Water
Unable to display preview. Download preview PDF.
- Anderson, A., Gane, C. R., and Scruton, B., “NUMEG suite of Whole Plant Models for performance optimisation and condition monitoring,” EPRI Nuclear Plant Performance Improvement Seminar, Ashville, N. Carolina, USA, September 3–4, 1996.Google Scholar
- Nemhauser, G. L. and Wolsey, L.A., Integer and combinatorial optimisation, John Wiley, New York (1989).Google Scholar
- Goldberg, D.E., Genetic algorithms in search, optimization and machine learning, Addison Wesley (1989).Google Scholar
- Parmee, I. C. and Denham, M. J., “The integration of adaptive search with current engineering design practice,” Proc. Adaptive Computing in Engineering Design and Control, PEDC, University of Plymouth, September 21–22, 1994.Google Scholar
- Parmee, I. C., “Diverse evolutionary search for preliminary whole system design,” Proc. 4th Int. Conf. on AI in Civil & Structural Engineering, Cambridge University, Civil-Comp Press, August 1995.Google Scholar
- Parmee, I. C., “The development of a dual-agent strategy for efficient search across whole system engineering hierarchies,” Proc. 4th Int. Conf. on Parallel Solving from Nature, Berlin, September 22–27, 1996.Google Scholar
- Parmee, I. C., Gane, C. R., Donne, M., and Chen, K. “Genetic strategies for the design and optimal operation of thermal systems,” Proc. 4th European Congress on Intelligent Techniques and Soft Computing, Aachen, Germany, September 2–5, 1996.Google Scholar
- Chen, K., Set membership state and parameter estimation and operational control of quality and quantity models of water supply and distribution systems, PhD thesis, Birmingham Univ., United Kingdom (1997).Google Scholar
- Rossman, L.A., Epanet users manual, Technical report, United States Environment Protection Agency (EPA), (1993).Google Scholar
- Chen, K., Parmee, I. C., and Gane, C. R., “Dual mutation strategies for mixed-integer optimisation in power station design,” Proc. 1997 IEEE Int. Conf. on Evolutionary Computations (ICEC’97), Indianapolis, USA, April 13–16, 1997.Google Scholar
- Siddal, J. N., Optimal engineering design: principles and applications, Marced Dekker Inc., New York (1982).Google Scholar
- Dasguta, D. and McGregor, D., A structured genetic algorithm, Technical Report Research Report IKBS-2–91, University of Strathclyde, Glasgow, United Kingdom (1991).Google Scholar