Multi-Objective tool to optimize the Water Resources Management using Genetic Algorithm and the Pareto Optimality Concept
- 641 Downloads
This paper examines the development of a multi-objective tool, called “ALL_WATER”, in optimizing Water Resources Management. The objectives of satisfying demand and reducing costs were taken into consideration while at the same time respecting water salinity requirements and hydraulic constraints. A Multi-Objective Genetic Algorithm (MOGA) and the PARETO optimality concept were used to resolve the formulated problem. The tool developed was used to help optimize the daily management schedule of a real case study in Tunisia. The hydraulic system is made up of three surface water sources, one demand site, two transfer links and three supply links. Within a short computation time, a PARETO front was identified made up of a set of 72 optimal solutions. The modeling approach and the decision-making flexibility, both shown in the case study, prove that the developed tool is able to efficiently identify a set of optimal solutions on a PARETO front. The developed tool will be able to be used for a large variety of water management problems.
KeywordsALL_WATER Water Management Optimization Genetic Algorithm Tunisia
The authors would like to thank the National Institute of Agronomy of Tunisia (INAT) and the Tunisian National Drinking Water Utility for their support of this work.
- Collette Y and Siarry P (2003) Multiobjective Optimization: Principles and Case Studies. Springer.Google Scholar
- Deb K, Agrawal S, Pratap A and Meyarivan T (2000) A Fast Non-Dominated Genetic Algorithm for Multi-Objective Optimisation : NSGA-II. KanGAL Report No. 200001, Indian Institute of Technology, Kanpur, India.Google Scholar
- Esquivel S, Leiva HA and Gallard RH (1999) Multiplicity in genetic algorithms to face multicriteria optimization. Congress on Evolutionary Computation. Washington D.C., July 1999. IEEE Service Centre.Google Scholar
- Goldberg DE (1991) Genetic Algorithms. Addison-Wesley.Google Scholar
- Knowles J and Corne D (1999) The Pareto Archived Evolution Strategy: A New Baseline Algorithm for Pareto Multiobjective Optimisation. Proceedings of the 1999 Congress on Evolutionary Computation (CEC'99), 1, 98–105.Google Scholar
- Koutsoyiannis D, Karavokiros G, Efstratiadis A. Mamassis N, Koukouvinos A and Christofides A (2003) A decision support system for the management of the water resource system of Athens, Physics and Chemistry of the Earth, Parts A/B/C, 28, 599–609Google Scholar
- Leiva HA, Esquivel SC and Gallard RH (2000) Miltiplicity and Local Search in Evolutionary Algorithms to Build the Pareto Front. Proceedings of the XX international conf. of the Chilean Computer Science Society (SCCC’00). 0-7695-0810-1/00. IEEE Service Centre.Google Scholar
- Lis J and Eiben A E (1997) Multi-sexual genetic algorithm for multiobjective optimization. 4th International conf. On evolutionary computation (ICEC’97). Indiapolis, USA, 59–64.Google Scholar
- Liu S, Gikas P, Papageorgiou LG (2010) An Optimisation-based Approach for Integrated Water Resources Management. Computer & Chemical Engineering 28:1075–1080Google Scholar
- Zitzler E, Thiele L (1998) Multiobjective optimization using evolutionary algorithms – A comparative case study. Proceeding of Parallel Problem Solving Nature V Amsterdam 292–301Google Scholar