A novel evolutionary meta-heuristic for the multi-objective optimization of real-world water distribution networks |
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Authors: | Edward Keedwell Soon-Thiam Khu |
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Affiliation: | 1. Centre for Water Systems, University of Exeter , Harrison Building, North Park Road, Exeter, EX4 4QF, UK E.C.Keedwell@exeter.ac.uk;3. Centre for Water Systems, University of Exeter , Harrison Building, North Park Road, Exeter, EX4 4QF, UK |
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Abstract: | Genetic algorithms are currently one of the state-of-the-art meta-heuristic techniques for the optimization of large engineering systems such as the design and rehabilitation of water distribution networks. They are capable of finding near-optimal cost solutions to these problems given certain cost and hydraulic parameters. Recently, multi-objective genetic algorithms have become prevalent in the water industry due to the conflicting nature of these hydraulic and cost objectives. The Pareto-front of solutions can aid decision makers in the water industry as it provides a set of design solutions which can be examined by experienced engineers. However, multi-objective genetic algorithms tend to require a large number of objective function evaluations to arrive at an acceptable Pareto-front. This article investigates a novel hybrid cellular automaton and genetic approach to multi-objective optimization (known as CAMOGA). The proposed method is applied to two large, real-world networks taken from the UK water industry. The results show that the proposed cellular automaton approach can provide a good approximation of the Pareto-front with very few network simulations, and that CAMOGA outperforms the standard multi-objective genetic algorithm in terms of efficiency in discovering similar Pareto-fronts. |
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Keywords: | Genetic algorithms Meta-heuristics Large-scale optimization Multi-objective Network design |
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