Journal Article10.1007/S11269-016-1320-Z
Decomposition based Multi Objective Evolutionary Algorithms for Design of Large-Scale Water Distribution Networks
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TL;DR: Experimental results show that decomposition based multiobjective evolutionary algorithms are very promising in dealing with complicated large-scale WDN design problems and suggest that MOEA/D-HS in particular could provide very high quality solutions with a uniform distribution along the Pareto front preserving the diversity and dominating the solutions of the other algorithms.
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Abstract: In last two decades, multiobjective evolutionary algorithms (MOEAs) have shown their merit for solving different optimization problems within the context of water resources and environmental engineering. MOEAs mainly use the concept of Pareto dominance for obtaining the trade-off solutions considering different criteria. A new alternative method for solving multiobjective problems is multiobjective evolutionary algorithm based on decomposition (MOEA/D) which uses scalarizing the objective functions. In this paper, decomposition strategies are developed for the large-scale water distribution network (WDN) design problems by integrating the concepts of harmony search (HS) and genetic algorithm (GA) within the MOEA/D framework. The proposed algorithms are then compared with two well-known non-dominance based MOEAs: NSGA2 and SPEA2 across four different WDN design problems. Experimental results show that MOEA/D outperform the Pareto dominance methods in terms of both non-domination and diversity criteria. MOEA/D-HS in particular could provide very high quality solutions with a uniform distribution along the Pareto front preserving the diversity and dominating the solutions of the other algorithms. It suggests that decomposition based multiobjective evolutionary algorithms are very promising in dealing with complicated large-scale WDN design problems.
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References
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TL;DR: This study proposes a novel parameter-setting-free technique for two major algorithm parameters (HMCR and PAR) and combines it with the harmony search algorithm and reaches the global optimum with good results.
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Self-Adaptive PSO-GA Hybrid Model for Combinatorial Water Distribution Network Design
TL;DR: In this paper, a hybrid model PSO-GA is presented to effectively utilize local and global search capabilities of particle swarm optimization (PSO) for optimal pipe sizing in a water distribution network.
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Robust multi‐objective optimization for water distribution system design using a meta‐metaheuristic
TL;DR: A meta-algorithm called AMALGAM is applied for the first time to WDS design and uses multiple metaheuristics simultaneously in an attempt to improve optimization performance, demonstrating large cost savings and reliability improvements.
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A novel evolutionary meta-heuristic for the multi-objective optimization of real-world water distribution networks
Edward Keedwell,Soon-Thiam Khu +1 more
TL;DR: 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 Pare to-fronts.
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An efficient hybrid approach for multiobjective optimization of water distribution systems
TL;DR: Results show that the proposed NLP‐SAMODE method consistently generates better‐quality Pareto fronts than the full‐search methods with significantly improved efficiency; and the proposed SAMODE algorithm (no parameter tuning) exhibits better performance than the NSGA‐II with calibrated parameter values in efficiently offering optimal fronts.
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