Multiobjective Evolutionary Algorithms Applied to the Rehabilitation of a Water Distribution System: A Comparative Study

Cheung, Peter Batista; Reis, Luisa Fernanda Ribeiro; Formiga, Klebber Teodomiro Martins; Chaudhry, Fazal Hussain; Ticona, Waldo Gonzalo Cancino

doi:10.1007/3-540-36970-8_47

Cited by 35 publications

(20 citation statements)

References 14 publications

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“…In addition, algorithm performance measures can be used to gain insight into algorithm searching behaviour (e.g. Cheung et al, 2003;Zecchin et al, 2012), as discussed in Section 2.3, or as a stopping criterion (see Section 3.6).…”

Section: Algorithm Performance Assessmentmentioning

confidence: 99%

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Maier

Kapelan

Kasprzyk

et al. 2014

Environmental Modelling & Software

503

306

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In all cases accepted manuscripts should:  link to the formal publication via its DOI  bear a CC-BY-NC-ND license -this is easy to do, click here to find out how  if aggregated with other manuscripts, for example in a repository or other site, be shared in alignment with our hosting policy  not be added to or enhanced in any way to appear more like, or to substitute for, the published journal article AbstractThe development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g., model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts.

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Section: Algorithm Performance Assessmentmentioning

confidence: 99%

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Maier

Kapelan

Kasprzyk

et al. 2014

Environmental Modelling & Software

503

306

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“…A compact measure of the hydraulic performance has then to be considered as the second objective of the WDS design. In this context, variants of pressure surplus to maximise (Gessler and Walski, 1985) or pressure deficit to minimise (Cheung et al, 2003;Farmani et al, 2005;Olsson et al, 2009) were initially used. However, these aforementioned formulations did not necessarily lead to looped networks, which are reliable configurations under abnormal conditions (e.g., pipe burst).…”

Section: Two-objective Design Of a Wdsmentioning

confidence: 99%

“…In the context of network design, the multi-objective approach (Cheung et al, 2003;Farmani et al, 2003;Halhal et al, 1997;McClymont, 2012;Perelman et al, 2008) has recently been gaining more and more favour than the singleobjective approach (Babayan et al, 2005;Cisty, 2010;Savic and Walters, 1997), which may lead to network solutions featuring poor hydraulic performance since it is only based on economic concerns (Walski, 2001;. Various multi-objective evolutionary algorithms (MOEAs), which are capable of approximating the trade-off among different objectives (Pareto front-PF) in a single run (Zitzler and Thiele, 1999), have then been applied to solve small-to-medium sized benchmark problems and some large problems based on the real-world networks.…”

Section: Introductionmentioning

confidence: 99%

Comparing Low and High-Level Hybrid Algorithms on the Two-Objective Optimal Design of Water Distribution Systems

Wang

Creaco

Franchini

et al. 2014

Water Resour Manage

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This paper presents the comparison of two hybrid methodologies for the two-objective (cost and resilience) design of water distribution systems. The first of them is a low level hybrid algorithm (LLHA), in which a main controller (the non-dominated genetic algorithm II, NSGA-II) coordinates various subordinate algorithms. The second methodology is a high level hybrid algorithm (HLHA), in which various sub-algorithms collaborate in parallel.Applications to four case studies of increasing complexity enable the performances of the hybrid algorithms to be compared with each other and with the performance of the benchmark NSGA-II. In the case study featuring low/intermediate complexity, the hybrid algorithms (especially the HLHA) successfully capture a more diversified Pareto front, although the NSGA-II shows the best convergence. When network complexity increases, instead, the hybrid algorithms (especially the LLHA) turn out to be superior in terms of both convergence and Pareto front diversification. With respect to both the HLHA and the NSGA-II, the LLHA is capable of detecting the final front in a single run with a small computation burden; the HLHA and the NSGA-II, which are more affected by the initial random seed, require, instead, numerous runs with an attempt to reach the definitive Pareto front, as the envelope/tangle of the Pareto fronts obtained at the end of the various runs. On the other hand, a drawback of the LLHA lies in its reduced ability to deal with general problem formulations, i.e., those not relating to water distribution optimal design).

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“…These problems are typically multi-objective (often trading off financial cost against requirements for pressure, speed of flow and other aspects of the required design), and there is an increasing body of published research which addresses such problems, e.g. : [2,26,9,23,6,4,7,1] However, the need to accelerate optimisation for such problems has not so far been seriously tackled. This is presumably because the problems involved are those of design, and sufficient time often exists to allow long optimisation runs before a final design is to be scrutinised and approved.…”

Section: Introductionmentioning

confidence: 99%

Preliminary Investigation of the ‘Learnable Evolution Model’ for Faster/Better Multiobjective Water Systems Design

Jourdan

Corne

Savić

et al. 2005

Lecture Notes in Computer Science

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Abstract. The design of large scale water distribution systems is a very difficult optimisation problem which invariably requires the use of timeexpensive simulations within the fitness function. The need to accelerate optimisation for such problems has not so far been seriously tackled. However, this is a very important issue, since as MOEAs become more and more recognised as the 'industry standard' technique for water system design, the demands placed on such systems (larger and larger water networks) will quickly meet with problems of scaleup. Meanwhile, LEM (Learnable Evolution Model') has appeared in the Machine Learning literature, and provides a general approach to integrating machine learning into evolutionary search. Published results using LEM show very great promise in terms of finding near-optimal solutions with significantly reduced numbers of evaluations. Here we introduce LEMMO (Learnable Evolution Model for Multi-Objective optimization), which is a multiobjective adaptation of LEM, and we apply it to certain problems commonly used as benchmarks in the water systems community. Compared with NSGA-II, we find that LEMMO both significantly improves performance, and significantly reduces the number of evaluations needed to reach a given target. We conclude that the general approach used in LEMMO is a promising direction for meeting the scale-up challenges in multiobjective water system design.

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Multiobjective Evolutionary Algorithms Applied to the Rehabilitation of a Water Distribution System: A Comparative Study

Cited by 35 publications

References 14 publications

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Comparing Low and High-Level Hybrid Algorithms on the Two-Objective Optimal Design of Water Distribution Systems

Preliminary Investigation of the ‘Learnable Evolution Model’ for Faster/Better Multiobjective Water Systems Design

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