Hybridizing Cellular Automata Principles and NSGAII for Multi-objective Design of Urban Water Networks

Guo, Yufeng; Keedwell, Edward; Walters, Godfrey A.; Khu, Soon‐Thiam

doi:10.1007/978-3-540-70928-2_42

Cited by 22 publications

(11 citation statements)

References 22 publications

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“…Although it does not directly change the optimisation algorithm, the results of preconditioning often improve the computational efficiency of the methodology. Examples from the field of the design of water distribution networks (Kang and Lansey, 2011;Keedwell and Khu, 2005), sewer networks (Guo et al, 2007), and groundwater remediation (Becker et al, 2006) use a set of domain specific rules to improve a solution before using it to seed a GA run. Alternative approaches are based on global sensitivity analysis , the use of engineering judgement and historical information (Pasha and Lansey, 2010), or graph decomposition methods.…”

Section: Current Statusmentioning

confidence: 99%

“…While the advantages of having a universally applicable methodology are obvious, the price to pay is often the loss of computational efficiency or poor quality results when applied to a problem with a large search space. For example, Guo et al (2007), Kadu et al (2008), Keedwell and Khu (2005) and Zheng et al (2011Zheng et al ( , 2013a demonstrate that carefully structured problem specific EA implementations outperform their generic counterparts. From this standpoint, an important question to be answered is: Can more generic methods be developed (e.g.…”

Section: Research Challenges and Future Directionsmentioning

confidence: 99%

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

Maier

Kapelan

Kasprzyk

et al. 2014

Environmental Modelling & Software

532

317

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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: Current Statusmentioning

confidence: 99%

Section: Research Challenges and Future Directionsmentioning

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

532

317

View full text Add to dashboard Cite

show abstract

“…Guo et al [48] hybridized a Non-dominated Sorting GA (NSGAII) with CA and used it for multiobjective design of both water distribution and storm sewer networks. The rst use of CA as a stand-alone optimizer in water resources problems was by Guo et al [49] who proposed a CA model for the optimal design of storm-sewer networks with the objective of minimizing the capital cost of the sewer network and total ooding volume in the system.…”

Section: Cellular Automatamentioning

confidence: 99%

An Adaptive Relaxed Cellular Automata Method for Reliability-Based Hydropower Operation of Reservoirs

Azizipour

Afshar

2017

Scientia Iranica

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Abstract. This paper presents a novel cellular automata Approach as the solution to reliability-based reservoir hydropower operation problems. The method is based on the observation that a low value of the penalty parameter would lead to partial enforcement of the constraints. Therefore, in this method, the constraints of the chance-constrained operation problem, namely operational and reliability constraints, are dealt with di erently. A high enough value of the penalty parameter is used for the rst set while a lower than enough value is used for the second set, leading to strong enforcement of the rst set of the constraint and partial ful llment of the second set. Since the proper value of the penalty parameter to be used for the reliability constraints is not known a priori, an adaptive method is proposed to nd the proper value. The proposed model is used for operation of Dez reservoir in Iran and the results are presented and compared with those of a Genetic Algorithm. Hydropower operation is considered over short, medium, and long-term periods to demonstrate the e ciency and e ectiveness of the proposed method for problems of di erent scales. The proposed model has proven to produce results superior to the results of GA with much reduced computational e ort.

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“…The first use of CA as a stand-alone optimizer was demonstrated by Guo, Walters, Khu, & Keedwell (2007b) for optimal design of storm sewer networks based on the simplifying assumption of known slopes. Afshar and Shahidi (2009) were the first to propose CA with mathematically derived transition rules for the optimal water supply and hydropower operation of a single reservoir.…”

Section: Cellular Automatamentioning

confidence: 99%

Sewer Networks Optimization Using Cellular Automata

Rohani

Afshar²

2013

SET

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The Hybrid Cellular Automata (HCA) method is used in this paper for the optimal design of sewer network problems with the fixed layout. The HCA method decomposes the problem into two sub-problems with considering the pipe diameters and nodal cover depths as decision variables. Two stages are solved iteratively for determining the decision variables in a manner to minimize the total cost of the sewer network subject to the operational constraints. The HCA method is used to optimally solve three benchmark examples with different sizes and the results are presented and compared to those of the existing methods. The results indicate that the HCA method is more efficient and effective than the alternative methods.

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Hybridizing Cellular Automata Principles and NSGAII for Multi-objective Design of Urban Water Networks

Cited by 22 publications

References 22 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

An Adaptive Relaxed Cellular Automata Method for Reliability-Based Hydropower Operation of Reservoirs

Sewer Networks Optimization Using Cellular Automata

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