Aaron C. Zecchin

Parametric study for an ant algorithm applied to water distribution system optimization

Much research has been carried out on the optimization of water distribution systems (WDSs). Within the last decade, the focus has shifted from the use of traditional optimization methods, such as linear and nonlinear programming, to the use of heuristics derived from nature (HDNs), namely, genetic algorithms, simulated annealing and more recently, ant colony optimization (ACO), an optimization algorithm based on the foraging behavior of ants. HDNs have been seen to perform better than more traditional optimization methods and amongst the HDNs applied to WDS optimization, a recent study found ACO to outperform other HDNs for two well-known case studies. One of the major problems that exists with the use of HDNs, particularly ACO, is that their searching behavior and, hence, performance, is governed by a set of user-selected parameters. Consequently, a large calibration phase is required for successful application to new problems. The aim of this paper is to provide a deeper understanding of ACO parameters and to develop parametric guidelines for the application of ACO to WDS optimization. For the adopted ACO algorithm, called AS/sub i-best/ (as it uses an iteration-best pheromone updating scheme), seven parameters are used: two decision policy control parameters /spl alpha/ and /spl beta/, initial pheromone value /spl tau//sub 0/, pheromone persistence factor /spl rho/, number of ants m, pheromone addition factor Q, and the penalty factor (PEN). Deterministic and semi-deterministic expressions for Q and PEN are developed. For the remaining parameters, a parametric study is performed, from which guidelines for appropriate parameter settings are developed. Based on the use of these heuristics, the performance of AS/sub i-best/ was assessed for two case studies from the literature (the New York Tunnels Problem, and the Hanoi Problem) and an additional larger case study (the Doubled New York Tunnels Problem). The results show that AS/sub i-best/ achieves the best performance presented in the literature, in terms of efficiency and solution quality, for the New York Tunnels Problem. Although AS/sub i-best/ does not perform as well as other algorithms from the literature for the Hanoi Problem (a notably difficult problem), it successfully finds the known least cost solution for the larger Doubled New York Tunnels Problem.

Battle of the Water Networks II

The Battle of the Water Networks II (BWN-II) is the latest of a series of competitions related to the design and operation of water distribution systems (WDSs) undertaken within the Water Distribution Systems Analysis (WDSA) Symposium series. The BWN-II problem specification involved a broadly defined design and operation problem for an existing network that has to be upgraded for increased future demands, and the addition of a new development area. The design decisions involved addition of new and parallel pipes, storage, operational controls for pumps and valves, and sizing of backup power supply. Design criteria involved hydraulic, water quality, reliability, and environmental performance measures. Fourteen teams participated in the Battle and presented their results at the 14th Water Distribution Systems Analysis conference in Adelaide, Australia, September 2012. This paper summarizes the approaches used by the participants and the results they obtained. Given the complexity of the BWN-II problem and the innovative methods required to deal with the multiobjective, high dimensional and computationally demanding nature of the problem, this paper represents a snap-shot of state of the art methods for the design and operation of water distribution systems. A general finding of this paper is that there is benefit in using a combination of heuristic engineering experience and sophisticated optimization algorithms when tackling complex real-world water distribution system design problems

ANT COLONY OPTIMIZATION FOR THE DESIGN OF WATER DISTRIBUTION SYSTEMS

During the last decade, evolutionary methods such as genetic algorithms have been used extensively for the optimal design and operation of water distribution systems. More recently, ant colony optimization algorithms (ACOAs), which are evolutionary methods based on the foraging behavior of ants, have been successfully applied to a number of benchmark combinatorial optimization problems. In this paper, a formulation is developed which enables ACOAs to be used for the optimal design of water distribution systems. This formulation is applied to two benchmark water distribution system optimization problems and the results are compared with those obtained using genetic algorithms. The findings of this study indicate that ACOAs are an attractive alternative to GAs for the optimal design of water distribution systems, as they outperformed GAs for the two case studies considered both in terms of computational efficiency and their ability to find near global optimal solutions.

An efficient decomposition and dual-stage multi-objective optimization method for water distribution systems with multiple supply sources

This paper proposes an efficient decomposition and dual-stage multi-objective optimization (DDMO) method for designing water distribution systems with multiple supply sources (WDS-MSSs). Three phases are involved in the proposed DDMO approach. In Phase 1, an optimal source partitioning cut-set is identified for a WDS-MSS, allowing the entire WDS-MSS to be decomposed into sub-networks. Then in Phase 2 a non-dominated sorting genetic algorithm (NSGA-II) is employed to optimize the sub-networks separately, thereby producing an optimal front for each sub-network. Finally in Phase 3, another NSGA-II implementation is used to drive the combined sub-network front (an approximate optimal front) towards the Pareto front for the original complete WDS-MSS. Four WDS-MSSs are used to demonstrate the effectiveness of the proposed approach. Results obtained show that the proposed DDMO significantly outperforms the NSGA-II that optimizes the entire network as a whole in terms of efficiently finding good quality optimal fronts. Incorporate graph decomposition for multi-objective optimization of water networks.Develop a two-stage algorithm for multi-objective optimization of water networks.Use real-world networks up to 525 decision variables to verify the proposed method.Proposed method shows great efficiency in terms of finding Parent fronts.Provide an efficient decision-making tool for the water network optimization.

Improved PMI-based input variable selection approach for artificial neural network and other data driven environmental and water resource models

Abstract Input variable selection (IVS) is one of the most important steps in the development of artificial neural network and other data driven environmental and water resources models. Partial mutual information (PMI) is one of the most promising approaches to IVS, but has the disadvantage of requiring kernel density estimates (KDEs) of the data to be obtained, which can become problematic when the data are non-normally distributed, as is often the case for environmental and water resources problems. In order to overcome this issue, preliminary guidelines for the selection of the most appropriate methods for obtaining the required KDEs are determined based on the results of 3780 trials using synthetic data with distributions of varying degrees of non-normality and six different KDE techniques. The validity of the guidelines is confirmed for two semi-real case studies developed based on the forecasting of river salinity and rainfall-runoff modelling problems.

Detection of Distributed Deterioration in Single Pipes Using Transient Reflections

A number of different methods that use signal processing of fluid transients (water hammer waves) for fault detection in pipes have been proposed in the past two decades. However, most of them focus solely on the detection of discrete deterioration, such as leaks or discrete blockages. Few studies have been conducted on the detection of distributed deterioration, such as extended sections of corrosion and extended blockages. This is despite the fact that they commonly exist and can have a severe negative impact on the operation of pipelines. The research reported here proposes a method of detecting distributed deterioration by investigating the time-domain water hammer response trace from a single pipe with a deteriorated section. Through wave analysis using a step pressure input, a theoretical square-shaped perturbation is found to exist in the transient pressure trace as a result of distributed deterioration. The hydraulic impedance of this section can be derived from the magnitude of the reflected pressure perturbation, while the location and length of the corresponding deteriorated section can be determined by using the arrival time and duration of the perturbation. The proposed method has been validated by analyzing experimental data measured from a pipe with a section of wall thickness change.

Single-Event Leak Detection in Pipeline Using First Three Resonant Responses

Hydraulic transients (water hammer waves) can be used to excite a pressurized pipeline, yielding the frequency response diagram (FRD) of the system. The FRD of a pipeline system is useful for condition assessment and fault detection, because it is closely related to the physical properties of the pipeline. Most previous FRD-based leak detection techniques use the sinusoidal leak-induced pattern recorded on the FRD, either shown on the resonant responses or the antiresonant responses. In contrast, the technique reported in the current paper only uses the responses at the first three resonant frequencies to determine the location and size of a leak. The bandwidth of the excitation only needs to be five times that of the fundamental frequency of the tested pipeline, which is much less than the requirement in conventional FRD-based techniques. Sensitivity analysis and numerical simulations are performed to assess the robustness and applicable range of the proposed leak location technique. The proposed leak location technique is verified by both numerical simulations and by using an experimental FRD obtained from a laboratory pipeline.

Comparison of the Searching Behavior of NSGA-II, SAMODE, and Borg MOEAs Applied to Water Distribution System Design Problems

AbstractA number of multiobjective evolutionary algorithms (MOEAs) have been developed and applied to water resource optimization problems over the past decade. The comparative performance of these MOEAs has been investigated often according to their overall end-of-run results (quality of optimal fronts) within prespecified computational budgets. Despite the importance of such comparative analyses, these studies have provided little knowledge of how different MOEAs navigate through the decision space toward the Pareto front. To address this issue, this paper uses a range of metrics to quantitively characterize MOEAs’ run-time searching behavior, with a focus on the statistics of search quality and convergence progress. The metrics are applied to three state-of-the-art MOEAs, including the nondominated sorting genetic algorithm-II (NSGA-II), self-adaptive multiobjective differential evolution (SAMODE), and Borg, for six water distribution system (WDS) design problems with the objectives of minimizing netwo...

An efficient hybrid approach for multiobjective optimization of water distribution systems

An efficient hybrid approach for the design of water distribution systems (WDSs) with multiple objectives is described in this paper. The objectives are the minimization of the network cost and maximization of the network resilience. A self-adaptive multiobjective differential evolution (SAMODE) algorithm has been developed, in which control parameters are automatically adapted by means of evolution instead of the presetting of fine-tuned parameter values. In the proposed method, a graph algorithm is first used to decompose a looped WDS into a shortest-distance tree (T) or forest, and chords (Ω). The original two-objective optimization problem is then approximated by a series of single-objective optimization problems of the T to be solved by nonlinear programming (NLP), thereby providing an approximate Pareto optimal front for the original whole network. Finally, the solutions at the approximate front are used to seed the SAMODE algorithm to find an improved front for the original entire network. The proposed approach is compared with two other conventional full-search optimization methods (the SAMODE algorithm and the NSGA-II) that seed the initial population with purely random solutions based on three case studies: a benchmark network and two real-world networks with multiple demand loading cases. Results show that (i) the proposed NLP-SAMODE method consistently generates better-quality Pareto fronts than the full-search methods with significantly improved efficiency; and (ii) the proposed SAMODE algorithm (no parameter tuning) exhibits better performance than the NSGA-II with calibrated parameter values in efficiently offering optimal fronts.

Coupled Binary Linear Programming–Differential Evolution Algorithm Approach for Water Distribution System Optimization

AbstractA coupled binary linear programming–differential evolution (BLP-DE) approach is proposed in this paper to optimize the design of water distribution systems (WDS). Three stages are involved in the proposed BLP-DE optimization method. In the first stage, the WDS that is being optimized is decomposed into trees and the core using a graph algorithm. Binary linear programming is then used to optimize the design of the trees during the second stage. In the third stage, a differential evolution (DE) algorithm is utilized to deal with the core design while incorporating the optimal solutions for the trees obtained in the second stage, thereby yielding near-optimal solutions for the original whole WDS. The proposed method takes advantage of both the BLP and DE algorithms: BLP is capable of providing a global optimal solution for the trees (no loops involved) with great efficiency, and a DE is able to efficiently generate good quality solutions for the core (loops involved) with a reduced search space compa...