Masoud Asadzadeh

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

Pareto archived dynamically dimensioned search with hypervolume-based selection for multi-objective optimization

Pareto archived dynamically dimensioned search (PA-DDS) is a parsimonious multi-objective optimization algorithm with only one parameter to diminish the users effort for fine-tuning algorithm parameters. This study demonstrates that hypervolume contribution (HVC) is a very effective selection metric for PA-DDS and Monte Carlo sampling-based HVC is very effective for higher dimensional problems (five objectives in this study). PA-DDS with HVC performs comparably to algorithms commonly applied to water resources problems (ϵ-NSGAII and AMALGAM under recommended parameter values). Comparisons on the CEC09 competition show that with sufficient computational budget, PA-DDS with HVC performs comparably to 13 benchmark algorithms and shows improved relative performance as the number of objectives increases. Lastly, it is empirically demonstrated that the total optimization runtime of PA-DDS with HVC is dominated (90% or higher) by solution evaluation runtime whenever evaluation exceeds 10 seconds/solution. Therefore, optimization algorithm runtime associated with the unbounded archive of PA-DDS is negligible in solving computationally intensive problems.

A benchmarking framework for simulation-based optimization of environmental models

Simulation models assist with designing and managing environmental systems. Linking such models with optimization algorithms yields an approach for identifying least-cost solutions while satisfying system constraints. However, selecting the best optimization algorithm for a given problem is non-trivial and the community would benefit from benchmark problems for comparing various alternatives. To this end, we?propose a set of six guidelines for developing effective benchmark problems for simulation-based optimization.The proposed guidelines were used to investigate problems involving sorptive landfill liners for containing and treating hazardous waste. Two solution approaches were applied to these types of problems for the first time - a pre-emptive (i.e. terminating simulations early when appropriate) particle swarm optimizer (PSO), and a hybrid discrete variant of the dynamically dimensioned search algorithm (HD-DDS). Model pre-emption yielded computational savings of up to 70% relative to non-pre-emptive counterparts. Furthermore, HD-DDS often identified globally optimal designs while incurring minimal computational expense, relative to alternative algorithms. Results also highlight the usefulness of organizing decision variables in terms of cost values rather than grouping by material type.

A new multi-objective algorithm, pareto archived DDS

The dynamically Dimensioned Search (DDS) continuous global optimization algorithm [5] is modified to solve continuous multi-objective unconstrained optimization problems. Inspired by Pareto Archived Evolution Strategy (PAES), the proposed multi-objective optimization, PA-DDS uses DDS as a search engine and archives all the non-dominated solutions during the search. In order to maintain the diversity of solutions, PA-DDS, which is single solution based, samples from less crowded parts of the external set of non-dominated solutions in each iteration. This tool inherits the parsimonious characteristic of DDS, so it has only one algorithm parameter from DDS, which does not need tuning, and one new parameter that defines the portion of computational budget for finding individual minima. PA-DDS uses crowding distance measure to sample from less populated parts of the tradeoff. The performance of the proposed tool is assessed in solving two test problems ZDT4 and ZDT6 [8] that have multiple local Pareto fronts. Results show that PA-DDS is promising relative to two high quality benchmark algorithms NSGA-II [3, 7] and AMALGAM [7].

A new selection metric for multiobjective hydrologic model calibration

A novel selection metric called Convex Hull Contribution (CHC) is introduced for solving multiobjective (MO) optimization problems with Pareto fronts that can be accurately approximated by a convex curve. The hydrologic model calibration literature shows that many biobjective calibration problems with a proper setup result in such Pareto fronts. The CHC selection approach identifies a subset of archived nondominated solutions whose map in the objective space forms convex approximation of the Pareto front. The optimization algorithm can sample solely from these solutions to more accurately approximate the convex shape of the Pareto front. It is empirically demonstrated that CHC improves the performance of Pareto Archived Dynamically Dimensioned Search (PA-DDS) when solving MO problems with convex Pareto fronts. This conclusion is based on the results of several benchmark mathematical problems and several hydrologic model calibration problems with two or three objective functions. The impact of CHC on PA-DDS performance is most evident when the computational budget is somewhat limited. It is also demonstrated that 1,000 solution evaluations (limited budget in this study) is sufficient for PA-DDS with CHC-based selection to achieve very high quality calibration results relative to the results achieved after 10,000 solution evaluations.

A TWO STAGE OPTIMIZATION APPROACH FOR CALIBRATING WATER DISTRIBUTION SYSTEMS

BWCN is a competition for calibrating pipe roughness coefficients and demand pattern multipliers of CTown Water Distribution System (WDS) to measured SCADA (hourly tank levels and pump flows) and fire flow test data in a 1-week operation. In a pre-calibration step, quality of the data is assessed, base demands for the fire flow tests are estimated, by mass balance, and pipes are grouped and their nominal values and variation range are determined. In this study, the calibration problem is solved in two stages, each of which tunes a portion of decision variables (DVs) that significantly impact the corresponding objectives while other DVs are set to their nominal (or calibrated) values. Dynamically Dimensioned Search based optimization algorithms are used in both stages because the default algorithm parameter setting is robust. Stage-1 aims to fit the fire flow test measurements that are highly affected by pipe roughness coefficients. Also, demand pattern multipliers for hour-1 SCADA must be calibrated in this stage because the base demand during the fire flow tests is roughly the same as those in hour 1. Ideally, a single solution must minimize the calibration error for all the measurements simultaneously. However, since no perfect model and/or data set exist, objective functions (error metrics) are usually in conflict. Therefore, this stage is set up as a bi-objective optimization problem to minimize the calibration error in simulating fire flow test measurements versus simulating hour-1 SCADA measurements. At the end of this stage, multi-criteria decision making is utilized to select candidate solutions to be evaluated in stage-2. In stage-2 demand pattern multipliers are calibrated to fit the SCADA (tank levels and pump flows). The WDS model performance in each hour is independent from subsequent hours; therefore, stage-2 is set up to calibrate demand pattern multipliers hour by hour starting from the hour 2 to 168 (1 week). All candidate solutions from stage-1 are evaluated in stage-2 and one of them is selected as the final solution to the C-Town calibration problem based on multi-criteria decision making. On average, the final calibrated model estimates static pressure, fire flow tests, tank levels and pumping flow rates of SCADA to within 3.5%, 1.5%, 1.0%, and 2.5% of the measured data respectively.

Evaluation of New Control Structures for Regulating the Great Lakes System: Multiscenario, Multireservoir Optimization Approach

AbstractWater levels in the Great Lakes–St. Lawrence system located in northeastern North America are critically important to the Canadian and U.S. economies. Water managers are concerned that this system, which is currently managed by control structures at the outlets of Lakes Superior and Ontario, is not able to cope with the highly uncertain impacts of climate change. In particular, the frequency of extreme water levels throughout the system might be substantially increased. This study provides an exploratory conceptual analysis to determine the extent that new control structures at the outlet of Lake Huron or Erie (or both) and corresponding excavation along the St. Clair or Niagara River (or both) might mitigate the risks posed by future extreme water supply scenarios. Multilake parametric rule curves were developed to regulate systems enabled with these new control structures as a whole. Multiple stochastic water supply sequences were adopted that represented different future extreme climate scenari...