scholarly journals Optimization of Groundwater Pumping and River-Aquifer Exchanges for Management of Water Resources

2021 ◽  
Author(s):  
Mayank Bajpai ◽  
Shishir Gaur ◽  
Anurag Ohri ◽  
Shreyansh Mishra ◽  
Hervé Piégay ◽  
...  
Keyword(s):  
Water Resources ◽  
Boundary Conditions ◽  
Simulation Optimization ◽  
Optimization Models ◽  
Multi Objective Optimization ◽  
Groundwater Pumping ◽  
Multi Objective ◽  
Model Domain ◽  
Pumping Rates ◽  
Pareto Fronts

Groundwater pumping influences the rate of River-Aquifer (R-A) exchanges and alters the water budget of the aquifer. Therefore, fulfilling the total water demand of the area, with an optimal pumping rate of wells and optimal R-A exchanges rate, is important for the sustainable management of water resources and aquatic ecosystems. Meanwhile, comparison of the output of different simulation-optimization techniques, which is used for the solution of water resource management problems, is a very challenging task where different Pareto fronts are compared to identify the best results. In the present work, mathematical models were developed to simulate the R-A exchanges for the lower part of the River Ain, France. The developed models were coupled with optimization models in MATLAB environment and were executed to solve the multi-objective optimization problem based on the maximization of pumping rates of wells and maximization of groundwater input into the river Ain through R-A exchanges. The Pareto front developed by different simulation-optimization models was compared and analyzed. The Pareto fronts were juxtaposed based on the convergence, total diversity, and uniformity with the help of different performance metrics like hypervolume, generational distance, inverted generational distance, etc. The impact of different groundwater models based on domain size and boundary conditions was also examined. Results show the dominance of MOPSO over other optimization algorithms and concluded that the maximization of pumping rates significantly changes after considering the R-A exchanges-based objective function. It is observed that the model domain also alters the output of simulation-optimization, therefore the model domain and corresponding boundary conditions should be selected carefully for the field application of management models. ANN models were also developed to deal with the computationally expensive simulation model by reducing the processing time and were found efficient. Keywords: Simulation-Optimization, Multi-Objective optimization, Artificial Neural Network, River-Aquifer exchanges.

scholarly journals Localized probability of improvement for kriging based multi-objective optimization

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 954-958
Author(s):  
Yinjiang Li ◽  
Song Xiao ◽  
Paolo Di Barba ◽  
Mihai Rotaru ◽  
Jan K. Sykulski
Keyword(s):  
Uniform Distribution ◽  
Nearest Neighbor ◽  
Higher Dimensions ◽  
Multi Objective Optimization ◽  
New Approach ◽  
Multi Objective ◽  
Local Probability ◽  
Pareto Fronts ◽  
Probability Of Improvement ◽  
Infill Sampling

AbstractThe paper introduces a new approach to kriging based multi-objective optimization by utilizing a local probability of improvement as the infill sampling criterion and the nearest neighbor check to ensure diversification and uniform distribution of Pareto fronts. The proposed method is computationally fast and linearly scalable to higher dimensions.

scholarly journals The dilemma between eliminating dominance-resistant solutions and preserving boundary solutions of extremely convex Pareto fronts

2021 ◽  
Author(s):  
Zhenkun Wang ◽  
Qingyan Li ◽  
Qite Yang ◽  
Hisao Ishibuchi
Keyword(s):  
Evolutionary Algorithms ◽  
Coping Strategies ◽  
Test Problem ◽  
Pareto Front ◽  
Optimization Problems ◽  
Feasible Region ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Pareto Fronts ◽  
Boundary Solutions

AbstractIt has been acknowledged that dominance-resistant solutions (DRSs) extensively exist in the feasible region of multi-objective optimization problems. Recent studies show that DRSs can cause serious performance degradation of many multi-objective evolutionary algorithms (MOEAs). Thereafter, various strategies (e.g., the $$\epsilon $$ ϵ -dominance and the modified objective calculation) to eliminate DRSs have been proposed. However, these strategies may in turn cause algorithm inefficiency in other aspects. We argue that these coping strategies prevent the algorithm from obtaining some boundary solutions of an extremely convex Pareto front (ECPF). That is, there is a dilemma between eliminating DRSs and preserving boundary solutions of the ECPF. To illustrate such a dilemma, we propose a new multi-objective optimization test problem with the ECPF as well as DRSs. Using this test problem, we investigate the performance of six representative MOEAs in terms of boundary solutions preservation and DRS elimination. The results reveal that it is quite challenging to distinguish between DRSs and boundary solutions of the ECPF.

A multi-objective evolutionary algorithm based on niche selection in solving irregular Pareto fronts

2021 ◽  
pp. 1-21
Author(s):  
Xin Li ◽  
Xiaoli Li ◽  
Kang Wang
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Benchmark Problems ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Environmental Selection ◽  
Optimal Solution Set ◽  
Pareto Fronts

The key characteristic of multi-objective evolutionary algorithm is that it can find a good approximate multi-objective optimal solution set when solving multi-objective optimization problems(MOPs). However, most multi-objective evolutionary algorithms perform well on regular multi-objective optimization problems, but their performance on irregular fronts deteriorates. In order to remedy this issue, this paper studies the existing algorithms and proposes a multi-objective evolutionary based on niche selection to deal with irregular Pareto fronts. In this paper, the crowding degree is calculated by the niche method in the process of selecting parents when the non-dominated solutions converge to the first front, which improves the the quality of offspring solutions and which is beneficial to local search. In addition, niche selection is adopted into the process of environmental selection through considering the number and the location of the individuals in its niche radius, which improve the diversity of population. Finally, experimental results on 23 benchmark problems including MaF and IMOP show that the proposed algorithm exhibits better performance than the compared MOEAs.

The Multi-Objective Optimization Allocation Model of Water Resources in Xiaokai River Irrigation Area

2015 ◽  
Vol 1092-1093 ◽  
pp. 1289-1294
Author(s):  
Xin Wang ◽  
Jing Xu ◽  
Ke Kong ◽  
Lei Yan ◽  
Fang Wu
Keyword(s):  
Water Resources ◽  
Optimization Model ◽  
Supply And Demand ◽  
Reference Value ◽  
Multi Objective Optimization ◽  
Allocation Model ◽  
Irrigation Area ◽  
Multi Objective ◽  
Groundwater Environment ◽  
Sequence Method

For the three big problems of water resources supply and demand contradiction, protection of groundwater environment and sediment over long distances in Xiaokai river irrigation area, the model of water utilization benefit maximization, groundwater level optimal control and the goal of sediment transport effect optimization model are established, and coupled into a multi-objective optimization model. The model is solved by using The delaminating sequence method, obtained the rational allocation plan of water resources in water years, and analyzing the rationality of the plan. The results show that, the scheme comprehensively considers the economic and environmental issues and has great reference value to promote sustainable development of irrigation area.

scholarly journals A Generalized SDP Multi-Objective Optimization Method for EM-Based Microwave Device Design

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3065 ◽  
Author(s):  
Ying Liu ◽  
Qingsha S. Cheng ◽  
Slawomir Koziel
Keyword(s):  
Computational Cost ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Uwb Antenna ◽  
Nsga Ii ◽  
Multi Objective ◽  
Iot Applications ◽  
Comparison Results ◽  
Pareto Fronts ◽  
Better Than

In this article, a generalized sequential domain patching (GSDP) method for efficient multi-objective optimization based on electromagnetics (EM) simulation is proposed. The GSDP method allowing fast searching for Pareto fronts for two and three objectives is elaborated in detail in this paper. The GSDP method is compared with the NSGA-II method using multi-objective problems in the DTLZ series, and the results show the GSDP method saved computational cost by more than 85% compared to NSGA-II method. A diversity comparison indicator (DCI) is used to evaluate approximate Pareto fronts. The comparison results show the diversity performance of GSDP is better than that of NSGA-II in most cases. We demonstrate the proposed GSDP method using a practical multi-objective design example of EM-based UWB antenna for IoT applications.

Multi-Objective Optimization of Large Pipeline Networks Using Genetic Algorithm

2004 ◽  
Author(s):  
K. K. Botros ◽  
D. Sennhauser ◽  
K. J. Jungowski ◽  
G. Poissant ◽  
H. Golshan ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Gas Pipeline ◽  
Multi Objective Optimization ◽  
Maximum Throughput ◽  
Multi Objective ◽  
Decision Variables ◽  
Pipeline Networks ◽  
Multiple Unit ◽  
Pareto Fronts ◽  
Selection Of

This paper presents application of Genetic Algorithm (GA) methodologies to multi-objective optimization of two complex gas pipeline networks to achieve specific operational objectives. The first network contains 10 compressor stations resulting in 20 decision variables and an optimization space of 6.3 × 1029 cases. The second system contains 25 compressor stations resulting in 54 decision variables and an optimization space of 1.85 × 1078 cases. Compressor stations generally included multiple unit sites, where the compressor characteristics of each unit is taken into account constraining the solution by the surge and stonewall limits, maximum and minimum speeds and maximum power available. A key challenge to the optimization of such large systems is the number of constraints and associated penalty functions, selection of the GA operators such as crossover, mutation, selection criteria and elitism, as well as the population size and number of generations. The paper discusses the approach taken to arrive at optimal values for these parameters for large gas pipeline networks. Examples for two-objective optimizations, referred to as Pareto fronts, include maximum throughput and minimum fuel, as well as, minimum linepack and maximum throughput in typical linepack/throughput/fuel envelopes.

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