Multi-Objective Optimization for Urban Drainage or Sewer Networks Rehabilitation through Pipes Substitution and Storage Tanks Installation

Drainage networks are civil constructions which do not generally attract the attention of decision-makers. However, they are of crucial importance for cities; this can be seen when a city faces floods resulting in extensive and expensive damage. The increase of rain intensity due to climate change may cause deficiencies in drainage networks built for certain defined flows which are incapable of coping with sudden increases, leading to floods. This problem can be solved using different strategies; one is the adaptation of the network through rehabilitation. A way to adapt the traditional network approach consists of substituting some pipes for others with greater diameters. More recently, the installation of storm tanks makes it possible to temporarily store excess water. Either of these solutions can be expensive, and an economic analysis must be done. Recent studies have related flooding with damage costs. In this work, a novel solution combining both approaches (pipes and tanks) is studied. A multi-objective optimization algorithm based on the NSGA-II is proposed for the rehabilitation of urban drainage networks through the substitution of pipes and the installation of storage tanks. Installation costs will be offset by damage costs associated with flooding. As a result, a set of optimal solutions that can be implemented based on the objectives to be achieved by municipalities or decisions makers. The methodology is finally applied to a real network located in the city of Bogotá, Colombia.

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LAND-USE PLANNING FOR SUSTAINABLE URBAN DEVELOPMENT IN AFRICA: A SPATIAL AND MULTI-OBJECTIVE OPTIMIZATION APPROACH

Geodesy and Cartography ◽

10.3846/gac.2019.6691 ◽

2019 ◽

Vol 45 (5) ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

Alex Lubida ◽

Mozafar Veysipanah ◽

Petter Pilesjo ◽

Ali Mansourian

Keyword(s):

Land Use ◽

Land Use Planning ◽

Geographical Information ◽

Sustainable Urban Development ◽

Optimization Approach ◽

Objective Functions ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Multi Objective ◽

The City

Land-use planning, which requires finding a balance among different conflicting social, economic and environment factors, is a complex task needed everywhere, including Africa. One example is the city of Zanzibar in Tanzania, which is under special consideration for land-use revision. From one side, the city has high potentials for tourist industry and at the other side there are major challenges with the city structure and poor accessibilities. In order to prepare a proper land-use plan for the city, a variety of influencing conflicting factors needs to be considered and satisfied. This can be regarded as a common problem in many African cities, which are under development. This paper aims to address the problem by proposing and demonstrating the use of Geographical Information System (GIS) and multi-objective optimization for land-use planning, in Zanzibar as a case study. The measures which have been taken by Zanzibar government to address the development challenges through the Zanzibar Strategy for Growth and Reduction of Poverty (ZSGRP) were identified by studying related documents and interviewing experts. Based on these, two objective functions were developed for land-use planning. Optimum base land-use plans were developed and mapped by optimizing the objective functions using the NSGA-II algorithm. The results show that the proposed approach and outputs can considerably facilitate land-use planning in Zanzibar. Similar approaches are highly recommended for other cities in Africa which are under development.

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Urban Drainage Networks Rehabilitation Using Multi-Objective Model and Search Space Reduction Methodology

Infrastructures ◽

10.3390/infrastructures4020035 ◽

2019 ◽

Vol 4 (2) ◽

pp. 35 ◽

Cited By ~ 1

Author(s):

Ulrich A. Ngamalieu-Nengoue ◽

Pedro L. Iglesias-Rey ◽

F. Javier Martínez-Solano

Keyword(s):

Genetic Algorithm ◽

Search Space ◽

Urban Drainage ◽

Multi Objective Optimization ◽

Multi Objective ◽

Space Reduction ◽

Decision Variables ◽

Drainage Networks ◽

Search Space Reduction ◽

Pareto Fronts

The drainage network always needs to adapt to environmental and climatic conditions to provide best quality services. Rehabilitation combining pipes substitution and storm tanks installation appears to be a good solution to overcome this problem. Unfortunately, the calculation time of such a rehabilitation scenario is too elevated for single-objective and multi-objective optimization. In this study, a methodology composed by search space reduction methodology whose purpose is to decrease the number of decision variables of the problem to solve and a multi-objective optimization whose purpose is to optimize the rehabilitation process and represent Pareto fronts as the result of urban drainage networks optimization is proposed. A comparison between different model results for multi-objective optimization is made. To obtain these results, Storm Water Management Model (SWMM) is first connected to a Pseudo Genetic Algorithm (PGA) for the search space reduction and then to a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for multi-objective optimization. Pareto fronts are designed for investment costs instead of flood damage costs. The methodology is applied to a real network in the city of Medellin in Colombia. The results show that search space reduction methodology provides models with a considerably reduced number of decision variables. The multi-objective optimization shows that the models’ results used after the search space reduction obtain better outcomes than in the complete model in terms of calculation time and optimality of the solutions.

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NSGA-II-RJG applied to multi-objective optimization of polymeric nanoparticles synthesis with silicone surfactants

Open Chemistry ◽

10.2478/s11532-011-0096-5 ◽

2011 ◽

Vol 9 (6) ◽

pp. 1080-1095 ◽

Cited By ~ 2

Author(s):

Renata Furtuna ◽

Silvia Curteanu ◽

Carmen Racles

Keyword(s):

Storage Temperature ◽

Polymeric Nanoparticles ◽

Particle Diameter ◽

Multi Objective Optimization ◽

Network Modelling ◽

Nsga Ii ◽

Multi Objective ◽

Silicone Surfactants ◽

Nanoparticles Synthesis ◽

And Storage

AbstractPolydimethylsiloxane nanoparticles were obtained by nanoprecipitation, using a siloxane surfactant as stabilizer. Two neural networks and a genetic algorithm were used to optimize this process, by minimizing the particle diameter and the polydispersity, finding in this way the optimum values for surfactant and polymer concentrations, and storage temperature. In order to improve the performance of the non-dominated sorting genetic algorithm, NSGA-II, a genetic operator was introduced in this study — the transposition operator — “real jumping genes”, resulting NSGA-II-RJG. It was implemented in original software and was applied to the multi-objective optimization of the polymeric nanoparticles synthesis with silicone surfactants. The multi-objective function of the algorithm included two fitness functions. One fitness function was calculated with a neural network modelling the variation of the particle diameter on the surfactant concentration, polymer concentration, and storage temperature, and the other was computed by a neural network modelling the dependence of polydispersity index on surfactant and polymer concentrations. The performance of the software program that implemented NSGA-II-RJG was highlighted by comparing it with the software implementation of NSGA-II. The results obtained from simulations showed that NSGA-II-RJG is able to find non-dominated solutions with a greater diversity and a faster convergence time than NSGA-II.

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Synchronous R-NSGA-II: An Extended Preference-Based Evolutionary Algorithm for Multi-Objective Optimization

Informatica ◽

10.15388/informatica.2015.37 ◽

2015 ◽

Vol 26 (1) ◽

pp. 33-50 ◽

Cited By ~ 16

Author(s):

Ernestas Filatovas ◽

Olga Kurasova ◽

Karthik Sindhya

Keyword(s):

Evolutionary Algorithm ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Multi Objective

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Multi-Objective Lightweight Optimization of Parameterized Suspension Components Based on NSGA-II Algorithm Coupling with Surrogate Model

Machines ◽

10.3390/machines9060107 ◽

2021 ◽

Vol 9 (6) ◽

pp. 107

Author(s):

Rongchao Jiang ◽

Zhenchao Jin ◽

Dawei Liu ◽

Dengfeng Wang

Keyword(s):

Ride Comfort ◽

Lightweight Design ◽

Dynamic Performance ◽

Vehicle Dynamic ◽

Multi Objective Optimization ◽

Original Design ◽

Dynamic Simulations ◽

Nsga Ii ◽

Torsion Beam ◽

Multi Objective

In order to reduce the negative effect of lightweighting of suspension components on vehicle dynamic performance, the control arm and torsion beam widely used in front and rear suspensions were taken as research objects for studying the lightweight design method of suspension components. Mesh morphing technology was employed to define design variables. Meanwhile, the rigid–flexible coupling vehicle model with flexible control arm and torsion beam was built for vehicle dynamic simulations. The total weight of control arm and torsion beam was taken as optimization objective, as well as ride comfort and handling stability performance indexes. In addition, the fatigue life, stiffness, and modal frequency of control arm and torsion beam were taken as the constraints. Then, Kriging model and NSGA-II were adopted to perform the multi-objective optimization of control arm and torsion beam for determining the lightweight scheme. By comparing the optimized and original design, it indicates that the weight of the optimized control arm and torsion beam are reduced 0.505 kg and 1.189 kg, respectively, while structural performance and vehicle performance satisfy the design requirement. The proposed multi-objective optimization method achieves a remarkable mass reduction, and proves to be feasible and effective for lightweight design of suspension components.

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Elite Exploitation: A Combination of Mathematical Concept and EMO Approach for Multi-Objective Decision Making

Symmetry ◽

10.3390/sym13010136 ◽

2021 ◽

Vol 13 (1) ◽

pp. 136

Author(s):

Wenxiao Li ◽

Yushui Geng ◽

Jing Zhao ◽

Kang Zhang ◽

Jianxin Liu

Keyword(s):

Decision Making ◽

Optimization Problems ◽

Hybrid Genetic Algorithm ◽

Decision Makers ◽

Superior Performance ◽

Test Problems ◽

Mathematical Function ◽

Hyperbolic Tangent ◽

Nsga Ii ◽

Multi Objective

This paper explores the combination of a classic mathematical function named “hyperbolic tangent” with a metaheuristic algorithm, and proposes a novel hybrid genetic algorithm called NSGA-II-BnF for multi-objective decision making. Recently, many metaheuristic evolutionary algorithms have been proposed for tackling multi-objective optimization problems (MOPs). These algorithms demonstrate excellent capabilities and offer available solutions to decision makers. However, their convergence performance may be challenged by some MOPs with elaborate Pareto fronts such as CFs, WFGs, and UFs, primarily due to the neglect of diversity. We solve this problem by proposing an algorithm with elite exploitation strategy, which contains two parts: first, we design a biased elite allocation strategy, which allocates computation resources appropriately to elites of the population by crowding distance-based roulette. Second, we propose a self-guided fast individual exploitation approach, which guides elites to generate neighbors by a symmetry exploitation operator, which is based on mathematical hyperbolic tangent function. Furthermore, we designed a mechanism to emphasize the algorithm’s applicability, which allows decision makers to adjust the exploitation intensity with their preferences. We compare our proposed NSGA-II-BnF with four other improved versions of NSGA-II (NSGA-IIconflict, rNSGA-II, RPDNSGA-II, and NSGA-II-SDR) and four competitive and widely-used algorithms (MOEA/D-DE, dMOPSO, SPEA-II, and SMPSO) on 36 test problems (DTLZ1–DTLZ7, WGF1–WFG9, UF1–UF10, and CF1–CF10), and measured using two widely used indicators—inverted generational distance (IGD) and hypervolume (HV). Experiment results demonstrate that NSGA-II-BnF exhibits superior performance to most of the algorithms on all test problems.

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Design and characterization of effective solar cells

Energy Systems ◽

10.1007/s12667-021-00451-x ◽

2021 ◽

Author(s):

Varun Ojha ◽

Giorgio Jansen ◽

Andrea Patanè ◽

Antonino La Magna ◽

Vittorio Romano ◽

...

Keyword(s):

Solar Cells ◽

Quantum Efficiency ◽

Cost Benefit Analysis ◽

Cost Minimization ◽

Cell Structure ◽

Cost Benefit ◽

Structure Design ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Multi Objective

AbstractWe propose a two-stage multi-objective optimization framework for full scheme solar cell structure design and characterization, cost minimization and quantum efficiency maximization. We evaluated structures of 15 different cell designs simulated by varying material types and photodiode doping strategies. At first, non-dominated sorting genetic algorithm II (NSGA-II) produced Pareto-optimal-solutions sets for respective cell designs. Then, on investigating quantum efficiencies of all cell designs produced by NSGA-II, we applied a new multi-objective optimization algorithm II (OptIA-II) to discover the Pareto fronts of select (three) best cell designs. Our designed OptIA-II algorithm improved the quantum efficiencies of all select cell designs and reduced their fabrication costs. We observed that the cell design comprising an optimally doped zinc-oxide-based transparent conductive oxide (TCO) layer and rough silver back reflector (BR) offered a quantum efficiency ($$Q_e$$ Q e ) of 0.6031. Overall, this paper provides a full characterization of cell structure designs. It derives relationship between quantum efficiency, $$Q_e$$ Q e of a cell with its TCO layer’s doping methods and TCO and BR layer’s material types. Our solar cells design characterization enables us to perform a cost-benefit analysis of solar cells usage in real-world applications.

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Multi-objective optimization of energy efficiency and thermal comfort in an existing office building using NSGA-II with fitness approximation: a case study

Journal of Building Engineering ◽

10.1016/j.jobe.2021.102440 ◽

2021 ◽

pp. 102440

Author(s):

Mohammadamin Ghaderian ◽

Farzad Veysi

Keyword(s):

Energy Efficiency ◽

Thermal Comfort ◽

Office Building ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Multi Objective ◽

Fitness Approximation

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Multi-objective optimization of squirrel cage fan for range hood based on Kriging model

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406221995863 ◽

2021 ◽

pp. 095440622199586

Author(s):

Qianhao Xiao ◽

Jun Wang ◽

Boyan Jiang ◽

Weigang Yang ◽

Xiaopei Yang

Keyword(s):

Flow Rate ◽

Optimization Design ◽

Volume Flow Rate ◽

Kriging Model ◽

Volume Flow ◽

Design Parameters ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Multi Objective ◽

Squirrel Cage

In view of the multi-objective optimization design of the squirrel cage fan for the range hood, a blade parameterization method based on the quadratic non-uniform B-spline (NUBS) determined by four control points was proposed to control the outlet angle, chord length and maximum camber of the blade. Morris-Mitchell criteria were used to obtain the optimal Latin hypercube sample based on the evolutionary operation, and different subsets of sample numbers were created to study the influence of sample numbers on the multi-objective optimization results. The Kriging model, which can accurately reflect the response relationship between design variables and optimization objectives, was established. The second-generation Non-dominated Sorting Genetic algorithm (NSGA-II) was used to optimize the volume flow rate at the best efficiency point (BEP) and the maximum volume flow rate point (MVP). The results show that the design parameters corresponding to the optimization results under different sample numbers are not the same, and the fluctuation range of the optimal design parameters is related to the influence of the design parameters on the optimization objectives. Compared with the prototype, the optimized impeller increases the radial velocity of the impeller outlet, reduces the flow loss in the volute, and increases the diffusion capacity, which improves the volume flow rate, and efficiency of the range hood system under multiple working conditions.

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