scholarly journals Comparison of Approaches for Irrigation Scheduling Using AquaCrop and NSGA-III Models under Climate Uncertainty

2020 ◽  
Vol 12 (18) ◽  
pp. 7694
Author(s):  
Richwell Mubita Mwiya ◽  
Zhanyu Zhang ◽  
Chengxin Zheng ◽  
Ce Wang
Keyword(s):  
Optimization Problem ◽  
Irrigation Scheduling ◽  
Irrigated Agriculture ◽  
Climate Data ◽  
Maize Crop ◽  
Maize Production ◽  
Multi Objective ◽  
Study Approach ◽  
Aquacrop Model ◽  
Conflicting Objectives

In the face of increased competition for water resources, optimal irrigation scheduling is necessary for sustainable development of irrigated agriculture. However, optimal irrigation scheduling is a nonlinear problem with many competing and conflicting objectives and constraints, and deals with an environment in which conditions are uncertain. In this study, a multi-objective optimization problem for irrigation scheduling was presented in which maximization of net benefits and water use efficiency and minimization of risk were the objectives. The presented optimization problem was solved using four different approaches, all of which used the AquaCrop model and nondominated sorting genetic algorithm III. Approach 1 used dynamic climate data without adaption; Approach 2 used dynamic climate data with adaption; Approach 3 used static climate data without adaption; and Approach 4 used static climate data with adaption. The dynamic climate data were generated using the bootstrap resampling of original climate data. A case study of maize production in north Jiangsu Province of China was used to evaluate the proposed approaches. Under the multi-objective scenario presented and other conditions of the study, Approach 4 gave the best results, and showed that irrigation depths of 400, 325, and 200 mm were required to produce a maize crop in a dry, normal, and wet year, respectively.

An Efficient and Accurate Solution Methodology for Bilevel Multi-Objective Programming Problems Using a Hybrid Evolutionary-Local-Search Algorithm

2010 ◽  
Vol 18 (3) ◽  
pp. 403-449 ◽  
Author(s):  
Kalyanmoy Deb ◽  
Ankur Sinha
Keyword(s):  
Problem Solving ◽  
Local Search ◽  
Bilevel Programming ◽  
Optimization Problem ◽  
Practical Importance ◽  
Test Problems ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Bilevel Programming Problems ◽  
Solution Methodology

Bilevel optimization problems involve two optimization tasks (upper and lower level), in which every feasible upper level solution must correspond to an optimal solution to a lower level optimization problem. These problems commonly appear in many practical problem solving tasks including optimal control, process optimization, game-playing strategy developments, transportation problems, and others. However, they are commonly converted into a single level optimization problem by using an approximate solution procedure to replace the lower level optimization task. Although there exist a number of theoretical, numerical, and evolutionary optimization studies involving single-objective bilevel programming problems, not many studies look at the context of multiple conflicting objectives in each level of a bilevel programming problem. In this paper, we address certain intricate issues related to solving multi-objective bilevel programming problems, present challenging test problems, and propose a viable and hybrid evolutionary-cum-local-search based algorithm as a solution methodology. The hybrid approach performs better than a number of existing methodologies and scales well up to 40-variable difficult test problems used in this study. The population sizing and termination criteria are made self-adaptive, so that no additional parameters need to be supplied by the user. The study indicates a clear niche of evolutionary algorithms in solving such difficult problems of practical importance compared to their usual solution by a computationally expensive nested procedure. The study opens up many issues related to multi-objective bilevel programming and hopefully this study will motivate EMO and other researchers to pay more attention to this important and difficult problem solving activity.

Multi-Objective Optimization of Piezoelectric Actuator Placement for Shape Control of Plates Using Genetic Algorithms

2009 ◽  
Vol 131 (9) ◽  
Author(s):  
Rajesh Kudikala ◽  
Deb Kalyanmoy ◽  
Bishakh Bhattacharya
Keyword(s):  
Optimization Algorithm ◽  
Shape Control ◽  
Optimization Problem ◽  
Piezoelectric Actuators ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Input Control ◽  
Wide Range ◽  
Conflicting Objectives ◽  
Lag Model

Shape control of adaptive structures using piezoelectric actuators has found a wide range of applications in recent years. In this paper, the problem of finding optimal distribution of piezoelectric actuators and corresponding actuation voltages for static shape control of a plate is formulated as a multi-objective optimization problem. The two conflicting objectives considered are minimization of input control energy and minimization of mean square deviation between the desired and actuated shapes with constraints on the maximum number of actuators and maximum induced stresses. A shear lag model of the smart plate structure is created, and the optimization problem is solved using an evolutionary multi-objective optimization algorithm: nondominated sorting genetic algorithm-II. Pareto-optimal solutions are obtained for different case studies. Further, the obtained solutions are verified by comparing them with the single-objective optimization solutions. Attainment surface based performance evaluation of the proposed optimization algorithm has been carried out.

scholarly journals Optimizing Irrigation Water and N Levels for Higher Yield and Reduced Blossom End Rot Incidence on Tomato

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Mebrahtu Gebremariam ◽  
Teklay Tesfay
Keyword(s):  
Irrigation Water ◽  
Irrigation Scheduling ◽  
Water Levels ◽  
Climate Data ◽  
Marketable Yield ◽  
Significant Interaction Effect ◽  
Combined Application ◽  
Combined Use ◽  
Aquacrop Model ◽  
Blossom End Rot

This study was conducted during 2018/19 under drip irrigation in the dry season to examine the effect of irrigation and N levels on yield, economic performance, and incidence of blossom end rot (BER) on tomato. A 3 × 4 factorial design with subdivided plots was implemented. Three irrigation levels (50%, 75%, and 100% ETc) were randomly assigned in the main plots and four N levels (0, 46, 92, and 138 kg ha−1) to the subplots. Climate data were imported into AquaCrop model climate dataset for determining irrigation water amount and irrigation scheduling. Irrigation scheduling was determined using the FAO AquaCrop model. Data were subjected to analysis of variance (ANOVA) using GenStat software. There was significant interaction effect of irrigation and N levels on yield, yield parameters, and BER incidence on tomato. Highest fruit diameter and fruit length were attained from the combined application of 75% ETc and 138 kg N ha−1. Besides, maximum fruits per plant and marketable yield were obtained under combined use of 100% ETc with 138 kg N ha−1 and 75% ETc with 92 kg N ha−1, respectively, whereas lowest yield performance was recorded when 50% ETc is coupled with 0 kg N ha−1. However, highest (21.91%) and lowest (7.03%) BER incidence was found under the combined use of 50% ETc and 0 kg N ha−1 100% ETc and 92 kg N ha−1, respectively. The economic analysis revealed that application of 46 kg N ha−1 was economically feasible irrespective of the irrigation water levels.

scholarly journals Optimization of a Power Line Communication System to Manage Electric Vehicle Charging Stations in a Smart Grid

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1767 ◽  
Author(s):  
Sara Carcangiu ◽  
Alessandra Fanni ◽  
Augusto Montisci
Keyword(s):  
Frequency Response ◽  
Optimization Problem ◽  
Power Line ◽  
Power Line Communication ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Charging Station ◽  
Conflicting Objectives ◽  
Charging Stations ◽  
Line Design

In this paper, a procedure is proposed to design a power line communication (PLC) system to perform the digital transmission in a distributed energy storage system consisting of fleets of electric cars. PLC uses existing power cables or wires as data communication multicarrier channels. For each vehicle, the information to be transmitted can be, for example: the models of the batteries, the level of the charge state, and the schedule of charging/discharging. Orthogonal frequency division multiplexing modulation (OFDM) is used for the bit loading, whose parameters are optimized to find the best compromise between the communication conflicting objectives of minimizing the signal power, maximizing the bit rate, and minimizing the bit error rate. The off-line design is modeled as a multi-objective optimization problem, whose solution supplies a set of Pareto optimal solutions. At the same time, as many charging stations share part of the transmission line, the optimization problem includes also the assignment of the sub-carriers to the single charging stations. Each connection between the control node and a charging station has its own frequency response and is affected by a noise spectrum. In this paper, a procedure is presented, called Chimera, which allows one to solve the multi-objective optimization problem with respect to a unique frequency response, representing the whole set of lines connecting each charging station with the central node. Among the provided Pareto solutions, the designer will make the final decision based on the control system requirements and/or the hardware constraints.

Multi-Objective Optimization of Piezoelectric Actuator Placement for Shape Control of Plate Using Genetic Algorithms

2008 ◽  
Author(s):  
Rajesh Kudikala ◽  
Deb Kalyanmoy ◽  
Bishakh Bhattacharya
Keyword(s):  
Shape Control ◽  
Optimization Problem ◽  
Piezoelectric Actuators ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Input Control ◽  
Wide Range ◽  
Conflicting Objectives ◽  
Lag Model

Shape control of adaptive structures using piezoelectric actuators has found a wide range of applications in recent years. In this paper, the problem of finding optimal distribution of piezoelectric actuators and corresponding actuation voltages for static shape control of a plate is formulated as a multi objective optimization problem. Two conflicting objectives: minimization of input control energy and minimization of mean square deviation between the desired and actuated shapes are considered with constraints on maximum number of actuators and maximum induced stresses. A shear lag model of the smart plate structure is created and the optimization problem is solved using an evolutionary multi-objective optimization (EMO) algorithm NSGA-II. Pareto-optimal solutions are obtained for different case studies. Further, the obtained solutions are verified by comparing with single-objective optimization solutions.

scholarly journals Calibration and Validation of the AquaCrop Model to Estimate Maize Production in Campos Gerais, Paraná State, Brazil

2020 ◽  
Vol 35 (2) ◽  
pp. 243-253
Author(s):  
Jorge Luiz Moretti de Souza ◽  
Cibelle Tamiris de Oliveira ◽  
Stefanie Lais Kreutz Rosa ◽  
Rodrigo Yoiti Tsukahara
Keyword(s):  
Maize Yield ◽  
Crop Productivity ◽  
Simple Linear Regression ◽  
Validation Process ◽  
Maize Crop ◽  
Maize Production ◽  
Calibration And Validation ◽  
Aquacrop Model ◽  
And Performance ◽  
Relative Errors

Abstract Crop productivity evaluation with models simulations can help in the prediction of harvests and in the understanding of the interactions resulting from the soil-plant-atmosphere continuum. The aim of this study was to calibrate and validate the AquaCrop model for maize crop in the edaphoclimatic conditions of Campos Gerais region, Paraná State, Brazil. The analyses were carried out for maize crop with model input data (climate, crop, soil and soil management) obtained from the ABC Foundation Experimental Station in Castro, Ponta Grossa and Socavão. The climate in the region is humid subtropical, with rainfall evenly distributed. The relief varies from flat to gently undulating. The period analyzed in the calibration and validation process comprised 2011 to 2016 and 2012 to 2016 harvests, respectively. The data used in the calibration of AquaCrop was different from those used in the validation process. Observed and simulated yields were evaluated by simple linear regression analyses, absolute and relative errors, correlation coefficient (r), concordance (d) and performance (c) indexes. The calibration of AquaCrop was satisfactory in the locations studied for maize crop, obtaining absolute errors varying from 6 to 121 kg ha–1. The highest calibration errors occurred in Castro. However, the errors were not enough to reduce the performance in the validation process for this localitie. The model validation resulted in “excellent” performance in all locations evaluated. The AquaCrop can be used to predict the maize yield with acceptable accuracy in the Campos Gerais Region, Paraná State, Brazil.

An evolutionary approach for optimal multi-objective resource allocation in distributed computing systems

2020 ◽  
Vol 28 (2) ◽  
pp. 97-109
Author(s):  
Avadh Kishor ◽  
Rajdeep Niyogi
Keyword(s):  
Resource Allocation ◽  
Distributed Computing ◽  
Optimization Problem ◽  
Computing System ◽  
System Size ◽  
Performance Criteria ◽  
Distributed Computing Systems ◽  
System Utilization ◽  
Multi Objective ◽  
Conflicting Objectives

Resource allocation in a distributed computing system is the process of allocating the workload across multiple computing resources to optimize the required performance criteria. In this article, a resource allocation problem that arises in a distributed system consisting of multiple heterogeneous servers is addressed. The problem is modeled as a multi-objective problem with two conflicting objectives: (a) to minimize the users’ expected response time and (b) to reduce the utilization imbalance between servers. To satisfy these objectives simultaneously, first, both the objectives are considered in an integrated manner, and an optimization problem is formulated. Second, the optimization problem is cast into a game-theoretic setting and modeled as a non-cooperative game, called a non-cooperative resource allocation game. Finally, to solve the game, a differential evolution-based co-evolutionary framework (DECEF) is proposed. To evaluate the performance of DECEF, a rigorous simulation study is carried out. Furthermore, to assess the relative performance of DECEF, it is compared against two existing approaches, from various aspects, including system utilization, system heterogeneity, and system size. The experimental results show that DECEF provides better system-wide performance while optimizing both the objectives.

scholarly journals Model based irrigation water application of maize (zeamays l.) to improve water productivity in irrigated agriculture

2020 ◽  
pp. 37918-37925
Author(s):  
Keyword(s):  
Crop Yields ◽  
Water Productivity ◽  
Irrigation Schedule ◽  
Irrigation Scheduling ◽  
Water Requirement ◽  
Irrigated Agriculture ◽  
Accurate Estimation ◽  
Crop Water ◽  
Maize Crop ◽  
Irrigation Requirements

Under the semiarid and arid climate of Eastern Europe, accurate estimation of crop water requirement and irrigation scheduling is important for water management and planning. The objectives of this study were to estimate maize water requirement and irrigation scheduling in variable climatic conditions. CROPWAT model is decision support system developed by United Nations Food and Agriculture Organization (FAO) and it is used as a practical tool to carry out standard calculations for reference evapotranspiration, crop water requirements, irrigation scheduling, and also allows helps in planning and decision making in the areas where water resource availability is varying and scarce. The study result indicated that Maize seasonal amounts of irrigation requirements varied from 439.5 to 615.0 mm. Maize actual daily evapotranspiration (ETa) varied from 0.12 to 4.13 mm and from 0.27 to 4.68 mm in 2010 and 2011 respectively. Net irrigation schedule for all growing periods in 2010 was zero for initial and late but for development 138.9 mm and 45.9 mm for mid-stage of the growing period. However, 2011 were zero, 83.7 mm, 178 mm, and 98.2 mm in initial, mid, and development and late stages respectively. Besides in the study area, 2010 was the wettest year but 2011 was determined as the driest year this may cause adverse conditions on maize crop yields quantity and quality. Irrigation requirements for maize should be adjusted to the local meteorological conditions for optimizing maize irrigation requirements and improving maize water productivity under such climatic variable conditions.

scholarly journals Multi-Objective Sizing Optimization of Hybrid Renewable Energy Microgrid in a Stand-Alone Marine Context

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 174
Author(s):  
Wenqiang Zhu ◽  
Jiang Guo ◽  
Guo Zhao
Keyword(s):  
Renewable Energy ◽  
Optimization Problem ◽  
System Optimization ◽  
Economic Benefits ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Energy Management Strategy ◽  
Sizing Optimization ◽  
Multi Objective ◽  
Hybrid Renewable Energy

Islands are the main platforms for exploration and utilization of marine resources. In this paper, an island hybrid renewable energy microgrid devoted to a stand-alone marine application is established. The specific microgrid is composed of wind turbines, tidal current turbines, and battery storage systems considering the climate resources and precious land resources. A multi-objective sizing optimization method is proposed comprehensively considering the economy, reliability and energy utilization indexes. Three optimization objectives are presented: minimizing the Loss of Power Supply Probability, the Cost of Energy and the Dump Energy Probability. An improved multi-objective grey wolf optimizer based on Halton sequence and social motivation strategy (HSMGWO) is proposed to solve the proposed sizing optimization problem. MATLAB software is utilized to program and simulate the optimization problem of the hybrid energy system. Optimization results confirm that the proposed method and improved algorithm are feasible to optimally size the system, and the energy management strategy effectively matches the requirements of system operation. The proposed HSMGWO shows better convergence and coverage than standard multi-objective grey wolf optimizer (MOGWO) and multi-objective particle swarm optimization (MOPSO) in solving multi-objective sizing problems. Furthermore, the annual operation of the system is simulated, the power generation and economic benefits of each component are analyzed, as well as the sensitivity.

scholarly journals Braille Block Detection via Multi-Objective Optimization from an Egocentric Viewpoint

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2775
Author(s):  
Tsubasa Takano ◽  
Takumi Nakane ◽  
Takuya Akashi ◽  
Chao Zhang
Keyword(s):  
Optimization Problem ◽  
Line Pair ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimization Framework ◽  
Visually Impaired People ◽  
Comprehensive Comparison ◽  
Basic Characteristics ◽  
Support Devices

In this paper, we propose a method to detect Braille blocks from an egocentric viewpoint, which is a key part of many walking support devices for visually impaired people. Our main contribution is to cast this task as a multi-objective optimization problem and exploits both the geometric and the appearance features for detection. Specifically, two objective functions were designed under an evolutionary optimization framework with a line pair modeled as an individual (i.e., solution). Both of the objectives follow the basic characteristics of the Braille blocks, which aim to clarify the boundaries and estimate the likelihood of the Braille block surface. Our proposed method was assessed by an originally collected and annotated dataset under real scenarios. Both quantitative and qualitative experimental results show that the proposed method can detect Braille blocks under various environments. We also provide a comprehensive comparison of the detection performance with respect to different multi-objective optimization algorithms.

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