Multi Objective Sustainable Irrigation Planning with Decision Parameters and Decision Variables Fuzzy in Nature

2012 ◽  
Vol 26 (10) ◽  
pp. 3005-3021 ◽  
Author(s):  
Jyotiba B. Gurav ◽  
D. G. Regulwar
Keyword(s):  
Irrigation Planning ◽  
Multi Objective ◽  
Decision Variables ◽  
Sustainable Irrigation

scholarly journals Multi-objective fuzzy optimization for sustainable irrigation planning

2020 ◽  
Vol 3 (1) ◽  
pp. 373-389 ◽  
Author(s):  
Jyotiba B. Gurav ◽  
D. G. Regulwar
Keyword(s):  
Crop Production ◽  
Fuzzy Optimization ◽  
Fuzzy Linear Programming ◽  
Cropping Pattern ◽  
Irrigation Planning ◽  
Multi Objective ◽  
Function Coefficient ◽  
Decision Variables ◽  
Optimal Cropping ◽  
Sustainable Irrigation

Abstract The objective of the present work is to determine an optimal cropping pattern under uncertainty, which maximizes four objectives simultaneously, including net benefits (NBF), crop production (CPD), employment generation (EGN) and manure utilization (MUT). Except the objective of maximizing the NBF, the other objectives are related to sustainability. To deal with uncertainty, a multi-objective fuzzy linear programming (MOFLP) model has developed along with fuzziness in decision parameters (objective function coefficient, cost coefficients, technological coefficients and resources) and decision variables (area to be irrigated under each crop in each season) and applied the same to Jayakwadi Project Stage-I, Maharashtra, India. The present study is in the form of a successful attempt to deal with irrigation planning associated with sustainability and uncertainty.

Swarm Intelligence for Multi-Objective Optimization in Engineering Design

2018 ◽  
pp. 239-250 ◽  
Author(s):  
Janga Reddy Manne
Keyword(s):  
Swarm Intelligence ◽  
Engineering Design ◽  
Random Search ◽  
Search Space ◽  
Population Based ◽  
Design Problems ◽  
Multi Objective ◽  
Hard Constraints ◽  
Decision Variables ◽  
Engineering Design Problems

Most of the engineering design problems are intrinsically complex and difficult to solve, because of diverse solution search space, complex functions, continuous and discrete nature of decision variables, multiple objectives and hard constraints. Swarm intelligence (SI) algorithms are becoming popular in dealing with these kind of complexities. The SI algorithms being population based random search techniques, use heuristics inspired from nature to enable effective exploration of optimal solutions to complex engineering problems. The SI algorithms derived based on principles of co-operative group intelligence and collective behavior of self-organized systems. This chapter presents key principles of multi-optimization, and swarm optimization for solving multi-objective engineering design problems with illustration through few examples.

scholarly journals Qom—A New Hydrologic Prediction Model Enhanced with Multi-Objective Optimization

2019 ◽  
Vol 10 (1) ◽  
pp. 251 ◽  
Author(s):  
Gustavo R. Zavala ◽  
José García-Nieto ◽  
Antonio J. Nebro
Keyword(s):  
River Basins ◽  
Hydrologic Model ◽  
Optimization Approach ◽  
Prediction Errors ◽  
Efficiency Coefficient ◽  
Multi Objective Optimization ◽  
Hydrologic Models ◽  
Multi Objective ◽  
Decision Variables ◽  
Hydrologic Prediction

The efficient calibration of hydrologic models allows experts to evaluate past events in river basins, as well as to describe new scenarios and predict possible future floodings. A difficulty in this context is the need to adjust a large number of parameters in the model to reduce prediction errors. In this work, we address this issue with two complementary contributions. First, we propose a new lumped rainfall-runoff hydrologic model—called Qom—which is featured by a limited set of continuous decision variables associated with soil moisture and direct runoff. Qom allows to separate and quantify the volume of losses and excesses of the rainwater falling in a hydrographic basin, while a Clark’s model is used to determine output hydrograms. Second, we apply a multi-objective optimization approach to find accurate calibrations of the model in a systematic and automatic way. The idea is to formulate the process as a bi-objective optimization problem where the Nash-Sutcliffe Efficiency coefficient and percent bias have to be minimized, and to combine the results found by a set of metaheuristics used to solve it. For validation purposes, we apply our proposal in six hydrographic scenarios, comprising river basins located in Spain, USA, Brazil and Argentina. The proposed approach is shown to minimize prediction errors of simulated streamflows with regards to those observed in these real-world basins.

Stochastic multi-objective decision making for sustainable irrigation in a changing environment

2019 ◽  
Vol 223 ◽  
pp. 928-945 ◽  
Author(s):  
Mo Li ◽  
Qiang Fu ◽  
Ping Guo ◽  
Vijay P. Singh ◽  
Chenglong Zhang ◽  
...  
Keyword(s):  
Decision Making ◽  
Changing Environment ◽  
Multi Objective ◽  
Sustainable Irrigation

scholarly journals GENERATING PARETO OPTIMAL SOLUTIONS OF MULTI-OBJECTIVE LFPP WITH INTERVAL COEFFICIENTS USING E-CONSTRAINT METHOD

2015 ◽  
Vol 20 (3) ◽  
pp. 329-345 ◽  
Author(s):  
Suvasis Nayak ◽  
Akshay Ojha
Keyword(s):  
Closed Interval ◽  
Fuzzy Programming ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Multi Objective ◽  
Decision Variables ◽  
Interval Coefficients ◽  
Constraint Method ◽  
Linear Fractional Programming Problem

This paper illustrates a procedure to generate pareto optimal solutions of multi-objective linear fractional programming problem (MOLFPP) with closed interval coefficients of decision variables both in objective and constraint functions. E-constraint method is applied to produce pareto optimal solutions comprising most preferred solution to satisfy all objectives. A numerical example is solved using our proposed method and the result so obtained is compared with that of fuzzy programming which justifies the efficiency and authenticity of the proposed method.

Development of sustainable irrigation planning with multi-objective fuzzy linear programming for Ukai–Kakrapar irrigation project, Gujarat, India

2013 ◽  
Vol 40 (7) ◽  
pp. 663-673 ◽  
Author(s):  
A.B. Mirajkar ◽  
P.L. Patel
Keyword(s):  
Linear Programming ◽  
Water Resources ◽  
Fuzzy Linear Programming ◽  
Exceedance Probability ◽  
Command Area ◽  
Irrigation Planning ◽  
Multi Objective ◽  
Irrigation Project ◽  
Conflicting Objectives ◽  
Inflow Conditions

Multi-objective fuzzy linear programming (MOFLP) approach is applied with four conflicting objectives, viz maximization of net benefits, employment generation, minimization of cost of cultivation and maximization of revenue generation from municipal and industrial supplies (M and I), on a water resources project (Ukai), Gujarat, India. The results from the model are reported for the most critical year (90% exceedance probability), critical year (85% exceedance probability), normal year (75% exceedance probability), and wet year (60% exceedance probability) inflow conditions. The degree of satisfaction of the proposed MOFLP model, considering all objectives together, for wet year, normal year, critical year and most critical year are found to be 0.527, 0.515, 0.50, and 0.46 respectively; and corresponding net irrigation benefits for different inflow conditions are computed as 10 611.91 Million Rs, 10 476.67 Million Rs, 8 311.0044 Million Rs, and 6 900.051 Million Rs, respectively. The proposed MOFLP model indicated that probable inflow corresponding to 75% dependability level is marginally sufficient to meet the requirement of the study area, and water availability becomes deficit in the command area for 85% dependability inflow condition. The optimized crop areas from the model, complying with the requirement of existing flood rules, and satisfying relevant conflicting objectives would help the decision makers in sustainable management of water resources in Ukai command area.

A Memetic Optimization Strategy Based on Dimension Reduction in Decision Space

2015 ◽  
Vol 23 (1) ◽  
pp. 69-100 ◽  
Author(s):  
Handing Wang ◽  
Licheng Jiao ◽  
Ronghua Shang ◽  
Shan He ◽  
Fang Liu
Keyword(s):  
Dimension Reduction ◽  
Optimization Problems ◽  
State Of The Art ◽  
Search Space ◽  
Optimization Strategy ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Decision Space ◽  
Multi Objective ◽  
Decision Variables

There can be a complicated mapping relation between decision variables and objective functions in multi-objective optimization problems (MOPs). It is uncommon that decision variables influence objective functions equally. Decision variables act differently in different objective functions. Hence, often, the mapping relation is unbalanced, which causes some redundancy during the search in a decision space. In response to this scenario, we propose a novel memetic (multi-objective) optimization strategy based on dimension reduction in decision space (DRMOS). DRMOS firstly analyzes the mapping relation between decision variables and objective functions. Then, it reduces the dimension of the search space by dividing the decision space into several subspaces according to the obtained relation. Finally, it improves the population by the memetic local search strategies in these decision subspaces separately. Further, DRMOS has good portability to other multi-objective evolutionary algorithms (MOEAs); that is, it is easily compatible with existing MOEAs. In order to evaluate its performance, we embed DRMOS in several state of the art MOEAs to facilitate our experiments. The results show that DRMOS has the advantage in terms of convergence speed, diversity maintenance, and portability when solving MOPs with an unbalanced mapping relation between decision variables and objective functions.

scholarly journals Multi-objective optimization of an industrial styrene reactor using Harmony Search Algorithm

2013 ◽  
pp. 190-196
Author(s):  
Saurabh Shukla ◽  
Ankit Anand
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Operating Conditions ◽  
Multi Objective Optimization ◽  
Industrial Practice ◽  
Multi Objective ◽  
Decision Variables ◽  
Optimization Study ◽  
Study Results

Multi-objective optimization of industrial styrene reactor is done using Harmony Search algorithm. Harmony search algorithm is a recently developed meta-heuristic algorithm which is inspired by musical improvisation process aimed towards obtaining the best harmony. Three objective functions – productivity, selectivity and yield are optimized to get best combination of decision variables for styrene reactor. All possible cases of single and multi-objective optimization have been considered. Pareto optimal sets are obtained as a result of the optimization study. Results reveal that optimized solution using harmony search algorithm gives better operating conditions than industrial practice.

Sign in / Sign up

Export Citation Format

Share Document