scholarly journals Multipurpose Water Reservoir Management: An Evolutionary Multiobjective Optimization Approach

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Luís A. Scola ◽  
Ricardo H. C. Takahashi ◽  
Sérgio A. A. G. Cerqueira
Keyword(s):  
Multiobjective Optimization ◽  
Flood Control ◽  
Electric Energy ◽  
Water Reservoir ◽  
Optimization Approach ◽  
Operation Planning ◽  
Nsga Ii ◽  
Evolutionary Multiobjective Optimization ◽  
Hydropower Plants ◽  
The Cost

The reservoirs that feed large hydropower plants should be managed in order to provide other uses for the water resources. Those uses include, for instance, flood control and avoidance, irrigation, navigability in the rivers, and other ones. This work presents an evolutionary multiobjective optimization approach for the study of multiple water usages in multiple interlinked reservoirs, including both power generation objectives and other objectives not related to energy generation. The classical evolutionary algorithm NSGA-II is employed as the basic multiobjective optimization machinery, being modified in order to cope with specific problem features. The case studies, which include the analysis of a problem which involves an objective of navigability on the river, are tailored in order to illustrate the usefulness of the data generated by the proposed methodology for decision-making on the problem of operation planning of multiple reservoirs with multiple usages. It is shown that it is even possible to use the generated data in order to determine the cost of any new usage of the water, in terms of the opportunity cost that can be measured on the revenues related to electric energy sales.

scholarly journals Control of Systems of Reservoirs with the Use of Risk Analysis

10.14311/538 ◽  
2004 ◽  
Vol 44 (2) ◽  
Author(s):  
P. Fošumpaur ◽  
L. Satrapa
Keyword(s):  
Risk Analysis ◽  
Flood Control ◽  
Water System ◽  
Water Reservoir ◽  
The Czech Republic ◽  
Run Off ◽  
Hydropower Plants ◽  
Basic Functions ◽  
And Control

A system of reservoirs is usually defined as a system of water management elements, that are mutually linked by inner and outer connections in a purpose-built complex. Combined elements consist of reservoirs, river sections, dams, weirs, hydropower plants, water treatment plants and other hydraulic structures. These elements also include the rainfall system, the run-off system, the ground water system, etc. A system of reservoirs serves many purposes, which result from the basic functions of water reservoirs: storage, flood control and environmental functions. Most reservoirs serve several purposes at the same time. They are so called multi-purposes reservoirs. Optimum design and control of a system of reservoirs depends strongly on identifying the particular purposes. In order to assess these purposes and to evaluate the appropriate set of criteria, risk analysis can be used. Design and control of water reservoir functions is consequently solved with the use of multi-objective optimisation. This paper deals with the use of the risk analysis to determine criteria for controlling the system. This approach is tested on a case study of the Pastviny dam in the Czech Republic.

Evolutionary multiobjective optimization of cellular base station locations using modified NSGA-II

2010 ◽  
Vol 17 (3) ◽  
pp. 597-609 ◽  
Author(s):  
N. Lakshminarasimman ◽  
S. Baskar ◽  
A. Alphones ◽  
M. Willjuice Iruthayarajan
Keyword(s):  
Multiobjective Optimization ◽  
Base Station ◽  
Nsga Ii ◽  
Evolutionary Multiobjective Optimization

Interactive evolutionary multiobjective optimization driven by robust ordinal regression

2010 ◽  
Vol 58 (3) ◽  
pp. 347-358 ◽  
Author(s):  
J. Branke ◽  
S. Greco ◽  
R. Słowiński ◽  
P. Zielniewicz
Keyword(s):  
Multiobjective Optimization ◽  
Decision Maker ◽  
Pareto Front ◽  
Optimization Technique ◽  
Ordinal Regression ◽  
Value Functions ◽  
Nsga Ii ◽  
Evolutionary Multiobjective Optimization ◽  
Robust Ordinal Regression ◽  
Speed Up

Interactive evolutionary multiobjective optimization driven by robust ordinal regressionThis paper presents the Necessary-preference-enhanced Evolutionary Multiobjective Optimizer (NEMO), which combines an evolutionary multiobjective optimization with robust ordinal regression within an interactive procedure. In the course of NEMO, the decision maker is asked to express preferences by simply comparing some pairs of solutions in the current population. The whole set of additive value functions compatible with this preference information is used within a properly modified version of the evolutionary multiobjective optimization technique NSGA-II in order to focus the search towards solutions satisfying the preferences of the decision maker. This allows to speed up convergence to the most preferred region of the Pareto-front.

scholarly journals A Modified Pareto Ant Colony Optimization Approach to Solve Biobjective Weapon-Target Assignment Problem

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
You Li ◽  
Yingxin Kou ◽  
Zhanwu Li ◽  
An Xu ◽  
Yizhe Chang
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithms ◽  
Ant Colony ◽  
Optimization Approach ◽  
Data Simulation ◽  
Nsga Ii ◽  
Mutation Strategy ◽  
Target Assignment ◽  
Operation Factor ◽  
The Cost

The weapon-target assignment (WTA) problem, known as an NP-complete problem, aims at seeking a proper assignment of weapons to targets. The biobjective WTA (BOWTA) optimization model which maximizes the expected damage of the enemy and minimizes the cost of missiles is designed in this paper. A modified Pareto ant colony optimization (MPACO) algorithm is used to solve the BOWTA problem. In order to avoid defects in traditional optimization algorithms and obtain a set of Pareto solutions efficiently, MPACO algorithm based on new designed operators is proposed, including a dynamic heuristic information calculation approach, an improved movement probability rule, a dynamic evaporation rate strategy, a global updating rule of pheromone, and a boundary symmetric mutation strategy. In order to simulate real air combat, the pilot operation factor is introduced into the BOWTA model. Finally, we apply the MPACO algorithm and other algorithms to the model and compare the data. Simulation results show that the proposed algorithm is successfully applied in the field of WTA which improves the performance of the traditional P-ACO algorithm effectively and produces better solutions than the two well-known multiobjective optimization algorithms NSGA-II and SPEA-II.

scholarly journals A Multiobjective Optimization Approach to Solve a Parallel Machines Scheduling Problem

2010 ◽  
Vol 2010 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaohui Li ◽  
Lionel Amodeo ◽  
Farouk Yalaoui ◽  
Hicham Chehade
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problem ◽  
Parallel Machines ◽  
Experimental Results ◽  
Optimization Approach ◽  
Due Dates ◽  
Nsga Ii ◽  
Parallel Machines Scheduling ◽  
Dependent Setup Times ◽  
Sequence Dependent Setup Times

A multiobjective optimization problem which focuses on parallel machines scheduling is considered. This problem consists of scheduling independent jobs on identical parallel machines with release dates, due dates, and sequence-dependent setup times. The preemption of jobs is forbidden. The aim is to minimize two different objectives: makespan and total tardiness. The contribution of this paper is to propose first a new mathematical model for this specific problem. Then, since this problem is NP hard in the strong sense, two well-known approximated methods, NSGA-II and SPEA-II, are adopted to solve it. Experimental results show the advantages of NSGA-II for the studied problem. An exact method is then applied to be compared with NSGA-II algorithm in order to prove the efficiency of the former. Experimental results show the advantages of NSGA-II for the studied problem. Computational experiments show that on all the tested instances, our NSGA-II algorithm was able to get the optimal solutions.

scholarly journals A Generic WebLab Control Tuning Experience Using the Ball and Beam Process and Multiobjective Optimization Approach

Information ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 132 ◽  
Author(s):  
Ricardo Massao Kagami ◽  
Guinther Kovalski da Costa ◽  
Thiago Schaedler Uhlmann ◽  
Luciano Antônio Mendes ◽  
Roberto Zanetti Freire
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Technique ◽  
Open Loop ◽  
Optimization Approach ◽  
Nsga Ii ◽  
Control Engineering ◽  
Virtual Laboratories ◽  
Interesting Approach ◽  
Reducing Costs ◽  
Remote Experimentation

In control engineering education, the possibility of using a real control system in the learning process motivates professors to improve both students’ knowledge and skills, thus avoiding an approach only based on control theory. While considering that control engineering laboratories are expensive, mainly because educational plants should reproduce classical problems that are found in the industry, the use of virtual laboratories appears as an interesting strategy for reducing costs and improving the diversity of experiments. In this research, remote experimentation was assumed regarding the ball and beam process as an alternative didactic methodology. While assuming a nonlinear and unstable open-loop process, this study presents how students should proceed to control the plant focusing on the topic that is associated with multiobjective optimization. Proportional-Integral-Derivative (PID) controller was tuned considering the Non-dominated Sorting Genetic Algorithm (NSGA-II) to illustrate the WebLab learning procedures described in this research. The proposed strategy was compared to the Åström’s robust loop shaping method to emphasize the performance of the multiobjective optimization technique. Analyzing the feedback provided by the students, remote experimentation can be seen as an interesting approach for the future of engineering learning, once it can be directly associated with industry demand of connected machines and real-time information analysis.

Design of combinational logic circuits through an evolutionary multiobjective optimization approach

2002 ◽  
Vol 16 (1) ◽  
pp. 39-53 ◽  
Author(s):  
CARLOS A. COELLO COELLO ◽  
ARTURO HERNÁNDEZ AGUIRRE
Keyword(s):  
Multiobjective Optimization ◽  
Population Based ◽  
Logic Circuits ◽  
Computational Effort ◽  
Lower Number ◽  
Optimization Approach ◽  
Combinational Circuits ◽  
Evolutionary Multiobjective Optimization ◽  
Combinational Logic Circuits ◽  
Human Designer

In this paper, we propose a population-based evolutionary multiobjective optimization approach to design combinational circuits. Our results indicate that the proposed approach can significantly reduce the computational effort required by a genetic algorithm (GA) to design circuits at a gate level while generating equivalent or even better solutions (i.e., circuits with a lower number of gates) than a human designer or even other GAs. Several examples taken from the literature are used to evaluate the performance of the proposed approach.

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