scholarly journals Comparative Study of Optimization Algorithms for The Optimal Reservoir Operation

2018 ◽  
Vol 246 ◽  
pp. 01003
Author(s):  
Xinyuan Liu ◽  
Yonghui Zhu ◽  
Lingyun Li ◽  
Lu Chen
Keyword(s):  
Reservoir Operation ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Complex Problem ◽  
Optimization Techniques ◽  
Decision Variables ◽  
Handling Methods ◽  
Optimality Algorithm ◽  
Parameter Values

Apart from traditional optimization techniques, e.g. progressive optimality algorithm (POA), modern intelligence algorithms, like genetic algorithms, differential evolution have been widely used to solve optimization problems. This paper deals with comparative analysis of POA, GA and DE and their applications in a reservoir operation problem. The results show that both GA and DES are feasible to reservoir operation optimization, but they display different features. GA and DE have many parameters and are difficult in determination of these parameter values. For simple problems with mall number of decision variables, GA and DE are better than POA when adopting appropriate parameter values and constraint handling methods. But for complex problem with large number of variables, POA combined with simplex method are much superior to GA and DE in time-assuming and quality of optimal solutions. This study helps to select proper optimization algorithms and parameter values in reservoir operation.

scholarly journals Lie Group Methods in Blind Signal Processing

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 440 ◽  
Author(s):  
Dariusz Mika ◽  
Jerzy Jozwik
Keyword(s):  
Signal Processing ◽  
Lie Groups ◽  
Lie Group ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Search Space ◽  
Optimization Techniques ◽  
Blind Signal Processing ◽  
Blind Signal ◽  
Group Methods

This paper deals with the use of Lie group methods to solve optimization problems in blind signal processing (BSP), including Independent Component Analysis (ICA) and Independent Subspace Analysis (ISA). The paper presents the theoretical fundamentals of Lie groups and Lie algebra, the geometry of problems in BSP as well as the basic ideas of optimization techniques based on Lie groups. Optimization algorithms based on the properties of Lie groups are characterized by the fact that during optimization motion, they ensure permanent bonding with a search space. This property is extremely significant in terms of the stability and dynamics of optimization algorithms. The specific geometry of problems such as ICA and ISA along with the search space homogeneity enable the use of optimization techniques based on the properties of the Lie groups O ( n ) and S O ( n ) . An interesting idea is that of optimization motion in one-parameter commutative subalgebras and toral subalgebras that ensure low computational complexity and high-speed algorithms.

DETERMINATION OF SOLVABILITY BOUNDARIES OF OPTIMIZATION PROBLEMS IN PARAMETER SPACE

1993 ◽  
Vol 03 (04) ◽  
pp. 453-462
Author(s):  
A.P. CHERENKOV ◽  
V.V. MIKHAILENKO ◽  
B.S. SHUSTERMAN
Keyword(s):  
Decision Making ◽  
Resource Allocation ◽  
Linear Programming ◽  
Dynamic Model ◽  
Parameter Space ◽  
Optimization Problems ◽  
Monotone Function ◽  
Complex Program ◽  
Parameter Values

This paper is devoted to the determination of parameter values of optimization problems for which they are solvable. In relation to this, the concept of monotone solvability with respect to parameter is essentially used. The procedure of construction of solvability boundaries in parameter space is realized, and it is essentially reduced to decipher the monotone function. This procedure is used for the consideration of a dynamic model of simulative control of the geological-prospecting process (the resource allocation between stages of geological-prospecting work). On the basis of this procedure using the standard package of linear programming, the complex program of decision-making for personal computers compatible with IBM XT/AT is implemented.

scholarly journals Improving multi-objective reservoir operation optimization with sensitivity-informed problem decomposition

2015 ◽  
Vol 12 (4) ◽  
pp. 3719-3752 ◽  
Author(s):  
J. G. Chu ◽  
C. Zhang ◽  
G. T. Fu ◽  
Y. Li ◽  
H. C. Zhou
Keyword(s):  
Sensitivity Analysis ◽  
Reservoir Operation ◽  
Global Sensitivity Analysis ◽  
Optimization Problems ◽  
Liaoning Province ◽  
Problem Decomposition ◽  
Global Sensitivity ◽  
Multi Objective ◽  
Decision Variables ◽  
Efficiency And Effectiveness

Abstract. This study investigates the effectiveness of a sensitivity-informed method for multi-objective operation of reservoir systems, which uses global sensitivity analysis as a screening tool to reduce the computational demands. Sobol's method is used to screen insensitive decision variables and guide the formulation of the optimization problems with a significantly reduced number of decision variables. This sensitivity-informed problem decomposition dramatically reduces the computational demands required for attaining high quality approximations of optimal tradeoff relationships between conflicting design objectives. The search results obtained from the reduced complexity multi-objective reservoir operation problems are then used to pre-condition the full search of the original optimization problem. In two case studies, the Dahuofang reservoir and the inter-basin multi-reservoir system in Liaoning province, China, sensitivity analysis results show that reservoir performance is strongly controlled by a small proportion of decision variables. Sensitivity-informed problem decomposition and pre-conditioning are evaluated in their ability to improve the efficiency and effectiveness of multi-objective evolutionary optimization. Overall, this study illustrates the efficiency and effectiveness of the sensitivity-informed method and the use of global sensitivity analysis to inform problem decomposition when solving the complex multi-objective reservoir operation problems.

scholarly journals Application of Nontraditional Optimization Techniques for Airfoil Shape Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
R. Mukesh ◽  
K. Lingadurai ◽  
U. Selvakumar
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Shape Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Optimization Techniques ◽  
Aerodynamic Shape Optimization ◽  
Shape Optimization Problem ◽  
Aerodynamic Shape

The method of optimization algorithms is one of the most important parameters which will strongly influence the fidelity of the solution during an aerodynamic shape optimization problem. Nowadays, various optimization methods, such as genetic algorithm (GA), simulated annealing (SA), and particle swarm optimization (PSO), are more widely employed to solve the aerodynamic shape optimization problems. In addition to the optimization method, the geometry parameterization becomes an important factor to be considered during the aerodynamic shape optimization process. The objective of this work is to introduce the knowledge of describing general airfoil geometry using twelve parameters by representing its shape as a polynomial function and coupling this approach with flow solution and optimization algorithms. An aerodynamic shape optimization problem is formulated for NACA 0012 airfoil and solved using the methods of simulated annealing and genetic algorithm for 5.0 deg angle of attack. The results show that the simulated annealing optimization scheme is more effective in finding the optimum solution among the various possible solutions. It is also found that the SA shows more exploitation characteristics as compared to the GA which is considered to be more effective explorer.

scholarly journals IMPLEMENTASI ALGORITMA HILL CLIMBING PADA PENENTUAN JARAK TERPENDEK KOTA WISATA DI INDONESIA

2019 ◽  
Vol 1 (3) ◽  
pp. 127-132
Author(s):  
Desti Fitriati ◽  
Nura Meutia Nessrayasa
Keyword(s):  
Optimization Problems ◽  
Complex Problem ◽  
Hill Climbing ◽  
Hill Climbing Algorithm ◽  
Steepest Ascent ◽  
Shortest Route ◽  
Shortest Distance ◽  
Optimization Function ◽  
The Right

Searching and determining the shortest route is a complex problem, looking for the shortest route from a number of attractions and the distance between attractions. With varying access paths, the shortest route search becomes the right choice using a website-based app that provides the closest route on a map using the SAHC (Steepest Ascent Hill Climbing) algorithm. Steepest Ascent Hill Climbing is a method of an algorithm that is widely used for optimization problems. One application is to find the shortest route by maximizing or minimizing the value of the existing optimization function. In research ii study using 34 provinces in Indonesia and every province, there are 5 most popular tour, accuracy value obtained in research determination of the shortest distance of tourist city in Indonesia is 93,3%.  

scholarly journals Optimization of Reservoir Operation using Linear Programming

2020 ◽  
Vol 8 (5) ◽  
pp. 1028-1032
Keyword(s):  
Linear Programming ◽  
Reservoir Operation ◽  
Storage Capacity ◽  
Optimization Techniques ◽  
Water Levels ◽  
Rule Curves ◽  
Operating Policy ◽  
Decision Variables ◽  
Rule Curve ◽  
Operation Period

The paper aims to derive the optimal releases monthly through linear programming for a single purpose reservoir. The releases from the reservoir are usually based upon the rule curves or operating policy adopted. The rule curve is the storage, indicating the water levels to be maintained in-order to satisfy the demand during the operation period. Linear programming (LP) is one of the global optimization techniques that have gained popularity as a means to attain reservoir operation. In the present study Linear Programming was used to develop an operation policy for Hemavathy Reservoir, Hassan District Karnataka, India. The decision variables were monthly reservoir releases for irrigation and initial storages in reservoir at beginning of the month. The constraint bound for the reservoir releases was reservoir storage capacity. The results derived by using Linear Programming shows that the downstream irrigation demands were satisfied and also considerable amount of water was conserved from reduced spills.

scholarly journals Distributionally robust optimization with decision dependent ambiguity sets

2020 ◽  
Vol 14 (8) ◽  
pp. 2565-2594 ◽  
Author(s):  
Fengqiao Luo ◽  
Sanjay Mehrotra
Keyword(s):  
Robust Optimization ◽  
Probability Distributions ◽  
Optimization Techniques ◽  
Selling Price ◽  
Distributionally Robust Optimization ◽  
Decision Variables ◽  
Cutting Surface ◽  
Kolmogorov Smirnov ◽  
Distributionally Robust

Abstract We study decision dependent distributionally robust optimization models, where the ambiguity sets of probability distributions can depend on the decision variables. These models arise in situations with endogenous uncertainty. The developed framework includes two-stage decision dependent distributionally robust stochastic programming as a special case. Decision dependent generalizations of five types of ambiguity sets are considered. These sets are based on bounds on moments, covariance matrix, Wasserstein metric, Phi-divergence and Kolmogorov–Smirnov test. For the finite support case, we use linear, conic or Lagrangian duality to give reformulations of these models with a finite number of constraints. Reformulations are also given for the continuous support case for moment, covariance, Wasserstein and Kolmogorov–Smirnov based models. These reformulations allow solutions of such problems using global optimization techniques within the framework of a cutting surface algorithm. The importance of decision dependence in the ambiguity set is demonstrated with the help of a numerical example modeling simultaneous determination of order quantity and selling price for a newsvendor problem.

scholarly journals A Modified Jaya Algorithm for Mixed-Variable Optimization Problems

2018 ◽  
Vol 29 (1) ◽  
pp. 1007-1027 ◽  
Author(s):  
Prem Singh ◽  
Himanshu Chaudhary
Keyword(s):  
Optimization Problems ◽  
Optimization Algorithms ◽  
Computational Cost ◽  
Continuous Variable ◽  
Optimization Techniques ◽  
Jaya Algorithm ◽  
Continuous Variables ◽  
Middle Point ◽  
Integer Variables ◽  
Mixed Variable

Abstract Mixed-variable optimization problems consist of the continuous, integer, and discrete variables generally used in various engineering optimization problems. These variables increase the computational cost and complexity of optimization problems due to the handling of variables. Moreover, there are few optimization algorithms that give a globally optimal solution for non-differential and non-convex objective functions. Initially, the Jaya algorithm has been developed for continuous variable optimization problems. In this paper, the Jaya algorithm is further extended for solving mixed-variable optimization problems. In the proposed algorithm, continuous variables remain in the continuous domain while continuous domains of discrete and integer variables are converted into discrete and integer domains applying bound constraint of the middle point of corresponding two consecutive values of discrete and integer variables. The effectiveness of the proposed algorithm is evaluated through examples of mixed-variable optimization problems taken from previous research works, and optimum solutions are validated with other mixed-variable optimization algorithms. The proposed algorithm is also applied to two-plane balancing of the unbalanced rigid threshing rotor, using the number of balance masses on plane 1 and plane 2. It is found that the proposed algorithm is computationally more efficient and easier to use than other mixed optimization techniques.

scholarly journals Improving multi-objective reservoir operation optimization with sensitivity-informed dimension reduction

2015 ◽  
Vol 19 (8) ◽  
pp. 3557-3570 ◽  
Author(s):  
J. Chu ◽  
C. Zhang ◽  
G. Fu ◽  
Y. Li ◽  
H. Zhou
Keyword(s):  
Sensitivity Analysis ◽  
Dimension Reduction ◽  
Reservoir Operation ◽  
Global Sensitivity Analysis ◽  
Optimization Problems ◽  
Liaoning Province ◽  
Global Sensitivity ◽  
Multi Objective ◽  
Decision Variables ◽  
Efficiency And Effectiveness

Abstract. This study investigates the effectiveness of a sensitivity-informed method for multi-objective operation of reservoir systems, which uses global sensitivity analysis as a screening tool to reduce computational demands. Sobol's method is used to screen insensitive decision variables and guide the formulation of the optimization problems with a significantly reduced number of decision variables. This sensitivity-informed method dramatically reduces the computational demands required for attaining high-quality approximations of optimal trade-off relationships between conflicting design objectives. The search results obtained from the reduced complexity multi-objective reservoir operation problems are then used to pre-condition the full search of the original optimization problem. In two case studies, the Dahuofang reservoir and the inter-basin multi-reservoir system in Liaoning province, China, sensitivity analysis results show that reservoir performance is strongly controlled by a small proportion of decision variables. Sensitivity-informed dimension reduction and pre-conditioning are evaluated in their ability to improve the efficiency and effectiveness of multi-objective evolutionary optimization. Overall, this study illustrates the efficiency and effectiveness of the sensitivity-informed method and the use of global sensitivity analysis to inform dimension reduction of optimization problems when solving complex multi-objective reservoir operation problems.

Exergy analysis and optimization of the Rankine cycle in steam power plants using the firefly algorithm

2018 ◽  
Vol 19 (5) ◽  
pp. 505
Author(s):  
Samad Elahifar ◽  
Ehsanolah Assareh ◽  
Mojtaba Nedaei
Keyword(s):  
Power Plant ◽  
Firefly Algorithm ◽  
Exergy Analysis ◽  
Optimization Problems ◽  
Thermal Power ◽  
Optimization Algorithms ◽  
Exergy Efficiency ◽  
Steam Power ◽  
Steam Power Plant ◽  
Decision Variables

The analysis of the exergy efficiency has always been considered as a fundamental criterion to study the behavior of the thermodynamic cycles. In this research, the exergy analysis of a steam power plant for generating electricity with Rankine thermodynamic cycle is carried out. Zarand steam power plant, which is located in the Kerman province, is considered as a case study. In order to optimize these thermodynamic processes and to achieve the highest exergy efficiency value, some primary parameters were considered as the decision variables. By changing the values of these parameters, an attempt was made to enhance the exergy efficiency by using a novel approach. The six decision variables, which are, output temperature and pressure values of the boiler, as well as the output pressure values of the four stages of the turbine, were chosen on the basis of probability of variations in a certain range of electricity generation parameters for the studied power plant. The exergy efficiency was considered as the objective function. Afterwards, optimization of the power plant by employing the firefly algorithm, which is one of the relatively latest invented algorithms for solving the optimization problems, was carried out. The firefly model performs the optimization process inspired by the behavior and action of fireflies to attract mates and reject enemies. For the purpose of analysis of the exergy efficiency, at the first stage, the optimization of exergy efficiency function was performed for the studied steam power plant, and then the results were compared with the solutions obtained using the genetic and particle swarm optimization algorithms. Final results are indicative of the fact that by appropriate changes in the decision variables and employing the firefly algorithm, the exergy efficiency of the thermal power plant increased from 30.1 to 30.7037 percent. This increase was equivalent to 0.6037 for the cycle, and compared to the results obtained from the genetic and swarm particle optimization algorithms, it was 0.04% and 0.0398% higher, respectively.

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