scholarly journals Analysis of optimization methods in marine objects control synthesis

2021 ◽  
Vol 2 (1) ◽  
pp. 25-30
Author(s):  
Józef Lisowski
Keyword(s):  
Dynamic Optimization ◽  
Optimization Methods ◽  
Control Synthesis ◽  
Single Object ◽  
Static Optimization ◽  
Particle Swarms ◽  
Optimization Task ◽  
Single Criterion

The article presents four main chapters that allow you to formulate an optimization task and choose a method for solving it from static and dynamic optimization methods to single-criterion and multi-criteria optimization. In the group of static optimization methods, the methods are without constraints and with constraints, gradient and non-gradient and heuristic. Dynamic optimization methods are divided into basic - direct and indirect and special. Particular attention has been paid to multi-criteria optimization in single-object approach as static and dynamic optimization, and multi-object optimization in game control scenarios. The article shows not only the classic optimization methods that were developed many years ago, but also the latest in the field, including, but not limited to, particle swarms.

scholarly journals Optimization Methods in Maritime Transport and Logistics

2018 ◽  
Vol 25 (4) ◽  
pp. 30-38
Author(s):  
Józef Lisowski
Keyword(s):  
Literature Review ◽  
Dynamic Optimization ◽  
Optimization Methods ◽  
Maritime Transport ◽  
Single Criterion

Abstract The purpose of the article is to present the goal of optimization of transport and logistics processes, followed by literature review in the field of optimization methods. The optimization methods were categorized and the most commonly used methods were listed. The tasks of static and dynamic optimization were formulated. An example of the single-criterion static and dynamic optimization and multi-criteria game optimization are given.

scholarly journals Optimum Tracking with Evolution Strategies

2006 ◽  
Vol 14 (3) ◽  
pp. 291-308 ◽  
Author(s):  
Dirk V. Arnold ◽  
Hans-Georg Beyer
Keyword(s):  
Dynamic Optimization ◽  
Scaling Laws ◽  
Optimization Problems ◽  
Step Length ◽  
Moving Target ◽  
Dynamic Optimization Problems ◽  
Genetic Operators ◽  
Speed Scaling ◽  
The Past ◽  
Optimization Task

Evolutionary algorithms are frequently applied to dynamic optimization problems in which the objective varies with time. It is desirable to gain an improved understanding of the influence of different genetic operators and of the parameters of a strategy on its tracking performance. An approach that has proven useful in the past is to mathematically analyze the strategy's behavior in simple, idealized environments. The present paper investigates the performance of a multiparent evolution strategy that employs cumulative step length adaptation for an optimization task in which the target moves linearly with uniform speed. Scaling laws that quite accurately describe the behavior of the strategy and that greatly contribute to its understanding are derived. It is shown that in contrast to previously obtained results for a randomly moving target, cumulative step length adaptation fails to achieve optimal step lengths if the target moves in a linear fashion. Implications for the choice of population size parameters are discussed.

Three-Stage Low Pressure Compressor Modernization by Means of Optimization Methods

2015 ◽  
Author(s):  
Evgenii Goryachkin ◽  
Grigorii Popov ◽  
Oleg Baturin ◽  
Daria Kolmakova
Keyword(s):  
Mass Flow ◽  
Pressure Ratio ◽  
Optimization Methods ◽  
Low Pressure ◽  
Optimal Combination ◽  
Pareto Set ◽  
Optimization Task ◽  
Efficiency Increase ◽  
Optimization Parameters ◽  
Pressure Compressor

Low pressure compressor operation has some features. Firstly, the LPC stages work with cold air. For this reason there is transonic or subsonic flow in LPC. Secondly, the flow in LPC has complex spatial structure. Blade geometry of LPC is described by a large number of parameters. For this reason, it is difficult to pick up optimal combination of parameters manually. The solution of this problem is the usage of optimization methods to find the optimal combination of parameters. This approach was tested in this work. The main goal of this work was the LPC modernization for new parameters of gas turbine engine. Set of unimprovable solutions (Pareto set) was obtained as a result of solving optimization task. Pareto set was a compromise between the efficiency increase and the mass flow decrease. Each point from Pareto set had a correspondence with LPC unique geometry represented as an array of optimization parameters. One point of the Pareto set met all the required parameters of modernized LPC. The LPC geometry that guaranteed the efficiency increase by 1,3%, the total pressure ratio increase by 4% and mass flow rate decrease by 11% in comparison with the original LPC was obtained as a result of the investigation.

Design Optimization of Power Plants by Considering Multiple Partial Load Operation Points

2007 ◽  
Author(s):  
Marc Ju¨des ◽  
George Tsatsaronis
Keyword(s):  
Design Optimization ◽  
Power Plants ◽  
Optimization Procedure ◽  
Optimization Methods ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Optimization Task ◽  
Operation Conditions ◽  
Partial Load ◽  
Conventional Optimization

The design optimization of complex energy conversion systems requires the consideration of typical operation conditions. Due to the complex optimization task, conventional optimization methods normally take into account only one operation point that is, in the majority of cases, the full load case. To guarantee good operation at partial loads additional operation conditions have to be taken into account during the optimization procedure. The optimization task described in this article considers altogether four different operation points of a cogeneration plant. Modelling requirements, such as the equations that describe the partial load behavior of single components, are described as well as the problems that occur, when nonlinear and nonconvex equations are used. For the solution of the resulting non-convex mixed-integer nonlinear programming (MINLP) problem, the solver LaGO is used, which requires that the optimization problem is formulated in GAMS. The results of the conventional optimization approach are compared to the results of the new method. It is shown, that without consideration of different operation points, a flexible operation of the plant may be impossible.

scholarly journals Decomposition-Coordinating Method for Parallel Solution of a Multi-area Combined Economic Emission Dispatch Problem

2016 ◽  
Vol 6 (5) ◽  
pp. 2048
Author(s):  
Senthil Krishnamurthy ◽  
Raynitchka Tzoneva
Keyword(s):  
Power System ◽  
High Performance ◽  
Optimization Methods ◽  
System Control ◽  
Parallel Solution ◽  
Power System Control ◽  
Optimization Task ◽  
Tie Lines ◽  
Emission Dispatch ◽  
Economic Emission Dispatch

<p>Multi-area Combined Economic Emission Dispatch (MACEED) problem is an optimization task in power system operation for allocating the amount of generation to the committed units within the system areas. Its objective is to minimize the fuel cost and the quantity of emissions subject to the power balance, generator limits, transmission line and tie-line constraints. The solutions of the MACEED problem in the conditions of deregulation are difficult, due to the model size, nonlinearities, and the big number of interconnections, and require intensive computations in real-time. High-Performance Computing (HPC) gives possibilities for the reduction of the problem complexity and the time for calculation by the use of parallel processing techniques for running advanced application programs efficiently, reliably and quickly. These applications are considered as very new in the power system control centers because there are not available optimization methods and software based on them that can solve the MACEED problem in parallel, paying attention to the existence of the power system areas and the tie-lines between them. A decomposition-coordinating method based on Lagrange’s function is developed in this paper. Investigations of the performance of the method are done using IEEE benchmark power system models.</p>

Integration of an Automatic Optimizer Functionality Into the Design Process of Industrial Steam Turbines

2012 ◽  
Author(s):  
Dimitri Drapkin ◽  
Franz Kores ◽  
Thomas Polklas
Keyword(s):  
Design Process ◽  
Optimization Methods ◽  
Simultaneous Optimization ◽  
Design Parameters ◽  
Optimization Approach ◽  
Steam Turbines ◽  
Particle Swarm Optimizer ◽  
Optimization Task ◽  
Wide Range ◽  
Optimization Parameters

Industrial steam turbines are mostly tailor made machinery, varying in a wide range of specifications. This feature introduces high requirements on the design process which has to be flexible, efficient and fast at the same time. Given live steam and design parameters as input, the geometry corresponding to the valid design scheme can be calculated together with the required thermodynamic, aerodynamic and mechanical characteristics. By variation of design parameters a design may be achieved which optimizes both, efficiency and cost. The optimization task is formulated mathematically, e.g. crucial optimization parameters, criteria for evaluation of different designs and other required constraints are selected. The structure of the resulting optimization problem is analyzed. Based on this analysis a modular optimization system design is proposed. The choice of Genetic Algorithms and Adaptive Particle Swarm Optimizer as optimization methods is discussed, recommendations for their proper use are given. A bicriterial optimization approach for a simultaneous optimization of efficiency and cost is developed.

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