Multiobjective Design Optimization of B-Screw Series Propellers Using Evolutionary Algorithms

2003 ◽  
Vol 40 (04) ◽  
pp. 229-238
Author(s):  
Ernesto Benini
Keyword(s):  
Evolutionary Algorithms ◽  
Multiobjective Optimization ◽  
Personal Computer ◽  
Optimization Method ◽  
Preliminary Design ◽  
Thrust Coefficient ◽  
Marine Propellers ◽  
Complex Design ◽  
Conflicting Objectives ◽  
Multiobjective Design

Methodical series are still widely used in the preliminary design of light-and moderate-duty marine propellers. If the above series enables standard screws to be designed by simple chart methods, they do not help the designer to deal with a complex design involving multiple and conflicting objectives. In this paper, a multiobjective optimization method of B-screw series propeller is introduced that makes use of an evolutionary algorithm maximizing both efficiency and thrust coefficient with a constraint determined by cavitation. The capabilities of the method are illustrated and discussed in detail. The results following the application of the method are given in the form of diagrams, which can be implemented on a common personal computer and used to derive optimal screw configurations in real time.

A Tradeoff-Based Interactive Multi-Objective Optimization Method Driven by Evolutionary Algorithms

2017 ◽  
Vol 21 (2) ◽  
pp. 284-292 ◽  
Author(s):  
Lu Chen ◽  
◽  
Bin Xin ◽  
Jie Chen ◽  
◽  
...  
Keyword(s):  
Evolutionary Algorithms ◽  
Optimal Solution ◽  
Optimization Method ◽  
Normal Vector ◽  
Pareto Optimal ◽  
Management Problem ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Tangent Hyperplane ◽  
Conflicting Objectives

Multi-objective optimization problems involve two or more conflicting objectives, and they have a set of Pareto optimal solutions instead of a single optimal solution. In order to support the decision maker (DM) to find his/her most preferred solution, we propose an interactive multi-objective optimization method based on the DM’s preferences in the form of indifference tradeoffs. The method combines evolutionary algorithms with the gradient-based interactive step tradeoff (GRIST) method. An evolutionary algorithm is used to generate an approximate Pareto optimal solution at each iteration. The DM is asked to provide indifference tradeoffs whose projection onto the tangent hyperplane of the Pareto front provides a tradeoff direction. An approach for approximating the normal vector of the tangent hyperplane is proposed which is used to calculate the projection. A water quality management problem is used to demonstrate the interaction process of the interactive method. In addition, three benchmark problems are used to test the accuracy of the normal vector approximation approach and compare the proposed method with GRIST.

Metrics for Quality Assessment of a Multiobjective Design Optimization Solution Set

2000 ◽  
Author(s):  
Jin Wu ◽  
Shapour Azarm
Keyword(s):  
Multiobjective Optimization ◽  
Pareto Frontier ◽  
Optimization Methods ◽  
Optimization Method ◽  
Solution Set ◽  
Pareto Solution ◽  
Vibrating Platform ◽  
Pareto Solution Sets ◽  
Multiobjective Design

Abstract In this paper, several new set quality metrics are introduced that can be used to evaluate the ‘goodness’ of an observed Pareto solution set. These metrics, which are formulated in closed-form and geometrically illustrated, include coverage difference, Pareto spread, accuracy of an observed Pareto frontier, number of distinct choices and cluster. The metrics should enable a designer either monitor the quality of an observed Pareto solution set as obtained by a multiobjective optimization method, or compare the quality of observed Pareto solution sets as reported by different multiobjective optimization methods. A vibrating platform example is used to demonstrate the calculation of these metrics for an observed Pareto solution set.

Metrics for Quality Assessment of a Multiobjective Design Optimization Solution Set

2000 ◽  
Vol 123 (1) ◽  
pp. 18-25 ◽  
Author(s):  
Jin Wu ◽  
Shapour Azarm
Keyword(s):  
Multiobjective Optimization ◽  
Pareto Frontier ◽  
Optimization Methods ◽  
Optimization Method ◽  
Solution Set ◽  
Pareto Solution ◽  
Vibrating Platform ◽  
Pareto Solution Sets ◽  
Multiobjective Design

In this paper, several new set quality metrics are introduced that can be used to evaluate the “goodness” of an observed Pareto solution set. These metrics, which are formulated in closed-form and geometrically illustrated, include hyperarea difference, Pareto spread, accuracy of an observed Pareto frontier, number of distinct choices and cluster. The metrics should enable a designer to either monitor the quality of an observed Pareto solution set as obtained by a multiobjective optimization method, or compare the quality of observed Pareto solution sets as reported by different multiobjective optimization methods. A vibrating platform example is used to demonstrate the calculation of these metrics for an observed Pareto solution set.

RBFN Based Efficiency Optimization Method of Induction Motor Utilized in Electrically Driven Marine Propellers

2011 ◽  
Vol 131 (1) ◽  
pp. 68-75
Author(s):  
Supari ◽  
Syafaruddin ◽  
I Made Yulistya Negara ◽  
Mochamad Ashari ◽  
Takashi Hiyama
Keyword(s):  
Induction Motor ◽  
Optimization Method ◽  
Efficiency Optimization ◽  
Marine Propellers

scholarly journals A Two-Stage Mono- and Multi-Objective Method for the Optimization of General UPS Parallel Manipulators

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 543
Author(s):  
Alejandra Ríos ◽  
Eusebio E. Hernández ◽  
S. Ivvan Valdez
Keyword(s):  
Genetic Algorithm ◽  
Performance Metrics ◽  
Tracking Error ◽  
Parallel Manipulators ◽  
Optimization Method ◽  
Statistical Hypothesis ◽  
Two Stage ◽  
Multi Objective ◽  
Second Stage ◽  
Conflicting Objectives

This paper introduces a two-stage method based on bio-inspired algorithms for the design optimization of a class of general Stewart platforms. The first stage performs a mono-objective optimization in order to reach, with sufficient dexterity, a regular target workspace while minimizing the elements’ lengths. For this optimization problem, we compare three bio-inspired algorithms: the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO), and the Boltzman Univariate Marginal Distribution Algorithm (BUMDA). The second stage looks for the most suitable gains of a Proportional Integral Derivative (PID) control via the minimization of two conflicting objectives: one based on energy consumption and the tracking error of a target trajectory. To this effect, we compare two multi-objective algorithms: the Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) and Non-dominated Sorting Genetic Algorithm-III (NSGA-III). The main contributions lie in the optimization model, the proposal of a two-stage optimization method, and the findings of the performance of different bio-inspired algorithms for each stage. Furthermore, we show optimized designs delivered by the proposed method and provide directions for the best-performing algorithms through performance metrics and statistical hypothesis tests.

Multiobjective Optimization of the Heating Process for Forging Automotive Crankshaft

2015 ◽  
Vol 137 (3) ◽  
Author(s):  
Hong-Seok Park ◽  
Xuan-Phuong Dang
Keyword(s):  
Multiobjective Optimization ◽  
Heating System ◽  
Optimization Method ◽  
Heating Process ◽  
Temperature Deviation ◽  
Engineering Data ◽  
Optimization Of Process Parameters ◽  
Mathematical Approximation ◽  
Nondominated Sorting ◽  
Induction Heating System

This paper presents potential approaches that increase the energy efficiency of an in-line induction heating system for forging of an automotive crankshaft. Both heat loss reduction and optimization of process parameters are proposed scientifically in order to minimize the energy consumption and the temperature deviation in the workpiece. We applied the numerical multiobjective optimization method in conjunction with the design of experiment (DOE), mathematical approximation with metamodel, nondominated sorting genetic algorithm (GA), and engineering data mining. The results show that using the insulating covers reduces heat by an amount equivalent to 9% of the energy stored in the heated workpiece, and approximately 5.8% of the energy can be saved by process parameter optimization.

Generative Optimization for Automatic Creation of 3D CAD Platforms

2021 ◽  
Author(s):  
William F. Quintero-Restrepo ◽  
Brian K. Smith ◽  
Junfeng Ma
Keyword(s):  
Evolutionary Algorithms ◽  
Six Sigma ◽  
Design Space ◽  
Optimization Method ◽  
Efficiency Improvement ◽  
3D Cad ◽  
Systems Model ◽  
Electric Machinery ◽  
Definition Of ◽  
Six Sigma Approach

Abstract The efficient creation of 3D CAD platforms can be achieved by the optimization of their design process. The research presented in this article showcases a method for allowing such efficiency improvement. The method is based on the DMADV six sigma approach. During the Define step, the definition of the scope and design space is established. In the Measure step, the initial evaluation of the platforms to be improved is done with the help of a Metrics framework for 3D CAD platforms. The Analyze Step includes the identification and optimization of the systems’ model of the process based on the architecture and the multiple objectives required for the improvement. The optimization method used that is based on evolutionary algorithms allows for the identification of the best improvement alternatives for the next step. During Design step of the method, the improvement alternatives are planned and executed. In the final Verification step, the evaluation of the improved process is tested against the previous status with the help of the Metrics Framework for 3D CAD platforms. The method is explained with an example case of a 3D CAD platform for creating metallic boxes for electric machinery.

Multiobjective optimization of the flaxseed mucilage extraction process using normal-boundary intersection approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mariana Souza Rocha ◽  
Luiz Célio Souza Rocha ◽  
Marcia Barreto da Silva Feijó ◽  
Paula Luiza Limongi dos Santos Marotta ◽  
Samanta Cardozo Mourão
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Method ◽  
Extraction Process ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Fiber Concentration ◽  
Trade Off ◽  
Content Type ◽  
Normal Boundary

PurposeThe mucilage of the Linum usitatissimum L. seed (Linseed) is one of the natural mucilages that presents a great potential to provide a food hydrocolloid with potential applications in both food and pharmaceutical industries. To increase the yield and quality of linseed oil during its production process, it is necessary to previously extract its polysaccharides. Because of this, flax mucilage production can be made viable as a byproduct of oil extraction process, which is already a product of high commercial value consolidated in the market. Thus, the purpose of this work is to optimize the mucilage extraction process of L. usitatissimum L. using the normal-boundary intersection (NBI) multiobjective optimization method.Design/methodology/approachCurrently, the variables of the process of polysaccharide extraction from different sources are optimized using the response surface methodology. However, when the optimal points of the responses are conflicting it is necessary to study the best conditions to achieve a balance between these conflicting objectives (trade-offs) and to explore the available options it is necessary to formulate an optimization problem with multiple objectives. The multiobjective optimization method used in this work was the NBI developed to find uniformly distributed and continuous Pareto optimal solutions for a nonlinear multiobjective problem.FindingsThe optimum extraction point to obtain the maximum fiber concentration in the extracted material was pH 3.81, temperature of 46°C, time of 13.46 h. The maximum extraction yield of flaxseed was pH 6.45, temperature of 65°C, time of 14.41 h. This result confirms the trade-off relationship between the objectives. NBI approach was able to find uniformly distributed Pareto optimal solutions, which allows to analyze the behavior of the trade-off relationship. Thus, the decision-maker can set extraction conditions to achieve desired characteristics in mucilage.Originality/valueThe novelty of this paper is to confirm the existence of a trade-off relationship between the productivity parameter (yield) and the quality parameter (fiber concentration in the extracted material) during the flaxseed mucilage extraction process. The NBI approach was able to find uniformly distributed Pareto optimal solutions, which allows us to analyze the behavior of the trade-off relationship. This allows the decision-making to the extraction conditions according to the desired characteristics of the final product, thus being able to direct the extraction for the best applicability of the mucilage.

scholarly journals Single- versus Multiobjective Optimization for Evolution of Neural Controllers in Ms. Pac-Man

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Tse Guan Tan ◽  
Jason Teo ◽  
Kim On Chin
Keyword(s):  
Neural Network ◽  
Multiobjective Optimization ◽  
Dynamic Environment ◽  
Automatic Generation ◽  
Fixed Number ◽  
Evolution Strategy ◽  
Pareto Optimal Set ◽  
Conflicting Objectives ◽  
Hidden Neurons ◽  
Single Versus

The objective of this study is to focus on the automatic generation of game artificial intelligence (AI) controllers for Ms. Pac-Man agent by using artificial neural network (ANN) and multiobjective artificial evolution. The Pareto Archived Evolution Strategy (PAES) is used to generate a Pareto optimal set of ANNs that optimize the conflicting objectives of maximizing Ms. Pac-Man scores (screen-capture mode) and minimizing neural network complexity. This proposed algorithm is called Pareto Archived Evolution Strategy Neural Network or PAESNet. Three different architectures of PAESNet were investigated, namely, PAESNet with fixed number of hidden neurons (PAESNet_F), PAESNet with varied number of hidden neurons (PAESNet_V), and the PAESNet with multiobjective techniques (PAESNet_M). A comparison between the single- versus multiobjective optimization is conducted in both training and testing processes. In general, therefore, it seems that PAESNet_F yielded better results in training phase. But the PAESNet_M successfully reduces the runtime operation and complexity of ANN by minimizing the number of hidden neurons needed in hidden layer and also it provides better generalization capability for controlling the game agent in a nondeterministic and dynamic environment.

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