Multi-Objective Design Optimization for Product Platform and Product Family Design Using Genetic Algorithms

2005 ◽  
Author(s):  
Satish V. K. Akundi ◽  
Timothy W. Simpson ◽  
Patrick M. Reed
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Genetic Algorithms ◽  
Product Family ◽  
Optimization Techniques ◽  
Product Platform ◽  
Product Family Design ◽  
Nsga Ii ◽  
Pareto Solution ◽  
Multi Objective

Many companies are using product families and platform-based product development to reduce costs and time-to-market while increasing product variety and customization. Multi-objective optimization is increasingly becoming a powerful tool to support product platform and product family design. In this paper, a genetic algorithm-based optimization method for product family design is suggested, and its application is demonstrated using a family of universal electric motors. Using an appropriate representation for the design variables and by adopting a suitable formulation for the genetic algorithm, a one-stage approach for product family design can be realized that requires no a priori platform decision-making, eliminating the need for higher-level problem-specific domain knowledge. Optimizing product platforms using multi-objective algorithms gives the designer a Pareto solution set, which can be used to make better decisions based on the trade-offs present across different objectives. Two Non-Dominated Sorting Genetic Algorithms, namely, NSGA-II and ε-NSGA-II, are described, and their performance is compared. Implementation challenges associated with the use of these algorithms are also discussed. Comparison of the results with existing benchmark designs suggests that the proposed multi-objective genetic algorithms perform better than conventional single-objective optimization techniques, while providing designers with more information to support decision making during product family design.

Study of the Non-Dominated Sorting Genetic Algorithm for the Optimal Structural Parameter of the Tooth-Arrangement Multi-Fingered Dextrous Hand

2013 ◽  
Vol 756-759 ◽  
pp. 4082-4089
Author(s):  
Zhan Li Li ◽  
Xiang Ting He
Keyword(s):  
Genetic Algorithm ◽  
Parameter Optimization ◽  
Experimental Results ◽  
Structural Parameter ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Pareto Solution ◽  
Multi Objective ◽  
Dextrous Hand

Firstly, the structural parameter optimization of the tooth-arrangement multi-fingered dextrous hand is studied. Secondly, as to the shortcomings that the Pareto solution of multi-objective optimization was distributed unevenly in NSGA-II, a non-dominated sorting genetic algorithm based on immune principle is proposed. Lastly, the structural parameter of the medical tooth-arrangement multi-fingered dextrous hand is optimized using the proposed algorithm. The experimental results show that this algorithm can optimize structural parameter of tooth-arrangement multi-fingered dextrous hand very well.

Research on Multi-Objective Transmission Planning in Electricity Markets

2012 ◽  
Vol 516-517 ◽  
pp. 1429-1432
Author(s):  
Yang Liu ◽  
Xu Liu ◽  
Feng Xian Cui ◽  
Liang Gao
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Genetic Algorithms ◽  
Electricity Markets ◽  
Optimization Problem ◽  
Nsga Ii ◽  
Transmission Planning ◽  
Multi Objective ◽  
Complex Optimization ◽  
The Mathematical Model

Abstract. Transmission planning is a complex optimization problem with multiple deciding variables and restrictions. The mathematical model is non-linear, discrete, multi-objective and dynamic. It becomes complicated as the system grows. So the algorithm adopted affects the results of planning directly. In this paper, a fast non-dominated sorting genetic algorithm (NSGA-II) is employed. The results indicate that NSGA-II has some advantages compared to the traditional genetic algorithms. In transmission planning, NSGA-II is feasible, flexible and effective.

Applications of Multi-Objective Genetic Algorithm in Optimizing Steering Performances for Submersible

2013 ◽  
Vol 756-759 ◽  
pp. 3136-3140
Author(s):  
Zhuo Yi Yang ◽  
Yong Jie Pang ◽  
Shao Lian Ma
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Approximation Model ◽  
Horizontal Movement ◽  
Nsga Ii ◽  
Hydrodynamic Coefficient ◽  
Pareto Solution ◽  
Multi Objective ◽  
Minimum Drag ◽  
Multi Objective Genetic Algorithm

Multi-objective arithmetic NSGA-II based on Pareto solution is investigated to deal with integrated optimal design of speedability and manoeuvre performances for submersible. Approximation model of resistance for serial revolving shape is constructed by hydrodynamic numerical calculations. The appraisement criterions of stability and mobility are calculated from linear equation of horizontal movement by estimating hydrodynamic coefficient of submersible. After optimization, the scattered Pareto solution of drag and turning diameter are gained, and from the solutions designer can select the reasonable one based on the actual requirement. The Pareto solution can ensure the minimum drag in this manoeuvre performance or the best manoeuvre performance in this drag value.

scholarly journals An Extensive Study for a Wide Utilization of Green Architecture Parameters in Built Environment Based on Genetic Schemes

Buildings ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 507
Author(s):  
Ghada Elshafei ◽  
Silvia Vilčeková ◽  
Martina Zeleňáková ◽  
Abdelazim M. Negm
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Green Building ◽  
Extensive Study ◽  
Nsga Ii ◽  
Multi Objective ◽  
Green Architecture ◽  
Multi Objective Programming ◽  
The Neural Network ◽  
Economic Future

Recently, green structures turned into a huge path to an economic future. Green building outlines include finding the harmony between agreeable home living and a maintainable environment. Furthermore, the usage of modern technologies is seen as part of greener construction changes to make the urban environment more viable. This paper introduces an exhaustive state-of-art review and current practices to look for the ideal green arrangement’s models, procedures, and parameters utilizing the genetic algorithms innovations to help for settling on the most ideal choice from various options. The integrated Genetic Algorithm (GA) along with the Nondominated Sorting Genetic Algorithm strategy GA-NSGA-II is considered to be more accurate for predicting a viable future. The above methodology is widely relevant for its humility, ease of execution, and enormous durability. Besides other approaches, the GA was incorporated as well as the Neural Network (NN), Simulated Annealing (SA), Fuzzy Set theory, decision-making multicriteria, and multi-objective programming. The most fashionable methods are moderately the embedded GA-NSGA-II approaches. This paper gives an outline of the capability of GA-based MOO in supporting the advancement of methodologies of the techniques and parameters to find the best solution for the building decision-making cycle. The GA combined schemes can fulfill all the requirements for finding the optimality in the case of multi-objective problem-solving.

Assessment of Multi-Objective Genetic Algorithms With Different Niching Strategies and Regression Methods for Engine Optimization and Design

2009 ◽  
Author(s):  
Yu Shi ◽  
Rolf D. Reitz
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Prediction Accuracy ◽  
Optimization Problems ◽  
Design Parameters ◽  
Hybrid Mode ◽  
Nsga Ii ◽  
Multi Objective ◽  
Regression Methods ◽  
Objective Space

In previous study [1] the Non-dominated Sorting Genetic Algorithm II (NSGA II) [2] performed better than other popular Multi-Objective Genetic Algorithms (MOGA) in engine optimization that sought optimal combinations of the piston bowl geometry, spray targeting, and swirl ratio. NSGA II is further studied in this paper using different niching strategies that are applied to the objective-space and design-space, which diversify the optimal objectives and design parameters accordingly. Convergence and diversity metrics are defined to assess the performance of NSGA II using different niching strategies. It was found that use of the design niching achieved more diversified results with respect to design parameters, as expected. Regression was then conducted on the design datasets that were obtained from the optimizations with two niching strategies. Four regression methods, including K-nearest neighbors (KN), Kriging (KR), Neural Networks (NN), and Radial Basis Functions (RBF), were compared. The results showed that the dataset obtained from optimization with objective niching provided a more fitted learning space for the regression methods. The KN, KR, outperformed the other two methods with respect to the prediction accuracy. Furthermore, a log transformation to the objective-space improved the prediction accuracy for the KN, KR, and NN methods but not the RBF method. The results indicate that it is appropriate to use a regression tool to partly replace the actual CFD evaluation tool in engine optimization designs using the genetic algorithm. This hybrid mode saves computational resources (processors) without losing optimal accuracy. A Design of Experiment (DoE) method (the Optimal Latin Hypercube method) was also used to generate a dataset for the regression processes. However, the predicted results were much less reliable than results that were learned using the dynamically increasing datasets from the NSGA II generations. Applying the dynamical learning strategy during the optimization processes allows computationally expensive CFD evaluations to be partly replaced by evaluations using the regression techniques. The present study demonstrates the feasibility of applying the hybrid mode to engine optimization problems, and the conclusions can also extend to other optimization studies (numerical or experimental) that feature time-consuming evaluations and have highly non-linear objective-spaces.

scholarly journals Multi-Objective Approach for Production Line Equipment Selection

2012 ◽  
Vol 3 (1) ◽  
pp. 4-17 ◽  
Author(s):  
H. Chehade ◽  
A. Dolgui ◽  
F. Dugardin ◽  
L. Makdessian ◽  
F. Yalaoui
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Local Search ◽  
Production Line ◽  
Nsga Ii ◽  
Equipment Selection ◽  
Multi Objective ◽  
Numerical Tests ◽  
Machining Lines ◽  
Direct Measures

Multi-Objective Approach for Production Line Equipment Selection A novel problem dealing with design of reconfigurable automated machining lines is considered. Such lines are composed of workstations disposed sequentially. Each workstation needs the most suitable equipment. Each available piece of equipment is characterized by its cost, can perform a set of operations and requires skills of a given level for its maintenance. A multi-objective approach is proposed to assign tasks, choose and allocate pieces of equipment to workstations taking into account all the problem parameters and constraints. The techniques developed are based on a genetic algorithm of type NSGA-II. The NSGA-II suggested is also combined with a local search. These two genetic algorithms (with and without local search) are tested for several line examples and for two versions of the considered problem: bi-objective and four-objective cases. The results of numerical tests are reported. What is the most interesting is that the assessment of these algorithms is accomplished by using three measuring criteria: the direct measures of gaps, the measures proposed by Zitzler and Thiele in 1999 and the distances suggested by Riise in 2002.

A Non-Dominated Sorting Genetic Algorithm Approach for Optimization of Multi-Objective Airport Gate Assignment Problem

2018 ◽  
Vol 2672 (23) ◽  
pp. 59-70 ◽  
Author(s):  
Seyedmirsajad Mokhtarimousavi ◽  
Danial Talebi ◽  
Hamidreza Asgari
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Supply And Demand ◽  
Multiple Criteria Decision Making ◽  
Financial Loss ◽  
Nsga Ii ◽  
Solution Approach ◽  
Multi Objective ◽  
Wide Range ◽  
Gate Assignment

Gate assignment problems (GAP) are one of the most substantial issues in airport operation. The ever-increasing demand producing high occupancy rates of gates, the potential financial loss from imbalances between supply and demand in congested airports, and the limited scope for expanding facilities present challenges that require an advanced methodology for optimal supply allocation. In principle, tackling GAP involves seeking to maintain an airport’s maximum capacity through the best possible allocation of resources (gates). There are a wide range of dependent and independent resources and limitations involved in the problem, adding to the complexity of GAP from both theoretical and practical perspectives. In this study, GAP is extended and mathematically formulated as a three-objective problem, taking into account all resources and restrictions, which can be directly linked to airport authorities’ multiple criteria decision-making processes. The preliminary goal of multi-objective formulation is to consider a wider scope, in which a higher number of objectives are simultaneously optimized, and thus to increase the practical efficiency of the final solution. The problem is solved by applying the second version of Non-dominated Sorting Genetic Algorithm (NSGA-II) as a parallel evolutionary optimization algorithm. Results illustrate that the proposed mathematical model could address most of the major criteria in the decision-making process in airport management in terms of passenger walking distances, robustness, and traditional costs. Moreover, the proposed solution approach shows promise in finding acceptable and plausible solutions compared with other multi-objective algorithms (BAT, PSO, ACO, and ABC).

Automatic calibration of urban drainage model using a novel multi-objective genetic algorithm

2005 ◽  
Vol 52 (5) ◽  
pp. 43-52 ◽  
Author(s):  
F. di Pierro ◽  
S. Djordjević ◽  
Z. Kapelan ◽  
S.-T. Khu ◽  
D. Savić ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Optimization Techniques ◽  
Urban Drainage ◽  
Network Modelling ◽  
Nsga Ii ◽  
Multi Objective ◽  
Sewer Network ◽  
Multi Objective Genetic Algorithm ◽  
Physically Based ◽  
Feasible Solutions

In order to successfully calibrate an urban drainage model, multiple calibration criteria should be considered. This raises the issue of adopting a method for comparing different solutions (parameter sets) according to a set of objectives. Amongst the global optimization techniques that have blossomed in recent years, Multi Objective Genetic Algorithms (MOGA) have proved effective in numerous engineering applications, including sewer network modelling. Most of the techniques rely on the condition of Pareto efficiency to compare different solutions. However, as the number of criteria increases, the ratio of Pareto optimal to feasible solutions increases as well. The pitfalls are twofold: the efficiency of the genetic algorithm search worsens and decision makers are presented with an overwhelming number of equally optimal solutions. This paper proposes a new MOGA, the Preference Ordering Genetic Algorithm, which alleviates the drawbacks of conventional Pareto-based methods. The efficacy of the algorithm is demonstrated on the calibration of a physically-based, distributed sewer network model and the results are compared with those obtained by NSGA-II, a widely used MOGA.

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