A multi-objective batch infill strategy for efficient global optimization

2016 ◽  
Author(s):  
Ahsanul Habib ◽  
Hemant Kumar Singh ◽  
Tapabrata Ray
Keyword(s):  
Global Optimization ◽  
Efficient Global Optimization ◽  
Multi Objective

Simulation-based multi-objective muffler optimization using efficient global optimization

2020 ◽  
Vol 68 (6) ◽  
pp. 441-458
Author(s):  
Jobin Puthuparampil ◽  
Pierre Sullivan
Keyword(s):  
Global Optimization ◽  
Transmission Loss ◽  
Effective Means ◽  
Plug Flow ◽  
Efficient Global Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Simulation Based ◽  
Gas Generators ◽  
Computer Based

Noise control of large diesel and natural gas generators is achieved through industrial mufflers. Design of such mufflers relies heavily on general guidelines. However, these guidelines are not suitable for complex mufflers; instead, computer-based optimization provides an effective means of design. Optimization of a plug flow muffler is conducted in this work with a multi-objective (transmission loss and pressure drop) finite element simulation-based optimization using the efficient global optimization (EGO) algorithm. The EGO algorithm is shown to be well suited for computationally expensive muffler optimization, performing vastly better than genetic algorithms, such as the commonly used NSGA-II algorithm.

Multi-Objective Selection of Cutting Conditions in Advanced Machining Processes via an Efficient Global Optimization Approach

2014 ◽  
Author(s):  
Mohamed Aly ◽  
Karim Hamza ◽  
Mohammed Tauhiduzzaman ◽  
Mouhab Meshreki ◽  
Ashraf O. Nassef ◽  
...  
Keyword(s):  
Global Optimization ◽  
High Speed ◽  
Diamond Turning ◽  
Cutting Conditions ◽  
Efficient Global Optimization ◽  
Experimental Sample ◽  
Machining Processes ◽  
Multi Objective ◽  
Ultra Precision ◽  
Selection Of

Optimum selection of cutting conditions in high-speed and ultra-precision machining processes often poses a challenging task due to several reasons; such as the need for costly experimental setup and the limitation on the number of experiments that can be performed before tool degradation starts becoming a source of noise in the readings. Moreover, oftentimes there are several objectives to consider, some of which may be conflicting, while others may be somewhat correlated. Pareto-optimality analysis is needed for conflicting objectives; however the existence of several objectives (high-dimension Pareto space) makes the generation and interpretation of Pareto solutions difficult. The approach adopted in this paper is a modified multi-objective efficient global optimization (m-EGO). In m-EGO, sample data points from experiments are used to construct Kriging meta-models, which act as predictors for the performance objectives. Evolutionary multi-objective optimization is then conducted to spread a population of new candidate experiments towards the zones of search space that are predicted by the Kriging models to have favorable performance, as well as zones that are under-explored. New experiments are then used to update the Kriging models, and the process is repeated until termination criteria are met. Handling a large number of objectives is improved via a special selection operator based on principle component analysis (PCA) within the evolutionary optimization. PCA is used to automatically detect correlations among objectives and perform the selection within a reduced space in order to achieve a better distribution of experimental sample points on the Pareto frontier. Case studies show favorable results in ultra-precision diamond turning of Aluminum alloy as well as high-speed drilling of woven composites.

An exploitation-enhanced multi-objective efficient global optimization algorithm for expensive aerodynamic shape optimizations

2021 ◽  
pp. 095441002110324
Author(s):  
Feng Deng ◽  
Ning Qin
Keyword(s):  
Global Optimization ◽  
Shape Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Efficient Global Optimization ◽  
Test Problems ◽  
Aerodynamic Shape Optimization ◽  
Aerodynamic Shape ◽  
Multi Objective ◽  
Complex Landscape

The traditional multi-objective efficient global optimization (EGO) algorithms have been hybridized and adapted to solving the expensive aerodynamic shape optimization problems based on high-fidelity numerical simulations. Although the traditional EGO algorithms are highly efficient in solving some of the optimization problems with very complex landscape, it is not preferred to solve most of the aerodynamic shape optimization problems with relatively low-degree multi-modal design spaces. A new infill criterion encouraging more local exploitation has been proposed by hybridizing two traditional multi-objective expected improvements (EIs), namely, statistical multi-objective EI and expected hypervolume improvement, in order to improve their robustness and efficiency in aerodynamic shape optimization. Different analytical test problems and aerodynamic shape optimization problems have been investigated. In comparison with traditional multi-objective EI algorithms and a standard evolutionary multi-objective optimization algorithm, the proposed method is shown to be more robust and efficient in the tests due to its hybrid characteristics, easier handling of sub-optimization problems, and enhanced exploitation capability.

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