Improving Pareto Optimal Designs Using Genetic Algorithms

1995 ◽  
Vol 10 (4) ◽  
pp. 239-247 ◽  
Author(s):  
John S. Gero ◽  
Sushil J. Louis
Keyword(s):  
Genetic Algorithms ◽  
Optimal Designs ◽  
Pareto Optimal

Determining the best practice – Optimal designs of composite helical structures using Genetic Algorithms

2021 ◽  
Vol 268 ◽  
pp. 113982
Author(s):  
Jiang-Bo Bai ◽  
Tian-Wei Liu ◽  
Zhen-Zhou Wang ◽  
Qiu-Hong Lin ◽  
Qiang Cong ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Best Practice ◽  
Optimal Designs ◽  
Helical Structures

Approach for Negotiation Problems in Multi-Agent Systems for DC Micro Grid Using Multi-Objective Evolutionary Algorithms

2015 ◽  
Vol 713-715 ◽  
pp. 2106-2109
Author(s):  
Mauricio Mauledoux ◽  
Edilberto Mejía-Ruda ◽  
Oscar I. Caldas
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Pareto Optimal ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Objective ◽  
Multi Agent ◽  
Micro Grid ◽  
Optimal Space ◽  
Multi Agents

The work is devoted to solve allocation task problem in multi agents systems using multi-objective genetic algorithms and comparing the technique with methods used in game theories. The paper shows the main advantages of genetic algorithms and the way to apply a parallel approach dividing the population in sub-populations saving time in the search and expanding the coverage of the solution in the Pareto optimal space.

Multiobjective optimization of journal bearing using mass conserving and genetic algorithms

2005 ◽  
Vol 219 (3) ◽  
pp. 235-248 ◽  
Author(s):  
H Hirani
Keyword(s):  
Thermal Analysis ◽  
Genetic Algorithms ◽  
Film Thickness ◽  
Journal Bearing ◽  
Operating Conditions ◽  
Radial Clearance ◽  
Pareto Optimal ◽  
Optimization Strategy ◽  
Oil Viscosity ◽  
Film Pressure

An optimal design of hydrodynamic journal bearing using mass conserving thermal analysis and genetic algorithms is presented. Simultaneous minimization of power loss and oil flow, subjected to constraints on film thickness, film pressure, and temperature rise between the bearing surfaces, is the objective of this study. The radial clearance, L/D ratio, oil groove location, feed pressure, and the oil viscosity are the design variables. The rank-based genetic algorithm is used to deal with the discrete variables and multimodal objective functions and to capture Pareto optimal points. In view of computation economics and robustness, initial guesses of oil film pressure distribution, eccentricity ratio, and attitude angle obtained by two-dimensional analytical approach are provided for mass conserving thermal analysis. The complete optimization strategy is illustrated by a step-by-step (in four steps) approach. A comparative study of thermal and isothermal analyses is illustrated. Effects of constraints on temperature, pressure, and film thickness on the design vector are enlightened. The mass conserving thermal analysis is validated against experimental results. Pareto optimal fronts for various operating conditions are presented.

Cluster Analysis and Switching Algorithm of Multi-Objective Optimal Control Design

2016 ◽  
Vol 139 (1) ◽  
Author(s):  
Zhi-Chang Qin ◽  
Jian-Qiao Sun
Keyword(s):  
Optimal Control ◽  
Cluster Analysis ◽  
Control Algorithm ◽  
Control Design ◽  
Optimal Designs ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Multi Objective ◽  
Switching Algorithm ◽  
Optimal Controllers

The multi-objective optimal control design usually generates hundreds or thousands of Pareto optimal solutions. How to assist a user to select an appropriate controller to implement is a postprocessing issue. In this paper, we develop a method of cluster analysis of the Pareto optimal designs to discover the similarity of the optimal controllers. After we identify the clusters of optimal controllers, we develop a switching strategy to select controls from different clusters to improve the performance. Numerical and experimental results show that the switching control algorithm is quite promising.

Design for Product-Embedded Disassembly

2005 ◽  
Author(s):  
Shingo Takeuchi ◽  
Kazuhiro Saitou
Keyword(s):  
Computational Method ◽  
Optimal Designs ◽  
Pareto Optimal ◽  
Regulatory Requirements ◽  
Component Retrieval ◽  
Disassembly Process ◽  
Multi Objective Genetic Algorithm ◽  
Design Alternatives ◽  
Spatial Configurations

This paper presents a computational method for designing assemblies with a built-in disassembly pathway that maximizes the profit of disassembly while satisfying regulatory requirements for component retrieval. Given component revenues and components to be retrieved, the method simultaneously determines the spatial configurations of components and locator features on the components, such that the product can be disassembled in the most profitable sequence, via a domino-like “self-disassembly” process triggered by the removal of one or a few fasteners. The problem is posed as optimization and a multi-objective genetic algorithm is utilized to search for Pareto-optimal designs in terms of three objectives: 1) the satisfaction of distance specification among components, 2) the efficient use of locator features on components, and 3) the profit of overall disassembly process under the regulatory requirements. A case study with different costs for removing fasteners demonstrates the effectiveness of the method in generating design alternatives under various disassembly scenarios.

Optimization of a Hybrid Double-Side Jet Impingement Cooling System for High-Power Light Emitting Diodes

2014 ◽  
Vol 136 (1) ◽  
Author(s):  
Sun-Min Kim ◽  
Kwang-Yong Kim
Keyword(s):  
High Power ◽  
Light Emitting Diodes ◽  
Cooling System ◽  
Pressure Coefficient ◽  
Jet Impingement ◽  
Optimal Designs ◽  
Pareto Optimal ◽  
Objective Functions ◽  
Light Emitting ◽  
Impingement Cooling

Optimization of a hybrid double-side jet impingement cooling system for high-power light emitting diodes (LEDs) was performed using a hybrid multi-objective evolutionary approach and three-dimensional numerical analysis for steady incompressible laminar flow and conjugate heat transfer using Navier–Stokes equations. For optimization, two design variables, i.e., ratios of the diameter of jet holes and the distance from the exit of upper impinging hole to chips to thickness of substrate were chosen out of the various geometric parameters affecting the performance of the cooling system. To evaluate cooling performance and pressure loss of the system, two objective functions, viz., the ratio of the maximum temperature to average temperature on the chips and pressure coefficient, were selected. Surrogate modeling of the objective functions was performed using response surface approximation. The Pareto-optimal solutions were obtained using a multi-objective evolutionary algorithm, and performances of three representative Pareto-optimal designs were discussed compared to a reference design. In the optimal designs, higher level of uniform cooling was generally achieved with higher pressure coefficient. The Pareto-sensitivity analysis between the objective function and design variable was also performed.

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