Tracing the Points in Search Space in Plant Biology Genetics Algorithm Optimization

Decomposition Templates and Joint Morphing Operators for Genetic Algorithm Optimization of Multicomponent Structural Topology

Journal of Mechanical Design ◽

10.1115/1.4026030 ◽

2013 ◽

Vol 136 (2) ◽

Cited By ~ 4

Author(s):

Zebin Zhou ◽

Karim Hamza ◽

Kazuhiro Saitou

Keyword(s):

Genetic Algorithm ◽

Search Space ◽

Structural Topology ◽

Algorithm Optimization ◽

Two Stage ◽

Multi Objective ◽

Multi Objective Genetic Algorithm ◽

Design Variables ◽

Loaded Structure ◽

Monolithic Structure

This paper presents a continuum-based approach for multi-objective topology optimization of multicomponent structures. Objectives include minimization of compliance, weight, and cost of assembly and manufacturing. Design variables are partitioned into two main groups: those pertaining to material allocation within a design domain (base topology problem), and those pertaining to decomposition of a monolithic structure into multiple components (joint allocation problem). Generally speaking, the two problems are coupled in the sense that the decomposition of an optimal monolithic structure is not always guaranteed to produce an optimal multicomponent structure. However, for spot-welded sheet-metal structures (such as those often found in automotive applications), certain assumptions can be made about the performance of a monolithic structure that favor the adoption of a two-stage approach that decouples the base topology and joint allocation problems. A multi-objective genetic algorithm (GA) is used throughout the studies in this paper. While the problem decoupling in two-stage approaches significantly reduces the size of the search space and allows better performance of the GA, the size of the search space can still be quite enormous in the second stage. To further improve the performance, a new mutation operator based on decomposition templates and localized joints morphing is proposed. A cantilever-loaded structure is used to study and compare various setups of single and two-stage GA approaches and establish the merit of the proposed GA operators. The approach is then applied to a simplified model of an automotive vehicle floor subject to global bending loading condition.

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Decomposition Templates and Joint Morphing Operators for Genetic Algorithm Optimization of Multi-Component Topology

Volume 5: 37th Design Automation Conference, Parts A and B ◽

10.1115/detc2011-48572 ◽

2011 ◽

Author(s):

Zebin Zhou ◽

Karim Hamza ◽

Kazuhiro Saitou

Keyword(s):

Genetic Algorithm ◽

Search Space ◽

Algorithm Optimization ◽

Two Stage ◽

Component Structure ◽

Multi Objective ◽

Multi Objective Genetic Algorithm ◽

Decision Variables ◽

Loaded Structure ◽

Monolithic Structure

This paper presents a continuum-based approach for multi-objective topology optimization of multi-component structures. Objectives include minimization of compliance, weight and as cost of assembly and manufacturing. Decision variables are partitioned into two main groups: those pertaining to material allocation within a design domain (base topology problem), and those pertaining to decomposition of a monolithic structure into multiple components (joint allocation problem). Generally speaking, the two problems are coupled in the sense that the decomposition of an optimal monolithic structure is not always guaranteed to produce an optimal multi-component structure. However, for spot-welded sheet-metal structures (such as those often found in automotive applications), certain assumptions can be about the performance of a monolithic structure that favor the adoption of a two-stage approach that decouples the base topology and joint allocation problems. A multi-objective genetic algorithm (GA) is used throughout the studies in this paper. While the problem decoupling in two-stage approaches significantly reduces the size of the search space and allows better performance of the GA, the size of the search space can still be quite enormous in the second stage. To further improve the performance, we propose a new mutation operator based on decomposition templates and localized joints morphing. A cantilever-loaded structure is then used as a metric to study and compare various setups of single and two-stage GA approaches.

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Naval Hull Form Multicriterion Hydrodynamic Optimization for the Conceptual Design Phase

Journal of Ship Research ◽

10.5957/jsr.2009.53.4.199 ◽

2009 ◽

Vol 53 (04) ◽

pp. 199-213

Author(s):

Steven F. Zalek ◽

Michael G. Parsons ◽

Robert F. Beck

Keyword(s):

Pareto Front ◽

Problem Formulation ◽

Search Space ◽

Population Based ◽

Global Optimum ◽

Optimization Process ◽

Algorithm Optimization ◽

Ship Hulls ◽

Conceptual Design Phase ◽

Seakeeping Performance

For a given set of ship design and operational criteria, there exists a trade-off between the ship's calm water powering performance and its seakeeping performance. Ship hulls that are designed for optimal powering performance can have poor seakeeping performance, and vice versa. It is typically not possible to obtain the global optimum for both of these objectives because of the competition between the powering and seakeeping performance functions and the design constraints. The set of globally nondominated solutions, or Pareto front, considering these competing criteria is searched for by navigating the multimodal search space using a multicriterion, population-based evolutionary algorithm optimization process. The optimization process uses a nontraditional objective function formulation that eliminates the need to tune the penalty function parameters for each new problem formulation and appears to provide a more thorough representation of the numerically approximated Pareto front. Results show that properly integrating this optimization process with the design criteria yields a set of hydrodynamically superior design solutions. The problem formulation and development is applicable to naval surface vessels and applied to a monohull frigate type example.

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Noise Characteristics Based on Genetic Algorithm Optimizations of Image Reconstruction

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.121-126.2116 ◽

2011 ◽

Vol 121-126 ◽

pp. 2116-2120

Author(s):

He Yan ◽

Xiu Feng Wang

Keyword(s):

Genetic Algorithm ◽

Object Recognition ◽

Simple Model ◽

Image Reconstruction ◽

Design Optimization ◽

Search Space ◽

Algorithm Optimization ◽

Noise Characteristics ◽

Enormous Amount ◽

Rich Information

GA approach suitable for solving object Recognition problem is described and evaluated using a series of simple model problems. Usually GA calculation produces enormous amount of data that contains rich information, of which only a tiny section is commonly utilized runs through the design optimization. An application of Genetic Algorithm optimization with Noise characteristics would give many insights into how to allocate various image reconstruction options in different echelons, GA employing a global search might identify a better solution in an different area of the search space.

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Towards Life-Cycle Fastening and Joining Cost Optimization Using Genetic Algorithms

Volume 1: Design for Manufacturing Conference ◽

10.1115/96-detc/dfm-1709 ◽

1996 ◽

Author(s):

Lily H. Shu ◽

Woodie C. Flowers

Keyword(s):

Life Cycle ◽

Cost Optimization ◽

Optimization Methods ◽

Search Space ◽

Life Cycle Costs ◽

Algorithm Optimization ◽

Search Spaces ◽

Material Recycling ◽

Joint Reliability ◽

Product Service

Abstract This paper extends from our previous results which suggest that fastening and joining methods preferred for assembly and scrap-material recycling do not necessarily facilitate remanufacture. We identify, through collaboration with three remanufacturing companies, primary factors that determine the life-cycle fastening and joining cost of products that are remanufactured. These factors, that also determine life-cycle joint reliability, are the fastening or joining method specified during design, the disassembly and reassembly method used during remanufacture, and the repair policy. A reliability model was applied to describe the failure characteristics of the joint so that part failure and replacement costs can be estimated over a specified length of product service. We then present an illustrative sample search space and the resulting life-cycle costs. A larger search space would require the use of optimization methods to minimize life-cycle costs. The fastening and joining plan, consisting of the above three factors, is represented for genetic algorithm optimization to be used on larger search spaces in the future.

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