Topology Optimization of Multi-Component Structures via Decomposition-Based Assembly Synthesis

2003 ◽  
Author(s):  
Naesung Lyu ◽  
Kazuhiro Saitou
Keyword(s):  
Genetic Algorithm ◽  
Pareto Front ◽  
Structural Performance ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Structural Stiffness ◽  
Optimal Solutions ◽  
Ground Structure ◽  
Multi Objective Genetic Algorithm ◽  
Trade Offs

A method is presented for synthesizing multi-component structural assemblies with maximum structural performance and manufacturability. The problem is posed as a relaxation of decomposition-based assembly synthesis [1,2,3], where both topology and decomposition of a structure are regarded as variables over a ground structure with non-overlapping beams. A multi-objective genetic algorithm [4,5] with graph-based crossover [6,7,8], coupled with FEM analyses, is used to obtain Pareto optimal solutions to this problem, exhibiting trade-offs among structural stiffness, total weight, component manufacturability (size and simplicity), and the number of joints. Case studies with a cantilever and a simplified automotive floor frame are presented, and representative designs in the Pareto front are examined for the trade-offs among the multiple criteria.

Multi-Objective Genetic Algorithm for Tasks Allocation in Cloud Computing

2019 ◽  
Vol 9 (3) ◽  
pp. 37-57
Author(s):  
Youssef Harrath ◽  
Rashed Bahlool
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Lower Bound ◽  
Experimental Results ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Multi Objective ◽  
Mutation Operators ◽  
Multi Objective Genetic Algorithm

The problem of allocating real-time tasks to cloud computing resources minimizing the makespan is defined as a NP-hard problem. This work studies the same problem with two realistic multi-objective criteria; the makespan and the total cost of execution and communication between tasks. A mathematical model including objective functions and constraints is proposed. In addition, a theoretical lower bound for the makespan which served later as a baseline to benchmark the experimental results is theoretically determined and proven. To solve the studied problem, a multi-objective genetic algorithm is proposed in which new crossover and mutation operators are proposed. Pareto-optimal solutions are retrieved using the genetic algorithm. The experimental results show that genetic algorithm provides efficient solutions in term of makespan for different-size problem instances with reference to the lower bound. Moreover, the proposed genetic algorithm produces many Pareto optimal solutions that dominate the solution given by greedy algorithm for both criteria.

A method of multiobjective optimization using a genetic algorithm and an artificial immune system

2009 ◽  
Vol 223 (5) ◽  
pp. 1243-1252 ◽  
Author(s):  
H Park ◽  
N-S Kwak ◽  
J Lee
Keyword(s):  
Genetic Algorithm ◽  
Pattern Recognition ◽  
Immune System ◽  
Artificial Immune System ◽  
Affinity Maturation ◽  
Pareto Optimal ◽  
Mathematical Function ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Artificial Immune

The immune system has pattern recognition capabilities based on reinforced learning, memory, and affinity maturation interacting between antigens (Ags) and antibodies (Abs). This article deals with an adaptation of artificial immune system (AIS) into genetic-algorithm (GA)-based multi-objective optimization. The present study utilizes the pattern recognition from an AIS and the evolution from a GA. Using affinity measures between Ags and Abs, GA-based immune simulation discovers a generalist Ab that represents the common pattern among Ags. Non-dominated Pareto-optimal solutions are obtained via GA-based immune simulation in which dominated designs are considered as Ags, whereas non-dominated designs are assigned to Abs. This article discusses the procedure of identifying Pareto-optimal solutions through the immune system-based pattern recognition. A number of mathematical function problems that are described by discontinuity or disconnection in the shape of Pareto surface are first examined as test examples. Subsequently, engineering optimization problems such as rotating flywheel disc and ten-bar planar truss are explored to support the present study.

Presorting as a method of acceleration of algorithms in multi-objective optimization problems

2016 ◽  
Vol 0 (0) ◽  
pp. 5-11
Author(s):  
Andrzej Ameljańczyk
Keyword(s):  
Distance Function ◽  
Pareto Front ◽  
Optimization Problems ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Minkowski Distance ◽  
Finite Set ◽  
Feasible Solutions

The paper presents a method of algorithms acceleration for determining Pareto-optimal solutions (Pareto Front) multi-criteria optimization tasks, consisting of pre-ordering (presorting) set of feasible solutions. It is proposed to use the generalized Minkowski distance function as a presorting tool that allows build a very simple and fast algorithm Pareto Front for the task with a finite set of feasible solutions.

scholarly journals An Imanishism-based Genetic Algorithm for sampling Various Pareto-optimal Solutions

2001 ◽  
Vol 121 (6) ◽  
pp. 992-1000
Author(s):  
Kiyoharu Tagawa ◽  
Noboru Wakabayashi ◽  
Hiromasa Haneda ◽  
Katsumi Inoue
Keyword(s):  
Genetic Algorithm ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions

Decomposition-Based Assembly Synthesis of a 3D Body-in-White Model for Structural Stiffness

2003 ◽  
Author(s):  
Naesung Lyu ◽  
Kazuhiro Saitou
Keyword(s):  
Pareto Front ◽  
Element Model ◽  
Vehicle Body ◽  
Structural Stiffness ◽  
Multi Objective Genetic Algorithm ◽  
First Case ◽  
Trade Offs ◽  
Body In White ◽  
Minimum Distortion

This paper presents an extension of our previous work on decomposition-based assembly synthesis for structural stiffness [1], where the 3D finite element model of a vehicle body-in-white (BIW) is optimally decomposed into a set of components considering the stiffness of the assembled structure under given loading conditions, as well as the manufacturability and assembleability or components. Two case studies, each focusing on the decomposition of a different portion of a BIW, are discussed. In the first case study, the side frame is decomposed for the minimum distortion of front door frame geometry under global bending. In the second case study, the side/floor frame and floor panels are decomposed for the minimum floor deflections under global bending. In each case study, multi-objective genetic algorithm [2,3] with graph-based crossover [4,5], combined with FEM analyses, is used to obtain Pareto optimal solutions. Representative designs are selected from the Pareto front and trade-offs among stiffness, manufacturability, and assembleability are discussed.

Analysis and Modeling of Supply Chain Management of Fresh Products Based on Genetic Algorithm in E-commerce Environment

2020 ◽  
Author(s):  
Yaoting Chen
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Weighted Sum ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Optimal Process ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Proposed Model ◽  
Fresh Products

Abstract BackgroundSupply chain provides the chance to enhance chain performances by decrease these uncertainties. It is a demand for some level of co-ordination of activities and processes within and between organization in the supply chain to decrease uncertainties and increase more cost for customers. Partner selection is an important issue in the supply chain management of fresh products in E-commerce environment. In this paper, we utilized a multi-objective genetic algorithm for evaluation supply chain of fresh products in E-commerce environment. ResultsThe proposed multi-objective genetic algorithm is to search the set of Pareto-optimal solutions for these conflicting objectives using by weighted sum approach. The proposed model suitable for fresh products in E-commerce environment to optimize supply chain are derived. The value of objective 1 (f1) performs approximately nonlinearly with the increasing the value of objective 2,3 and 4 (f2,f3 and f4). At the value of objective 1 of 3.2*105, f2, f3 and f4 is about 4.3*105, 86 and 5.6*104. When the value of objective 1 is increased to 7.6*105, the minimum f2, f3 and f4 is about 3.0*105, 38 and 2.56*104. It is noted that the value of objective 1 is increased from 6.4*105 to 7.6*105, the variation of f2, f3 and f4 is 11.7%, 17.4% and 3.4% respectively. It is pointed out that the variation of f2 and f3 with f1 and f4 is kept within obvious ranges. This practical result highlights the fact that the effects of the fact that effects of f2 and f3 are important factors affecting the performance supply chain network of fresh product in E-commerce environment.ConclusionsIn this paper, we utilized a multi-objective genetic algorithm for evaluation supply chain of fresh products in E-commerce environment. Four objectives for optimal process are included in the proposed model: (1) maximization of green appraisal score, (2) minimization of transportation time and total time comprised of product time, (3) maximization of average product quality, (4) minimization of transportation cost and total cost comprised of product cost. In order to evaluate optimal process, set of Pareto-optimal solutions is obtained based on the weighted sum method.

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