An Optimization Framework for The Design of Cable Harness Layouts in Planar Interconnected Systems

2021 ◽  
pp. 1-29
Author(s):  
Nafiseh Masoudi ◽  
Georges Fadel
Keyword(s):  
Genetic Algorithm ◽  
Complex Systems ◽  
Geometric Structure ◽  
Optimization Problem ◽  
Interconnected Systems ◽  
Test Cases ◽  
Computationally Efficient ◽  
Cable Harness ◽  
Multiobjective Genetic Algorithm ◽  
Optimization Framework

Abstract The components of complex systems such as automobiles or ships communicate via connectors, including wires, hoses, or pipes whose weight could substantially increase the total weight of the system. Hence, it is of paramount importance to lay out these connectors such that their overall weight is minimized. In this paper, a computationally efficient approach is proposed to optimize the layout of flexible connectors (e.g., cable harnesses) by minimizing their overall length while maximizing their common length. The approach provides a framework to mathematically model the cable harness layout optimization problem. A Multiobjective Genetic Algorithm (MOGA) solver is then applied to solve the optimization problem, which outputs a set of non-dominated solutions to the bi-objective problem. Finally, the effects of the workspace’s geometric structure on the optimal layouts of cable harnesses are discussed using test cases. The overarching objective of this study is to provide insight for designers of cable harnesses when deciding on the final layout of connectors considering issues such as accessibility to and maintainability of these connectors.

scholarly journals A Multiobjective Genetic Algorithm for the Localization of Optimal and Nearly Optimal Solutions Which Are Potentially Useful: nevMOGA

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-22 ◽  
Author(s):  
Alberto Pajares ◽  
Xavier Blasco ◽  
Juan M. Herrero ◽  
Gilberto Reynoso-Meza
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Decision Maker ◽  
Pareto Front ◽  
Optimization Problem ◽  
Engineering Problem ◽  
Multiobjective Optimization Problem ◽  
Optimal Solutions ◽  
Multiobjective Genetic Algorithm ◽  
Pi Controllers

Traditionally, in a multiobjective optimization problem, the aim is to find the set of optimal solutions, the Pareto front, which provides the decision-maker with a better understanding of the problem. This results in a more knowledgeable decision. However, multimodal solutions and nearly optimal solutions are ignored, although their consideration may be useful for the decision-maker. In particular, there are some of these solutions which we consider specially interesting, namely, the ones that have distinct characteristics from those which dominate them (i.e., the solutions that are not dominated in their neighborhood). We call these solutions potentially useful solutions. In this work, a new genetic algorithm called nevMOGA is presented, which provides not only the optimal solutions but also the multimodal and nearly optimal solutions nondominated in their neighborhood. This means that nevMOGA is able to supply additional and potentially useful solutions for the decision-making stage. This is its main advantage. In order to assess its performance, nevMOGA is tested on two benchmarks and compared with two other optimization algorithms (random and exhaustive searches). Finally, as an example of application, nevMOGA is used in an engineering problem to optimally adjust the parameters of two PI controllers that operate a plant.

A Genetic Algorithm Based Procedure for Extracting Optimal Solutions From a Morphological Chart

2007 ◽  
Author(s):  
Santosh Tiwari ◽  
Joshua Summers ◽  
Georges Fadel
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Mathematical Representation ◽  
Optimal Solutions ◽  
Proof Of Concept ◽  
Optimization Framework ◽  
Mutation Operators ◽  
Novel Approach ◽  
Morphological Chart ◽  
Crossover And Mutation

A novel approach using a genetic algorithm is presented for extracting globally satisfycing (Pareto optimal) solutions from a morphological chart where the evaluation and combination of “means to sub-functions” is modeled as a combinatorial multi-objective optimization problem. A fast and robust genetic algorithm is developed to solve the resulting optimization problem. Customized crossover and mutation operators specifically tailored to solve the combinatorial optimization problem are discussed. A proof-of-concept simulation on a practical design problem is presented. The described genetic algorithm incorporates features to prevent redundant evaluation of identical solutions and a method for handling of the compatibility matrix (feasible/infeasible combinations) and addressing desirable/undesirable combinations. The proposed approach is limited by its reliance on the quantifiable metrics for evaluating the objectives and the existence of a mathematical representation of the combined solutions. The optimization framework is designed to be a scalable and flexible procedure which can be easily modified to accommodate a wide variety of design methods that are based on the morphological chart.

Genetic Algorithm Based Optimum Semi-Active Control of Building Frames Using Limited Number of Magneto-Rheological Dampers and Sensors

2018 ◽  
Vol 140 (10) ◽  
Author(s):  
Vishisht Bhaiya ◽  
S. D. Bharti ◽  
M. K. Shrimali ◽  
T. K. Datta
Keyword(s):  
Genetic Algorithm ◽  
Active Control ◽  
Optimization Problem ◽  
Numerical Study ◽  
Accurate Estimation ◽  
Computationally Efficient ◽  
Linear Quadratic ◽  
Building Frames ◽  
Mr Dampers ◽  
Magneto Rheological

Optimum semi-active control with a limited number of magneto-rheological (MR) dampers and measurement sensors has certain requirements. Most important of them is the accurate estimation of control forces developed in the MR dampers from the observations made in the structure. Therefore, the observation strategy should form an integral part of the optimization problem. The existing literature on the subject does not address this issue properly. The paper presents a computationally efficient optimization scheme for semi-active control of partially observed building frames using a limited number of MR dampers and sensors for earthquakes. The control scheme duly incorporates the locations of measurement sensors as variables into the genetic algorithm (GA) based optimization problem. A ten-storied building frame is taken as an illustrative example. The optimum control strategy utilizes two well-known control laws, namely, the linear quadratic Gaussian (LQG) with clipped optimal control and the bang-bang control to find the time histories of voltage to be applied to the MR dampers. The results of the numerical study show that the proposed scheme of sensor placement provides the optimum reduction of response with more computational efficiency. Second, optimal locations of sensors vary with the response quantities to be controlled, the nature of earthquake, and the control algorithm. Third, optimal locations of MR dampers are invariant of the response quantities to be controlled and the nature of earthquake.

A GENETIC ALGORITHM-BASED APPROACH FOR OPTIMIZATION OF SCHEDULING IN JOB SHOP ENVIRONMENT

2011 ◽  
Vol 10 (02) ◽  
pp. 223-240 ◽  
Author(s):  
KUMAR RITWIK ◽  
SANKHA DEB
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Job Shop ◽  
Shop Floor ◽  
Successful Implementation ◽  
Test Cases ◽  
Manufacturing Environment ◽  
Scheduling Optimization ◽  
Encoding Scheme ◽  
Chromosome Representation

The present work aims to develop a genetic algorithm-based approach to solve the scheduling optimization problem in the Job Shop manufacturing environment. A new encoding scheme for chromosome representation has been developed for this purpose that denotes a priority sequence of operations, from which a schedule can be generated if the precedence constraints are known. The successful implementation of the proposed encoding scheme has been presented and its performance has been compared with the existing operation-based scheme found in literatures across different test cases by varying the number of jobs and machines in the shop floor.

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