A Genetic Algorithm for Scheduling and Decomposition of Multidisciplinary Design Problems

1995 ◽  
Author(s):  
Stephen S. Altus ◽  
Ilan M. Kroo ◽  
Peter J. Gage
Keyword(s):  
Genetic Algorithm ◽  
Numerical Optimization ◽  
Heuristic Method ◽  
Merit Function ◽  
Multidisciplinary Design ◽  
Design Problems ◽  
Design Studies ◽  
Complex Design ◽  
Complex Engineering

Abstract Complex engineering studies typically involve hundreds of analysis routines and thousands of variables. The sequence of operations used to evaluate a design strongly affects the speed of each analysis cycle. This influence is particularly important when numerical optimization is used, because convergence generally requires many iterations. Moreover, it is common for disciplinary teams to work simultaneously on different aspects of a complex design. This practice requires decomposition of the analysis into subtasks, and the efficiency of the design process critically depends on the quality of the decomposition achieved. This paper describes the development of software to plan multidisciplinary design studies. A genetic algorithm is used, both to arrange analysis subroutines for efficient execution, and to decompose the task into subproblems. The new planning tool is compared with an existing heuristic method. It produces superior results when the same merit function is used, and it can readily address a wider range of planning objectives.

A Genetic Algorithm for Scheduling and Decomposition of Multidisciplinary Design Problems

1996 ◽  
Vol 118 (4) ◽  
pp. 486-489 ◽  
Author(s):  
Stephen S. Altus ◽  
Ilan M. Kroo ◽  
Peter J. Gage
Keyword(s):  
Genetic Algorithm ◽  
Numerical Optimization ◽  
Heuristic Method ◽  
Merit Function ◽  
Multidisciplinary Design ◽  
Design Problems ◽  
Design Studies ◽  
Complex Design ◽  
Complex Engineering

Complex engineering studies typically involve hundreds of analysis routines and thousands of variables. The sequence of operations used to evaluate a design strongly affects the speed of each analysis cycle. This influence is particularly important when numerical optimization is used, because convergence generally requires many iterations. Moreover, it is common for disciplinary teams to work simultaneously on different aspects of a complex design. This practice requires decomposition of the analysis into subtasks, and the efficiency of the design process critically depends on the quality of the decomposition achieved. This paper describes the development of software to plan multidisciplinary design studies. A genetic algorithm is used, both to arrange analysis subroutines for efficient execution, and to decompose the task into subproblems. The new planning tool is compared with an existing heuristic method. It produces superior results when the same merit function is used, and it can readily address a wider range of planning objectives.

scholarly journals An improved arithmetic optimization algorithm with forced switching mechanism for global optimization problems

2022 ◽  
Vol 19 (1) ◽  
pp. 473-512
Author(s):  
Rong Zheng ◽  
◽  
Heming Jia ◽  
Laith Abualigah ◽  
Qingxin Liu ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Heuristic Method ◽  
Population Diversity ◽  
Test Functions ◽  
Design Problems ◽  
Local Optima ◽  
Switching Mechanism ◽  
Engineering Design Problems

<abstract> <p>Arithmetic optimization algorithm (AOA) is a newly proposed meta-heuristic method which is inspired by the arithmetic operators in mathematics. However, the AOA has the weaknesses of insufficient exploration capability and is likely to fall into local optima. To improve the searching quality of original AOA, this paper presents an improved AOA (IAOA) integrated with proposed forced switching mechanism (FSM). The enhanced algorithm uses the random math optimizer probability (<italic>RMOP</italic>) to increase the population diversity for better global search. And then the forced switching mechanism is introduced into the AOA to help the search agents jump out of the local optima. When the search agents cannot find better positions within a certain number of iterations, the proposed FSM will make them conduct the exploratory behavior. Thus the cases of being trapped into local optima can be avoided effectively. The proposed IAOA is extensively tested by twenty-three classical benchmark functions and ten CEC2020 test functions and compared with the AOA and other well-known optimization algorithms. The experimental results show that the proposed algorithm is superior to other comparative algorithms on most of the test functions. Furthermore, the test results of two training problems of multi-layer perceptron (MLP) and three classical engineering design problems also indicate that the proposed IAOA is highly effective when dealing with real-world problems.</p> </abstract>

scholarly journals ІНФОРМАЦІЙНА СИСТЕМА ДЛ Я РЕЄСТРУ ПАЦІЄНТІВ ІЗ МЕТАБОЛІЧНИМИ ЗАХВОРЮВАННЯМИ

2015 ◽  
Author(s):  
N. H. Horovenko ◽  
V. Z. Stetsyuk ◽  
N. V. Olhovych ◽  
A. Yo. Savytskyi ◽  
A. V. Malyei
Keyword(s):  
Electronic Medical Records ◽  
Medical Records ◽  
Web Application ◽  
Metabolic Diseases ◽  
Design Problems ◽  
Database Server ◽  
Complex Design ◽  
Quality Of Patient Care ◽  
Software Product

<p>This article describes the problems encountered in the management of medical records of patients with metabolic diseases, and also provides a general solution to these problems through the introduction of a software product.</p><p>Objective was to reduce the burden on the healthcare registrars and medical genetics center, improving the speed and quality of patient care. In the software implementation the main features of the complex design problems are described: the programming language Java, IDE NetBeans, MySQL database server and web application to work with database server phpMyAdmin and put forward requirements. Also, medical receptionist is able to keep track of patients to form an extract, view statistics.</p><p>During development were numerous consultations with experienced doctors, medical registrars. With the convenient architecture in the future will be easy to add custom modules in the program. Development of the program management of electronic medical records of patients the center of metabolic diseases is essential, because today in Ukraine all the software that can keep track of patients who did not drawn enough attention to patients with metabolic diseases. Currently the software is installed in the center of metabolic diseases NCSH “OKHMATDYT.”</p>

A Study of Convergence in Decentralized Design

2003 ◽  
Author(s):  
Vincent Chanron ◽  
Kemper Lewis
Keyword(s):  
Efficient Solutions ◽  
Engineering Systems ◽  
Efficient Design ◽  
Numerical Series ◽  
Design Problems ◽  
Customer Expectations ◽  
Complex Design ◽  
Complex Engineering ◽  
Decentralized Design ◽  
Market Pressures

The decomposition and coordination of decisions in the design of complex engineering systems is a great challenge. Companies who design these systems routinely allocate design responsibility of the various subsystems and components to different people, teams or even suppliers. The mechanisms behind this network of decentralized design decisions create difficult management and coordination issues. However, developing efficient design processes is paramount, especially with market pressures and customer expectations. Standard techniques to modeling and solving decentralized design problems typically fail to understand the underlying dynamics of the decentralized processes and therefore result in suboptimal solutions. This paper aims to model and understand the mechanisms and dynamics behind a decentralized set of decisions within a complex design process. By using concepts from the fields of mathematics and economics, including Game Theory and the Cobweb Model, we model a simple decentralized design problem and provide efficient solutions. This new approach uses numerical series and linear algebra as tools to determine conditions for convergence of such decentralized design problems. The goal of this paper is to establish the first steps towards understanding the mechanisms of decentralized decision processes. This includes two major steps: studying the convergence characteristics, and finding the final equilibrium solution of a decentralized problem. Illustrations of the developments are provided in the form of two decentralized design problems with different underlying behavior.

scholarly journals Rosetta:MSF:NN: Boosting performance of multi-state computational protein design with a neural network

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256691
Author(s):  
Julian Nazet ◽  
Elmar Lang ◽  
Rainer Merkl
Keyword(s):  
Genetic Algorithm ◽  
Protein Design ◽  
Pearson Correlation ◽  
Three Dimensional ◽  
3D Models ◽  
Design Problems ◽  
Computational Costs ◽  
Complex Design ◽  
Rational Protein Design ◽  
Structural Ensemble

Rational protein design aims at the targeted modification of existing proteins. To reach this goal, software suites like Rosetta propose sequences to introduce the desired properties. Challenging design problems necessitate the representation of a protein by means of a structural ensemble. Thus, Rosetta multi-state design (MSD) protocols have been developed wherein each state represents one protein conformation. Computational demands of MSD protocols are high, because for each of the candidate sequences a costly three-dimensional (3D) model has to be created and assessed for all states. Each of these scores contributes one data point to a complex, design-specific energy landscape. As neural networks (NN) proved well-suited to learn such solution spaces, we integrated one into the framework Rosetta:MSF instead of the so far used genetic algorithm with the aim to reduce computational costs. As its predecessor, Rosetta:MSF:NN administers a set of candidate sequences and their scores and scans sequence space iteratively. During each iteration, the union of all candidate sequences and their Rosetta scores are used to re-train NNs that possess a design-specific architecture. The enormous speed of the NNs allows an extensive assessment of alternative sequences, which are ranked on the scores predicted by the NN. Costly 3D models are computed only for a small fraction of best-scoring sequences; these and the corresponding 3D-based scores replace half of the candidate sequences during each iteration. The analysis of two sets of candidate sequences generated for a specific design problem by means of a genetic algorithm confirmed that the NN predicted 3D-based scores quite well; the Pearson correlation coefficient was at least 0.95. Applying Rosetta:MSF:NN:enzdes to a benchmark consisting of 16 ligand-binding problems showed that this protocol converges ten-times faster than the genetic algorithm and finds sequences with comparable scores.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

scholarly journals Genetic Algorithm for the Retailers’ Shelf Space Allocation Profit Maximization Problem

2021 ◽  
Vol 11 (14) ◽  
pp. 6401
Author(s):  
Kateryna Czerniachowska ◽  
Karina Sachpazidu-Wójcicka ◽  
Piotr Sulikowski ◽  
Marcin Hernes ◽  
Artur Rot
Keyword(s):  
Genetic Algorithm ◽  
Profit Maximization ◽  
Allocation Rule ◽  
Maximization Problem ◽  
Shelf Space ◽  
Allocation Model ◽  
Space Allocation ◽  
Shelf Space Allocation ◽  
To Receive

This paper discusses the problem of retailers’ profit maximization regarding displaying products on the planogram shelves, which may have different dimensions in each store but allocate the same product sets. We develop a mathematical model and a genetic algorithm for solving the shelf space allocation problem with the criteria of retailers’ profit maximization. The implemented program executes in a reasonable time. The quality of the genetic algorithm has been evaluated using the CPLEX solver. We determine four groups of constraints for the products that should be allocated on a shelf: shelf constraints, shelf type constraints, product constraints, and virtual segment constraints. The validity of the developed genetic algorithm has been checked on 25 retailing test cases. Computational results prove that the proposed approach allows for obtaining efficient results in short running time, and the developed complex shelf space allocation model, which considers multiple attributes of a shelf, segment, and product, as well as product capping and nesting allocation rule, is of high practical relevance. The proposed approach allows retailers to receive higher store profits with regard to the actual merchandising rules.

scholarly journals Heuristic rank selection with progressively searching tensor ring network

2021 ◽  
Author(s):  
Nannan Li ◽  
Yu Pan ◽  
Yaran Chen ◽  
Zixiang Ding ◽  
Dongbin Zhao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Compression Ratio ◽  
State Of The Art ◽  
Heuristic Method ◽  
Ring Networks ◽  
Ring Network ◽  
Narrow Region ◽  
Network Search ◽  
Deep Networks ◽  
Evolutionary Phase

AbstractRecently, tensor ring networks (TRNs) have been applied in deep networks, achieving remarkable successes in compression ratio and accuracy. Although highly related to the performance of TRNs, rank selection is seldom studied in previous works and usually set to equal in experiments. Meanwhile, there is not any heuristic method to choose the rank, and an enumerating way to find appropriate rank is extremely time-consuming. Interestingly, we discover that part of the rank elements is sensitive and usually aggregate in a narrow region, namely an interest region. Therefore, based on the above phenomenon, we propose a novel progressive genetic algorithm named progressively searching tensor ring network search (PSTRN), which has the ability to find optimal rank precisely and efficiently. Through the evolutionary phase and progressive phase, PSTRN can converge to the interest region quickly and harvest good performance. Experimental results show that PSTRN can significantly reduce the complexity of seeking rank, compared with the enumerating method. Furthermore, our method is validated on public benchmarks like MNIST, CIFAR10/100, UCF11 and HMDB51, achieving the state-of-the-art performance.

scholarly journals A Unique Transdisciplinary Engineering-Based Integrated Approach for the Design of Temporary Refugee Housing Using Kano, HOQ/QFD, TRIZ, AD, ISM and DSM Tools

Designs ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 31
Author(s):  
Daniel Moran ◽  
Atila Ertas ◽  
Utku Gulbulak
Keyword(s):  
Rural Areas ◽  
Social Issues ◽  
Integrated Approach ◽  
Interpretive Structural Modeling ◽  
Design Problems ◽  
Design Structure ◽  
Function Design ◽  
Complex Engineering ◽  
Design Conflicts

The continued displacement of refugees from their homes and homelands (now greater than 50 million people worldwide) places increased focus and attention on evolving the designs of temporary housing that is available to be provided to the refugee population, especially in rural areas where housing does not already exist and must be constructed in very little time. Complex engineering problems involving social issues, such as this case study, benefit from the use of Integrated Transdisciplinary (TD) Tools (ITDT) to effectively and efficiently address the design questions related to them. The integrated use of TD Tools such as Kano Analysis, KJ Diagrams, Critical to Quality (CTQ), House of Quality (HOQ)/Quality Function Design (QFD), Theory of Inventive Problem Solving (TRIZ), Axiomatic Design (AD), Interpretive Structural Modeling (ISM), and Design Structure Matrix (DSM) through an end-to-end unique design process leads to innovation and elimination of design conflicts for especially complicated design problems. The objective of this study is to examine the design of temporary refugee housing using integrated TD tools mentioned above. This research concludes that the use of the ITDT approach provides an innovative, decoupled design.

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