EVOLUTIONARY METHODS AND GENETIC ALGORITHMS FOR NUMERICAL CALCULATION OF THE OPTIMAL DESIGN OF NON-RIGID ROAD SURFACES

2021 ◽  
pp. 84-90
Author(s):  
А.А. Тайлакова ◽  
А.Г. Пимонов
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Numerical Calculation ◽  
Optimal Design ◽  
Evolutionary Algorithms ◽  
Hybrid Genetic Algorithm ◽  
Road Surfaces ◽  
Cost Of Materials ◽  
The Cost ◽  
Full Search

В статье представлена оптимизационная модель для расчета конструкций нежестких дорожных одежд, обосновано применение эволюционных алгоритмов в сочетании с полным перебором и параллельными вычислениями и описан разработанный гибридный генетический алгоритм The article presents an optimization model for calculating structures of non-rigid road surfaces, justifies the use of evolutionary algorithms in combination with full search and parallel calculations, and describes the developed hybrid genetic algorithm for optimizing the design of non-rigid road surfaces at the cost of materials of structural layers.

RDF Query Path Optimization Using Hybrid Genetic Algorithms

2022 ◽  
Vol 12 (1) ◽  
pp. 1-16
Author(s):  
Qazi Mudassar Ilyas ◽  
Muneer Ahmad ◽  
Sonia Rauf ◽  
Danish Irfan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Execution Time ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Solution Space ◽  
Path Optimization ◽  
Initial Population ◽  
Description Framework ◽  
The Cost

Resource Description Framework (RDF) inherently supports data mergers from various resources into a single federated graph that can become very large even for an application of modest size. This results in severe performance degradation in the execution of RDF queries. As every RDF query essentially traverses a graph to find the output of the Query, an efficient path traversal reduces the execution time of RDF queries. Hence, query path optimization is required to reduce the execution time as well as the cost of a query. Query path optimization is an NP-hard problem that cannot be solved in polynomial time. Genetic algorithms have proven to be very useful in optimization problems. We propose a hybrid genetic algorithm for query path optimization. The proposed algorithm selects an initial population using iterative improvement thus reducing the initial solution space for the genetic algorithm. The proposed algorithm makes significant improvements in the overall performance. We show that the overall number of joins for complex queries is reduced considerably, resulting in reduced cost.

Optimal Design and Multicoils Quench Analysis of the EMPS Superconducting Magnet Employing Hybrid Genetic Algorithm

2018 ◽  
Vol 28 (3) ◽  
pp. 1-5 ◽  
Author(s):  
Jae Young Jang ◽  
Young Jin Hwang ◽  
Sangjin Lee ◽  
Yeon Suk Choi
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Hybrid Genetic Algorithm ◽  
Superconducting Magnet ◽  
Quench Analysis

Towards a Hybrid Solver: Integration of a Genetic Algorithm Within “DSIDES”

2002 ◽  
Author(s):  
Shuiwei Xie ◽  
Warren F. Smith
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Linear Programming ◽  
Decision Support ◽  
Hybrid Genetic Algorithm ◽  
Systems Design ◽  
Nonlinear Problems ◽  
The Body ◽  
Hybrid Solver ◽  
Decision Based Design

In contributing to the body of knowledge for decision-based design, the work reported in this paper has involved steps towards building a hybrid genetic algorithm to address systems design. Highlighted is a work in progress at the Australian Defence Force Academy (ADFA). A genetic algorithm (GA) is proposed to deal with discrete aspects of a design model (e.g., allocation of space to function) and a sequential linear programming (SLP) method for the continuous aspects (e.g., sizing). Our historical Decision Based Design (DBD) tool has been the code DSIDES (Decision Support In the Design of Engineering Systems). The original functionality of DSIDES was to solve linear and non-linear goal programming styled problems using linear programming (LP) and sequential (adaptive) linear programming (SLP/ALP). We seek to enhance DSIDES’s solver capability by the addition of genetic algorithms. We will also develop the appropriate tools to deal with the decomposition and synthesis implied. The foundational paradigm for DSIDES, which remains unchanged, is the Decision Support Problem Technique (DSPT). Through introducing genetic algorithms as solvers in DSIDES, the intention is to improve the likelihood of finding the global minimum (for the formulated model) as well as the ability of dealing more effectively with nonlinear problems which have discrete variables, undifferentiable objective functions or undifferentiable constraints. Using some numerical examples and a practical ship design case study, the proposed GA based method is demonstrated to be better in maintaining diversity of populations, preventing premature convergence, compared with other similar GAs. It also has similar effectiveness in finding the solutions as the original ALP DSIDES solver.

Optimal Design of 6-UPU Parallel Mechanism

2011 ◽  
Vol 480-481 ◽  
pp. 1055-1060
Author(s):  
Guang Hua Wu ◽  
Lie Hang Gong ◽  
Xin Wei Ji ◽  
Zhong Jun Wu ◽  
Yong Jun Gai
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimal Design ◽  
Objective Function ◽  
Parallel Mechanism ◽  
Design Space ◽  
Jacobian Matrix ◽  
Optimal Model ◽  
The Real ◽  
Universal Joints

The methodology of the optimal design for the 6-UPU parallel mechanism (PM) is presented based on genetic algorithms. The optimal index which expressed by Jacobian matrix of the PM is first deduced. An optimal model is established, in which the kinematic dexterity of a parallel mechanism is considered as the objective function. The design space, the limiting length of the electric actuators and the limit angles of universal joints are taken as constraints. The real-encoding genetic algorithm is applied to the optimal design of a parallel mechanism, which is proved the validity and advantage for the optimal design of a similar mechanism.

scholarly journals Optimal design of DFG-based wavelength conversion based on hybrid genetic algorithm

2003 ◽  
Vol 11 (14) ◽  
pp. 1677 ◽  
Author(s):  
Xueming Liu ◽  
Yanhe Li
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Wavelength Conversion ◽  
Hybrid Genetic Algorithm

Improved Genetic Algorithms Based Optimal Design of The Shuangji River Aqueduct

2011 ◽  
Vol 201-203 ◽  
pp. 1288-1291
Author(s):  
Xin Li Bai ◽  
Wei Yu ◽  
Dan Fei Wang ◽  
Yuan Yuan Fan
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Structural Optimization ◽  
Large Scale ◽  
Hybrid Genetic Algorithm ◽  
Computer Software ◽  
Search Method ◽  
Discrete Variables ◽  
Original Design ◽  
Design Plan

The simple genetic algorithm (SGA) is taken as the global search method, and the traditional direct search method for mixed-discrete variables as the local search method. The improved (hybrid) genetic algorithm (IGA) is obtained by improving the SGA. And through the introduction of penalty constraints, the problem dealing with the constraints in GA is successfully resolved. A mathematical model for structural optimization of aqueduct is established, and computer software is developed for structural optimization of large-scale aqueduct based on IGA. Using this program, the Shuangji River aqueduct is optimized and Rectangle-sectioned aqueduct design plan is obtained. Compared with the original design plan, optimal design is very economical and was adopted by Design Institute.

scholarly journals Power Factor Compensation in Non-Sinusoidal Circuits Using Geometric Algebra and Evolutionary Algorithms

2019 ◽  
Author(s):  
Francisco G. Montoya ◽  
Alfredo Alcayde ◽  
Francisco M. Arrabal-Campos ◽  
Raul Baños
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Power Factor ◽  
Geometric Algebra ◽  
Active Filters ◽  
High Quality ◽  
Passive Filters ◽  
Algebra Theory ◽  
Open Question

Non-linear loads in circuits cause the appearance of harmonic disturbances both in voltage and current. In order to minimize the effects of these disturbances and, therefore, to control over the flow of electricity between the source and the load, they are often used passive or active filters. Nevertheless, determining the type of filter and the characteristics of their elements is not a trivial task. In fact, the development of algorithms for calculating the parameters of filters is still an open question. This paper analyzes the use of genetic algorithms to maximize the power factor compensation in non-sinusoidal circuits using passive filters, while concepts of geometric algebra theory are used to represent the flow of power in the circuits. According to the results obtained in different case studies, it can be concluded that the genetic algorithm obtain high quality solutions that could be generalized to similar problems of any dimension.

An Object-Oriented Framework for Rapid Genetic Algorithm Development

2007 ◽  
pp. 608-624 ◽  
Author(s):  
A. L. Medaglia
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Optimization Problems ◽  
Lot Sizing ◽  
Rapid Development ◽  
Object Oriented ◽  
Lot Sizing Problem ◽  
Fast Development ◽  
Complex Optimization

JGA, the acronym for Java Genetic Algorithm, is a computational object-oriented framework for rapid development of evolutionary algorithms for solving complex optimization problems. This chapter describes the JGA framework and illustrates its use on the dynamic inventory lot-sizing problem. Using this problem as benchmark, JGA is compared against three other tools, namely, GAlib, an open C++ implementation; GADS, a commercial MatlabÒ toolbox; and PROC GA, a commercial (yet experimental) SASÒ procedure. JGA has proved to be a flexible and extensible object-oriented framework for the fast development of single (and multi-objective) genetic algorithms by providing a collection of ready-to-use modules (Java classes) that comprise the nucleus of any genetic algorithm. Furthermore, JGA has also been designed to be embedded in larger applications that solve complex business problems.

A meta-parameter tuning model to improve the genetic algorithms design of labeling diversity mappers

2021 ◽  
Author(s):  
Shaheen Solwa ◽  
Ayodeji James Bamisaye
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Parameter Tuning ◽  
Space Time ◽  
Diversity Gain ◽  
Parameter Setting ◽  
Fitness Value ◽  
Best Parameter ◽  
Diversity Gains

Evolutionary algorithms (EAs) have recently been applied to Uncoded Space-Time Labeling Diversity (USTLD) systems to produce labeling diversity mappers. However, the most challenging task is choosing the best parameter setting for the EA to create a more ‘optimal’ mapper design. This paper proposes a ‘meta-Genetic Algorithm (GA)’ used to tune hyperparameters for the Labeling Diversity EA. The algorithm is examined on 16, 32 and 64QAM; 32 and 64PSK; 16, 32 and 64APSK and 16APSK constellations that do not show diagonal symmetry. Furthermore, the meta-GA settings and original GA settings are compared in terms of the number of generations taken to converge to a solution. For QAM constellations, the output using the meta-GA settings matched but did not improve with the original settings. However, the number of generations needed to converge to a solution took 120 times less than the number of generations using the original settings. In the 64PSK constellation, a diversity gain of [Formula: see text][Formula: see text]dB was observed while improving on the actual fitness value from 0.0575 to 0.0661. Similarly, with 32APSK constellation, an improvement in fitness value from 0.1457 to 0.1748 was made while showing diversity gains of [Formula: see text][Formula: see text]dB. 64APSK constellation fitness value improved from 0.0708 to 0.0957, and a [Formula: see text][Formula: see text]dB gain was observed. The most significant improvement was made by the asymmetric 16APSK constellation, with gains of [Formula: see text][Formula: see text]dB and increasing its fitness value three times (0.0981 to 0.3000). A study of the effects of optimizing the GA parameters shows that the number of swaps during crossover [Formula: see text] and the radius [Formula: see text] were the two most important variables to optimize when executing this GA.

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