THE APPLICATION OF MULTIPLE-OBJECTIVE GENETIC ALGORITHMS FOR CONTROLLING SOUND QUALITY BY ARCHITECTURAL FORM DESIGN

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
CHAWEE BUSAYARAT
Keyword(s):  
Genetic Algorithms ◽  
Architectural Design ◽  
New Technologies ◽  
Fitness Function ◽  
Sound Quality ◽  
Suitable Model ◽  
Architectural Form ◽  
Acoustic Environment ◽  
Simulation And Optimization ◽  
Sound Environment

Presently, the architectural design responding to the sound environment has become to play an important role in the design process. New technologies such as simulation and optimization models through a computer allow us to discover the possibility of shapes, forms, and materials that respond to a specific condition of the acoustic environment. However, due to the complexity of variables, we have to face the difficulty of how to evaluate the calculated result. How should one know if the said design is suitable successful for the activities in that area? One of the solutions is using the genetic algorithm, which refers to the scenario simulation and adjustment to find the most suitable model. It provides the possibility of shape and materials to be the best result to design the architecture responding to the sound environment. The objective of this research is to propose a guideline for using genetic algorithms for architectural design by using sound quality as the Fitness function to find the best results terms of shape and material used. The proposed process takes reverberation time of sound and audio ray-tracing into consideration. The result of this research shows applications, abilities, and limitations of genetic algorithms in the domain of architectural parametric design.

A fast genetic algorithm for solving architectural design optimization problems

2015 ◽  
Vol 29 (4) ◽  
pp. 457-469 ◽  
Author(s):  
Zhouzhou Su ◽  
Wei Yan
Keyword(s):  
Genetic Algorithms ◽  
Design Optimization ◽  
Domain Knowledge ◽  
Architectural Design ◽  
Optimization Problems ◽  
Computing Time ◽  
Divide And Conquer ◽  
Specific Domain ◽  
Building Performance Simulation ◽  
Simulation And Optimization

AbstractBuilding performance simulation and genetic algorithms are powerful techniques for helping designers make better design decisions in architectural design optimization. However, they are very time consuming and require a significant amount of computing power. More time is needed when two techniques work together. This has become the primary impediment in applying design optimization to real-world projects. This study focuses on reducing the computing time in genetic algorithms when building simulation techniques are involved. In this study, we combine two techniques (offline simulation and divide and conquer) to effectively improve the run time in these architectural design optimization problems, utilizing architecture-specific domain knowledge. The improved methods are evaluated with a case study of a nursing unit design to minimize the nurses’ travel distance and maximize daylighting performance in patient rooms. Results show the computing time can be saved significantly during the simulation and optimization process.

Comprehensive Promotion Measures of Urban Residence Acoustic Environment Quality

2013 ◽  
Vol 438-439 ◽  
pp. 1690-1693
Author(s):  
Zhi Guo Wang ◽  
Mei Jun Lu
Keyword(s):  
Architectural Design ◽  
Rapid Development ◽  
Residential Building ◽  
Living Environment ◽  
Control Measures ◽  
Living Standard ◽  
Urban Residence ◽  
Acoustic Environment ◽  
Environment Quality ◽  
Sound Environment

As the urban rapid development and the continuous improvement of people's living standard, residents put forward higher request for sound environment quality of residential building. By analyzing the main factors influencing the urban residence acoustic environment, in view of site selection planning, architectural design, structural measures and other appropriate measures, reasonable noise control measures are put forward to improve urban residential environment quality and provide residents with a quiet sound living environment.

scholarly journals Application of Genetic Algorithm Elements to Modelling of Rotation Processes in Motion Transmission Including a Long Shaft

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 115
Author(s):  
Andriy Chaban ◽  
Marek Lis ◽  
Andrzej Szafraniec ◽  
Radoslaw Jedynak
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fitness Function ◽  
Least Square ◽  
Distributed Parameter ◽  
Parameter Method ◽  
Analytical Mechanics ◽  
Parameter System ◽  
Rotational Systems ◽  
Elastic Elements

Genetic algorithms are used to parameter identification of the model of oscillatory processes in complicated motion transmission of electric drives containing long elastic shafts as systems of distributed mechanical parameters. Shaft equations are generated on the basis of a modified Hamilton–Ostrogradski principle, which serves as the foundation to analyse the lumped parameter system and distributed parameter system. They serve to compute basic functions of analytical mechanics of velocity continuum and rotational angles of shaft elements. It is demonstrated that the application of the distributed parameter method to multi-mass rotational systems, that contain long elastic elements and complicated control systems, is not always possible. The genetic algorithm is applied to determine the coefficients of approximation the system of Rotational Transmission with Elastic Shaft by equivalent differential equations. The fitness function is determined as least-square error. The obtained results confirm that application of the genetic algorithms allow one to replace the use of a complicated distributed parameter model of mechanical system by a considerably simpler model, and to eliminate sophisticated calculation procedures and identification of boundary conditions for wave motion equations of long elastic elements.

scholarly journals Adaptive Gene Level Mutation

Algorithms ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 16
Author(s):  
Jalal Al-afandi ◽  
Horváth András
Keyword(s):  
Genetic Algorithms ◽  
Stochastic Optimization ◽  
Specific Problem ◽  
Traditional Approach ◽  
Fitness Function ◽  
Optimization Methods ◽  
Analytic Solutions ◽  
Problem Representation ◽  
Travelling Salesman ◽  
Gene Level

Genetic Algorithms are stochastic optimization methods where solution candidates, complying to a specific problem representation, are evaluated according to a predefined fitness function. These approaches can provide solutions in various tasks even, where analytic solutions can not be or are too complex to be computed. In this paper we will show, how certain set of problems are partially solvable allowing us to grade segments of a solution individually, which results local and individual tuning of mutation parameters for genes. We will demonstrate the efficiency of our method on the N-Queens and travelling salesman problems where we can demonstrate that our approach always results faster convergence and in most cases a lower error than the traditional approach.

scholarly journals Interior Sound Quality Prediction of Pure Electric Vehicles Based on Transfer Path Synthesis

2021 ◽  
Vol 11 (10) ◽  
pp. 4385
Author(s):  
Kun Qian ◽  
Zhichao Hou ◽  
Jie Liang ◽  
Ruixue Liu ◽  
Dengke Sun
Keyword(s):  
Electric Vehicles ◽  
Sound Quality ◽  
Interior Noise ◽  
Experimental Comparison ◽  
Noise Component ◽  
Acoustic Environment ◽  
Evaluation Indexes ◽  
Transfer Path ◽  
New Ideas ◽  
Path Synthesis

The interior sound quality (SQ) of pure electric vehicles (PEVs) has become an important consideration for users purchasing vehicles. At present, it is insufficient to take the sound pressure level as the interior acoustics design index of PEVs. Transfer path analysis (TPA) and transfer path synthesis (TPS) that take the SQ of interior noise as the improvement target remains in the preliminary exploration stage. In this paper, objective psychoacoustic parameters of SQ were taken as evaluation indexes of interior PEV noise. A virtual interior SQ synthesis model was designed on the basis of TPA and TPS, which combines experimentation and simulation. The SQ synthesis model demonstrates each noise component contribution in a PEV by new SQ separation technology. First, the interior noise transfer path and noise source of the PEV were determined in a synthesis analysis method of the interior PEV noise. Second, on the basis of the composition mechanism of interior noise and the basic principle of TPA, the excitation signal and transfer function of each interior noise path in the PEV were tested. On the basis of TPS, the interior SQ synthesis model of PEV was then established. Finally, the accuracy of the prediction model was verified in simulation and experimental comparison studies on the psychoacoustic objective parameters of SQ. The SQ objective parameter value of each transfer path was quantified by using contribution analysis. The results are expected to improve the comfort of the interior acoustic environment and enhance the competitiveness of vehicle products. They also provide an effective reference and new ideas for the development of interior SQ in PEVs.

Assembly Planning: A Genetic Approach

1998 ◽  
Author(s):  
Shiang-Fong Chen
Keyword(s):  
Genetic Algorithms ◽  
Fitness Function ◽  
Assembly Planning ◽  
Genetic Approach ◽  
Genetic Operators ◽  
Complex Problems ◽  
All Solutions ◽  
Assembly Problem ◽  
Optimal Assembly

Abstract The difficulty of an assembly problem is the inherent complexity of possible solutions. If the most suitable plan is selected after all solutions are found, it will be very time consuming and unrealistic. Motivated by the success of genetic algorithms (GAs) in solving combinatorial and complex problems by examining a small number of possible candidate solutions, GAs are employed to find a near-optimal assembly plan for a general environment. Five genetic operators are used: tree crossover, tree mutation, cut-and-paste, break-and-joint, and reproduction. The fitness function can adapt to different criteria easily. This assembly planner can help an inexperienced technician to find a good solution efficiently. The algorithm has been fully implemented. One example product is given to show the applications and results.

scholarly journals Strategic Team AI Path Plans: Probabilistic Pathfinding

2008 ◽  
Vol 2008 ◽  
pp. 1-6 ◽  
Author(s):  
Tng C. H. John ◽  
Edmond C. Prakash ◽  
Narendra S. Chaudhari
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Computer Games ◽  
Fitness Function ◽  
New Method ◽  
High Quality ◽  
Plan Generation ◽  
Novel Method ◽  
Games And Simulations

This paper proposes a novel method to generate strategic team AI pathfinding plans for computer games and simulations using probabilistic pathfinding. This method is inspired by genetic algorithms (Russell and Norvig, 2002), in that, a fitness function is used to test the quality of the path plans. The method generates high-quality path plans by eliminating the low-quality ones. The path plans are generated by probabilistic pathfinding, and the elimination is done by a fitness test of the path plans. This path plan generation method has the ability to generate variation or different high-quality paths, which is desired for games to increase replay values. This work is an extension of our earlier work on team AI: probabilistic pathfinding (John et al., 2006). We explore ways to combine probabilistic pathfinding and genetic algorithm to create a new method to generate strategic team AI pathfinding plans.

scholarly journals Application of the genetic algorithm to the task of compiling the curriculum

2018 ◽  
Author(s):  
V. A. Turchina ◽  
D. O. Tanasienko
Keyword(s):  
Higher Education ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Necessary Conditions ◽  
Fitness Function ◽  
Educational Process ◽  
Heuristic Methods ◽  
Educational Groups ◽  
The Individual ◽  
Negative Property

One of the main tasks in organizing the educational process in higher education is the drawing up of a schedule of classes. It reflects the weekly student and faculty load. At the same time, when compiling, there are a number of necessary conditions and a number of desirable. The paper considers seven required and four desirable conditions. In this paper, one of the well-known approaches that can be used in drawing up a curriculum is consid-ered. The proposed scheme of the genetic algorithm, the result of which is to obtain an approximate solution to the problem of scheduling with the need to further improve it by other heuristic methods. To solve the problem, an island model of the genetic algorithm was selected and its advantages were considered. In the paper, the author's own structure of the individual, which includes chromosomes in the form of educational groups and genes as a lesson at a certain time, is presented and justified. The author presents his own implementations of the genetic algorithms. During the work, many variants of operators were tested, but they were rejected due to their inefficiency. The biggest problem was to maintain the consistency of information encoded in chromosomes. Also, two post-steps were added: to try to reduce the number of teacher conflict conflicts and to normalize the schedule - to remove windows from the schedule. The fitness function is calculated according to the following principles: if some desired or desired property is present in the individual, then a certain number is deducted from the individual's assessment, if there is a negative property, then a certain number is added to the assessment. Each criterion has its weight, so the size of the fine or rewards may be different. In this work, fines were charged for non-fulfillment of mandatory conditions, and rewards for fulfilling the desired

scholarly journals Subjective Experience of Speech Depending on the Acoustic Treatment in an Ordinary Room

2021 ◽  
Vol 18 (23) ◽  
pp. 12274
Author(s):  
Emma Arvidsson ◽  
Erling Nilsson ◽  
Delphine Bard-Hagberg ◽  
Ola J. I. Karlsson
Keyword(s):  
Speech Perception ◽  
Subjective Experience ◽  
Sound Quality ◽  
Reverberation Time ◽  
Acoustic Environment ◽  
Acoustic Environments ◽  
The Right ◽  
Acoustic Treatment ◽  
High Degree ◽  
Speech Clarity

In environments such as classrooms and offices, complex tasks are performed. A satisfactory acoustic environment is critical for the performance of such tasks. To ensure a good acoustic environment, the right acoustic treatment must be used. The relation between different room acoustic treatments and how they affect speech perception in these types of rooms is not yet fully understood. In this study, speech perception was evaluated for three different configurations using absorbers and diffusers. Twenty-nine participants reported on their subjective experience of speech in respect of different configurations in different positions in a room. They judged sound quality and attributes related to speech perception. In addition, the jury members ranked the different acoustic environments. The subjective experience was related to the different room acoustic treatments and the room acoustic parameters of speech clarity, reverberation time and sound strength. It was found that people, on average, rated treatments with a high degree of absorption as best. This configuration had the highest speech clarity value and lowest values for reverberation time and sound strength. The perceived sound quality could be correlated to speech clarity, while attributes related to speech perception had the strongest association with reverberation time.

scholarly journals Analysis of Selection Methods Used in Genetic Algorithms

2021 ◽  
Vol 4 ◽  
pp. 29-43
Author(s):  
Nataliya Gulayeva ◽  
Artem Ustilov
Keyword(s):  
Growth Rate ◽  
Genetic Algorithms ◽  
Selection Pressure ◽  
Fitness Function ◽  
Selection Method ◽  
Initial Population ◽  
Reproduction Rate ◽  
Selection Methods ◽  
Selection Scheme ◽  
Fitness Value

This paper offers a comprehensive review of selection methods used in the generational genetic algorithms.Firstly, a brief description of the following selection methods is presented: fitness proportionate selection methods including roulette-wheel selection (RWS) and its modifications, stochastic remainder selection with replacement (SRSWR), remainder stochastic independent selection (RSIS), and stochastic universal selection (SUS); ranking selection methods including linear and nonlinear rankings; tournament selection methods including deterministic and stochastic tournaments as well as tournaments with and without replacement; elitist and truncation selection methods; fitness uniform selection scheme (FUSS).Second, basic theoretical statements on selection method properties are given. Particularly, the selection noise, selection pressure, growth rate, reproduction rate, and computational complexity are considered. To illustrate selection method properties, numerous runs of genetic algorithms using the only selection method and no other genetic operator are conducted, and numerical characteristics of analyzed properties are computed. Specifically, to estimate the selection pressure, the takeover time and selection intensity are computed; to estimate the growth rate, the ratio of best individual copies in two consecutive populations is computed; to estimate the selection noise, the algorithm convergence speed is analyzed based on experiments carried out on a specific fitness function assigning the same fitness value to all individuals.Third, the effect of selection methods on the population fitness distribution is investigated. To do this, there are conducted genetic algorithm runs starting with a binomially distributed initial population. It is shown that most selection methods keep the distribution close to the original one providing an increased mean value of the distribution, while others (such as disruptive RWS, exponential ranking, truncation, and FUSS) change the distribution significantly. The obtained results are illustrated with the help of tables and histograms.

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