Optimization Models of Sound Systems Using Genetic Algorithms

2003 ◽  
Vol 29 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Jinyun Ke ◽  
Mieko Ogura ◽  
William S.-Y. Wang
Keyword(s):  
Genetic Algorithms ◽  
Optimization Problems ◽  
Scalar Function ◽  
Explanatory Models ◽  
Multiple Criteria ◽  
Ranking Method ◽  
Optimization Models ◽  
Pareto Ranking ◽  
Vowel Systems ◽  
Perceptual Contrast

In this study, optimization models using genetic algorithms (GAs) are proposed to study the configuration of vowels and tone systems. As in previous explanatory models that have been used to study vowel systems, certain criteria, which are assumed to be the principles governing the structure of sound systems, are used to predict optimal vowels and tone systems. In most of the earlier studies only one criterion has been considered. When two criteria are considered, they are often combined into one scalar function. The GA model proposed for the study of tone systems uses a Pareto ranking method that is highly applicable for dealing with optimization problems having multiple criteria. For optimization of tone systems, perceptual contrast and markedness complexity are considered simultaneously. Although the consistency between the predicted systems and the observed systems is not as significant as those obtained for vowel systems, further investigation along this line is promising.

Solving marketing optimization problems using genetic algorithms

1995 ◽  
Vol 29 (4) ◽  
pp. 39-56 ◽  
Author(s):  
S. Hurley ◽  
L. Moutinho ◽  
N.M. Stephens
Keyword(s):  
Genetic Algorithms ◽  
Optimization Problems

Implementation of Eurocode Load Cases in Optimization Problems of Steel Frames, Based on Genetic Algorithms

2013 ◽  
Vol 310 ◽  
pp. 609-613
Author(s):  
Ioana D. Balea ◽  
Radu Hulea ◽  
Georgios E. Stavroulakis
Keyword(s):  
Finite Element ◽  
Genetic Algorithms ◽  
Global Optimization ◽  
Optimal Design ◽  
Optimization Problems ◽  
Steel Frames ◽  
Global Optimization Algorithm ◽  
Planar Structures ◽  
Discrete Global Optimization ◽  
Steel Sections

This paper presents an implementation of Eurocode load cases for discrete global optimization algorithm for planar structures based on the principles of finite element methods and genetic algorithms. The final optimal design is obtained using IPE sections chosen as feasible by the algorithm, from the available steel sections from industry. The algorithm is tested on an asymmetric planar steel frame with promising results.

scholarly journals Utilizing Model Knowledge for Design Developed Genetic Algorithm to Solving Problem

2018 ◽  
Vol 3 (2) ◽  
pp. 172
Author(s):  
Hamidreza Salmani mojaveri
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Parallel Machines ◽  
Job Shop ◽  
Optimization Problems ◽  
Premature Convergence ◽  
Local Optimum ◽  
Scheduling Problems ◽  
Algorithm Implementation

One of the discussed topics in scheduling problems is Dynamic Flexible Job Shop with Parallel Machines (FDJSPM). Surveys show that this problem because of its concave and nonlinear nature usually has several local optimums. Some of the scheduling problems researchers think that genetic algorithms (GA) are appropriate approach to solve optimization problems of this kind. But researches show that one of the disadvantages of classical genetic algorithms is premature convergence and the probability of trap into the local optimum. Considering these facts, in present research, represented a developed genetic algorithm that its controlling parameters change during algorithm implementation and optimization process. This approach decreases the probability of premature convergence and trap into the local optimum. The several experiments were done show that the priority of proposed procedure of solving in field of the quality of obtained solution and convergence speed toward other present procedure.

scholarly journals Genetic Algorithms with Memory- and Elitism-Based Immigrants in Dynamic Environments

2008 ◽  
Vol 16 (3) ◽  
pp. 385-416 ◽  
Author(s):  
Shengxiang Yang
Keyword(s):  
Genetic Algorithms ◽  
Optimization Problems ◽  
Dynamic Environments ◽  
Population Diversity ◽  
Dynamic Problems ◽  
Dynamic Optimization Problems ◽  
Hybrid Memory ◽  
Memory Schemes ◽  
Memory Scheme ◽  
With Memory

In recent years the genetic algorithm community has shown a growing interest in studying dynamic optimization problems. Several approaches have been devised. The random immigrants and memory schemes are two major ones. The random immigrants scheme addresses dynamic environments by maintaining the population diversity while the memory scheme aims to adapt genetic algorithms quickly to new environments by reusing historical information. This paper investigates a hybrid memory and random immigrants scheme, called memory-based immigrants, and a hybrid elitism and random immigrants scheme, called elitism-based immigrants, for genetic algorithms in dynamic environments. In these schemes, the best individual from memory or the elite from the previous generation is retrieved as the base to create immigrants into the population by mutation. This way, not only can diversity be maintained but it is done more efficiently to adapt genetic algorithms to the current environment. Based on a series of systematically constructed dynamic problems, experiments are carried out to compare genetic algorithms with the memory-based and elitism-based immigrants schemes against genetic algorithms with traditional memory and random immigrants schemes and a hybrid memory and multi-population scheme. The sensitivity analysis regarding some key parameters is also carried out. Experimental results show that the memory-based and elitism-based immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments.

scholarly journals Multi-Objective Robust Optimization Using a Postoptimality Sensitivity Analysis Technique: Application to a Wind Turbine Design

2015 ◽  
Vol 137 (1) ◽  
Author(s):  
Weijun Wang ◽  
Stéphane Caro ◽  
Fouad Bennis ◽  
Ricardo Soto ◽  
Broderick Crawford
Keyword(s):  
Sensitivity Analysis ◽  
Robust Optimization ◽  
Wind Turbine ◽  
Optimization Design ◽  
Optimization Problems ◽  
Design Environment ◽  
Multi Objective ◽  
Pareto Ranking ◽  
Analysis Technique ◽  
Robust Optimization Design

Toward a multi-objective optimization robust problem, the variations in design variables (DVs) and design environment parameters (DEPs) include the small variations and the large variations. The former have small effect on the performance functions and/or the constraints, and the latter refer to the ones that have large effect on the performance functions and/or the constraints. The robustness of performance functions is discussed in this paper. A postoptimality sensitivity analysis technique for multi-objective robust optimization problems (MOROPs) is discussed, and two robustness indices (RIs) are introduced. The first one considers the robustness of the performance functions to small variations in the DVs and the DEPs. The second RI characterizes the robustness of the performance functions to large variations in the DEPs. It is based on the ability of a solution to maintain a good Pareto ranking for different DEPs due to large variations. The robustness of the solutions is treated as vectors in the robustness function space (RF-Space), which is defined by the two proposed RIs. As a result, the designer can compare the robustness of all Pareto optimal solutions and make a decision. Finally, two illustrative examples are given to highlight the contributions of this paper. The first example is about a numerical problem, whereas the second problem deals with the multi-objective robust optimization design of a floating wind turbine.

DIFFERENT APPROACHES IN MULTIPLE-CRITERIA OPTIMIZATION USING THE TAGUCHI METHOD: A CASE STUDY IN A COLD HEADING PROCESS

2004 ◽  
Vol 03 (01) ◽  
pp. 53-68 ◽  
Author(s):  
A. S. MILANI ◽  
C. EL-LAHHAM ◽  
J. A. NEMES
Keyword(s):  
Taguchi Method ◽  
Optimization Problems ◽  
Real Life ◽  
Multiple Attribute Decision Making ◽  
Multiple Criteria ◽  
Multiple Criteria Optimization ◽  
Multiple Attribute ◽  
Overall Evaluation ◽  
Decision Making Model

Real life engineering problems usually require the satisfaction of different, potentially conflicting criteria. Design optimization, on the other hand, based on the conventional Taguchi method cannot accommodate more than one response. However, by the use of the overall evaluation criterion approach, the method can be applied to multiple-criteria optimization problems. This paper presents the use of different utility function methods as well as a multiple attribute decision-making model in the multiple-criteria optimization of a cold heading process. Different aspects of each method are discussed and compared.

A rank-based constraint handling technique for engineering design optimization problems solved by genetic algorithms

2017 ◽  
Vol 187 ◽  
pp. 77-87 ◽  
Author(s):  
Rafael de Paula Garcia ◽  
Beatriz Souza Leite Pires de Lima ◽  
Afonso Celso de Castro Lemonge ◽  
Breno Pinheiro Jacob
Keyword(s):  
Genetic Algorithms ◽  
Design Optimization ◽  
Engineering Design ◽  
Optimization Problems ◽  
Constraint Handling ◽  
Engineering Design Optimization ◽  
Handling Technique

Hookes-Jeeves-Based Variant of Memetic Algorithm

2016 ◽  
pp. 450-475
Author(s):  
Dipti Singh ◽  
Kusum Deep
Keyword(s):  
Genetic Algorithms ◽  
Memetic Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Memetic Algorithms ◽  
Global Optimum ◽  
Benchmark Problems ◽  
New Variant ◽  
Optimum Solution ◽  
Global Optimum Solution

Due to their wide applicability and easy implementation, Genetic algorithms (GAs) are preferred to solve many optimization problems over other techniques. When a local search (LS) has been included in Genetic algorithms, it is known as Memetic algorithms. In this chapter, a new variant of single-meme Memetic Algorithm is proposed to improve the efficiency of GA. Though GAs are efficient at finding the global optimum solution of nonlinear optimization problems but usually converge slow and sometimes arrive at premature convergence. On the other hand, LS algorithms are fast but are poor global searchers. To exploit the good qualities of both techniques, they are combined in a way that maximum benefits of both the approaches are reaped. It lets the population of individuals evolve using GA and then applies LS to get the optimal solution. To validate our claims, it is tested on five benchmark problems of dimension 10, 30 and 50 and a comparison between GA and MA has been made.

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