scholarly journals Optimization of engineering systems using genetic algorithm enhanced computational techniques

2021 ◽  
Author(s):  
Nicholas Farouk Ali
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Mechanical Engineering ◽  
Optimization Methods ◽  
System Optimization ◽  
Optimization Techniques ◽  
Engineering Applications ◽  
Engineering Problems ◽  
New Ideas

The field of optimization has been and continues to be an area of significant importance in the industry. From financial, industrial, social and any other sector conceivable, people are interested in improving the scheme of existing methodologies and products and/or in creating new ideas. Due to the growing need for humans to improve their lives and add efficiency to a system, optimization has been and still is an area of active research. Typically optimization methods seek to improve rather than create new ideas. However, the ability of optimization methods to mold new ideas should not be ruled out, since optimized solutions usually lead to new designs, which are in most cases unique. Combinatorial optimization is the term used to define the method of finding the best sequence or combination of variables or elements in a large complex system in order to attain a particular objective. This thesis promises to provide a panoramic view of optimization in general before zooming into a specific artificial intelligence technique in optimization. Detailed information on optimization techniques commonly used in mechanical engineering is first provided to ensure a clear understanding of the thesis. Moreover, the thesis highlights the differences and similarities, advantages and disadvantages of these techniques. After a brief study of the techniques entailed in optimization, an artificial intelligence algorithm, namely genetic algorithm, was selected, developed, improved and later applied to a wide variety of mechanical engineering problems. Ample examples from various fields of engineering are provided to illustrate the versatility of genetic algorithms. The major focus of this thesis is therefore the application of genetic algorithms to solve a broad range of engineering problems. The viability of the genetic algorithm (GA) as an optimization tool for mechanical engineering applications is assessed and discussed. Comparison between GA generated results and results found in the literature are presented when possible to underscore the power of GA to solve problems. Moreover, the disadvantages and advantages of the genetic algorithms are discussed based on the results obtained. The mechanical engineering applications studied include conceptual aircraft design, design of truss structures under various constraints and loading conditions, and armour design using established penetration analytical models. Results show that the genetic algorithm developed is capable of handling a wide range of problems, is an efficient cost effective tool, and often provides superior results when compared to other optimization methods found in the literature.

scholarly journals Optimization of engineering systems using genetic algorithm enhanced computational techniques

2021 ◽  
Author(s):  
Nicholas Farouk Ali
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Mechanical Engineering ◽  
Optimization Methods ◽  
System Optimization ◽  
Optimization Techniques ◽  
Engineering Applications ◽  
Engineering Problems ◽  
New Ideas

The field of optimization has been and continues to be an area of significant importance in the industry. From financial, industrial, social and any other sector conceivable, people are interested in improving the scheme of existing methodologies and products and/or in creating new ideas. Due to the growing need for humans to improve their lives and add efficiency to a system, optimization has been and still is an area of active research. Typically optimization methods seek to improve rather than create new ideas. However, the ability of optimization methods to mold new ideas should not be ruled out, since optimized solutions usually lead to new designs, which are in most cases unique. Combinatorial optimization is the term used to define the method of finding the best sequence or combination of variables or elements in a large complex system in order to attain a particular objective. This thesis promises to provide a panoramic view of optimization in general before zooming into a specific artificial intelligence technique in optimization. Detailed information on optimization techniques commonly used in mechanical engineering is first provided to ensure a clear understanding of the thesis. Moreover, the thesis highlights the differences and similarities, advantages and disadvantages of these techniques. After a brief study of the techniques entailed in optimization, an artificial intelligence algorithm, namely genetic algorithm, was selected, developed, improved and later applied to a wide variety of mechanical engineering problems. Ample examples from various fields of engineering are provided to illustrate the versatility of genetic algorithms. The major focus of this thesis is therefore the application of genetic algorithms to solve a broad range of engineering problems. The viability of the genetic algorithm (GA) as an optimization tool for mechanical engineering applications is assessed and discussed. Comparison between GA generated results and results found in the literature are presented when possible to underscore the power of GA to solve problems. Moreover, the disadvantages and advantages of the genetic algorithms are discussed based on the results obtained. The mechanical engineering applications studied include conceptual aircraft design, design of truss structures under various constraints and loading conditions, and armour design using established penetration analytical models. Results show that the genetic algorithm developed is capable of handling a wide range of problems, is an efficient cost effective tool, and often provides superior results when compared to other optimization methods found in the literature.

CONVERGENCE RATE OF THE SUCCESSIVE ZOOMING GENETIC ALGORITHM FOR BAND-WIDTHS OF EQUALITY CONSTRAINT

2010 ◽  
Vol 24 (15n16) ◽  
pp. 2731-2736
Author(s):  
Y. D. KWON ◽  
S. W. HAN ◽  
J.W. DO
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Descent Method ◽  
Optimization Techniques ◽  
Steepest Descent ◽  
Equality Constraint ◽  
Steepest Descent Method ◽  
Band Width ◽  
Rapid Convergence ◽  
Engineering Problems

Modern optimization techniques, such as the steepest descent method, Newton's method, Rosen's gradient projection method, genetic algorithms, etc., have been developed and quickly improved with the progress of digital computers. The steepest descent method and Newton's method are applied efficiently to unconstrained problems. For many engineering problems involving constraints, the genetic algorithm and SUMT1are applied with relative ease. Genetic algorithms2have global search characteristics and relatively good convergence rates. Recently, a Successive Zooming Genetic Algorithm (SZGA)3,4 was introduced that can search the precise optimal solution at any level of desired accuracy. In the case of engineering problems involving an equality constraint, even if good optimization techniques are applied to the constraint problems, a proper constraint range can lead to a more rapid convergence and precise solution. This study investigated the proper band-width of an equality constraint using the Successive Zooming Genetic Algorithm (SZGA) technique both theoretically and numerically. We were able to find a certain band-width range of the rapid convergence for each problem, and a broad but more general one too.

Comparison of Two Multiobjective Optimization Techniques With and Within Genetic Algorithms

1999 ◽  
Author(s):  
Shapour Azar ◽  
Brian J. Reynolds ◽  
Sanjay Narayanan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Pareto Set ◽  
Design Solution ◽  
Multiple Objective ◽  
Multiple Objective Optimization ◽  
Continuous Variables ◽  
Optimal Set

Abstract Engineering decision making involving multiple competing objectives relies on choosing a design solution from an optimal set of solutions. This optimal set of solutions, referred to as the Pareto set, represents the tradeoffs that exist between the competing objectives for different design solutions. Generation of this Pareto set is the main focus of multiple objective optimization. There are many methods to solve this type of problem. Some of these methods generate solutions that cannot be applied to problems with a combination of discrete and continuous variables. Often such solutions are obtained by an optimization technique that can only guarantee local Pareto solutions or is applied to convex problems. The main focus of this paper is to demonstrate two methods of using genetic algorithms to overcome these problems. The first method uses a genetic algorithm with some external modifications to handle multiple objective optimization, while the second method operates within the genetic algorithm with some significant internal modifications. The fact that the first method operates with the genetic algorithm and the second method within the genetic algorithm is the main difference between these two techniques. Each method has its strengths and weaknesses, and it is the objective of this paper to compare and contrast the two methods quantitatively as well as qualitatively. Two multiobjective design optimization examples are used for the purpose of this comparison.

scholarly journals A novel pseudoderivative-based mutation operator for real-coded adaptive genetic algorithms

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 139
Author(s):  
Maxinder S Kanwal ◽  
Avinash S Ramesh ◽  
Lauren A Huang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Mutation Rate ◽  
Optimal Solution ◽  
Global Optimum ◽  
Optimization Techniques ◽  
Adaptive Mutation ◽  
Local Optima ◽  
3 Dimensional ◽  
Successive Generations

Recent development of large databases, especially those in genetics and proteomics, is pushing the development of novel computational algorithms that implement rapid and accurate search strategies. One successful approach has been to use artificial intelligence and methods, including pattern recognition (e.g. neural networks) and optimization techniques (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate that is derived from comparing the fitness values of successive generations. We propose a novel pseudoderivative-based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemented to solve real problems in neurology and bioinformatics. As a first step towards this goal, we tested our algorithm on two 3-dimensional surfaces with multiple local optima, but only one global optimum, as well as on the N-queens problem, an applied problem in which the function that maps the curve is implicit. For all tests, the adaptive mutation rate allowed the genetic algorithm to find the global optimal solution, performing significantly better than other search methods, including genetic algorithms that implement fixed mutation rates.

Research on Software Optimization Solutions of E-Commerce Site

2012 ◽  
Vol 198-199 ◽  
pp. 626-630
Author(s):  
Xin Wang
Keyword(s):  
Electronic Commerce ◽  
Dynamic Load ◽  
Performance Optimization ◽  
Optimization Methods ◽  
System Optimization ◽  
Optimization Techniques ◽  
Load Optimization ◽  
Software Optimization ◽  
Database Performance ◽  
Cluster Technology

There are generally two types of E-commerce platform optimized programs: hardware optimization and software optimization, This paper first analyzes the system optimization techniques of software optimization, Including dynamic load optimization technology and cluster technology; Then studies the database performance optimization methods from the table, connection pooling, query and several other aspects; Finally to carry on the research to optimization electronic commerce platform used the cache technology. Proposes a universal significance of E-commerce platform software optimization solutions, these studies have some references for relevant E-commerce website designers and maintainers, and provides a strategy for the corresponding E-commerce enterprises to optimize platform environments.

scholarly journals Implementation of Genetic Algorithms and Momentum Backpropagation in Classification of Subtype Cells Acute Myeloid Leukimia

2020 ◽  
Vol 14 (2) ◽  
pp. 189
Author(s):  
Dian Mustikaningrum ◽  
Retantyo Wardoyo
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
White Blood Cells ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Learning Rate ◽  
Algorithm Optimization ◽  
Selection Parameter ◽  
Acute Myeloid ◽  
Random Trial

 Acute Myeloid Leukimia (AML) is a type of cancer which attacks white blood cells from myeloid. AML subtypes M1, M2, and M3 are affected by the same type of cells called myeloblasts, so it needs more detailed analysis to classify.Momentum Backpropagation  is used to classified. In its application, optimal selection of architecture, learning rate, and momentum is still done by random trial. This is one of the disadvantage of Momentum Backpropagation. This study uses a genetic algorithm (GA) as an optimization method to get the best architecture, learning rate, and momentum of artificial neural network. Genetic algorithms are one of the optimization techniques that emulate the process of biological evolution.The dataset used in this study is numerical feature data resulting from the segmentation of white blood cell images taken from previous studies which has been done by Nurcahya Pradana Taufik Prakisya. Based on these data, an evaluation of the Momentum Backpropagation process was conducted the selection parameter in a random trial with the genetic algorithm. Furthermore, the comparison of accuracy values was carried out as an alternative to the ANN learning method that was able to provide more accurate values with the data used in this study.The results showed that training and testing with genetic algorithm optimization of ANN parameters resulted in an average memorization accuracy of 83.38% and validation accuracy of 94.3%. Whereas in other ways, training and testing with momentum backpropagation random trial resulted in an average memorization accuracy of 76.09% and validation accuracy of 88.22%.

scholarly journals Educational Information System Optimization for Artificial Intelligence Teaching Strategies

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Taotang Liu ◽  
Zhongxin Gao ◽  
Honghai Guan
Keyword(s):  
Artificial Intelligence ◽  
Information System ◽  
Network Architecture ◽  
Optimization Problems ◽  
Optimization Methods ◽  
System Optimization ◽  
Engineering Problem ◽  
Engineering Practice ◽  
Local Optimum ◽  
Clustering Method

Under the background of the information age, scientific research and engineering practice have developed vigorously, resulting in many complex optimization problems that are difficult to solve. How to design more effective optimization methods has become the focus of urgent solutions in many academic fields. Under the guidance of such demand, intelligent optimization algorithms have emerged. This article analyzes and optimizes the modern artificial intelligence teaching information system in detail. On the basis of determining the network architecture, a detailed demand analysis was carried out, and the overall structure optimization of the network was given; the business process and data flow of the main modules of the website (resource center module and collaborative learning module) were optimized. In order to further enhance the local search ability of the algorithm, a multiclass interactive optimization algorithm is proposed in combination with the Euclidean distance-based clustering method, which changes the teaching mode from “one-person teaching” to “multiperson teaching.” This clustering method has lower complexity and is beneficial to enhance the utilization of neighborhood information. At the same time, in order to enhance the diversity of the population and strengthen the connection between the subgroups, after the teaching phase, the worst students in each subgroup are allowed to learn from the best teachers of the population, and after the learning phase, individuals in a random subgroup are allowed to learn from other subgroups. The algorithm was tested in the experimental environment of unconstrained, constrained, and an engineering problem. From the test results, it can be seen that the algorithm is not easy to fall into the local optimum. Compared with other algorithms, the solution accuracy is higher and the stability is better. And it performed well in engineering optimization problems, thus verifying the effectiveness of the strategy.

Genetic Algorithm Learning of Nash Equilibrium

2015 ◽  
Vol 13 (3) ◽  
pp. 1-14 ◽  
Author(s):  
M'hamed Outanoute ◽  
Mohamed Baslam ◽  
Belaid Bouikhalene
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Quality Of Service ◽  
Nash Equilibrium ◽  
Service Providers ◽  
Optimization Methods ◽  
Great Power ◽  
Dynamical Behaviors ◽  
Game Theoretic

To select or change a service provider, customers use the best compromise between price and quality of service (QoS). In this work, the authors formulate a game theoretic framework for the dynamical behaviors of Service Providers (SPs). They share the same market and are competing to attract more customers to gain more profit. Due to the divergence of SPs interests, it is believed that this situation is a non-cooperative game of price and QoS. The game converges to an equilibrium position known Nash Equilibrium. Using Genetic Algorithms (GAs), the authors find strategies that produce the most favorable profile for players. GAs are from optimization methods that have shown their great power in the learning area. Using these meta-heuristics, the authors find the price and QoS that maximize the profit for each SP and illustrate the corresponding strategy in Nash Equilibrium (NE). They also show the influence of some parameters of the problem on this equilibrium.

A Review of Multi-objective Optimization: Methods and Algorithms in Mechanical Engineering Problems

2021 ◽  
Author(s):  
João Luiz Junho Pereira ◽  
Guilherme Antônio Oliver ◽  
Matheus Brendon Francisco ◽  
Sebastião Simões Cunha ◽  
Guilherme Ferreira Gomes
Keyword(s):  
Mechanical Engineering ◽  
Optimization Methods ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Engineering Problems

OntoQuery-An Evolutionary Approach for Query Formation and Information Retrieval

2011 ◽  
pp. 140-160
Author(s):  
Sheng-Uei Guan ◽  
Chang Ching Chng ◽  
Fangming Zhu
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Information Retrieval ◽  
Query Optimization ◽  
Formation Process ◽  
Relevance Feedback ◽  
Fitness Function ◽  
Optimization Techniques ◽  
Retrieval Scheme

This chapter proposes the establishment of OntoQuery in an m-commerce agent framework. OntoQuery represents a new query formation approach that combines the usage of ontology and keywords. This approach takes advantage of the tree pathway structure in ontology to form queries visually and efficiently. Also, it uses keywords to complete the query formation process more efficiently. Present query optimization techniques like relevance feedback use expensive iterations. The proposed information retrieval scheme focuses on using genetic algorithms to improve computational effectiveness. Mutations are done on queries formed in the earlier part by replacing terms with synonyms. Query optimization techniques used include query restructuring by logical terms and numerical constraints replacement. Also, the fitness function of the genetic algorithm is defined by three elements, number of documents retrieved, quality of documents, and correlation of queries. The number and quality of documents retrieved give the basic strength of a mutated query.

Sign in / Sign up

Export Citation Format

Share Document