scholarly journals Hybrid Optimization Based Mathematical Procedure for Dimensional Synthesis of Slider-Crank Linkage

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1581
Author(s):  
Alfonso Hernández ◽  
Aitor Muñoyerro ◽  
Mónica Urízar ◽  
Enrique Amezua
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Linear Equations ◽  
Optimization Procedure ◽  
Optimization Techniques ◽  
Dimensional Synthesis ◽  
Hybrid Strategy ◽  
Dimensional Parameters ◽  
Optimization Methodology ◽  
Crank Mechanism

In this paper, an optimization procedure for path generation synthesis of the slider-crank mechanism will be presented. The proposed approach is based on a hybrid strategy, mixing local and global optimization techniques. Regarding the local optimization scheme, based on the null gradient condition, a novel methodology to solve the resulting non-linear equations is developed. The solving procedure consists of decoupling two subsystems of equations which can be solved separately and following an iterative process. In relation to the global technique, a multi-start method based on a genetic algorithm is implemented. The fitness function incorporated in the genetic algorithm will take as arguments the set of dimensional parameters of the slider-crank mechanism. Several illustrative examples will prove the validity of the proposed optimization methodology, in some cases achieving an even better result compared to mechanisms with a higher number of dimensional parameters, such as the four-bar mechanism or the Watt’s mechanism.

An Energy Formulation for Parametric Size and Shape Optimization of Compliant Mechanisms

1999 ◽  
Vol 121 (2) ◽  
pp. 229-234 ◽  
Author(s):  
J. A. Hetrick ◽  
S. Kota
Keyword(s):  
Shape Optimization ◽  
Compliant Mechanisms ◽  
Compliant Mechanism ◽  
Optimization Procedure ◽  
Stress Constraints ◽  
Optimization Techniques ◽  
Mechanical Transmission ◽  
Dimensional Synthesis ◽  
Size And Shape ◽  
Mechanical Devices

Compliant mechanisms are jointless mechanical devices that take advantage of elastic deformation to achieve a force or motion transformation. An important step toward automated design of compliant mechanisms has been the development of topology optimization techniques. The next logical step is to incorporate size and shape optimization to perform dimensional synthesis of the mechanism while simultaneously considering practical design specifications such as kinematic and stress constraints. An improved objective formulation based on maximizing the energy throughput of a linear static compliant mechanism is developed considering specific force and displacement operational requirements. Parametric finite element beam models are used to perform the size and shape optimization. This technique allows stress constraints to limit the maximum stress in the mechanism. In addition, constraints which restrict the kinematics of the mechanism are successfully applied to the optimization problem. Resulting optimized mechanisms exhibit efficient mechanical transmission and meet kinematic and stress requirements. Several examples are given to demonstrate the effectiveness of the optimization procedure.

Weight Optimization of Steel Frames Using Genetic Algorithm

2002 ◽  
Vol 5 (2) ◽  
pp. 99-111 ◽  
Author(s):  
Ribelito F. Torregosa ◽  
Worsak Kanok-Nukulchai
Keyword(s):  
Genetic Algorithm ◽  
Steel Frames ◽  
Optimization Procedure ◽  
Optimization Techniques ◽  
Weight Optimization ◽  
Discrete Variables ◽  
Design Problems ◽  
Plane Frames ◽  
Population Sizes ◽  
A New Technique

Genetic Algorithm (GA) is a new technique in optimization procedure that works best in design problems with discrete variables. It employs the survival of the fittest philosophy in determining the optimum combination. GA optimization procedure is applied to weight optimization of steel plane frames subjected to different load cases. Database of steel beam sizes is provided as the discrete variables. Both elitist and non-elitist search procedures are used to optimize the total weight of steel frames. Crossover types used are 20- and 50-percent uniform. Optimization result using population sizes 10, 20, and 40 are compared. Elitist search procedure showed superior results when compared to non-elitist for higher population sizes search because of its faster convergence rate. Performance of non-elitist is superior when using lesser population sizes. To examine the performance of genetic algorithms, case studies are conducted by varying material groups and the results are compared with the results from other optimization techniques. Genetic optimization showed superior results when compared to other techniques especially to problems with few material groupings.

scholarly journals Application of Genetic Algorithm for Binary Optimization of Microstrip Antennas: A Review

2021 ◽  
Vol 5 (4) ◽  
pp. 315-333
Author(s):  
Jeevani W. Jayasinghe ◽  
Keyword(s):  
Genetic Algorithm ◽  
Performance Enhancement ◽  
Optimization Procedure ◽  
Microstrip Antennas ◽  
Optimization Techniques ◽  
Global Optimizer ◽  
Trial And Error Method ◽  
Miniature Antennas ◽  
Local Optimizer ◽  
Error Method

<abstract> <p>Researchers have proposed applying optimization techniques to improve performance of microstrip antennas (MSAs) in terms of bandwidth, radiation characteristics, polarization, directivity and size. The drawbacks of the conventional MSAs can be overcome by optimizing the antenna parameters while keeping a compact configuration. Applying a global optimizer is a better technique than using a local optimizer or a trial and error method for performance enhancement. This paper discusses genetic algorithm (GA) optimization of microstrip antennas presented by the antenna research community. The GA optimization procedure, antenna parameters optimized by using GA and the optimization objectives are presented by reviewing the literature. Further, evolution of GA in the field of MSAs and its significance are explored. Application of GA optimization to design broadband, multiband, high-directivity and miniature antennas is demonstrated with the support of several case studies giving an insight for further developments in the field.</p> </abstract>

scholarly journals Swine Influenza Mimicked Optimization (SIMO)

2020 ◽  
Vol 9 (5) ◽  
pp. 937-943
Keyword(s):  
Resonant Frequency ◽  
Patch Antenna ◽  
Fitness Function ◽  
Swine Influenza ◽  
Microstrip Patch Antenna ◽  
Optimization Techniques ◽  
Treatment Control ◽  
Microstrip Patch ◽  
Global Optima ◽  
Dimensional Parameters

In this paper, Swine Influenza Model based Optimization (SIMBO) is modified as Swine Influenza mimicked Optimization (SIMO) and explored through the application in the antenna design. Different researchers proposed different optimization techniques and algorithms so that the accuracy of optima will be improved. For fast convergence and better accuracy SIMBO with its variants such as treatment (SIMBOT), vaccination (SIMBOV) and quarantine (SIMBOQ). We have modified Swine Influenza model to propose new technique known as SIMO. In SIMO, health of individuals is improved by synchronizing vaccination, quarantine and treatment. Test 1 and Test 2 are used before vaccine and quarantine respectively to adjust antiviral dose dynamically during its treatment. The additional feature in SIMO such as infection factor control and improve the health of individuals gradually without checking the state/health at every day. SIMO changes solution indirectly during treatment and directly via vaccination and quarantine. The time varying treatment control the health of individuals in defined days such that individual will not feel illness for longer duration. An available closed form formula of equilateral triangular microstrip patch antenna resonant frequency has been used to form the fitness function in order to optimize the dimensional parameters. The proposed technique is used to determine the accurate resonant frequency of equilateral triangular microstrip patch antenna of different dimension. In order to optimize the technique with speedy convergence and effective precision of global optima, this method also used for the optimization of standard benchmark functions.

scholarly journals Performance Analysis of Simulated Annealing and Genetic Algorithm on systems of linear equations

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1297
Author(s):  
Md. Shabiul Islam ◽  
Most Tahamina Khatoon ◽  
Kazy Noor-e-Alam Siddiquee ◽  
Wong Hin Yong ◽  
Mohammad Nurul Huda
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Real Time ◽  
Large Scale ◽  
Fitness Function ◽  
Random Search ◽  
Linear Equations ◽  
Simultaneous Equations ◽  
Function Evaluation ◽  
Solution Sets

Problem solving and modelling in traditional substitution methods at large scale for systems using sets of simultaneous equations is time consuming. For such large scale global-optimization problem, Simulated Annealing (SA) algorithm and Genetic Algorithm (GA) as meta-heuristics for random search technique perform faster. Therefore, this study applies the SA to solve the problem of linear equations and evaluates its performances against Genetic Algorithms (GAs), a population-based search meta-heuristic, which are widely used in Travelling Salesman problems (TSP), Noise reduction and many more. This paper presents comparison between performances of the SA and GA for solving real time scientific problems. The significance of this paper is to solve the certain real time systems with a set of simultaneous linear equations containing different unknown variable samples those were simulated in Matlab using two algorithms-SA and GA. In all of the experiments, the generated random initial solution sets and the random population of solution sets were used in the SA and GA respectively. The comparison and performances of the SA and GA were evaluated for the optimization to take place for providing sets of solutions on certain systems. The SA algorithm is superior to GA on the basis of experimentation done on the sets of simultaneous equations, with a lower fitness function evaluation count in MATLAB simulation. Since, complex non-linear systems of equations have not been the primary focus of this research, in future, performances of SA and GA using such equations will be addressed. Even though GA maintained a relatively lower number of average generations than SA, SA still managed to outperform GA with a reasonably lower fitness function evaluation count. Although SA sometimes converges slowly, still it is efficient for solving problems of simultaneous equations in this case. In terms of computational complexity, SA was far more superior to GAs.

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.

Size and Shape Optimization of Compliant Mechanisms: An Efficiency Formulation

1998 ◽  
Author(s):  
Joel A. Hetrick ◽  
Sridhar Kota
Keyword(s):  
Shape Optimization ◽  
Compliant Mechanisms ◽  
Compliant Mechanism ◽  
Optimization Procedure ◽  
Stress Constraints ◽  
Mechanical Efficiency ◽  
Optimization Techniques ◽  
Mechanical Transmission ◽  
Dimensional Synthesis ◽  
Size And Shape

Abstract Compliant mechanisms are jointless mechanical devices that take advantage of elastic deformation to achieve a force or motion transformation. A milestone toward systematic design of compliant mechanisms has been the development of topology optimization techniques. The next logical step is to incorporate size and shape optimization to identify the exact dimensional form of the mechanism. A new objective formulation based on maximizing the mechanical efficiency of a compliant mechanism is developed in order to perform the size and shape optimization. An advantage of this formulation is that precise control over the mechanism’s mechanical or geometric advantage can be enforced during optimization. Finite element beam models are used to perform dimensional synthesis of planar compliant mechanisms. This technique allows stress constraints to limit the maximum stress in the mechanism which improves the mechanism’s durability and flexibility. Resulting optimized mechanisms exhibit efficient mechanical transmission and meet kinematic and stress requirements. Several examples are given to demonstrate the effectiveness of the optimization procedure.

scholarly journals Total Internal Reflection-Based High Efficiency Grating Design for a Metal-Free Polarizing Filter Applications Using Hybrid Optimization Procedure

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
ChaBum Lee
Keyword(s):  
Genetic Algorithm ◽  
Taguchi Method ◽  
Total Internal Reflection ◽  
Optimization Problems ◽  
Optimization Procedure ◽  
Optimization Techniques ◽  
Internal Reflection ◽  
Hybrid Optimization ◽  
Design Parameters ◽  
Metal Free

This paper presents a fast and rigorous design method for grating-based metal-free polarizing filter applications using two-step hybrid optimization techniques. Grating structures utilizing the total internal reflection in a lamellar configuration were used to achieve metal-free solution, which is a key technology in the chirped pulse amplification for high power laser system. Here two polarizing filters were designed: polarization sensitive and polarization insensitive. Those polarization performances were characterized by the rigorous coupled-wave analysis (RCWA), and the design parameters of grating structures, pitch, depth, and filling factor were optimized by two-step hybrid optimization procedure because the diffraction characteristics of grating-based polarizing filters are highly sensitive to small changes in design parameters. The Taguchi method is incorporated into selection process in the genetic algorithm, which indicates that the Taguchi method optimizes the design parameters in a coarse manner, and then, coarsely optimized parameters are finely optimized using the genetic algorithm. Therefore the proposed method could solve global numerical optimization problems with continuous variables. The proposed two-step hybrid optimization algorithm could effectively optimize the grating structures for the purpose of polarization filter applications, and the optimized grating structures could selectively filter the incident light up to 99.8% as to TE or TM waves.

Application of Genetic Algorithms to the Synthesis of Riser Configurations

2003 ◽  
Author(s):  
Luciano T. Vieira ◽  
Beatriz de S. L. P. de Lima ◽  
Alexandre G. Evsukoff ◽  
Breno P. Jacob
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Optimization Techniques ◽  
Objective Functions ◽  
Random Choice ◽  
Large Space ◽  
Steel Catenary Riser ◽  
Deep Waters ◽  
Optimization Methodology

The purpose of this work is to describe the application of Genetic Algorithms in the search of the best configuration of catenary riser systems in deep waters. Particularly, an optimization methodology based on genetic algorithms is implemented on a computer program, in order to seek an optimum geometric configuration for a steel catenary riser in a lazy-wave configuration. This problem is characterized by a very large space of possible solutions; the use of traditional methods is an exhaustive work, since there is a large number of variables and parameters that define this type of system. Genetic algorithms are more robust than the more commonly used optimization techniques. They use random choice as a tool to guide a search toward regions of the search space with likely improvements. Some differences such as the coding of the parameter set, the search from a population of points, the use of objective functions and randomized operators are factors that contribute to the robustness of a genetic algorithm and result in advantages over traditional techniques. The implemented methodology has as baseline one or more criteria established by the experience of the offshore engineer. The implementation of an intelligent methodology oriented specifically to the optimization and synthesis of riser configurations will not only facilitate the work of manipulating a huge mass of data, but also assure the best alternative between all the possible ones, searching in a much larger space of possible solutions than classical methods.

scholarly journals Optimizing Software Modularity with Minimum Possible Variations

2018 ◽  
Vol 29 (1) ◽  
pp. 1135-1150
Author(s):  
Amarjeet Prajapati ◽  
Jitender Kumar Chhabra
Keyword(s):  
Genetic Algorithm ◽  
Software Maintenance ◽  
Fitness Function ◽  
Optimization Technique ◽  
Quality Criteria ◽  
Optimization Techniques ◽  
Software Systems ◽  
Design Quality ◽  
Early Stages ◽  
Software Modularity

Abstract Poor design choices at the early stages of software development and unprincipled maintenance practices usually deteriorate software modularity and subsequently increase system complexity. In object-oriented software, improper distribution of classes among packages is a key factor, responsible for modularity degradation. Many optimization techniques to improve the software modularity have been proposed in the literature. The focus of these optimization techniques is to produce modularization solutions by optimizing different design quality criteria. Such modularization solutions are good from the different aspect of quality; however, they require huge modifications in the existing modular structure to realize the suggested solution. Thus these techniques are costly and time consuming if applied at early stages of software maintenance. This paper proposes a search-based optimization technique to improve the modularity of the software system with minimum possible variation between the existing and produced modularization solution. To this contribution, a penalized fitness function, namely, penalized modularization quality, is designed in terms of modularization quality and the Move or Join Effectiveness Measure metric. Furthermore, this fitness function is used in both single-objective genetic algorithm (SGA) and multi-objective genetic algorithm (MGA) to generate the modularization. The effectiveness of the proposed remodularization approach is evaluated over five open-source and three random generated software systems. The experimentation results show that the proposed approach is able to generate modularization solutions with improved quality along with lesser perturbation compared to their non-penalty counterpart and at the same time it performs better with the MGA compared to the SGA. The proposed approach can be very useful, especially when total remodularization is not feasible/desirable due to lack of time or high cost.

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