Comparative study of genetic algorithm and simulated annealing for optimal tolerance design formulated with discrete and continuous variables

2005 ◽  
Vol 219 (10) ◽  
pp. 735-758 ◽  
Author(s):  
P K Sing ◽  
S C Jain ◽  
P K Jain
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Design Problem ◽  
Optimal Solution ◽  
Solution Space ◽  
Optimization Techniques ◽  
Manufacturing Processes ◽  
Tolerance Design ◽  
Solution Algorithms ◽  
Selection Of

Optimal tolerance design has been the focus of extensive research for a few decades. This has resulted in several formulations and solution algorithms for systematic tolerance design considering various aspects. Availability of different alternative manufacturing processes or machines for realization of a dimension is frequently encountered. In such cases optimal tolerance design must also consider optimal selection of a set of manufacturing processes or machines as appropriate. Such a non-linear multivariate optimal tolerance design problem results in a combinatorial and multi-modal solution space. Optimal solution of this advanced tolerance design problem is difficult using traditional optimization techniques. The problem formulation becomes more complex with simultaneous selection of design and manufacturing tolerances. The focus of the current research is on the optimal solution of this advanced and complex tolerance design problem. Genetic algorithm and simulated annealing as non-traditional global optimization techniques have been used to obtain the solution. Application of the solution techniques has been demonstrated with the help of appropriate examples. Comparison of the results establishes that the genetic algorithm is the superior of the two approaches.

Parallel Dynamic Optimization of Steel Risers

2010 ◽  
Vol 133 (1) ◽  
Author(s):  
Rafael L. Tanaka ◽  
Clóvis de A. Martins
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Natural Selection ◽  
Dynamic Optimization ◽  
Solution Space ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Objective Functions ◽  
Definition Of

This paper addresses the use of optimization techniques in the design of a steel riser. Two methods are used: the genetic algorithm, which imitates the process of natural selection, and the simulated annealing, which is based on the process of annealing of a metal. Both of them are capable of searching a given solution space for the best feasible riser configuration according to predefined criteria. Optimization issues are discussed, such as problem codification, parameter selection, definition of objective function, and restrictions. A comparison between the results obtained for economic and structural objective functions is made for a case study. Optimization method parallelization is also addressed.

Application of a Genetic Algorithm to Concept Variant Selection

2006 ◽  
Author(s):  
Ryan S. Hutcheson ◽  
Robert L. Jordan ◽  
Robert B. Stone ◽  
Janis P. Terpenny ◽  
Xiaomeng Chang
Keyword(s):  
Genetic Algorithm ◽  
Product Development ◽  
Solution Space ◽  
Variant Selection ◽  
Functional Modeling ◽  
Detailed Design ◽  
Modeling Techniques ◽  
Break Even Point ◽  
Future Work ◽  
Selection Of

This paper outlines a framework for applying a genetic algorithm to the selection of component variants between the conceptual and detailed design stages of product development. A genetic algorithm (GA) is defined for the problem and an example is presented that demonstrates its application and usefulness. Functional modeling techniques are used to formulate the design problem and generate the chromosomes that are evaluated with the algorithm. In the presented example, suitable GA parameters and the break-even point where the GA surpassed an enumerated search of the same solution space were found. Recommend uses of the GA along with limitations of the method and future work are presented as well.

scholarly journals Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization

2017 ◽  
Vol 1 (2) ◽  
pp. 82 ◽  
Author(s):  
Tirana Noor Fatyanosa ◽  
Andreas Nugroho Sihananto ◽  
Gusti Ahmad Fanshuri Alfarisy ◽  
M Shochibul Burhan ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Function Optimization ◽  
Non Linear ◽  
Speed Up ◽  
Linear Problems

The optimization problems on real-world usually have non-linear characteristics. Solving non-linear problems is time-consuming, thus heuristic approaches usually are being used to speed up the solution’s searching. Among of the heuristic-based algorithms, Genetic Algorithm (GA) and Simulated Annealing (SA) are two among most popular. The GA is powerful to get a nearly optimal solution on the broad searching area while SA is useful to looking for a solution in the narrow searching area. This study is comparing performance between GA, SA, and three types of Hybrid GA-SA to solve some non-linear optimization cases. The study shows that Hybrid GA-SA can enhance GA and SA to provide a better result

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.

Modeling and Optimization for Steel Grade Selection Based on Hierarchical Genetic Algorithm

2012 ◽  
Vol 505 ◽  
pp. 203-208
Author(s):  
Jian Yi ◽  
Bin Du ◽  
Qiang Liu ◽  
Yun Lin ◽  
Ke Wei Huang
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Solution Space ◽  
Difficult Problem ◽  
Steel Grade ◽  
Reasonable Time ◽  
Steel Making ◽  
Steel Grades ◽  
The Mathematical Model ◽  
Hierarchical Genetic Algorithm

In steel-making process, when a furnace is charged, there are many optional steel grades for each slab. It is a difficult problem to select the appropriate steel grade for each slab. In this paper, based on the analysis of technics constraints in steel-making process, the steel grade intensivism problem is described, and the mathematical model is also established. To solve the above problem, a newly designed hierarchical genetic algorithm is proposed, where the hierarchical manner is used to decrease the solution space. The effectiveness of the approach is demonstrated by a simulation. The optimal solution can be obtained in reasonable time, which will be helpful to decrease the scraps between two steel grades while casting, to decrease the sum of surplus, and eventually to cut down the stock.

scholarly journals Optimization and Design of Hammerheads and Fenders on Scrap Metal Shredders Based on Improved Genetic Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
He Tian ◽  
Guoqiang Wang ◽  
Kangkang Sun ◽  
Zeren Chen ◽  
Chuliang Yan ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Dynamic Balance ◽  
Optimal Solution ◽  
Scrap Metal ◽  
Improved Genetic Algorithm ◽  
Multiple Traveling Salesman Problem ◽  
Local Optimal Solution ◽  
Annealing Algorithm

Dynamic unbalance force is an important factor affecting the service life of scrap metal shredders (SMSs) as the product of mass error. Due to the complexity of hammerheads arrangement, it is difficult to take all the parts of the hammerhead into account in the traditional methods. A novel optimization algorithm combining genetic algorithm and simulated annealing algorithm is proposed to improve the dynamic balance of scrap metal shredders. The optimization of hammerheads and fenders on SMS in this paper is considered as a multiple traveling salesman problem (MTSP), which is a kind of NP-hard problem. To solve this problem, an improved genetic algorithm (IGA) combined with the global optimization characteristics of genetic algorithm (GA) and the local optimal solution of simulated annealing algorithm (SA) is proposed in this paper, which adopts SA in the process of selecting subpopulations. The optimization results show that the resultant force of the shredder central shaft by using IGA is less than the traditional metaheuristic algorithm, which greatly improves the dynamic balance of the SMS. Validated via ADAMS simulation, the results are in good agreement with the theoretical optimization analysis.

scholarly journals A COMPARISON OF SIMULATED ANNEALING, GENETIC ALGORITHM AND PARTICLE SWARM OPTIMIZATION IN OPTIMAL FIRST-ORDER DESIGN OF INDOOR TLS NETWORKS

2017 ◽  
Vol IV-2/W4 ◽  
pp. 75-82 ◽  
Author(s):  
F. Jia ◽  
D. Lichti
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Network Design Problem ◽  
Design System ◽  
Swarm Optimization ◽  
First Order

The optimal network design problem has been well addressed in geodesy and photogrammetry but has not received the same attention for terrestrial laser scanner (TLS) networks. The goal of this research is to develop a complete design system that can automatically provide an optimal plan for high-accuracy, large-volume scanning networks. The aim in this paper is to use three heuristic optimization methods, simulated annealing (SA), genetic algorithm (GA) and particle swarm optimization (PSO), to solve the first-order design (FOD) problem for a small-volume indoor network and make a comparison of their performances. The room is simplified as discretized wall segments and possible viewpoints. Each possible viewpoint is evaluated with a score table representing the wall segments visible from each viewpoint based on scanning geometry constraints. The goal is to find a minimum number of viewpoints that can obtain complete coverage of all wall segments with a minimal sum of incidence angles. The different methods have been implemented and compared in terms of the quality of the solutions, runtime and repeatability. The experiment environment was simulated from a room located on University of Calgary campus where multiple scans are required due to occlusions from interior walls. The results obtained in this research show that PSO and GA provide similar solutions while SA doesn’t guarantee an optimal solution within limited iterations. Overall, GA is considered as the best choice for this problem based on its capability of providing an optimal solution and fewer parameters to tune.

Gear Design Optimization Algorithms: A Review

2020 ◽  
Vol 27 (1) ◽  
Author(s):  
YO Usman ◽  
PO Odion ◽  
EO Onibere ◽  
AY Egwoh
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Design Optimization ◽  
Ant Colony Optimization ◽  
Optimization Algorithms ◽  
Optimization Techniques ◽  
Ant Colony ◽  
Design Parameters ◽  
Gear Design ◽  
Complex Design

Gearing is one of the most efficient methods of transmitting power from a source to its application with or without change of speed or direction. Gears are round mechanical components with teeth arranged in their perimeter. Gear design is complex design that involves many design parameters and tables, finding an optimal or near optimal solution to this complex design is still a major challenge. Different optimization algorithms such as Genetic Algorithm (GA), Simulated Annealing, Ant-Colony Optimization, and Neural Network etc., have been used for design optimization of the gear design problems. This paper focuses on the review of the optimization techniques used for gear design optimization with a view to identifying the best of them. Nowadays, the method used for the design optimization of gears is the evolutionary algorithm specifically the genetic algorithm which is based on the evolution idea of natural selection. The study revealed that GA. has the ability to find optimal solutions in a short time of computation by making a global search in a large search space. Keywords: Firefly Algorithm, Ant-Colony Optimization, Simulated Annealing, Genetic Algorithm, Gear design, Optimization, Particle Swarm Optimization Algorithm

Yarn Selection Based on Simulated Annealing Genetic Algorithm

2014 ◽  
Vol 687-691 ◽  
pp. 1548-1551
Author(s):  
Li Jiang ◽  
Gang Feng Yan ◽  
Zhen Fan
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Original Image ◽  
Separation Algorithm ◽  
Global Optimal Solution ◽  
Simulated Annealing Genetic Algorithm ◽  
Color Separation ◽  
Global Optimal

Aiming at the bad performance when achieve rich colors of fabric with very limited yarns in the traditional woven industry, the paper comes up with a solution of selecting yarn from a set of yarns based on SAGA(simulated annealing genetic algorithm). In order to reduce the computational complexity, original image is compressed based on clustering algorithm. And the original yarns is divided into four regions based on color separation algorithm to narrow the feasible area. The result of experiments show that image compression and yarns division can greatly improve the speed of SAGA, and SAGA can effectively converges to global optimal solution.

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