scholarly journals Research on Social Talent Governance Based on Genetic Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yan Zeng
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Convergence Rate ◽  
Single Point ◽  
Optimal Solution ◽  
Implementation Process ◽  
Recall Rate ◽  
Acceptance Probability ◽  
High Data ◽  
Calculation Results

The existing social talent governance algorithms have a number of issues such as slow convergence rate, relatively low data accuracy, recall rate, and low anti-interference. To address these problems, this paper proposes a research on social talent governance algorithm based on genetic algorithm. We discuss the difference between the traditional and the genetic algorithms and determine the implementation process of genetic algorithm. On this basis, the excellent individuals are determined by independent computing fitness, and the initialization population is designed according to the individual similarity threshold. After the population is defined, the roulette and deterministic sampling selection method are integrated to clarify the selection calculation process. Based on the calculation results, we design the crossover operator by segmented single-point crossover between individuals. The mutation operator is designed by segmented mutation of different gene segments according to the calculation results. The results are incorporated into the simulated annealing acceptance probability to conduct simulated annealing for the individuals after the cross-mutation operation and set relevant conditions after the end of the algorithm. We seek the optimal solution of the data within the number of iterations and finally realize the whole process of social talent governance algorithm. The experimental results show that the proposed algorithm has fast convergence rate, high data precision and recall, and has certain feasibility.

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 About the influence of features of fitness-functions on the convergence of the genetic algorithm

2020 ◽  
pp. 362-367
Author(s):  
I.O. Lukianov ◽  
◽  
F.A. Lytvynenko ◽  
Keyword(s):  
Genetic Algorithm ◽  
Convergence Rate ◽  
Random Effect ◽  
Optimal Solution ◽  
Computer Experiments ◽  
Effective Control ◽  
Fitness Functions ◽  
Parallel Version ◽  
Adaptive Capabilities ◽  
Significant Factors

The adaptive capabilities of a parallel version of a multipopulation genetic algorithm are considered depending on the characteristics of certain classes of fitness-functions. Ways are proposed to increase the rate of convergence to the optimal solution based on effective control of algorithm parameters and strategies for the exchange of chromosome-solutions between populations. The results of computer experiments with the optimization of fitness-functions with various ratios of insignificant and significant factors are presented. The dependence of the convergence rate of the algorithm in the presence of a random effect on the values of fitness-functions is studied.

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.

Application of Genetic Algorithm for Fault Detection in Cracked Composite Structure

2014 ◽  
Author(s):  
A. K. Dash ◽  
D. K. Agarwalla ◽  
H. C. Das ◽  
M. K. Pradhan ◽  
S. K. Bhuyan
Keyword(s):  
Genetic Algorithm ◽  
Single Point ◽  
Optimal Solution ◽  
Search Space ◽  
Mode Shapes ◽  
Fault Identification ◽  
Reinforced Composite ◽  
Glass Fiber Reinforced ◽  
Intelligent Genetic Algorithm ◽  
Non Destructive

Machines and beam like structures used in various industries require continuous monitoring for the fault identification for ensuring uninterrupted service. Different non-destructive techniques (NDT) are generally used for this purpose, but they are costly and time consuming. Vibration based methods can be useful to detect cracks in structures using various artificial intelligence (AI) techniques. The modal parameters from the dynamic response of the structure are used for the purpose. In the current analysis, the vibration characteristics of a glass fiber reinforced composite cracked cantilever beam having different crack locations and depths have been studied. Numerical and finite element methods have been used to extract the diagnostic indices (natural frequencies, mode shapes) from cracked and intact beam structure. An intelligent Genetic Algorithm (GA) based controller has been designed to automate the fault identification and location process. Single point crossover and in some cases mutation procedure have been followed to find out the optimal solution from the search space. The controller has been trained in offline mode using the simulation and experimental results (initial data pool) under various healthy and faulty conditions of the structure. The outcome from the developed controller shows that the system could not only detect the cracks but also predict their locations and severities. Good agreement between the simulation, experimental and GA controller results confirms the effectiveness of the proposed controller.

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.

Research on Parallel Test Task Scheduling Based on Improved Genetic Algorithm and Petri Net

2014 ◽  
Vol 543-547 ◽  
pp. 1119-1122
Author(s):  
Pei Pei Chen ◽  
Bao Mei Qiu ◽  
Hao Ba
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Task Scheduling ◽  
Petri Net ◽  
Simulated Annealing Algorithm ◽  
Optimal Path ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Test Task ◽  
Parallel Test

Parallel test task scheduling is always complex and difficult to optimize. Aiming at this problem, an improved Genetic Simulated Annealing Algorithm based on Petri net is posed to. At first, a Petri net model is established for the system, then the transition sequence is used as task scheduling sequence set path. Genetic Algorithm is introduced in order to get the optimal path. In the process of search, the sequence will be able to stimulate changes as chromosomes, selection, crossover and mutation. In order to prevent premature convergence of the algorithm appears, into the phenomenon of local optimal solution, the individual needs simulated annealing operation, and finally, we can get the shortest time to complete the test task scheduling sequence.

scholarly journals An Effective Heuristic-Based Approach for Partitioning

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xibin Zhao ◽  
Hehua Zhang ◽  
Yu Jiang ◽  
Songzheng Song ◽  
Xun Jiao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Embedded System ◽  
Simulated Annealing Algorithm ◽  
Optimal Solution ◽  
Local Optimal Solution ◽  
Annealing Algorithm ◽  
The Cost ◽  
Combined Algorithm ◽  
Search Capability

As being one of the most crucial steps in the design of embedded systems, hardware/software partitioning has received more concern than ever. The performance of a system design will strongly depend on the efficiency of the partitioning. In this paper, we construct a communication graph for embedded system and describe the delay-related constraints and the cost-related objective based on the graph structure. Then, we propose a heuristic based on genetic algorithm and simulated annealing to solve the problem near optimally. We note that the genetic algorithm has a strong global search capability, while the simulated annealing algorithm will fail in a local optimal solution easily. Hence, we can incorporate simulated annealing algorithm in genetic algorithm. The combined algorithm will provide more accurate near-optimal solution with faster speed. Experiment results show that the proposed algorithm produce more accurate partitions than the original genetic algorithm.

scholarly journals BOOSTING SIMULATED ANNEALING WITH FITNESS LANDSCAPE PARAMETERS FOR BETTER OPTIMALITY

2015 ◽  
pp. 107-112
Author(s):  
Sunanda Gupta ◽  
Sakshi Arora
Keyword(s):  
Simulated Annealing ◽  
Probability Function ◽  
Fitness Landscape ◽  
Optimal Solution ◽  
Search Space ◽  
Solution Quality ◽  
Acceptance Probability ◽  
Evaluation Strategies ◽  
Fitness Distance Correlation ◽  
The Cost

Multi Dimensional Knapsack problem is a widely studied NP hard problem requiring extensive processing to achieve optimality. Simulated Annealing (SA) unlike other is capable of providing fast solutions but at the cost of solution quality. This paper focuses on making SA robust in terms of solution quality while assuring faster convergence by incorporating effective fitness landscape parameters. For this it proposes to modify the ‘Acceptance Probability’ function of SA. The fitness landscape evaluation strategies are embedded to Acceptance Probability Function to identify the exploitation and exploration of the search space and analyze the behavior on the performance of SA. The basis of doing so is that SA in the process of reaching optimality ignores the association between the search space and fitness space and focuses only on the comparison of current solution with optimal solution on the basis of temperature settings at that point. The idea is implemented in two different ways i.e. by making use of Fitness Distance Correlation and Auto Correlation functions. The experiments are conducted to evaluate the resulting SA on the range of MKP instances available in the OR library.

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