Genetic Algorithms Study in Switch Electrical Appliances Electric Arc Feature Extraction

2013 ◽  
Vol 365-366 ◽  
pp. 165-169
Author(s):  
Jing Sheng Yu ◽  
Li Li ◽  
Ting Liu
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Optimal Solution ◽  
Electric Arc ◽  
Network Computing ◽  
Electrical Arc ◽  
Pool Formation ◽  
Extraction Model ◽  
Crossover And Mutation

The genetic algorithm applied to switch electrical appliances electric arc feature extraction, based on genetic algorithm, the switch electrical arc feature extraction model was established. The initial pool formation, evaluation individual, reproduction, crossover and mutation have done a detailed representation. This model can eliminate the slow convergence and so easy to fall into the local minimum shortcomings of BP neural network computing graphics weights. The experiment showed that genetic algorithm can better converge to the global optimal solution, more in line with the arc Feature Extraction fact, and more effectively improving the quality of graphics extraction.

scholarly journals Service Composition in IoT using Genetic algorithm and Particle swarm optimization

2020 ◽  
Vol 10 (1) ◽  
pp. 56-64 ◽  
Author(s):  
Neeti Kashyap ◽  
A. Charan Kumari ◽  
Rita Chhikara
Keyword(s):  
Genetic Algorithm ◽  
Quality Of Service ◽  
Particle Swarm Optimization ◽  
Service Composition ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Critical Issue ◽  
Swarm Optimization

AbstractWeb service compositions are commendable in structuring innovative applications for different Internet-based business solutions. The existing services can be reused by the other applications via the web. Due to the availability of services that can serve similar functionality, suitable Service Composition (SC) is required. There is a set of candidates for each service in SC from which a suitable candidate service is picked based on certain criteria. Quality of service (QoS) is one of the criteria to select the appropriate service. A standout amongst the most important functionality presented by services in the Internet of Things (IoT) based system is the dynamic composability. In this paper, two of the metaheuristic algorithms namely Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are utilized to tackle QoS based service composition issues. QoS has turned into a critical issue in the management of web services because of the immense number of services that furnish similar functionality yet with various characteristics. Quality of service in service composition comprises of different non-functional factors, for example, service cost, execution time, availability, throughput, and reliability. Choosing appropriate SC for IoT based applications in order to optimize the QoS parameters with the fulfillment of user’s necessities has turned into a critical issue that is addressed in this paper. To obtain results via simulation, the PSO algorithm is used to solve the SC problem in IoT. This is further assessed and contrasted with GA. Experimental results demonstrate that GA can enhance the proficiency of solutions for SC problem in IoT. It can also help in identifying the optimal solution and also shows preferable outcomes over PSO.

TEST DATA GENERATION FOR SOFTWARE TESTING BASED ON QUANTUM-INSPIRED GENETIC ALGORITHM

2013 ◽  
Vol 12 (01) ◽  
pp. 1350004 ◽  
Author(s):  
CHENGYING MAO ◽  
XINXIN YU
Keyword(s):  
Genetic Algorithm ◽  
Software Testing ◽  
Test Data ◽  
Optimal Solution ◽  
Search Space ◽  
Structural Testing ◽  
Test Data Generation ◽  
Data Generation ◽  
Quantum Bit

The quality of test data has an important impact on the effect of software testing, so test data generation has always been a key task for finding the potential faults in program code. In structural testing, the primary goal is to cover some kinds of structure elements with some specific inputs. Search-based test data generation provides a rational way to handle this difficult problem. In the past, some well-known meta-heuristic search algorithms have been successfully utilized to solve this issue. In this paper, we introduce a variant of genetic algorithm (GA), called quantum-inspired genetic algorithm (QIGA), to generate the test data with stronger coverage ability. In this new algorithm, the traditional binary bit is replaced by a quantum bit (Q-bit) to enlarge the search space so as to avoid falling into local optimal solution. On the other hand, some other strategies such as quantum rotation gate and catastrophe operation are also used to improve algorithm efficiency and quality of test data. In addition, experimental analysis on eight real-world programs is performed to validate the effectiveness of our method. The results show that QIGA-based method can generate test data with higher coverage in much smaller convergence generations than GA-based method. More importantly, our proposed method is more robust for algorithm parameter change.

Satellite Module Layout Design Based on Adaptive Bee Evolutionary Genetic Algorithm

2014 ◽  
Vol 538 ◽  
pp. 193-197
Author(s):  
Jian Jiang Su ◽  
Chao Che ◽  
Qiang Zhang ◽  
Xiao Peng Wei
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Layout Design ◽  
Adaptive Genetic Algorithm ◽  
Local Optimality ◽  
Mutation Probability ◽  
Evolutionary Genetic ◽  
Local Optimal Solution ◽  
The Moment ◽  
Crossover And Mutation

The main problems for Genetic Algorithm (GA) to deal with the complex layout design of satellite module lie in easily trapping into local optimality and large amount of consuming time. To solve these problems, the Bee Evolutionary Genetic Algorithm (BEGA) and the adaptive genetic algorithm (AGA) are introduced. The crossover operation of BEGA algorithm effectively reinforces the information exploitation of the genetic algorithm, and introducing random individuals in BEGA enhance the exploration capability and avoid the premature convergence of BEGA. These two features enable to accelerate the evolution of the algorithm and maintain excellent solutions. At the same time, AGA is adopted to improve the crossover and mutation probability, which enhances the escaping capability from local optimal solution. Finally, satellite module layout design based on Adaptive Bee Evolutionary Genetic Algorithm (ABEGA) is proposed. Numerical experiments of the satellite module layout optimization show that: ABEGA outperforms SGA and AGA in terms of the overall layout scheme, enveloping circle radius, the moment of inertia and success rate.

Research on Multimodal Transport Shortest Path Based on the Genetic Algorithm

2015 ◽  
Vol 744-746 ◽  
pp. 1813-1816
Author(s):  
Shou Wen Ji ◽  
Shi Jin ◽  
Kai Lv
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Shortest Path ◽  
Optimal Solution ◽  
Path Model ◽  
Time Analysis ◽  
Multimodal Transportation ◽  
Network Time ◽  
Multimodal Transport ◽  
Crossover And Mutation

This paper focuses on the research of multimodal transportation optimization model and algorithm, designs an intermodal shortest time path model and gives a solution to algorithm, constructs a multimodal transport network time analysis chart. By using genetic algorithms, the transportation scheme will be optimized. And based on each path’s code, the population will be evolved to obtain the optimal solution by using crossover and mutation rules.

scholarly journals An Extended Genetic Algorithm for Distributed Integration of Fuzzy Process Planning and Scheduling

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Shuai Zhang ◽  
Zhinan Yu ◽  
Wenyu Zhang ◽  
Dejian Yu ◽  
Yangbing Xu
Keyword(s):  
Genetic Algorithm ◽  
Process Planning ◽  
Optimal Solution ◽  
Planning And Scheduling ◽  
Fuzzy Process ◽  
Short Period ◽  
Mutation Strategies ◽  
Encoding Method ◽  
Machine Replacement ◽  
Crossover And Mutation

The distributed integration of process planning and scheduling (DIPPS) aims to simultaneously arrange the two most important manufacturing stages, process planning and scheduling, in a distributed manufacturing environment. Meanwhile, considering its advantage corresponding to actual situation, the triangle fuzzy number (TFN) is adopted in DIPPS to represent the machine processing and transportation time. In order to solve this problem and obtain the optimal or near-optimal solution, an extended genetic algorithm (EGA) with innovative three-class encoding method, improved crossover, and mutation strategies is proposed. Furthermore, a local enhancement strategy featuring machine replacement and order exchange is also added to strengthen the local search capability on the basic process of genetic algorithm. Through the verification of experiment, EGA achieves satisfactory results all in a very short period of time and demonstrates its powerful performance in dealing with the distributed integration of fuzzy process planning and scheduling (DIFPPS).

scholarly journals Modified Genetic Algorithm to Traveling Salesman Problem for Large Input Datasets

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Vladimir Vladimirov* ◽  
Fatima Sapundzhi ◽  
Radoslava Kraleva ◽  
Velin Kralev
Keyword(s):  
Genetic Algorithm ◽  
Complete Graph ◽  
Input Data ◽  
Travelling Salesman Problem ◽  
Optimal Solution ◽  
Solution Space ◽  
Hamiltonian Cycles ◽  
Large Size ◽  
Parameter Values

The use of graphs is widely applied in modeling and solving problems in the field of computer science and bioinformatics.Therefore, it is essential to develop and improve algorithms reducing their computational complexity and  increasing the precision of the solutions generated by them as well as the size of the input data.In this study two well-known algorithms for solving the problem for finding a minimum Hamiltonian cycle in weighted, undirected and complete graph (also known as Travelling Salesman Problem –- TSP) are analyzed.The first algorithm is based on the backtracking method and it always finds the optimal solution, while with the second one, the genetic algorithm (GA), finding the optimal solution is not always guaranteed.The aims of the study are to determine: (1)which of the algorithms can be used so that the resulting solution is optimal or near-optimal and the execution time be reasonable depending on the size of the input data; (2)the influence of GA parameter values on the quality of the resulting solutions for large size of the input data. The parameters determine the number of solutions in each population and the number of all generations.The analysis of the results revealed that:(1) the algorithm that finds all possible solutions can be used for graphs with a small number of vertices (not more than 20), whereas GA can be used for graphs with a large number of vertices; (2) in graphs with a small number of vertices: n<20 (and n*(n-1)/2 edges) GA always finds the optimal solution as long as  enough  solution space is set. However, the number of all Hamiltonian cycles in a complete graph with n vertices ((n-1)!/2) is bigger than the solution space; (3) all input datasets showed that with the number increase of vertices in the graph it is necessary to increase the number of the current solutions in the population. In this way GA reaches a certain rate of convergence faster, i.e.,  a generation after which the space of solutions contains only optimal solutions or near optimal ones.Acknowledgments: This work is partially supported by the project of the Bulgarian National Science Fund, entitled: “Bioinformatics research: protein folding, docking and prediction of biological activity”, NSF I02/16, 12.12.14.

scholarly journals A Parallel Mix Self-Adaptive Genetic Algorithm for Solving the Dynamic Economic Dispatch Problem

2020 ◽  
Author(s):  
Egidio De Carvalho Ribeiro Júnior ◽  
Omar Andres Carmona Cortes ◽  
Osvaldo Ronald Saavedra
Keyword(s):  
Genetic Algorithm ◽  
Economic Dispatch ◽  
Adaptive Genetic Algorithm ◽  
Random Algorithm ◽  
Dynamic Economic Dispatch ◽  
Crossover And Mutation ◽  
Adaptive Code ◽  
Better Than ◽  
Self Adaptive

The purpose of this paper is to propose a parallel genetic algorithm that has adaptive and self-adaptive characteristics at the same time for solving the Dynamic Economic Dispatch (DED) problem that is a challenging problem to solve. The algorithm selects the proper operators (using adaptive features) and probabilities (using the self-adaptive code) that produce the most fittable individuals. Regarding operations, the choice is made between four different types of crossover: simple, arithmetical, non-uniform arithmetical, and linear. Concerning mutation, we used four types of mutations (uniform, non-uniform, creep, and enhanced apso). The choice is made scholastically, which is uniform at the beginning of the algorithm, being adapted as the AG  executes. The crossover and mutation probabilities are coded into the genes, transforming this part of the algorithm into self-adaptive. The multicore version was coded using OpenMP. An ANOVA test, along with a Tukey test, proved that the mixed self-adaptive algorithm works better than both: a random algorithm, which chooses operators randomly, and a combination of operators set previously in the DED optimization. Regarding the performance of the parallel approach, results have shown that a speedup of up to 3.19 can be reached with no loss in the quality of solutions.

scholarly journals Hybrid Evolutionary-Heuristic Algorithm for Capacitor Banks Allocation

2010 ◽  
Vol 61 (6) ◽  
pp. 332-340 ◽  
Author(s):  
Marinko Barukčić ◽  
Srete Nikolovski ◽  
Franjo Jović
Keyword(s):  
Genetic Algorithm ◽  
Heuristic Algorithm ◽  
Optimal Allocation ◽  
Hybrid Methods ◽  
Optimal Solution ◽  
Distribution Networks ◽  
Computing Methods ◽  
Evolutionary Stage ◽  
Capacitor Banks

Hybrid Evolutionary-Heuristic Algorithm for Capacitor Banks Allocation The issue of optimal allocation of capacitor banks concerning power losses minimization in distribution networks are considered in this paper. This optimization problem has been recently tackled by application of contemporary soft computing methods such as: genetic algorithms, neural networks, fuzzy logic, simulated annealing, ant colony methods, and hybrid methods. An evolutionaryheuristic method has been proposed for optimal capacitor allocation in radial distribution networks. An evolutionary method based on genetic algorithm is developed. The proposed method has a reduced number of parameters compared to the usual genetic algorithm. A heuristic stage is used for improving the optimal solution given by the evolutionary stage. A new cost-voltage node index is used in the heuristic stage in order to improve the quality of solution. The efficiency of the proposed two-stage method has been tested on different test networks. The quality of solution has been verified by comparison tests with other methods on the same test networks. The proposed method has given significantly better solutions for time dependent load in the 69-bus network than found in references.

scholarly journals A Hybrid Genetic Algorithm and Sperm Swarm Optimization (HGASSO) for Multimodal Functions

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Genetic Algorithm ◽  
Convergence Rates ◽  
Hybrid Genetic Algorithm ◽  
Main Idea ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Quality Of Results ◽  
Metaheuristic Methods ◽  
Crossover And Mutation

In this paper, we propose a hybrid algorithm combining two different metaheuristic methods, “Genetic Algorithms (GA)” and “Sperm Swarm Optimization (SSO)”, for the global optimization of multimodal benchmarks functions. The proposed Hybrid Genetic Algorithm and Sperm Swarm Optimization (HGASSO) operates based on incorporates concepts from GA and SSO in which generates individuals in a new iteration not only by crossover and mutation operations as proposed in GA, but also by techniques of local search of SSO. The main idea behind this hybridization is to reduce the probability of trapping in local optimum of multi modal problem. Our algorithm is compared against GA, and SSO metaheuristic optimization algorithms. The experimental results using a suite of multimodal benchmarks functions taken from the literature have evinced the superiority of the proposed HGASSO approach over the other approaches in terms of quality of results and convergence rates in which obtained good results in solving the multimodal benchmarks functions that include cosine, sine, and exponent in their formulation.

scholarly journals An Improved Method of Genetic Algorithm to Solve the Variable Speed Limit Problem with Constraint Conditions

2016 ◽  
Vol 12 (12) ◽  
pp. 16 ◽  
Author(s):  
Yishui Shui ◽  
Fang Li ◽  
Yichen Chen ◽  
Wei Chen
Keyword(s):  
Genetic Algorithm ◽  
Penalty Function ◽  
Optimal Solution ◽  
Limit Problem ◽  
Penalty Function Method ◽  
Speed Limit ◽  
Variable Speed ◽  
Variable Speed Limit ◽  
The Past ◽  
Crossover And Mutation

This paper analyses the genetic algorithm which is used to solve the problem of the variable speed limit （VSL). In order to ensure the safety of driving, the speed limit in the chromosome must meet the constraints in time and space. The past practice is to add a penalty function in the object function, but with the increase of the number of solutions in the chromosomes, the weight of the penalty function is difficult to determine, often leads to the bad results. In this paper, we design a method to generate the chromosomes which meet the constraints, and the chromosomes in crossover and mutation of the genetic algorithm still the meet the constraint conditions. By comparison, it is found that the method can converge faster than the penalty function method, and will generate an optimal solution under constraint conditions.<span style="font-size: 10px;"> </span>

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