scholarly journals An Allele Real-Coded Quantum Evolutionary Algorithm Based on Hybrid Updating Strategy

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Yu-Xian Zhang ◽  
Xiao-Yi Qian ◽  
Hui-Deng Peng ◽  
Jian-Hui Wang
Keyword(s):  
Genetic Algorithm ◽  
Global Convergence ◽  
Convergence Rate ◽  
Local Search ◽  
Evolutionary Algorithm ◽  
Optimization Problem ◽  
Continuous Optimization ◽  
Evolutionary Process ◽  
Experimental Results ◽  
Quantum Genetic Algorithm

For improving convergence rate and preventing prematurity in quantum evolutionary algorithm, an allele real-coded quantum evolutionary algorithm based on hybrid updating strategy is presented. The real variables are coded with probability superposition of allele. A hybrid updating strategy balancing the global search and local search is presented in which the superior allele is defined. On the basis of superior allele and inferior allele, a guided evolutionary process as well as updating allele with variable scale contraction is adopted. AndHεgate is introduced to prevent prematurity. Furthermore, the global convergence of proposed algorithm is proved byMarkovchain. Finally, the proposed algorithm is compared with genetic algorithm, quantum evolutionary algorithm, and double chains quantum genetic algorithm in solving continuous optimization problem, and the experimental results verify the advantages on convergence rate and search accuracy.

A Novel Quantum Genetic Algorithm for Continuous Function Optimization

2013 ◽  
Vol 816-817 ◽  
pp. 907-914
Author(s):  
Hao Li ◽  
Shi Yong Li
Keyword(s):  
Genetic Algorithm ◽  
Continuous Function ◽  
Global Convergence ◽  
Local Search ◽  
Rotation Angle ◽  
Binary Solution ◽  
Function Optimization ◽  
Quantum Genetic Algorithm ◽  
Function Extremum ◽  
Angle Of Rotation

In this paper, a novel quantum genetic algorithm is proposed. This algorithm compares the probability expectation of the quantum chromosome with the best binary solution to determine rotation angle of rotation gate. Different individual in population evolve with different rate to complete local search and global search simultaneously. Hε gate is used to prevent the algorithm from premature convergence. After analyzing the algorithm and its global convergence, applying this approach to the optimization of function extremum, and comparing with the simple genetic algorithm and the quantum genetic algorithm, the simulation result illustrates that the algorithm has the characteristic of quick convergence speed and high solution precision.

Bee Immune Evolutionary Algorithm

2011 ◽  
Vol 268-270 ◽  
pp. 1184-1187 ◽  
Author(s):  
Zuo Yong Li ◽  
Chun Xue Yu ◽  
Lei Zang
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Evolutionary Algorithm ◽  
Evolutionary Process ◽  
Optimal Solution ◽  
Global Optimal Solution ◽  
Second Best ◽  
Evolutionary Genetic ◽  
Global Optimal ◽  
Immune Evolutionary Algorithm

The bee immune evolutionary algorithm was proposed in order to improve effectively the optimal ability of bee evolutionary genetic algorithm. In the evolutionary process of bee, the algorithm made on immune evolutionary iteration calculation, generate next-generation population, in the proportions of fitness values for the best individual and second-best individuals in each generation. Because the algorithm takes in the neighborhood of space search as well out the neighborhood of space search for the some optimal individuals, meanwhile, with iterative numbers increase, capability of local search can be strengthened gradually; the bee immune evolutionary algorithm can approach the global optimal solution with higher accuracy. The calculated results for typical best functions show that the bee immune evolutionary algorithm has better optimal capability and stability.

Application of Hybrid Genetic Algorithm in Control Network Adjustment

2013 ◽  
Vol 353-356 ◽  
pp. 3434-3437
Author(s):  
Wei Chen
Keyword(s):  
Genetic Algorithm ◽  
Global Convergence ◽  
Local Search ◽  
Simplex Method ◽  
Hybrid Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Experimental Results ◽  
Control Network ◽  
Network Adjustment ◽  
Search Speed

In this paper a hybrid genetic algorithm which consists of the simplex method and the genetic algorithm is proposed for the defect of poor local search ability of genetic algorithm. The hybrid genetic algorithm has the advantages of good global convergence of the genetic algorithm and excellent local search ablility of the simplex method and can improve search speed and calculation accuracy.The hybrid algorithm is applied to the control network adjustment and experimental results demonstrates the effectiveness and superiority of the algorithm.

scholarly journals A Novel Hybrid Algorithm for Minimum Total Dominating Set Problem

Mathematics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 222 ◽  
Author(s):  
Fuyu Yuan ◽  
Chenxi Li ◽  
Xin Gao ◽  
Minghao Yin ◽  
Yiyuan Wang
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Evolutionary Algorithm ◽  
Hybrid Algorithm ◽  
Dominating Set ◽  
Experimental Results ◽  
Total Dominating Set ◽  
Dominating Set Problem ◽  
Algorithm Search ◽  
Better Than

The minimum total dominating set (MTDS) problem is a variant of the classical dominating set problem. In this paper, we propose a hybrid evolutionary algorithm, which combines local search and genetic algorithm to solve MTDS. Firstly, a novel scoring heuristic is implemented to increase the searching effectiveness and thus get better solutions. Specially, a population including several initial solutions is created first to make the algorithm search more regions and then the local search phase further improves the initial solutions by swapping vertices effectively. Secondly, the repair-based crossover operation creates new solutions to make the algorithm search more feasible regions. Experiments on the classical benchmark DIMACS are carried out to test the performance of the proposed algorithm, and the experimental results show that our algorithm performs much better than its competitor on all instances.

scholarly journals Heliostat Field Layout Optimization with Evolutionary Algorithms

10.29007/7p6t ◽  
2018 ◽  
Author(s):  
Pascal Richter ◽  
David Laukamp ◽  
Levin Gerdes ◽  
Martin Frank ◽  
Erika Ábrahám
Keyword(s):  
Power Plant ◽  
Convex Optimization ◽  
Evolutionary Algorithms ◽  
Convergence Rate ◽  
Evolutionary Algorithm ◽  
Computer Science ◽  
Energy Supply ◽  
Optimization Problem ◽  
Convex Optimization Problem ◽  
New Genotype

The exploitation of solar power for energy supply is of increasing importance. While technical development mainly takes place in the engineering disciplines, computer science offers adequate techniques for optimization. This work addresses the problem of finding an optimal heliostat field arrangement for a solar tower power plant.We propose a solution to this global, non-convex optimization problem by using an evolutionary algorithm. We show that the convergence rate of a conventional evolutionary algorithm is too slow, such that modifications of the recombination and mutation need to be tailored to the problem. This is achieved with a new genotype representation of the individuals.Experimental results show the applicability of our approach.

Optimizing Fourier Series Based Gaits for Modular Robots Using an Evolutionary Algorithm

2013 ◽  
Author(s):  
David Ko ◽  
Harry H. Cheng
Keyword(s):  
Genetic Algorithm ◽  
Fourier Series ◽  
Evolutionary Algorithm ◽  
Periodic Motion ◽  
Degrees Of Freedom ◽  
Series Representation ◽  
Robotic System ◽  
Experimental Results ◽  
New Method ◽  
Modular Robots

A new method of controlling and optimizing robotic gaits for a modular robotic system is presented in this paper. A robotic gait is implemented on a robotic system consisting of three Mobot modules for a total of twelve degrees of freedom using a Fourier series representation for the periodic motion of each joint. The gait implementation allows robotic modules to perform synchronized gaits with little or no communication with each other making it scalable to increasing numbers of modules. The coefficients of the Fourier series are optimized by a genetic algorithm to find gaits which move the robot cluster quickly and efficiently across flat terrain. Simulated and experimental results show that the optimized gaits can have over twice as much speed as randomly generated gaits.

Shape Modification of NURBS Curves Using Genetic Algorithm

2012 ◽  
Vol 532-533 ◽  
pp. 1450-1454
Author(s):  
Yan Hong Li ◽  
Guo Wang Mu ◽  
Zeng Guo
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Experimental Results ◽  
New Method ◽  
Shape Modification ◽  
Nurbs Curve ◽  
Nurbs Curves

In this paper, we propose a new method for shape modification of NURBS curves. For a given NURBS curve, we modify its one or more weights so that the curve passes through the point specified in advance. We convert this into an optimization problem and solve it by genetic algorithm. The experimental results show the feasibility and validity of our method.

scholarly journals A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Leilei Cao ◽  
Lihong Xu ◽  
Erik D. Goodman
Keyword(s):  
Genetic Algorithm ◽  
Global Optimization ◽  
Local Search ◽  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Its Region ◽  
Bat Algorithm ◽  
Greedy Strategy ◽  
Search Mechanism ◽  
Genotype Space

A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared.

A Video Abstraction Model Using a Genetic Algorithm

2012 ◽  
Vol 562-564 ◽  
pp. 2061-2064
Author(s):  
Zhi Cheng Wei
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Feature Space ◽  
Experimental Results ◽  
Visual Content ◽  
Amount Of Information ◽  
Video Abstraction ◽  
Video Frames ◽  
Key Frames ◽  
Standard Genetic Algorithm

In this paper, we present a standard genetic algorithm (SGA) based video abstraction framework, which can adaptively sample video frames in non-uniform way. We formulate the video abstraction as an optimization problem and apply a SGA in the feature space for video abstraction. The video abstraction is accomplished by applying genetic algorithm to search key frames from similar visual content source so that only a small but meaningful amount of information is retained. Experimental results and comparisons are presented to show good performance of our scheme on video static summarization and video skimming.

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