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.

Towards efficient local search for the minimum total dominating set problem

2021 ◽  
Author(s):  
Shuli Hu ◽  
Huan Liu ◽  
Yupan Wang ◽  
Ruizhi Li ◽  
Minghao Yin ◽  
...  
Keyword(s):  
Local Search ◽  
Dominating Set ◽  
Total Dominating Set ◽  
Dominating Set Problem

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.

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 Performance Study of Some Approximation Algorithms for Computing a Small Dominating Set in a Graph

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 339
Author(s):  
Jonathan Li ◽  
Rohan Potru ◽  
Farhad Shahrokhi
Keyword(s):  
Approximation Algorithms ◽  
Real World ◽  
Hybrid Algorithm ◽  
Dominating Set ◽  
Performance Ratio ◽  
Performance Study ◽  
Similar Time ◽  
Sparse Graphs ◽  
Lp Rounding ◽  
Better Than

We implement and test the performances of several approximation algorithms for computing the minimum dominating set of a graph. These algorithms are the standard greedy algorithm, the recent Linear programming (LP) rounding algorithms and a hybrid algorithm that we design by combining the greedy and LP rounding algorithms. Over the range of test data, all algorithms perform better than anticipated in theory, and have small performance ratios, measured as the size of output divided by the LP objective lower bound. However, each have advantages over the others. For instance, LP rounding algorithm normally outperforms the other algorithms on sparse real-world graphs. On a graph with 400,000+ vertices, LP rounding took less than 15 s of CPU time to generate a solution with performance ratio 1.011, while the greedy and hybrid algorithms generated solutions of performance ratio 1.12 in similar time. For synthetic graphs, the hybrid algorithm normally outperforms the others, whereas for hypercubes and k-Queens graphs, greedy outperforms the rest. Another advantage of the hybrid algorithm is to solve very large problems that are suitable for application of LP rounding (sparse graphs) but LP formulations become formidable in practice and LP solvers crash, as we observed on a real-world graph with 7.7 million+ vertices and a planar graph on 1,000,000 vertices.

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.

Based on Improved Genetic Algorithm for Task Scheduling of Heterogeneous Multi-Core Processor

2014 ◽  
Vol 1030-1032 ◽  
pp. 1671-1675
Author(s):  
Yue Qiu ◽  
Jing Feng Zang
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Execution Time ◽  
Scheduling Algorithm ◽  
Experimental Results ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
High Quality ◽  
Multi Core Processor ◽  
Better Than

This paper puts forward an improved genetic scheduling algorithm in order to improve the execution efficiency of task scheduling of the heterogeneous multi-core processor system and give full play to its performance. The attribute values and the high value of tasks were introduced to structure the initial population, randomly selected a method with the 50% probability to sort for task of individuals of the population, thus to get high quality initial population and ensured the diversity of the population. The experimental results have shown that the performance of the improved algorithm was better than that of the traditional genetic algorithm and the HEFT algorithm. The execution time of tasks was reduced.

scholarly journals Local Search for Minimum Weight Dominating Set with Two-Level Configuration Checking and Frequency Based Scoring Function (Extended Abstract)

2017 ◽  
Author(s):  
Yiyuan Wang ◽  
Shaowei Cai ◽  
Minghao Yin
Keyword(s):  
Local Search ◽  
Search Algorithm ◽  
Dominating Set ◽  
Minimum Weight ◽  
Scoring Function ◽  
Solution Quality ◽  
Configuration Checking ◽  
New Variant ◽  
New Ideas ◽  
Better Than

The Minimum Weight Dominating Set (MWDS) problem is an important generalization of the Minimum Dominating Set (MDS) problem with extensive applications. This paper proposes a new local search algorithm for the MWDS problem, which is based on two new ideas. The first idea is a heuristic called two-level configuration checking (CC2), which is a new variant of a recent powerful configuration checking strategy (CC) for effectively avoiding the recent search paths. The second idea is a novel scoring function based on the frequency of being uncovered of vertices. Our algorithm is called CC2FS, according to the names of the two ideas. The experimental results show that, CC2FS performs much better than some state-of-the-art algorithms in terms of solution quality on a broad range of MWDS benchmarks.

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