scholarly journals Optimization of Network Coding Resources Based on Improved Quantum Genetic Algorithm

Photonics ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 502
Author(s):  
Tianyang Liu ◽  
Qiang Sun ◽  
Huachun Zhou ◽  
Qi Wei
Keyword(s):  
Genetic Algorithm ◽  
Network Coding ◽  
Topological Structure ◽  
Rotation Angle ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Resource Optimization ◽  
Quantum Genetic Algorithm ◽  
Adjustment Mechanism ◽  
Adaptive Adjustment

The problem of network coding resource optimization with a known topological structure is NP-hard. Traditional quantum genetic algorithms have the disadvantages of slow convergence and difficulty in finding the optimal solution when dealing with this problem. To overcome these disadvantages, this paper proposes an adaptive quantum genetic algorithm based on the cooperative mutation of gene number and fitness (GNF-QGA). This GNF-QGA adopts the rotation angle adaptive adjustment mechanism. To avoid excessive illegal individuals, an illegal solution adjustment mechanism is added to the GNF-QGA. A solid demonstration was provided that the proposed algorithm has a fast convergence speed and good optimization capability when solving network coding resource optimization problems.

scholarly journals The Improvement of Quantum Genetic Algorithm and Its Application on Function Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Huaixiao Wang ◽  
Jianyong Liu ◽  
Jun Zhi ◽  
Chengqun Fu
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Evolutionary Process ◽  
Optimal Solution ◽  
Function Optimization ◽  
The Self ◽  
Improved Method ◽  
Simulation Test ◽  
Quantum Genetic Algorithm ◽  
Mutation Operation

To accelerate the evolutionary process and increase the probability to find the optimal solution, the following methods are proposed to improve the conventional quantum genetic algorithm: an improved method to determine the rotating angle, the self-adaptive rotating angle strategy, adding the quantum mutation operation and quantum disaster operation. The efficiency and accuracy to search the optimal solution of the algorithm are greatly improved. Simulation test shows that the improved quantum genetic algorithm is more effective than the conventional quantum genetic algorithm to solve some optimization problems.

Information Technology in an Improved Quantum Genetic Algorithm Based on Dynamic Adjustment of Quantum Rotation Angle

2013 ◽  
Vol 859 ◽  
pp. 577-581
Author(s):  
Hui Xia Li ◽  
Yun Can Xue ◽  
Jian Qiang Zhang ◽  
Qi Wen Yang
Keyword(s):  
Genetic Algorithm ◽  
Information Technology ◽  
Rotation Angle ◽  
Local Optimum ◽  
Dynamic Adjustment ◽  
Excellent Performance ◽  
Simulation Experiments ◽  
Standard Quantum ◽  
Quantum Genetic Algorithm ◽  
Chaos Search

To overcome the shortcomings of precocity and being easily trapped into local optimum of the standard quantum genetic algorithm (QGA) , Information Technology in An Improved Quantum Genetic Algorithm based on dynamic adjustment of the quantum rotation angle of quantum gate (DAAQGA) was proposed. Mutation operation using the quantum not-gate is also introduced to enhance the diversity of population. Chaos search are also introduced into the modified algorithm to improve the search accuracy. Simulation experiments have been carried and the results show that the improved algorithm has excellent performance both in the preventing premature ability and in the search accuracy.

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

Genetic Algorithm and Other Meta-Heuristics

2005 ◽  
pp. 144-173 ◽  
Author(s):  
Bernard K.S. Cheung
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Schema Theory ◽  
Heuristic Methods ◽  
Optimal Solutions ◽  
Corporate Globalization ◽  
Global Optimal ◽  
Mutation And Selection

Genetic algorithms have been applied in solving various types of large-scale, NP-hard optimization problems. Many researchers have been investigating its global convergence properties using Schema Theory, Markov Chain, etc. A more realistic approach, however, is to estimate the probability of success in finding the global optimal solution within a prescribed number of generations under some function landscapes. Further investigation reveals that its inherent weaknesses that affect its performance can be remedied, while its efficiency can be significantly enhanced through the design of an adaptive scheme that integrates the crossover, mutation and selection operations. The advance of Information Technology and the extensive corporate globalization create great challenges for the solution of modern supply chain models that become more and more complex and size formidable. Meta-heuristic methods have to be employed to obtain near optimal solutions. Recently, a genetic algorithm has been reported to solve these problems satisfactorily and there are reasons for this.

A Kind of New Immune Genetic Algorithm and its Application

2011 ◽  
Vol 48-49 ◽  
pp. 25-28
Author(s):  
Wei Jian Ren ◽  
Yuan Jun Qi ◽  
Wei Lv ◽  
Cheng Da Li
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Logistic Map ◽  
Optimal Solution ◽  
Optimization Method ◽  
Local Optimum ◽  
Immune Genetic Algorithm ◽  
Chaos Optimization ◽  
Scale Optimization

According to the phenomenon of falling into local optimum during solving large-scale optimization problems and the shortcomings of poor convergence of Immune Genetic Algorithm, a new kind of probability selection method based on the concentration for the genetic operation is presented. Considering the features of chaos optimization method, such like not requiring the solved problems with continuity or differentiability, which is unlike the conventional method, and also with a solving process within a certain range traverse in order to find the global optimal solution, a kind of Chaos Immune Genetic Algorithm based on Logistic map and Hénon map is proposed. Through the application to TSP problem, the results have showed the superior to other algorithms.

scholarly journals Genetic Algorithm for the Multi-Objective Optimization of Product Gene

2012 ◽  
Vol 6-7 ◽  
pp. 116-121
Author(s):  
Qing Song Ai ◽  
Zhou Liu ◽  
Yan Wang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Engineering ◽  
Manufacturing Industry ◽  
Optimization Problems ◽  
Rapid Development ◽  
Optimal Solution ◽  
Adaptive Mutation ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Modified Genetic Algorithm

In order to adapt to the rapid development of the manufacturing industry, product genetic engineering arises at the historic moment. Finding the optimal solution under more than one decision variables of the solution set is becoming the most important problems that we should solve. In this paper, we proposed a modified genetic algorithm to solve gene product genetic engineering of multi-objective optimization problems. The new concepts such as matrix encoding, column crossover and adaptive mutation are proposed as well. Experimental results show that the modified genetic algorithm can find the optimal solutions and match the customer’s expectations in modern manufacture.

scholarly journals Quantum Genetic Algorithm on Multilevel Quantum Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Valerii Tkachuk
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Quantum Systems ◽  
Point Of View ◽  
Lookup Table ◽  
Test Function ◽  
High Effectiveness ◽  
Quantum Genetic Algorithm ◽  
Influence Mechanism ◽  
Adaptive Nature

We present a new evolutionary algorithm on the basis of quantum computations technology for solving optimization problems. The algorithm is built using many-valued quantum logic concept, which is more prospective from the computing power’s point of view. We compare the suggested algorithm to the traditional quantum genetic algorithm to demonstrate its high effectiveness on the example of test function global optimization problems. The advantages can be observed in the running time, the convergence speed, and the solution precision. The proposed implementation for the algorithm of quantum gate operator has an adaptive nature and does not require a lookup table. The role and the influence mechanism of the quantum disaster operator on the proposed algorithm effectiveness are also analyzed.

scholarly journals Optimization of Traveling Salesman Problem Using Affinity Propagation Clustering and Genetic Algorithm

2015 ◽  
Vol 5 (4) ◽  
pp. 239-245 ◽  
Author(s):  
Ahmad Fouad El-Samak ◽  
Wesam Ashour
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Solution Space ◽  
Affinity Propagation ◽  
Computational Time ◽  
Combinatorial Optimization Problems ◽  
Affinity Propagation Clustering ◽  
Core Idea ◽  
Computation Algorithms

Abstract Combinatorial optimization problems, such as travel salesman problem, are usually NP-hard and the solution space of this problem is very large. Therefore the set of feasible solutions cannot be evaluated one by one. The simple genetic algorithm is one of the most used evolutionary computation algorithms, that give a good solution for TSP, however, it takes much computational time. In this paper, Affinity Propagation Clustering Technique (AP) is used to optimize the performance of the Genetic Algorithm (GA) for solving TSP. The core idea, which is clustering cities into smaller clusters and solving each cluster using GA separately, thus the access to the optimal solution will be in less computational time. Numerical experiments show that the proposed algorithm can give a good results for TSP problem more than the simple GA.

scholarly journals Design of Finite Impulse Response Filters Based on Genetic Algorithm

2013 ◽  
Vol 6 (3) ◽  
pp. 28-39
Author(s):  
Raaed Faleh Hassan ◽  
Ali Subhi Abbood
Keyword(s):  
Genetic Algorithm ◽  
Impulse Response ◽  
Optimization Problems ◽  
Filter Design ◽  
Finite Impulse Response ◽  
Optimal Solution ◽  
Fir Filter ◽  
Language Version ◽  
Finite Impulse Response Filters ◽  
Matlab Programming

Genetic Algorithms (GAs) are used to solve many optimization problems in science and engineering such as pattern recognition, robotics, biology, medicine, and many other applications. The aim of this paper is to describe a method of designing Finite Impulse Response (FIR) filter using Genetic Algorithm (GA). In this paper, the Genetic Algorithm not only used for searching the optimal coefficients, but also it is used to find the minimum number of Taps, and hence minimize the number of multipliers and adders that can be used in the design of the FIR filter. The Evolutionary Programming is the best search procedure and most powerful than Linear Programming in providing the optimal solution that is desired to minimize the ripple content in both passband and stopband. The algorithm generates a population of genomes that represents the filter coefficient and the number of taps, where new genomes are generated by crossover and mutation operations methods. Our proposed genetic technique has able to give better result compare to other method.The FIR filter design using Genetic Algorithm is simulated using MATLAB programming language version 7.6.0.324 (R2008a).

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