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Journal of Xi'an University of Technology

An Improved Quantum Genetic Algorithm

Bo Yang

Published Jan 1, 2012• 2 citations
OpenAlex

Abstract

This paper suggests an improved quantum genetic algorithm(IQGA).In encoding,a kind of encoding method to determine the quantum chromosome gene length is advanced in terms of accurate dynamics of the problem to be found out.This encoding method takes the equilibrium relations between the calculation solution accuracy and seeking for efficiency into consideration.Also,this paper discusses the dynamic adjustment and tactics of quantum rotation angle,whereby making operator realize the combination of a kind of rough searching with the fine searching under the different cases.The combination of quantum mutation operation can render the postmutation to be able to carry out searching in a larger neighborhood range in such a way that the algorithm is ensured to search for a solution with high accuracy within a rational calculation cost.In the end,IQGA is used to test several selected standard base functions.The tested results indicate that IQGA can obtain the best solusion,the best average value and convergence algebra as against the existing algorithm data cited in literature,and with a certain advantage.

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This paper suggests an improved quantum genetic algorithm(IQGA).In encoding,a kind of encoding method to determine the quantum chromosome gene length is advanced in terms of accurate dynamics of the problem to be found out.This encoding method takes the equilibrium relations between the calculation solution accuracy and seeking for efficiency into consideration.Also,this paper discusses the dynamic adjustment and tactics of quantum rotation angle,whereby making operator realize the combination of a kind of rough searching with the fine searching under the different cases.The combination of quantum mutation operation can render the postmutation to be able to carry out searching in a larger neighborhood range in such a way that the algorithm is ensured to search for a solution with high accuracy within a rational calculation cost.In the end,IQGA is used to test several selected standard base functions.The tested results indicate that IQGA can obtain the best solusion,the best average value and convergence algebra as against the existing algorithm data cited in literature,and with a certain advantage.
Quantum genetic algorithm (QGA) is the combination between genetic algorithm and quantum computing. In this paper, a chromosome of the standard QGA is seen as a node and the chromosome population is regarded as a network. Then the reasons for the prematurity and the stagnation of the standard QGA are analyzed from the perspective of network structure. To solve the two problems, an improved quantum genetic algorithm (IQGA) based on the small world theory is proposed. In IQGA, chromosomes encoded with qubits are divided into some sub-groups and the NW network model is introduced into the population structure. When updating chromosomes, an optimal chromosome in locality or in other sub-groups is chosen based on a certain probability as the evolution target for each chromosome. The new network structure of the chromosome population has a relatively moderate clustering coefficient and is favorable to the diversity of individual chromosomes. Tests of three classic functions prove the effectiveness and superiority of IQGA.
Aimed at the problem of large storage capacity and easily falling into local optimum,a novel improved quantum genetic algorithm is presented.The algorithm adopts an angle-coding method to reduce the storage space of chromosomes.For the quantum chromosomes are distributed averagely in space of initial value,small interval method is used to initialize quanta swarm.It uses the improved quantum rotation gates to renew the population and realizes adaptive search by the adjustment strategy of dynamic quantum step and uses the operation of quantum crossover and quantum mutation to prevent the premature problem.Through the typical multi-peak function optimization test,it shows that the algorithm has the faster convergence rate,the stronger global optimization ability and the shorter computing time.The algorithm can be used for multi-peak function optimization problem.
An improved quantum genetic algorithm(IQGA) was proposed to overcome the shortcoming of the quantum genetic algorithm(QGA),i.e. local optimization, when it is used for the optimization of continuous functions with many extreme values. In IQGA, the strategies of updating quantum gate using the best solution obtained and population catastrophe were adopted. The test results for two typical functions show that the convergence speed of IQGA is faster than that of QGA, and IQGA can converge in a global solution space, overcoming the shortcoming of QGA. The application results indicate that IQGA is better than QGA and other genetic algorithms.
An improved quantum genetic algorithm (IQGA) is proposed in this paper, which codes the chromosome with probability amplitudes represented by sine and cosine functions, and uses an adaptive strategy of the rotation angle to update the population. Then the mutation operation is considered in this improved quantum genetic algorithm (MIQGA). Rapid convergence and good global search capability characterize the performance of MIQGA. While testing, a variance function is introduced to estimate the stability of the algorithm. When solving 0-1 knapsack problem, greedy repair function is used to repair unfeasible solutions. Experimental results show MIQGA has better comprehensive performance than traditional genetic algorithm (GA), standard quantum genetic algorithm (QGA) and IQGA, especially the superiority in terms of optimization quality and population diversity.
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.
Aiming at the issue that quantum genetic algorithm is not suitable for continuons function optimization,an improved quantum genetic algorithm is proposed.This algorithm directly compares the quantum chromosomes with the current best solution to determine the rotating angle of the rotating gate.Every individual in population evolves with different rates to complete local search and global search simultaneously.Mutation operation is used to prevent the premature convergence.After analyzing the algorithm and its global convergence,this algorithm is applied to solve the function extremum and to optimize the PID controller parameters,and it is compared with the standard genetic algorithm and the quantum genetic algorithm.Experimental results illustrate that the proposed algorithm has better performance.