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Journal of Southwest Jiaotong University

Improvement of Quantum Genetic Algorithm and Its Application

Ge ZHANG

Published Jan 1, 2003• 12 citations
OpenAlex

Abstract

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.

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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.
Abstract With the in-depth study of quantum genetic algorithm (QGA), the defect of premature convergence limits its development more and more. In order to further improve QGA, we propose an improved QGA based on multi population (IQGA). In the process of population initialization in IQGA, we generate multiple populations to avoid a single population falling into a local optimal value. We also abandon the fixed angle used by the traditional quantum revolving gate, and design a strategy to adjust the angle adaptively according to the difference from the optimal solution. In addition, we introduce a population catastrophe strategy to deal with the premature convergence of each population. The populations are connected based on migration operation, and the optimal solution of each population obtained by each iteration is collected in the elite group. A series of simulation experiments show that our improvement strategies of IQGA are effective.
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.
This survey presents a comprehensive analysis of the Improved Quantum Genetic Algorithm (IQGA), an interesting way of applying quantum computing to genetic algorithms (GAs). The study begins with an introduction to the several variants of Quantum Genetic Algorithm (QGA), which are widely used in different applications of GAs. The article introduces the IQGA as a quantum version of the classical GA, detailing how it leverages quantum parallelism and superposition to reduce the time complexity of the genetic operations, and highlighting its crucial role in quantum algorithms. The analysis also addresses the mathematical foundations of the IQGA, and the key advantages and challenges associated with its use in several applications. Finally, it concludes with an exploration of the current and potential applications of IQGA and common characteristics of several implementations of IQGAs.
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.
This survey presents a comprehensive analysis of the Improved Quantum Genetic Algorithm (IQGA), an interesting way of applying quantum computing to genetic algorithms (GAs). The study begins with an introduction to the several variants of Quantum Genetic Algorithm (QGA), which are widely used in different applications of GAs. The article introduces the IQGA as a quantum version of the classical GA, detailing how it leverages quantum parallelism and superposition to reduce the time complexity of the genetic operations, and highlighting its crucial role in quantum algorithms. The analysis also addresses the mathematical foundations of the IQGA, and the key advantages and challenges associated with its use in several applications. Finally, it concludes with an exploration of the current and potential applications of IQGA and common characteristics of several implementations of IQGAs. Keywords: QGA, IQGA, GA.
Intelligent optimization algorithm based on quantum behavior is the hot spot of intelligent computation. Quantum clone genetic algorithm has many shortcomings such as: low efficiency, poor population diversity, slow convergence speed, easy to trap in local minimums, blindness in global optimal searching direction and so on. An improved quantum clone genetic algorithm (IQCGA) is proposed in this paper. The algorithm have many merits: (1) Adopting the nicking probability division initialization strategy, population diversity is enhanced; (2) Introducing the quantum whole interference crossover, the information is spread in the whole population, so it helps to avoid trapping in local minimums, and accelerates convergence speed; (3) Making use of adaptation strategy in quantum rotate gate updating, searching speed for optimal solution is accelerated; (4) In order to avoid premature and evolution stagnation, the superior individual whole crossover quantum catastrophe strategy is adopted, which helps the population to search the objective solution in different directions. IQCGA is composted of initialization stage and iteration evolution stage. Furthermore, theory analysis is given to prove the convergence. Finally, Simulation results prove that the algorithms can maintain the population diversity, reduce calculation complexity, accelerate searching speed for optimal solution, and avoid trapping in local minimums. So it has the better searching optimal ability and convergence speed.