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International Symposium on Information Science and Engineering

Network Optimization Method Based on Improved Quantum Genetic Algorithm

Xin Fan, Wei Li, Zhihuan Chen, Jun Jie Yi

Published Dec 1, 2012• 2 citations
Publisher

Abstract

To solve structure optimization problems of the information transmission networks, based on quantum genetic algorithm (QGA), the paper proposes an improved quantum genetic algorithm (IQGA). The core of the algorithm is to take full advantage of interference and entanglement of quantum states, through adopting the quantum gates rotation corner dynamic adjusting mechanism, quantum crossover and mutation operations, genetic operation parameters dynamic adaptive adjustment mechanism and solution elites control strategy. The algorithm has better performance in solving combinatorial optimization problems of the weak correlation between genes.

DOI
10.1109/isise.2012.101

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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.
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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 paper proposes a novel approach to optimization problems by integrating quantum entanglement with the Genetic Algorithm (GA). The core idea is to leverage the inherent parallelism and enhanced information transfer capabilities of quantum entanglement to significantly improve the search performance of the GA. We represent genetic algorithm chromosomes as quantum states and employ quantum entanglement to facilitate rapid information exchange and crossover operations across multiple qubits. This mechanism allows for parallel exploration of the search space, mitigating the limitations of traditional GA's tendency towards local optima. The theoretical framework and the underlying principles are presented, demonstrating the potential for the Quantum Entanglement Genetic Algorithm (QGA) to accelerate the solution of complex optimization problems. The algorithm's effectiveness is discussed through a mathematical formulation, focusing on the key components of the QGA and its advantages over conventional GA. The ultimate goal is to demonstrate a robust and efficient method for tackling challenging optimization tasks.