Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
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DOI：
10.1109/tevc.2002.804320
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发表时间：
2002-12-01
影响因子：
14.3
通讯作者：
Kim, JH
中科院分区：
文献类型：
--
作者：
Han, KH;Kim, JH
本文提出了它是受量子计算启发的新型进化算法，称为量子启发的进化算法（QEA），该算法基于量子计算的概念和原理，例如量子计算的概念和原理。像其他进化算法一样，QEA的特征也具有个体的表示，评估函数和人口动态。但是，QEA代替二进制，数字或符号表示，而是使用Q-bit（定义为最小的信息单位）作为概率表示形式，而Q-bit个体则作为Q-bits的字符串。引入了Q-gate作为变异操作员，以将个人推向更好的解决方案。为了证明其有效性和适用性，在背包问题上进行了实验，这是一个众所周知的组合优化问题。结果表明，与常规遗传算法相比，QEA的性能即使人口少，人口少，没有过早的收敛。
This paper proposes it novel evolutionary algorithm inspired by quantum computing, called a quantum-inspired evolutionary algorithm (QEA), which is based on the concept and principles of quantum computing, such as a quantum bit and super-position of states. Like other evolutionary algorithms, QEA is also characterized by the representation of the individual, the evaluation function, and the population dynamics. However, instead of binary, numeric, or symbolic representation, QEA uses a Q-bit, defined as the smallest unit of information, for the probabilistic representation and a Q-bit individual as a string of Q-bits. A Q-gate is introduced as a variation operator to drive the individuals toward better solutions. To demonstrate its effectiveness and applicability, experiments are carried out on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that QEA performs well, even with a small population, without premature convergence as compared to the conventional genetic algorithm.