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求解max-re-sat的离散混沌量子蝙蝠算法
首发时间：2024-05-29
摘要：针对最大正则可满足性问题求解算法的研究空缺，以及提升求解最大可满足性问题的智能优化算法的精度，基于蝙蝠算法（bat algorithm,ba），提出了一种基于离散混沌量子的蝙蝠算法。在该算法中，将连续数值转化为离散的二进制编码，对算法进行了离散化处理。运用量子理论，引入量子比特编码和启发式量子变异，通过量子旋转门改变非最优个体的概率振幅来实现变异，解决了早熟和收敛速度慢的问题。在位置更新中，使用混沌映射替代固定参数，增强了灵活性和多样性，提高了全局寻优能力和求解效率。实验结果表明，在随机正则可满足性问题实例产生模型产生的不同规模算例上，所提算法的求解精度远远高于传统启发式算法；同时，与获奖的求解器相比，也具有一定的竞争力，验证了该算法的有效性。
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discrete chaotic quantum bat algorithm for solving the max-re-sat
abstract：in response to the research gap in algorithms for solving maximum regularization satisfiability problems and the improvement of the accuracy of intelligent optimization algorithms for solving maximum satisfiability problems, this paper proposes a discrete chaotic quantum based bat algorithm based on the bat algorithm (ba). in this algorithm, continuous values are converted into discrete binary codes, and the algorithm is discretized. the bat algorithm for scattered chaos quantum. in this algorithm, continuous values are converted into discrete binary codes, and the algorithm is discretized. by applying quantum theory, introducing quantum bit encoding and heuristic quantum mutation, the mutation is achieved by changing the probability amplitude of non optimal individuals through quantum rotation gates, solving the problems of precocity and slow convergence speed. in position update, using chaotic maps to replace fixed parameters enhances flexibility and diversity, improves global optimization ability and solving efficiency. the experimental results show that the proposed algorithm has much higher accuracy than traditional heuristic algorithms in generating different scale examples in the stochastic regularization satisfiability problem instance generation model; at the same time, compared with the award-winning solver, it also has a certain level of competitiveness, verifying the effectiveness of the algorithm.
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求解max-re-sat的离散混沌量子蝙蝠算法
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