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Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As a possible countermeasure to the lack in algorithms for solving maximum regularization satisfiability problems as well as inadequate accuracy of intelligent optimization algorithms for solving maximum satisfiability problems, a discrete chaotic quantum based bat algorithm based on the bat algorithm (BA) was proposed herein. This algorithm was discretized by conversion of continuous values to discrete binary codes. 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, chaotic mapping is used to replace fixed parameters, which enhances flexibility and diversity, and 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. [ABSTRACT FROM AUTHOR]