摘要
可满足性问题(SatisfiabilityProblem,SAT)是计算科学的典型问题之一,目前有DP算法、SAT1.3算法和遗传算法等多种求解方法。文章根据Kennedy和Eberhart提出的二进制粒子群优化算法(BinaryParticleSwarmOptimizers),基于局部随机搜索策略,给出了一种求解3-SAT问题的新方法:基于局部随机搜索的改进二进制粒子群优化算法(ModifedBinaryParticleSwarmOptimizersBasedonlocalstochasticsearch,简称MBPSO)。数值实验表明,对于随机产生的3-SAT问题测试实例,该算法是一种高效实用的新方法。
Satisfiability Problem（SAT） is one of the typical problems in computer science.At present,there are many algorithms,for example,DP Algorithm,SAT1.3,simulated annealing Algorithm and Genetic Algorithm etc.In this paper,we present Binary Particle Swarm Optimiziser for solving Satisfiability Problem（SAT）,and advances an Modifed Binary Particle Swarm Optimiziser based on local stochastic search strategy（for short MBPSO）.The numerical experiment show that MBPSO algorithm is an efficient and practical method.
出处
《计算机工程与应用》
CSCD
北大核心
2006年第16期70-72,共3页
Computer Engineering and Applications
