摘要
基于量子理论提出一种量子混合蛙跳算法,该算法采用量子位的Bloch球面坐标编码个体,利用量子位在Bloch球面上绕轴旋转的方法更新个体,通过自适应混沌旋转角度算子提高子群内部局部搜索能力,采用Hadamard门实现个体变异避免早熟,有效扩展了解空间的搜索范围.实验结果表明,该方法优于普通的混合蛙跳算法、粒子群算法和遗传算法,具有较高的优化能力和效率,更适合高维复杂函数的优化.
A quantum shuffled frog leaping algorithm was proposed which combines with the quantum theory.In this algorithm,the individuals are expressed with Bloch spherical coordinates of qubits,the individual update is realized with the rotation of qubits in Bloch sphere,and the local search capabilities within the subgroup is improved with adaptive chaotic rotation angle operator.Then,to avoid premature convergence,the mutation of individuals is achieved with Hadamard gates.Above operations extend the search of the solution space effectively.Results of experiments show that compared with the SFLA,PSO and GA,the algorithm has a higher optimization capability and efficiency,and is more suitable for high-dimensional optimization of complex functions.
出处
《吉林大学学报（理学版）》
CAS
CSCD
北大核心
2013年第3期471-477,共7页
Journal of Jilin University:Science Edition
基金
国家自然科学基金(批准号:61170132)
黑龙江省教育厅科学技术研究项目(批准号:11551015)
黑龙江省教育厅科研基金(批准号:12511009)
关键词
量子计算
混合蛙跳算法
连续空间优化
仿真
quantum computing
shuffled frog leaping algorithm
continuous space optimizing
simulation
