基于演化有哪些信誉好的足球投注网站信息的量子行为粒子群优化算法-计算机工程与应用.pdfVIP

Computer Engineering and Applications 计算机工程与应用 2017 ，53（9 ） 41 基于演化有哪些信誉好的足球投注网站信息的量子行为粒子群优化算法 1，2 3 赵 吉 ，程 成 ZHAO Ji1，2 , CHENG Cheng3 1.无锡环境科学与工程研究中心，江苏 无锡 214063 2.江南大学 物联网工程学院，江苏 无锡 214122 3. 中船重工 七○二研究所，江苏 无锡 214122 1.Research Centre of Environment Science Engineering, Wuxi, Jiangsu 214063, China 2.School of IoT Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China 3.China Ship Scientific Research Center 702, Wuxi, Jiangsu 214122, China ZHAO Ji, CHENG Cheng. Improved QPSO algorithm based on search history. Computer Engineering and Appli- cations, 2017, 53 （9 ）：41-46. Abstract: An improved Non-revisited QPSO algorithm based on search history （NrQPSO ）is proposed to help prevent pre- mature convergence and stagnate at local optimal solutions. NrQPSO is an integration of the entire search history scheme and a standard Quantum-behaved Particle Swarm Optimization （QPSO ）. It guarantees that all updated positions are not re- visited before and the diversity of particles is increased by mutation. The search history scheme partitions the continuous search space into overlap sub-regions by using BSP tree. The partitioned sub-region serves as mutation range such that the corresponding mutation is adaptive and parameterless. Compared with other traditional algorithms, the experiment results on eight standard testing functions show that the proposed algorithm is superior regarding the optimization of multimodal and unimodal functions, with enhancement in both convergence speed and precision. Those demonstrate the effectiveness of the algorithm. Key words: quantum-behaved particle swarm optimization; search history; binary space partitioning; non-revisited 摘 要：针对量子行为粒子群优化算法可能过早收敛而陷入局部最优的问题，提出了基于演化有哪些信誉好的足球投注网站信息的非重复访 问量子行为粒子群优化算法（Non-revisited QPSO ，NrQPSO ）。该算法将演化有哪些信誉好的足球投注网站信息记录方案和标准QPSO 算法 结合起来，确保所有更新的粒子位置都是未被重复访问的，并通过变异操作增加粒子的多样性。演化有哪些信誉好的足球投注网站信息记录 方案利用二维空间分割树（BSP ）将连续有哪些信誉好的足球投注网站空间划分为不同的重叠子区域，并且将子区域作为粒子