量子混沌自适应粒子群优化算法的研究  

Research on Quantum Chaos Adaptive PSO Algorithm

作　　者：丁知平[1] DING Zhi-ping(Institute of information technology and creative design,Qingyuan Polytechnic,Qingyuan,Guangdong 511510,China)

机构地区：[1]清远职业技术学院信息技术与创意设计学院,广东清远511510

出　　处：《软件》2018年第4期9-14,共6页Software

基　　金：广东省高等学校优秀青年教师培养对象项目;清远市科技计划项目(2016B002);广东省质量工程项目(GDJG2015242)

摘　　要：为了提高粒子群算法求解连续函数优化问题的性能,提出一种量子混沌自适应粒子群优化算法。该算法首先采用量子位Bloch球面坐标编码方案对群体初始位置进行初始化,此种编码方式能扩展对搜索空间的遍历性,增加群体的多样性,进而加快算法的收敛速度;其次,采用Logistic混沌对种群的精英个体进行混沌搜索,有效地避免了粒子群算法陷入局部最优,从而获得更高质量的最优解。最后,采用自适应非线性惯性权值以进一步改善PSO算法的收敛速度和优化精度。通过对8个复杂高维函数寻优测试,仿真结果表明,改进算法更具竞争力,其性能整体较优,尤其适合复杂的高维函数寻优。To improve the performance of particle swarm optimization（PSO） for complex function optimization problems, a quantum chaotic self-adapting particle swarm optimization（QCA-PSO） algorithm is proposed. First, the Bloch spherical coordinate coding scheme is used to initialize the initial position of the population. This coding method can extend the ergodicity of the search space and increase the diversity of the population so as to accelerate the convergence speed of the algorithm. Then, logistic chaos is used to search the elite individuals of the population, which help to particle swarm optimization algorithm avoid into local optimum and obtain the higher quality optimal solution. Finally, the convergence speed and the optimization precision for PSO algorithm can be further accelerated by using self-adaptive inertia weight. Experiments on eight complex functions with high-dimension, simulation results demonstrate that the improved algorithm is competitive, and it has a better overall performance, especially for complex high-dimensional functions optimization.

关 键 词：粒子群优化 量子算法 混沌 函数优化 自适应 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]