基于量子行为的鸡群优化算法及其收敛性分析

Chicken Swarm Optimization Algorithm Based on Quantum Behavior and Its Convergence Analysis

针对鸡群算法易陷入局部最优、出现早熟收敛等缺陷,提出了一种基于量子行为的鸡群优化算法(Quantum-Behaved Chicken Swarm Optimization, QCSO)。通过利用鸡群中的个体信息建立量子化的势阱模型,根据原鸡群更新公式得到个体最优解和全局最优解,采用蒙特卡洛随机采样完成个体极值的更新,在个体极值和全局极值附近以并列的角度进行搜索,提高了算法的局部搜索性能。同时,结合随机算法全局收敛性的判别准则,研究了基于量子行为的鸡群优化算法的收敛性,证明了QCSO是一种全局收敛的优化算法。选取4个基本的测试函数对QCSO的优化能力进行测试,结果表明QCSO的寻优性能较原算法以及传统的优化算法都有较大的提升。

Aiming at the defects of chicken swarm optimization algorithm, such as easy to fall into local optimal, premature convergence and slow convergence, a chicken swarm optimization algorithm based on quantum behavior is proposed in this paper. A quantized potential well model was established based on the individual information of chicken swarm. According to the existing individual extremum and global extremum obtained by the original updating formula, Monte Carlo random sampling was adopted to complete the updating of individual extremum, and the search was conducted at a parallel Angle near individual extremum and global extremum, which improves the local search performance of the algorithm. At the same time, the convergence of quantum-behavior chicken swarm optimization algorithm was discussed in this paper, and QCSO was proved to be a globally convergent optimization algorithm. The optimization capability of QCSO was tested by using basic test function, and the results show that the optimization performance of this algorithm is greatly improved compared with the original algorithm.