In order to improve the performance of Particle Swarm Optimization (PSO) algorithm in solving continuous function optimization problems, a chaotic particle optimization algorithm for complex functions is proposed. Firstly, the algorithm uses qubit Bloch spherical coordinate coding scheme to initialize the initial position of the population. This coding method can expand the ergodicity of the search space, increase the diversity of the population, and further accelerate the convergence speed of the algorithm. Secondly, Logistic chaos is used to search the elite individuals of the population, which effectively prevents the PSO algorithm from falling into local optimization, thus obtaining higher quality optimal solution. Finally, complex functions are used to improve chaotic particles to further improve the convergence speed and optimization accuracy of PSO algorithm. Through the optimization tests of four complex high-dimensional functions, the simulation results show that the improved algorithm is more competitive and its overall performance is better, especially suitable for the optimization of complex high-dimensional functions.

为了提高粒子群算法在求解连续函数优化问题上的性能，提出了一种针对复杂函数的混沌粒子优化算法。首先，该算法使用qubit Bloch球面坐标编码方案来初始化总体的初始位置。该编码方法可以扩大搜索空间的遍历性，增加种群的多样性，并进一步加快算法的收敛速度。其次，利用逻辑混沌搜索人口中的精英个体，有效地防止了PSO算法陷入局部最优，从而获得了更高质量的最优解。最后，复杂函数用于改善混沌粒子，进一步提高了粒子群优化算法的收敛速度和优化精度。通过对四个复杂高维函数的优化测试，仿真结果表明，改进算法具有较强的竞争力，整体性能更好，特别适合于复杂高维函数的优化。