The advantages of genetic algorithm(GA),the particle swarm optimization(PSO)and chaotic motion characteristics are combined in this paper. The chaotic particle swarm genetic algorithm (DCPSO-GA)joined with the chaos perturbing is put forward,and the global optimization performance of the hybrid algorithm are analyzed by 5 high dimensional nonlinear test function. The stagnation phenomenon which appears in the optimal search is solved by DCPSO-GA. The search space of the global optimization is expanded and the diversity of the particle is enriched,while the function gradient information is not required. The global optimal solution can be found by DCPSO-GA for the 5 test function in this paper,and its convergence rate is very fast,greatly reducing the amount of computation. Moreover,it can be known that when the total number of target function calls is close to or less than other related algorithms,the improved algorithm has a great improvement in the calculation accuracy and convergence speed. The DCPSO-GA algorithm is applied to heavy oil cracking parameter estimation and prediction. It can be shown in the test results that the parameter estimation and prediction accuracy can be improved,the error can be reduced,the global optimal solution can be effectively found,the convergence speed can be improved and the amount of calculation can be greatly reduced.%结合遗传算法（GA）和粒子群算法（PSO）的优点以及混沌运动的特性，提出了加入混沌扰动的混沌粒子群遗传算法（DCPSO-GA），并使用5个高维非线性测试函数考察全局优化混合算法的性能。DCPSO-GA解决了在寻优搜索时出现的停滞现象，扩大了全局优化的搜索空间，丰富了粒子的多样性，且不需要函数梯度信息。测试结果证明，针对本文的5个测试函数DCPSO-GA能找到全局最优解，其收敛速度很快，大大减少了计算量。而且，经过与其他相关算法比较可知，当总的目标函数调用次数较接近或更少时，改进算法不论在计算精度还是收敛速度上，均有很大的提高。并将DCPSO-GA算法应用到重油裂解参数估计和预测中，测试结果证明，其提高了参数估计和预测的准确性，降低了误差，能有效找到全局最优解，收敛速度快，大大减少计算量。
展开▼
