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发表时间：2022-10-10
发表刊物：化工进展
期号：9
页面范围：2663-2669
ISSN号：1000-6613
摘要：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.
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