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In order to avoid particle swarm optimization algorithm easy to fall into local optimum in solving complex problems, this paper proposed a new adaptive dynamic cultural particle swarm optimization algorithm. It introduced the evaluation of particle swarm premature convergence indicators into population space. By calculating the evaluation of particle swarm premature convergence indicators, decisions whether to have mutated operation on population space. It made the improved algorithm could make better use of mechanism of dual evolution and dual promotion in cultural particle swarm optimization algorithm. It adjusted the inertia weight of the particle adaptively based on the premature convergence degree of the swarm. The diversity of inertia weight made a compromise between the global convergence and convergence speed. It tested the proposed algorithm with four well-known benchmark functions. The experimental results show that the new algorithm has great global search ability convergence accuracy and convergence velocity is also increased and avoid the premature convergence problem effectively. [ABSTRACT FROM AUTHOR]