In order to solve the problem that the existing multi-objective evolutionary algorithm is difficult to deal with the complex Pareto front(PF), an improved angle penalized distance and adaptive reference vector based multi-objective evolutionary algorithm(PDAREA) is proposed. An improved angle penalty distance strategy is used for individual selection to efficiently reduce the conflict between individual convergence and distribution in the population. The adaptive reference vector strategy can dynamically adjust the distribution of reference vectors according to the change of the objective function, which effectively improves the problem of uneven distribution of individuals on PF. Through the reference vector regeneration strategy, the ability and efficiency of the algorithm to deal with problems with irregular Pareto fronts is promoted. Finally, the proposed algorithm is compared with seven mainstream algorithms in simulation experiments, and applied to two practical applications. The results show that the proposed algorithm is highly competitive in solving many-objective optimization problems with complex Pareto fronts, which can effectively balance the convergence and distribution of the population.