Aiming at the problem that the moth-flame optimization algorithm is easy to fall into the local optimal trap and the global optimization ability is insufficient, chaotic sequences, simulated annealing algorithm, and genetic algorithm were utilized to propose a tent chaos and simulated annealing improved moth-flame optimization algorithm. First, the tent chaotic sequences were used to initialize the population and increase the diversity of the population. Then the new solution was obtained by adding disturbance to the current optimal solution which was further proportionally hybridized to generate a final new solution. According to the Metropolis criterion in the simulated annealing algorithm, the ultimate solution was finally obtained by determining whether the new hybridized solution was accepted. The performance of the proposed algorithm was tested through complex high-dimensional benchmark functions and trajectory planning problems. The results of six complex benchmark function optimization tests show that for the 10-dimensional benchmark function, the algorithm converged to the optimal value after about 0.25 seconds, and for 50 dimensions, it was about 0.5 seconds. Compared with the standard moth-flame optimization algorithm and other intelligent optimization algorithms, the proposed algorithm could effectively jump out of the local optimal solution in the discussed scenarios, and the accuracy precision and the convergence speed were improved in certain degree. The trajectory planning simulation results show that for an airspace environment with four no-fly zones and two threat sources, the proposed algorithm could obtain the optimal trajectory after about 100 iterations, which is more accurate than the standard moth-flame optimization algorithm. Therefore, it is valuable for engineering applications and has better performance.