The invention discloses a particle swarm improved algorithm based on chaotic backward learning. The algorithm mainly comprises a backward learning strategy and chaotic particle swarm optimization. The basic principle of the backward learning strategy includes: the backward learning strategy generates a corresponding backward solution for each initial candidate solution, and selects the solutions with short distance (high fitness) from two kinds of solutions (candidate solutions and corresponding backward solutions) as members of an initial population so that the convergence rate in an optimization process can be increased. In order to maintain the diversity of the population and enable individuals of the initial population to be uniformly distributed as much as possible, the initial population is generated by employing the backward learning strategy.