An improved discrete particle swarm optimization(PSO) algorithm is designed to tackle the general uncapacitated
Lot-sizing problem. The encoding scheme of particles is designed in terms of setup states of production units, while
an effective decoding procedure translates a particle into a feasible production plan. Different from the traditional PSO
algorithm, the improved PSO algorithm incorporates single cutting-point crossover operators to improve the intensification
ability of the algorithm. In addition, mutation operators and velocity disturbance strategies are also introduced into the PSO
algorithm to keep the diversity of swarm. By using those operators, the proposed algorithm can get good balance between
exploitation and exploration. Computational results on 90 randomly generated test instances show the good performance of
the proposed PSO algorithm.