Storage planning of automated pharmacy based on an improved adaptive chaotic particle swarm optimization algorithm

With the target of achieving dense storage of packed medicine of rapid dispensing system in automated pharmacy, an improved adaptive chaotic particle swarm optimization (IACPSO) algorithm was proposed to solve the storage planning problem. According to the proposed IACPSO algorithm, the diversity of the particle swarm was enhanced by using the ergodicity of the chaos motion to initialize the swarm; a part of particles were chosen on the basis of their fitness value and optimized by chaos optimization algorithm to help the inert ones jump out the local extremum region at each iteration; the capability of global and local search was improved by introducing an adaptive inertia weight factor for each particle to adjust its inertia weight factor adaptively in response to its fitness. The IACPSO algorithm was used to solve mathematical model. Simulation results showed that this algorithm got rid of the shortcomings that the Particle Swarm Optimization (PSO) was easy to fall into of the local extreme point, while kept the rapidity in early search. The algorithm improved the efficiency of intelligent storage system, and implemented intensive storage.