Abstract: An improved adaptive particle swarm optimization algorithm (IAPSO), which is inspired from hormone modulation mechanism, is used to minimize the maximal makespan of the permutation flow-shop scheduling problem (FSSP). The initial best position of each particle is no longer the randomly generated initial position of each particle; it is converted from the sequence of jobs, which is generated by greedy randomized adaptive search based on heuristics. Inspired from hormone modulation mechanism, the hormonal regular factor (HF) is used to modify the updating equations of particle swarm, which is based on the information of the particles around the single particle. it improves the flying function of the particle swarm in order to obtain better searching efficiency and searching quality. The simulation results based on benchmarks demonstrate its feasibility and effectiveness.

Key words: Hormonal factor, Hormone modulation mechanism, Improved adaptive particle swarm optimization algorithm (IAPSO), Permutation flow-shop scheduling problem (PFSP)