A hybrid particle swarm optimization algorithm and its application in hydrogen management

This paper proposes a hybrid particle swarm optimization algorithm with local chaos search. It combines the particle swarm optimization with the chaos optimization and takes full advantage of the quick and broad search of the former and the randomness and ergodicity of the latter simultaneously. This hybrid algorithm not only overcomes the basic particle swarm optimization algorithm´s disadvantage of easily getting into the local extremum in the later evolution period, but also keeps the rapidity in the previous period. In the final, this algorithm is used to resolve the static scheduling model of hydrogen balance in the refinery. The balance between hydrogen production and utilization is achieved, and at the same time, the cost of hydrogen utilization is reduced as much as possible.