A sequential quadratic programming integrated particle swarm optimization algorithm (CPSO-SQP) is proposed.
This new algorithm uses CPSO, which makes the best of ergodicity of chaos mapping, as the global optimizer while the SQP
is employed for accelerating the local search. Thus, the particles are able to search the whole space while finding local optima
fast, which increases the possibility of exploring a global optimum in problems with more local optima while ensuring the
convergence of algorithm. The simulation results for benchmark functions show that CPSO-SQP has better accuracy, more
probability of finding global optimum and faster speed of convergence than those reported in the literature. The feasibility
of the method is illustrated with the challenging ethylene piece yardage optimization problem of a cascade HDPE reaction
course.