A particle swarm optimization for solving NLP/MINLP process synthesis problems

A hybrid particle swarm optimization algorithm which includes two alternative gradient-based methods for handling constraints has been proposed to solve process synthesis and design problems which involve continuous and binary variables and equality and inequality constraints (a mixed integer non-linear programming problem, MINLP). The first method for handling constraints uses the Newton-Raphson algorithm (NR) and the other method transforms the problem of finding the feasible region into a constrained simulation problem (CSP). The efficiency of both hybrid PSO algorithms has been tested and compared with the original PSO method. The two hybrid algorithms are able to achieve the global optimum for a small planning problem chosen as case study.