The heat exchanger networks synthesis (HENS) still remains an open problem due to its non-linear characteristics but also due to a great number of local optima in its solution space. This paper deals with the development of effective techniques to generate optimal heat exchanger network(HEN) automatically aiming to simultaneously balance the energy recovery target and investment costs. The optimization formulations of such a problem turn out to be a non-convex NLP/MINLP problem with equality and inequality constraints. The stochastic or meta-heuristic optimization algorithms seem to have some special advantages for the synthesis of complex HENS. However, one of the major issues for stochastic algorithms is how to handle the constraints. In this paper, we develop a robust particle swarm algorithm coupled with a novel adaptive constraint-handling technique to search for the global optimum of these formulations. The problem can be transformed into with no equality constraints through analyzing and tearing equality constraints. Two Classic HENS problems are performed to prove the applicability and efficiency of the proposed algorithm.
