Solving heat exchanger network synthesis problems with Tabu Search

Abstract

This paper describes the implementation of a meta-heuristic optimization approach, Tabu Search (TS), for heat exchanger networks (HEN) synthesis and compares this approach to others presented in the literature. TS is a stochastic optimization approach that makes use of adaptive memory in the form of Tabu lists. Both recency- and frequency-based Tabu lists are used to provide short- and long-term knowledge of search history. TS is shown to locate the global optima with a high probability and low computation times, demonstrating the algorithm’s potential for solving a variety of other mixed integer nonlinear programming (MINLP) problems.