Solving heat exchanger network synthesis problems with Tabu Search

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-based and frequency-based tabu lists are used to provide short-term and longterm 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.