A novel random walk algorithm with compulsive evolution(RWCE) was proposed on the basis of different heuristic methods for global optimization of heat exchanger networks. In RWCE algorithm

both integer(e.g.

number of heat exchanger units) and continuous(e.g.

area of heat exchanger) variables were optimized simultaneously by expanding or contracting randomly area of heat exchangers in the direction of targeting cost reduction. Moreover

when individuals walked around local optima

the RWCE algorithm could compulsively accept imperfect networks at certain probability such that it had strong capability of jumping out of the local optima and continuing global optimization. Several case studies indicated that the proposed RWCE algorithm

compared to other heuristic methods

possessed characteristics of simple evolution strategy

strong algorithm suitability and global searchability

which significantly improved optimization performance.