Research Institute of New energy Science and Technology，University of Shanghai for Science and Technology，Shanghai，China, Post Code: 200093

Because differential evolution algorithm has weak local search ability and easily converges to a local minimum when optimizing the heat exchanger networks (HEN),a distributedmemetic differential evolution algorithm based on dynamic topology is developed for simultaneous synthesis of HEN. Firstly, subpopulations are connected by the von Neumann topology and individuals in the subpopulation are connected by a dynamic topology based on the Euclidean distance, which can speed up the information exchange and expand the search scope. Then the Hooke-Jeeves algorithm is introduced to improve the local search ability. Meanwhile, for handling the new solution generated by Hooke-Jeeves algorithm, a collaborative learning mechanism is proposed to balance the global search ability and the convergence speed. Finally, two optimizing strategies of integer variables are combined with the proposed algorithm in order to simultaneously optimize continuous and integer variables. Two case studies are selected to demonstrate the validity of this approach. The result of case 1 shows that the proposed algorithm is better than the standard DE algorithm and other improved versions of DE algorithm. The result of case 2 shows that the proposed algorithm can effectively tackle the largescale HEN problem and has a strong robustness.
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