A parallelized hybrid genetic algorithm with differential evolution for heat exchanger network retrofit

The challenge of heat exchanger network retrofit is often addressed using deterministic algorithms. However, the complexity of the retrofit problems, combined with multi-period operation, makes it very difficult to find any feasible solution. In contrast, stochastic algorithms are more likely to find feasible solutions in complex solution spaces. This work presents a customized evolutionary based optimization algorithm to address this challenge. The algorithm has two levels, whereby, a genetic algorithm optimizes the topology of the heat exchanger network on the top level. Based on the resulting topology, a differential evolution algorithm optimizes the heat loads of the heat exchangers in each operating period. The following bullet points highlight the customization of the algorithm: • The advantage of using both algorithms: the genetic algorithm is used for the topology optimization (discrete variables) and the differential evolution for the heat load optimization (continuous variables). • Penalizing and preserving strategies are used for constraint handling; • The evaluation of the genetic algorithm is parallelized, meaning the differential evolution algorithm is performed on each chromosome parallel on multiple cores.