Choosing the best evolutionary algorithm to optimize the multiobjective shell-and-tube heat exchanger design problem using PROMETHEE

The aim of this paper is twofold. First, to find Pareto solutions for minimization of the heat-transfer area and pumping power to solve a shell-and-tube heat exchanger multiobjective optimization problem using the Predator-Prey, Multiobjective Particle Swarm Optimization, and Non-Dominated Sorting Genetic Algorithm II evolutionary algorithms. Each algorithm's performance is analyzed using the following statistical metrics: Hypervolume, Spacing and Pair-wise Distance. Second, to use the Preference Ranking Organization Method for Enrichment Evaluations decision-making method to choose the best evolutionary algorithms. The criteria used in decision making are the statistical metrics and the annual cost heat exchanger operation. The results show the Multiobjective Particle Swarm Optimization as the most robust algorithm during decision making.