A comparison of multi-objective optimisation metaheuristics on the 2D airfoil design problem

Variants of the multi-objective particle swarm optimisation (MOPSO) algorithm are investigated, mainly focusing on swarm topology, to optimise the well-known 2D airfoil design problem. The topologies used are global best, local best, wheel, and von Neumann. The results are compared to the non-dominated sorting genetic algorithm (NSGA-ii) and multi-objective tabu search (MOTS) algorithm, and it is found that the attainment surfaces achieved by some of the mopso variants completely dominate those of NSGA-ii. In general, the mopso algorithms also significantly improve diversity of solutions compared to mots. The mopso algorithm proves its ability to exploit promising solutions in the presence of a large number of infeasible solutions, making it well suited to problems of this nature.