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

Seyedali Mirjalili, Tim Rawlins, Jan Hettenhausen, Andrew Lewis


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.

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Multi-Objective Particle Swarm Optimization; MOPSO; 2d Airfoil Design

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DOI: http://dx.doi.org/10.21914/anziamj.v54i0.6154

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