Airfoil shape optimization using improved Multiobjective Territorial Particle Swarm algorithm with the objective of improving stall characteristics

In this paper, a new robust optimization technique with the ability of solving multi-objective constrained design optimization problems in aerodynamics is presented. This new technique is Multi-objective Territorial Particle Swarm Optimization (MOTPSO) algorithm in which diversity is actively preserved by avoiding overcrowded clusters of particles and encouraging broader exploration. Adaptively varying “territories” are formed around promising individuals to prevent many of the lesser individuals from premature clustering and encouraged them to explore new neighborhoods based on a hybrid self-social metric. Also, a new social interaction scheme is introduced which guided particles towards the weighted average of their “elite” neighbors’ best found positions instead of their own personal bests which in turn helps the particles to exploit the candidate local optima more effectively. The MOTPSO algorithm takes into account multiple objective functions using a Pareto-Based approach. The non-dominated solutions found by swarm are stored in an external archive and nearest neighbor density estimator method is used to select a leader for the individual particles in the swarm. Efficiency and robustness of the proposed algorithm is demonstrated using multiple traditional and newly-composed optimization benchmark functions and aerodynamic design problems. In final airfoil designs obtained from the Multi Objective Territorial Particle Swarm Optimization algorithm, separation point is delayed to make the airfoil less susceptible to stall in critical operating conditions and it also reveal an evident improvement over the test case airfoil across all objective functions presented.