Different evolutionary algorithms, by their very nature, will have different search trajectory characteristics. Understanding these particularly for real world problems gives researchers and practitioners valuable insights into potential problem domains for the various algorithms, as well as an understanding for potential hybridisation. In this study, we examine three evolutionary techniques, namely, multi-objective particle swarm optimisation, extremal optimisation and tabu search. A problem that is to design optimal cross sectional areas of airfoils that maximise lift and minimise drag, is used. The comparison analyses actual parameter values, rather than just objective function values and computational costs. It reveals that the three algorithms had distinctive search patterns, and favoured different regions during exploration of the design space.
Randall, M., Rawlins, T., Lewis, A., & Kipouros, T. (2015). Performance comparison of evolutionary algorithms for airfoil design. In Procedia Computer Science (Vol. 51, pp. 2267–2276). Elsevier B.V. https://doi.org/10.1016/j.procs.2015.05.384