The Porcupine Measure for Comparing the Performance of Multi-Objective Optimization Algorithms

In spite of being introduced over twenty-five years ago, Fonseca and Fleming’s attainment surfaces have not been widely used. This article investigates some the shortcomings that may led to lack adoption this performance measure. The quantitative measure based on surfaces, by Knowles Corne, is analyzed. analysis shows results obtained Corne approach are influenced (biased) shape surface. Improvements for bi-objective pof comparisons proposed. Furthermore, assuming M objective functions, an M-dimensional attainment-surface-based measure, named porcupine proposed comparing multi-objective optimization algorithms. A computationally optimized version presented empirically