Migration NSGA: method to improve a non-elitist searching of Pareto front, with application in magnetics
Elisabetta Sieni, Paolo Di Barba, Michele Forzan · Inverse Problems in Science and Engineering · 2015
The paper describes a migration strategy to improve classical non-dominated sorting genetic algorithm (NSGA) to find optimal solution of a multi-objective problem. Migration NSGA has been tested to assess its performance using analytical functions for which the Pareto front is known in analytical form, as well as two case studies in electromagnetics, for which the Pareto front is not known a priori. This strategy improves the approximation of the Pareto-optimal solutions of a multi-objective problem by introducing new individuals in the population miming the effect of migrations.