nevMOGA Multiobjective Evolutionary Algorithm

The nevMOGA algorithm is aimed at finding the optimal and nearly optimal solutions nondominated in their neighborhood.

The nevMOGA algorithm is aimed at finding not only the optimal solutions but also the nearly optimal solutions nondominated in their neighborhood.

Including the nearly optimal solutions in the decision-making scenario increases the number of alternatives available to the designer. But nevertheless it is important to obtain these solutions without obtaining an excessive number of solutions (to avoid complicating the optimization process and the decision stage). Therefore, in a multiobjetive optimization problem is valuable obtains a set of solutions manageable but, at the same time, without neglecting the existing diversity in the characteristics of the nearly optimal solutions. We believe that the solutions that fulfill these characteristics are the optimal and almost optimal alternatives nondominated in their neighbohood.

A designer adopting this approach will not want to be given two nearly optimal solutions which have similar characteristics (neighboring solutions), if one of them is dominated by the other (i.e., worse for at least one of the objectives and not better for the rest), as he or she will logically choose the nondominated one. However, if these two solutions have significantly different characteristics (i.e., they are nonneighboring solutions), then both of them will be interesting for the designer. If the designer has these solutions available, they can analyze them a posteriori (for example, by including new indicators or by considering the physical sense of the solutions), in order to decide which one is the most suitable. We will call these solutions potentially useful solutions.

nevMOGA obtains the set of potentially useful alternatives by providing the designer with a manageable number of solutions without neglecting the existing diversity in the characteristics of the nearly optimal solutions.

Details about nevMOGA are described in (please, cite this algorithm as):

[1] A. Pajares, X. Blasco, J.M. Herrero and G. Reynoso-Meza. A multiobjective genetic algorithm for the localization of optimal and nearly optimal solutions which are potentially useful: nevMOGA. Complexity, 2018.

nevMOGA has also been employed in:

[2] A. Pajares, X. Blasco, J.M. Herrero and G. Reynoso-Meza. A new point of view in multivariable controller tuning under multiobjetive optimization by considering nearly optimal solutions. IEEEAccess, 2019.