Δp-MOEA: A new multi-objective evolutionary algorithm based on the Δp indicator
Adriana Menchaca-Méndez, Carlos Hernández, Carlos A. Coello Coello · 2016
In this paper, we propose a new selection scheme for Multi-Objective Evolutionary Algorithms (MOEAs) based on the Δρindicator. Our new selection scheme is incorporated into a MOEA giving rise to the “Δρ-MOEA.” Perhaps, one of the most important disadvantages of MOEAs based on Δρis the definition of the reference set. In this work, we propose to create a reference set at each generation using e-dominance and the set of nondominated solutions found so far. Our new selection scheme uses two different techniques to select solutions according to the modified generational distance indicator or the modified inverted generational distance indicator. Our proposed Δp-MOEA is validated using standard test functions taken from the specialized literature, having three to six objective functions and it is compared with respect to two well-known MOEAs: MOEA/D using Penalty Boundary Intersection (PBI), which is based on decomposition, and SMS-EMOA-HYPE (a version of SMS-EMOA that uses a fitness assignment scheme based on the use of an approximation of the hypervolume indicator).