Open AccessA Convergence Indicator for Multi-Objective Optimisation Algorithms
Author(s) -
Thiago Fontes Santos,
Sebastião Martins Xavier
Publication year - 2018
Publication title -
tema
Language(s) - English
Resource type - Journals
eISSN - 2179-8451
pISSN - 1677-1966
DOI - 10.5540/tema.2018.019.03.437
Subject(s) - convergence (economics) , measure (data warehouse) , entropy (arrow of time) , mathematics , key (lock) , mathematical optimization , algorithm , set (abstract data type) , pareto principle , computer science , data mining , physics , computer security , quantum mechanics , economics , programming language , economic growth
The algorithms of multi-objective optimisation had a relative growth in the last years. Thereby, it's requires some way of comparing the results of these. In this sense, performance measures play a key role. In general, it's considered some properties of these algorithms such as capacity, convergence, diversity or convergence-diversity. There are some known measures such as generational distance (GD), inverted generational distance (IGD), hypervolume (HV), Spread($\Delta$), Averaged Hausdorff distance ($\Delta_p$), R2-indicator, among others. In this paper, we focuses on proposing a new indicator to measure convergence based on the traditional formula for Shannon entropy. The main features about this measure are: 1) It does not require tho know the true Pareto set and 2) Medium computational cost when compared with Hypervolume.