Open AccessAnt colony optimisation algorithm for multiobjective subset selection problems
Author(s) -
Liu Yi,
Zhou Hao,
Wang Yanzhen,
Ren Xiaoguang,
Diao Xingchun
Publication year - 2019
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2019.1933
Subject(s) - selection (genetic algorithm) , ant colony optimization algorithms , mathematical optimization , computer science , ant colony , multi objective optimization , algorithm , mathematics , artificial intelligence
Multiobjective subset selection problems widely exist in many real‐world applications. Multiobjective ant colony optimisation (MOACO) is a strong and kind instrument for settling those issues. However, it still has two shortages which the authors must resolve. One, its solution construction process is inconsistent with the disorder characteristics of solutions, which prevent it from getting better solutions. Two, conventional MOACOs which most deal with biobjective optimisation problems are difficult to figure out high‐dimensional objectives optimisation problems. In this study, the authors propose a new MOACO to resolve those two disadvantages. They give its detailed descriptions and an exhaust experiment. And the results on C measure, spacing measure and inverted generational distance (IGD) show that the proposed algorithm has a powerful convergence ability and gets a better balance between convergence and diversity compared with other state of art approaches.