A note on multi-objective improved teaching–learning based optimization algorithm (MO-ITLBO)

Recently Multi-Objective Improved Teaching-Learning-Based-Optimization algorithm (MO-ITLBO) has been proposed to solve complex multi-objective optimization problems and has been shown to be competitive against various other state-of-the-art algorithms. The algorithm was demonstrated on the constrain...

Full description

Saved in:
Bibliographic Details
Published in:Information sciences 2016-12, Vol.373, p.337-350
Main Authors: Chinta, Sivadurgaprasad, Kommadath, Remya, Kotecha, Prakash
Format: Article
Language:English
Subjects:
Algorithms
Constraints
Epsilon dominance
Harnesses
Improved teachinglearning based optimization
Multi-objective optimization
Multiple objective analysis
Optimization
Pareto dominance
State of the art
Teaching-learning based optimization
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Recently Multi-Objective Improved Teaching-Learning-Based-Optimization algorithm (MO-ITLBO) has been proposed to solve complex multi-objective optimization problems and has been shown to be competitive against various other state-of-the-art algorithms. The algorithm was demonstrated on the constrained and unconstrained optimization problems of CEC 2009 and was reported to have shown impressive results. However, some critical steps in the algorithm have not been adequately described, and these have become major impediments even for the implementation of MO-ITLBO. In this note, we have explained all such issues which need to be convincingly addressed so that independent researchers could evaluate and use MO-ITLBO for various other applications. Also, two variants of MO-ITLBO have been suggested whose results enforce that the issues reported in this article are critical to harness the reported benefits of the MO-ITLBO.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2016.08.061