Work

Evaluating Multiobjective Evolutionary Algorithms Using MCDM Methods

Year: 2018

Type: article

Abstract: The evaluation of multiobjective evolutionary algorithms (MOEAs) involves many metrics, it can be considered as a multiple-criteria decision making (MCDM) problem. A framework is proposed to estimate MOEAs, in which six MOEAs, five performance metrics, and two MCDM methods are used. An experimental study is designed and thirteen benchmark functions are selected to validate the proposed framework. The experimental results have indicated that the framework is effective in evaluating MOEAs.

Source: Mathematical Problems in Engineering

DOI: 10.1155/2018/9751783

Authors: Xiaobing Yu, YiQun Lu, Xianrui Yu

Institution: Nanjing University of Information Science and Technology

Language: English

Topic: Advanced Multi-Objective Optimization Algorithms

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,550

Funder: National Natural Science Foundation of China