Exploiting characterization of dynamism for enhancing dynamic multi-objective evolutionary algorithms

Characterization of dynamism is an essential phase for some of the dynamic multi-objective evolutionary algorithms (DMOEAs) in order to improve their performance. Although frequency of change and severity of change are the two main perspectives of characterizing dynamic features of the dynamic multi...

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Bibliographic Details
Published in:Applied soft computing Vol. 85; p. 105783
Main Authors: Sahmoud, Shaaban, Topcuoglu, Haluk Rahmi
Format: Journal Article
Language:English
Published: Elsevier B.V 01.12.2019
Subjects:
Change detection
Characterization of change
Dynamic multi-objective evolutionary algorithms
Dynamic multi-objective optimization problems
Dynamic multi-objective evolutionary algorithms
Characterization of change
Change detection
Dynamic multi-objective optimization problems
ISSN:1568-4946, 1872-9681
Online Access:Get full text
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Summary:Characterization of dynamism is an essential phase for some of the dynamic multi-objective evolutionary algorithms (DMOEAs) in order to improve their performance. Although frequency of change and severity of change are the two main perspectives of characterizing dynamic features of the dynamic multi-objective optimization problems (DMOPs), they do not sufficiently attract attentions of the research community. In this paper, we propose a set of new sensor-based change detection schemes for the DMOPs that significantly outperform the current used change detection schemes. Additionally, a new technique is proposed for detecting the change severity for DMOPs. The experimental evaluation based on different test problems and change severity levels validates performance of our technique. We also propose a novel adaptive algorithm called change-responsive NSGA-II (CR-NSGA-II) algorithm that incorporates the change detection schemes, the technique for change severity and a new response mechanism into the NSGA-II algorithm. Our algorithm demonstrates competitive and significantly better results than the leading DMOEAs on majority of test problems and metrics considered. •We propose change detection schemes for Dynamic Multi-Objective Optimization Problems.•A new technique for detecting the severity of changes for DMOPs is also presented.•A new algorithm, change-responsive NSGA-II (CR-NSGA-II), is proposed for DMOPs.•Our algorithm utilizes severity of changes and incorporates a new response mechanism.•The CR-NSGA-II demonstrates better results than the related work on majority of tests.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2019.105783