A dynamic multi-objective evolutionary algorithm using a change severity-based adaptive population management strategy

In addition to the need for simultaneously optimizing several competing objectives, many real-world problems are also dynamic in nature. These problems are called dynamic multi-objective optimization problems. Applying evolutionary algorithms to solve dynamic optimization problems has obtained great...

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Bibliographic Details
Published in:	Soft computing (Berlin, Germany) Vol. 21; no. 4; pp. 885 - 906
Main Authors:	Azzouz, Radhia, Bechikh, Slim, Said, Lamjed Ben
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2017 Springer Nature B.V
Subjects:	Artificial Intelligence Classification Computational Intelligence Control Engineering Evolutionary algorithms Genetic algorithms Mathematical Logic and Foundations Mechatronics Methodologies and Application Multiple objective analysis Optimization techniques Pareto optimization Robotics Sorting algorithms Strategy Local search-based strategy Dynamic multi-objective optimization Adaptive population management Change severity-based strategy Change severity sensing Time-changing objective functions Memory-based strategy
ISSN:	1432-7643, 1433-7479
Online Access:	Get full text
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