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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Summary: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 attention among many researchers. However, most of works are restricted to the single-objective case. In this work, we propose an adaptive hybrid population management strategy using memory, local search and random strategies, to effectively handle environment dynamicity for the multi-objective case where objective functions change over time. Moreover, the proposed strategy is based on a new technique that detects the change severity, according to which it adjusts the number of memory and random solutions to be used. This ensures, on the one hand, a high level of convergence and on the other hand, the required diversity. We propose a dynamic version of the Non dominated Sorting Genetic Algorithm II, within which we integrate the above-mentioned strategies. Empirical results show that our proposal based on the use of the adaptive strategy is able to handle dynamic environments and to track the Pareto front as it changes over time. Moreover, when confronted with several recently proposed dynamic algorithms, it has presented competitive and better results on most problems.
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-015-1820-4