Solving dynamic multi-objective problems with a new prediction-based optimization algorithm

This paper proposes a new dynamic multi-objective optimization algorithm by integrating a new fitting-based prediction (FBP) mechanism with regularity model-based multi-objective estimation of distribution algorithm (RM-MEDA) for multi-objective optimization in changing environments. The prediction-...

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Bibliographic Details
Published in:PloS one 2021-08, Vol.16 (8), p.e0254839-e0254839
Main Authors: Zhang, Qingyang, Jiang, Shouyong, Yang, Shengxiang, Song, Hui
Format: Article
Language:English
Subjects:
Algorithms
Biology and Life Sciences
Changing environments
Computer and Information Sciences
Computer science
Dynamic characteristics
Earth Sciences
Ecology and Environmental Sciences
Environmental changes
Genetic algorithms
Linear prediction
Machine learning
Mathematical optimization
Methods
Multiple criteria decision making
Multiple objective analysis
Optimization
Optimization algorithms
Pareto optimization
Physical Sciences
Population
Prediction models
Reaction mechanisms
Research and Analysis Methods
Sampling
Social Sciences
Strategy
Subpopulations
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Summary:This paper proposes a new dynamic multi-objective optimization algorithm by integrating a new fitting-based prediction (FBP) mechanism with regularity model-based multi-objective estimation of distribution algorithm (RM-MEDA) for multi-objective optimization in changing environments. The prediction-based reaction mechanism aims to generate high-quality population when changes occur, which includes three subpopulations for tracking the moving Pareto-optimal set effectively. The first subpopulation is created by a simple linear prediction model with two different stepsizes. The second subpopulation consists of some new sampling individuals generated by the fitting-based prediction strategy. The third subpopulation is created by employing a recent sampling strategy, generating some effective search individuals for improving population convergence and diversity. Experimental results on a set of benchmark functions with a variety of different dynamic characteristics and difficulties illustrate that the proposed algorithm has competitive effectiveness compared with some state-of-the-art algorithms.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0254839