Transfer Learning-Based Dynamic Multiobjective Optimization Algorithms

One of the major distinguishing features of the dynamic multiobjective optimization problems (DMOPs) is that optimization objectives will change over time, thus tracking the varying Pareto-optimal front becomes a challenge. One of the promising solutions is reusing "experiences" to constru...

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Vydáno v:IEEE transactions on evolutionary computation Ročník 22; číslo 4; s. 501 - 514
Hlavní autoři: Jiang, Min, Huang, Zhongqiang, Qiu, Liming, Huang, Wenzhen, Yen, Gary G.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.08.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithm design and analysis
Algorithms
Classification
Dimensionality reduction
domain adaption
dynamic multiobjective optimization
evolutionary algorithm (EA)
Evolutionary algorithms
Genetic algorithms
Heuristic algorithms
Machine learning
Mathematical models
Multiple objective analysis
Optimization
Pareto optimization
Particle swarm optimization
Population (statistical)
Prediction algorithms
Predictive models
Sociology
Sorting algorithms
Statistics
transfer learning
ISSN:1089-778X, 1941-0026
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Shrnutí:One of the major distinguishing features of the dynamic multiobjective optimization problems (DMOPs) is that optimization objectives will change over time, thus tracking the varying Pareto-optimal front becomes a challenge. One of the promising solutions is reusing "experiences" to construct a prediction model via statistical machine learning approaches. However, most existing methods neglect the nonindependent and identically distributed nature of data to construct the prediction model. In this paper, we propose an algorithmic framework, called transfer learning-based dynamic multiobjective evolutionary algorithm (EA), which integrates transfer learning and population-based EAs to solve the DMOPs. This approach exploits the transfer learning technique as a tool to generate an effective initial population pool via reusing past experience to speed up the evolutionary process, and at the same time any population-based multiobjective algorithms can benefit from this integration without any extensive modifications. To verify this idea, we incorporate the proposed approach into the development of three well-known EAs, nondominated sorting genetic algorithm II, multiobjective particle swarm optimization, and the regularity model-based multiobjective estimation of distribution algorithm. We employ 12 benchmark functions to test these algorithms as well as compare them with some chosen state-of-the-art designs. The experimental results confirm the effectiveness of the proposed design for DMOPs.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2017.2771451