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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Published in:	IEEE transactions on evolutionary computation Vol. 22; no. 4; pp. 501 - 514
Main Authors:	Jiang, Min, Huang, Zhongqiang, Qiu, Liming, Huang, Wenzhen, Yen, Gary G.
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.08.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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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Abstract	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.
AbstractList	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.
Author	Jiang, Min Yen, Gary G. Huang, Zhongqiang Qiu, Liming Huang, Wenzhen
Author_xml	– sequence: 1 givenname: Min orcidid: 0000-0003-2946-6974 surname: Jiang fullname: Jiang, Min organization: Department of Cognitive Science and Technology, Fujian Key Laboratory of Machine Intelligence and Robotics, Xiamen University, Xiamen, China – sequence: 2 givenname: Zhongqiang surname: Huang fullname: Huang, Zhongqiang organization: Institute of Innovation Research, Sangfor Technologies, Shenzhen, China – sequence: 3 givenname: Liming surname: Qiu fullname: Qiu, Liming organization: Department of Cognitive Science and Technology, Fujian Key Laboratory of Machine Intelligence and Robotics, Xiamen University, Xiamen, China – sequence: 4 givenname: Wenzhen surname: Huang fullname: Huang, Wenzhen email: minjiang@xmu.edu.cn organization: Institute of Automation, Chinese Academy of Sciences, Beijing, China – sequence: 5 givenname: Gary G. orcidid: 0000-0001-8851-5348 surname: Yen fullname: Yen, Gary G. email: gyen@okstate.edu organization: School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA
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Snippet	One of the major distinguishing features of the dynamic multiobjective optimization problems (DMOPs) is that optimization objectives will change over time,...
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SubjectTerms	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
Title	Transfer Learning-Based Dynamic Multiobjective Optimization Algorithms
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