Transfer Learning-Based Dynamic Multiobjective Optimization Algorithms

By Min Jiang, Zhongqiang Huang, Liming Qiu, Wenzhen Huang, Gary G. Yen. IEEE Transactions on Evolutionary Computation, 2016

346 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/ac5d2f7d5f6082cd4e24effdc4e663017f9d550b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Bing Xue
2. Mengjie Zhang
3. Yanan Sun
4. Gary G. Yen
5. Jiancheng Lv
6. Yaochu Jin
7. Yuqiao Liu
8. Handing Wang

In Computational Theory and Mathematics

1. Bing Xue
2. Mengjie Zhang
3. Yanan Sun
4. Gary G. Yen
5. Jiancheng Lv
6. Yaochu Jin
7. Yong Wang
8. Tetsuyuki Takahama

Citations in the corpus

• Automatically Designing CNN Architectures Using Genetic Algorithm for Image Classification (2018, background): “…include genetic algorithms (GAs) [23], genetic programming [24], evolutionary strategy [25], etc., among which GAs are the most popular one mainly because of their theoretical evidences [26] and promising performance in solving different optimization problems [27], [28], [29], [30], [31].”
• Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification (2018, methodology): “Based on our previous work [58], the tournament selection with or without replacement almost has no different bias to the final performance, and the replacement mechanism used in the proposed algorithm is just to follow the common practice [27], [28], [30].”