dynamic multiobjective optimization
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2021 ◽  
Author(s):  
Tianyu Liu ◽  
Lei Cao ◽  
Zhu Wang

AbstractDynamic multiobjective optimization problems (DMOPs) require the evolutionary algorithms that can track the moving Pareto-optimal fronts efficiently. This paper presents a dynamic multiobjective evolutionary framework (DMOEF-MS), which adopts a novel multipopulation structure and Steffensen’s method to solve DMOPs. In DMOEF-MS, only one population deals with the original DMOP, while the others focus on single-objective problems that are generated by the weighted summation of the original DMOP. Then, Steffensen’s method is used to control the evolving process in two ways: prediction and diversity-maintenance. Particularly, the prediction strategy is devised to predict the next promising positions for the individuals that handle single-objective problems, and the diversity-maintenance strategy is used to increase population diversity before the environment changes and reinitialize the multiple populations after the environment changes. This paper gives a comprehensive comparison of DMOEF-MS with some state-of-the-art DMOEAs on 14 DMOPs and the experimental results demonstrate the effectiveness of the proposed algorithm.


2021 ◽  
Vol 560 ◽  
pp. 307-330
Author(s):  
Huipeng Xie ◽  
Juan Zou ◽  
Shengxiang Yang ◽  
Jinhua Zheng ◽  
Junwei Ou ◽  
...  

2021 ◽  
Author(s):  
Qiang He ◽  
Zheng Xiang ◽  
Peng Ren

Abstract In recent years, the dynamic multiobjective optimization problems (DMOPs), whose major strategy is to track the varying PS (Pareto Optimal Solution, PS) and/or PF (Pareto Optimal Frontier), caused a great deal of attention worldwide. As a promising solution, reusing of “experiences” to establish a prediction model is proved to be very useful and widely used in practice. However, most existing methods overlook the importance of environmental selection in the evolutionary processes. In this paper, we propose a dynamic multiobjective optimal evolutionary algorithm which is based on environmental selection and transfer learning (DMOEA-ESTL). This approach makes full use of the environmental selection and transfer learning technique to generate individuals for a new environment by reusing experience to maintain the diversity of the population and speed up the evolutionary process. As experimental validation, we embed this new scheme in the NSGA-II (non-dominated sorting genetic algorithm). We test the proposed algorithm with the help of six benchmark functions as well as compare it with the other two prediction based strategies FPS (Forward-looking Prediction Strategy, FPS) and PPS (Population Prediction Strategy, PPS). The experimental results testify that the proposed strategy can deal with the DMOPs effectively.


2021 ◽  
Vol 545 ◽  
pp. 1-24
Author(s):  
Xuemin Ma ◽  
Jingming Yang ◽  
Hao Sun ◽  
Ziyu Hu ◽  
Lixin Wei

2021 ◽  
Vol 546 ◽  
pp. 349-367 ◽  
Author(s):  
Ali Ahrari ◽  
Saber Elsayed ◽  
Ruhul Sarker ◽  
Daryl Essam ◽  
Carlos A. Coello Coello

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