New Evolutionary Algorithm for Dynamic Multiobjective Optimization Problems

2006 ◽  
pp. 889-892 ◽  
Author(s):  
Chun-an Liu ◽  
Yuping Wang
Keyword(s):  
Multiobjective Optimization ◽  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Multiobjective Optimization Problems ◽  
Dynamic Multiobjective Optimization

scholarly journals A Hybrid Predictive Strategy Carried through Simultaneously from Decision Space and Objective Space for Evolutionary Dynamic Multiobjective Optimization

2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Peng Xu ◽  
Xiaoming Wu ◽  
Man Guo ◽  
Shuai Wang ◽  
Qingya Li ◽  
...  
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problems ◽  
Ideal Point ◽  
Dynamic Environment ◽  
Decision Space ◽  
Center Point ◽  
Multiobjective Optimization Problems ◽  
Objective Space ◽  
Adaptive Diversity ◽  
Dynamic Multiobjective Optimization

There are many issues to consider when integrating 5G networks and the Internet of things to build a future smart city, such as how to schedule resources and how to reduce costs. This has a lot to do with dynamic multiobjective optimization. In order to deal with this kind of problem, it is necessary to design a good processing strategy. Evolutionary algorithm can handle this problem well. The prediction in the dynamic environment has been the very challenging work. In the previous literature, the location and distribution of PF or PS are mostly predicted by the center point. The center point generally refers to the center point of the population in the decision space. However, the center point of the decision space cannot meet the needs of various problems. In fact, there are many points with special meanings in objective space, such as ideal point and CTI. In this paper, a hybrid prediction strategy carried through from both decision space and objective space (DOPS) is proposed to handle all kinds of optimization problems. The prediction in decision space is based on the center point. And the prediction in objective space is based on CTI. In addition, for handling the problems with periodic changes, a kind of memory method is added. Finally, to compensate for the inaccuracy of the prediction in particularly complex problems, a self-adaptive diversity maintenance method is adopted. The proposed strategy was compared with other four state-of-the-art strategies on 13 classic dynamic multiobjective optimization problems (DMOPs). The experimental results show that DOPS is effective in dynamic multiobjective optimization.

scholarly journals Dynamic Multiobjective Optimization with Multiple Response Strategies Based on Linear Environment Detection

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-26
Author(s):  
Qiyuan Yu ◽  
Shen Zhong ◽  
Zun Liu ◽  
Qiuzhen Lin ◽  
Peizhi Huang
Keyword(s):  
Multiobjective Optimization ◽  
Prediction Models ◽  
Environmental Changes ◽  
Optimization Problems ◽  
Initial Population ◽  
Multiple Response ◽  
Response Strategies ◽  
Multiobjective Optimization Problems ◽  
Dynamic Multiobjective Optimization ◽  
Environment Detection

Dynamic multiobjective optimization problems (DMOPs) bring more challenges for multiobjective evolutionary algorithm (MOEA) due to its time-varying characteristic. To handle this kind of DMOPs, this paper presents a dynamic MOEA with multiple response strategies based on linear environment detection, called DMOEA-LEM. In this approach, different types of environmental changes are estimated and then the corresponding response strategies are activated to generate an efficient initial population for the new environment. DMOEA-LEM not only detects whether the environmental changes but also estimates the types of linear changes so that different prediction models can be selected to initialize the population when the environmental changes. To study the performance of DMOEA-LEM, a large number of test DMOPs are adopted and the experiments validate the advantages of our algorithm when compared to three state-of-the-art dynamic MOEAs.

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