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

Autor: Qiyuan Yu, Shen Zhong, Zun Liu, Qiuzhen Lin, Peizhi Huang
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Complexity, Vol 2020 (2020)
Druh dokumentu: article
ISSN: 1076-2787
1099-0526
DOI: 10.1155/2020/9053809
Popis: 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.
Databáze: Directory of Open Access Journals
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