NDLI: A population diversity maintaining strategy based on dynamic environment evolutionary model for dynamic multiobjective optimization

Content Provider	IEEE Xplore Digital Library
Author	Zhou Peng Jinhua Zheng Juan Zou
Copyright Year	2014
Description	Author affiliation: Coll. of Inf. Eng., Xiangtan Univ., Xiangtan, China (Zhou Peng; Jinhua Zheng; Juan Zou)
Abstract	Maintaining population diversity is a crucial issue for the performance of dynamic multiobjective optimization algorithms. However traditional dynamic multiobjective evolutionary algorithms usually imitate the biological evolution of their own, maintain population diversity through different strategies and make the population be able to track the Pareto optimal solution set after the change efficiently. Nevertheless, these algorithms neglect the role of dynamic environment in evolution, lead to the lacking of active and instructional search. In this paper, a population diversity maintaining strategy based on dynamic environment evolutionary model is proposed (DEE-PDMS). This strategy builds a dynamic environment evolutionary model when a change is detected, which makes use of the dynamic environment to record the different knowledge and information generated by population before and after environmental change, and in turn the knowledge and information guide the search in new environment. The model enhances population diversity by guided fashion, makes the simultaneous evolution of the environment and population. A comparison study with other two state-of-the-art strategies on five test problems with linear or nonlinear correlation between design variables has shown the effectiveness of the DEE-PDMS for dealing with dynamic environments.
Starting Page	274
Ending Page	281
File Size	471781
Page Count	8
File Format	PDF
e-ISBN	9781479914883
DOI	10.1109/CEC.2014.6900268
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sociology Statistics Heuristic algorithms Evolution (biology) Convergence Optimization Algorithm design and analysis
Content Type	Text
Resource Type	Article

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