NDLI: A novel cooperative coevolutionary dynamic multi-objective optimization algorithm using a new predictive model

Content Provider	Springer Nature Link
Author	Liu, Ruochen Chen, Yangyang Ma, Wenping Mu, Caihong Jiao, Licheng
Copyright Year	2013
Abstract	Dynamic multi-objective optimization problem (DMOP) is quite challenging and it dues to that there are multiple conflicting objects changing over with time or environment. In this paper, a novel cooperative coevolutionary dynamic multi-objective optimization algorithm (PNSCCDMO) is proposed. The main idea of a new cooperative coevolution based on non-dominated sorting is that it allows the decomposition process of the optimization problem according to the search space of decision variables, and each species subcomponents will cooperate to evolve for better solutions. This way derives from nature and can improve convergence significantly. A modified linear regression prediction strategy is used to make rapid response to the new changes in the environment. The effectiveness of PNSCCDMO is validated against various of DMOPs compared with the other four algorithms, and the experimental result indicates PNSCCDMO has a good capability to track the Pareto front as it is changed with time in dynamic environments.
Starting Page	1913
Ending Page	1929
Page Count	17
File Format	PDF
ISSN	14327643
Journal	Soft Computing
Volume Number	18
Issue Number	10
e-ISSN	14337479
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-11-24
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Coevolution Dynamic multi-objective optimization Evolutionary algorithms Predictive model Computational Intelligence Artificial Intelligence (incl. Robotics) Mathematical Logic and Foundations Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Software Geometry and Topology

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