NDLI: A directed search strategy for evolutionary dynamic multiobjective optimization

Content Provider	Springer Nature Link
Author	Wu, Yan Jin, Yaochu Liu, Xiaoxiong
Copyright Year	2014
Abstract	Many real-world multiobjective optimization problems are dynamic, requiring an optimization algorithm that is able to continuously track the moving Pareto front over time. In this paper, we propose a directed search strategy (DSS) consisting of two mechanisms for improving the performance of multiobjective evolutionary algorithms in changing environments. The first mechanism reinitializes the population based on the predicted moving direction as well as the directions that are orthogonal to the moving direction of the Pareto set, when a change is detected. The second mechanism aims to accelerate the convergence by generating solutions in predicted regions of the Pareto set according to the moving direction of the non-dominated solutions between two consecutive generations. The two mechanisms, when combined together, are able to achieve a good balance between exploration and exploitation for evolutionary algorithms to solve dynamic multiobjective optimization problems. We compare DSS with two existing prediction strategies on a variety of test instances having different changing dynamics. Empirical results show that DSS is powerful for evolutionary algorithms to deal with dynamic multiobjective optimization problems.
Starting Page	3221
Ending Page	3235
Page Count	15
File Format	PDF
ISSN	14327643
Journal	Soft Computing
Volume Number	19
Issue Number	11
e-ISSN	14337479
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2014-10-17
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Dynamic multiobjective optimization Evolutionary algorithm Prediction Local search 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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