WD2O: a novel wind driven dynamic optimization approach with effective change detection

WD2O: a novel wind driven dynamic optimization approach with effective change detection Dynamic optimization holds promise to solve real world problems that require adaptation to dynamic environments. The challenge is to track optima in an ever changing landscape. This paper describes a new computational intelligence approach to dynamic optimization termed as wind driven dynamic optimization (WD2O). Basically, it relies on an enhanced Multi-Region Modified Wind Driven Optimization (MR-MWDO) model and exhibits four main features. First, a multi-region approach is used to classify regions of the search space into promising and non-promising areas with accordance to low and high pressure regions in the natural model. Second, it uses an effective collision avoidance strategy to prevent collision between sub-populations. Third, it allows cost effective change detection. Fourth, it maintains two types of populations in order to achieve better balanced search. The proposed WD2O has been successfully applied to Moving Peaks Benchmark (MPB) problem. An extensive experimental study has shown that WD2O outperforms significantly the first prototype MR-MWDO. Furthermore, it has shown very competitive results compared to state of the art methods and has achieved the best performance for high dimensional problems while keeping an appreciable time complexity. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Intelligence Springer Journals

WD2O: a novel wind driven dynamic optimization approach with effective change detection

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Springer US
Copyright © 2017 by Springer Science+Business Media New York
Computer Science; Artificial Intelligence (incl. Robotics); Mechanical Engineering; Manufacturing, Machines, Tools
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