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Identifying and exploiting the scale of a search space in differential evolution
conference contribution
posted on 2023-05-23, 08:53 authored by Erin MontgomeryErin Montgomery, Chen, S, Gonzalez-Fernandez, YOptimisation in multimodal landscapes involves two distinct tasks: identifying promising regions and location of the (local) optimum within each region. Progress towards the second task can interfere with the first by providing a misleading estimate of a region’s value. Thresheld convergence is a generally applicable “meta”-heuristic designed to control an algorithm’s rate of convergence and hence which mode of search it is using at a given time. Previous applications of thresheld convergence in differential evolution (DE) have shown considerable promise, but the question of which threshold values to use for a given (unknown) function landscape remains open. This work explores the use of clustering-based method to infer the distances between local optima in order to set a series of decreasing thresholds in a multi-start DE algorithm. Results indicate that on those problems where normal DE converges, the proposed strategy can lead to sizable improvements.
History
Publication title
Proceedings of 2014 IEEE Congress on Evolutionary ComputationPagination
1427-1434ISBN
9781479966264Department/School
School of Information and Communication TechnologyPublisher
Institute of Electrical and Electronics EngineersPlace of publication
ChinaEvent title
2014 IEEE Congress on Evolutionary ComputationEvent Venue
Beijing, ChinaDate of Event (Start Date)
2014-07-06Date of Event (End Date)
2014-07-11Rights statement
Copyright 2014 IEEERepository Status
- Restricted