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A simple strategy for maintaining diversity and reducing crowding in differential evolution
conference contribution
posted on 2023-05-23, 08:56 authored by Erin MontgomeryErin Montgomery, Chen, SDifferential evolution (DE) is a widely-effective population-based continuous optimiser that requires convergence to automatically scale its moves. However, once its population has begun to converge its ability to conduct global search is diminished, as the difference vectors used to generate new solutions are derived from the current population members’ positions. In multi-modal search spaces DE may converge too rapidly, i.e., before adequately exploring the search space to identify the best region(s) in which to conduct its finer-grained search. Traditional crowding or niching techniques can be computationally costly or fail to compare new solutions with the most appropriate existing population member. This paper proposes a simple intervention strategy that compares each new solution with the population member it is most likely to be near, and prevents those moves that are below a threshold that decreases over the algorithm’s run, allowing the algorithm to ultimately converge. Comparisons with a standard DE algorithm on a number of multi-modal problems indicate that the proposed technique can achieve real and sizable improvements.
History
Publication title
Proceedings of 2012 IEEE Congress on Evolutionary ComputationPagination
2692-2699ISBN
978-1-4673-1510-4Department/School
School of Information and Communication TechnologyPublisher
IEEEPlace of publication
United States of AmericaEvent title
2012 IEEE Congress on Evolutionary ComputationEvent Venue
Brisbane, AustraliaDate of Event (Start Date)
2012-06-10Date of Event (End Date)
2012-06-15Rights statement
Copyright 2012 IEEERepository Status
- Restricted