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Simulated annealing with thresheld convergence
conference contribution
posted on 2023-05-23, 08:56 authored by Chen, S, Xudiera, C, Erin MontgomeryErin MontgomeryStochastic search techniques for multi-modal search spaces require the ability to balance exploration with exploitation. Exploration is required to find the best region, and exploitation is required to find the best solution (i.e. the local optimum) within this region. Compared to hill climbing which is purely exploitative, simulated annealing probabilistically allows “backward” steps which facilitate exploration. However, the balance between exploration and exploitation in simulated annealing is biased towards exploitation – improving moves are always accepted, so local (greedy) search steps can occur at even the earliest stages of the search process. The purpose of “thresheld convergence” is to have these early-stage local search steps “held” back by a threshold function. It is hypothesized that early local search steps can interfere with the effectiveness of a search technique’s (concurrent) mechanisms for global search. Experiments show that the addition of thresheld convergence to simulated annealing can lead to significant performance improvements in multi-modal search spaces.
History
Publication title
Proceedings of the 2012 IEEE Congress on Evolutionary ComputationPagination
1946-1952ISBN
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, AustraliaRights statement
Copyright 2012 IEEERepository Status
- Restricted