Non-dominated Sorting Bee Colony optimization in the presence of noise View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2016-03

AUTHORS

Pratyusha Rakshit, Amit Konar

ABSTRACT

The paper incorporates new extensional strategies into the traditional multi-objective optimization algorithms to proficiently obtain the Pareto-optimal solutions in the presence of noise in the fitness landscapes. The first strategy, referred to as adaptive selection of sample size, is employed to assess the trade-off between accuracy in fitness estimation and the associated run-time complexity. The second strategy is concerned with determining statistical expectation of fitness samples, instead of their conventional averaging, as the fitness measure of the trial solutions. The third strategy aims at improving Goldberg’s approach to examine possible accommodation of a seemingly inferior solution in the optimal Pareto front using a more statistically viable comparator. The traditional Non-dominated Sorting Bee Colony algorithm has been ameliorated by extending its selection step with the proposed strategies. Experiments undertaken to study the performance of the proposed algorithm reveal that the extended algorithm outperforms its contenders with respect to four performance metrics, when examined on a test suite of 23 standard benchmarks with additive noise of three statistical distributions. More... »

PAGES

1139-1159

References to SciGraph publications

Journal

TITLE

Soft Computing

ISSUE

3

VOLUME

20

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00500-014-1579-z

DOI

http://dx.doi.org/10.1007/s00500-014-1579-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1051161472


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