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


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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

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  • 2009. Noisy Multiobjective Optimization on a Budget of 250 Evaluations in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • 2003. The Measure of Pareto Optima Applications to Multi-objective Metaheuristics in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
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  • 1998. Averaging efficiently in the presence of noise in PARALLEL PROBLEM SOLVING FROM NATURE — PPSN V
  • 1995. Uniform Random Numbers, Theory and Practice in NONE
  • 2001-07-06. Evolutionary Multi-objective Ranking with Uncertainty and Noise in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • 2000. A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II in PARALLEL PROBLEM SOLVING FROM NATURE PPSN VI
  • 2014. Ant Colony Optimization on a Budget of 1000 in SWARM INTELLIGENCE
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  • 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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