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Related Concept Videos

Multiple Comparison Tests01:13

Multiple Comparison Tests

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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
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Related Experiment Video

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Many-objective BAT algorithm.

Uzman Perwaiz1, Irfan Younas1, Adeem Ali Anwar1

  • 1Department of Computer Science, National University of Computer and Emerging Sciences, Lahore, Pakistan.

Plos One
|June 12, 2020
PubMed
Summary

This study introduces the Many Objective Bat Algorithm (MaOBAT) to improve solutions for many-objective optimization problems. MaOBAT enhances Pareto front diversity and convergence using bat echolocation and dynamic reference points.

Area of Science:

  • Optimization Algorithms
  • Computational Intelligence
  • Evolutionary Computation

Background:

  • Many-objective optimization problems (MaOPs) present challenges in achieving both diversity and convergence in Pareto approximations.
  • Existing multi-objective evolutionary algorithms (MOEAs) and reference point techniques address these challenges with varying success.

Purpose of the Study:

  • To develop an effective algorithm for handling many-objective optimization problems.
  • To enhance the diversity and convergence of Pareto fronts in MaOPs.

Main Methods:

  • Adaptation of the bat algorithm, termed Many Objective Bat Algorithm (MaOBAT), inspired by bat echolocation.
  • Utilization of dominance rank for solution selection and dynamically allocated reference points to guide the search process.

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Main Results:

  • The proposed MaOBAT algorithm demonstrates significant advantages over state-of-the-art algorithms.
  • Experimental results indicate improved quality of solutions in terms of convergence and diversity.

Conclusions:

  • MaOBAT effectively addresses the challenges of diversity and convergence in many-objective optimization problems.
  • The algorithm's biologically inspired approach and use of reference points contribute to superior performance.