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Modified Cuckoo Search Algorithm using a New Selection Scheme for Unconstrained Optimization Problems.

Mohammad Shehab1, Ahamad Tajudin Khader2

  • 1Computer Science Department, Aqaba University of Technology, Aqaba 77110, Jordan.

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|May 16, 2020
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Summary
This summary is machine-generated.

The Modified Cuckoo Search Algorithm (MCSA) enhances optimization by replacing random selection with tournament selection. This improves result probability and maintains solution diversity, outperforming standard methods.

Keywords:
Cuckoo search algorithmMCSAglobal optimization problemspremature convergencerandom selectiontournament selection.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms

Background:

  • The Cuckoo Search Algorithm (CSA), introduced in 2009, is a popular metaheuristic.
  • Original CSA uses random selection, potentially limiting the discovery of optimal solutions and reducing diversity.

Purpose of the Study:

  • To enhance the performance of the Cuckoo Search Algorithm (CSA) for unconstrained optimization problems.
  • Introduce the Modified Cuckoo Search Algorithm (MCSA) to improve solution quality and maintain population diversity.

Main Methods:

  • Replaced CSA's default random selection with a tournament selection scheme.
  • Evaluated MCSA performance using a suite of benchmark functions for unconstrained optimization.

Main Results:

  • MCSA demonstrated superior performance compared to the standard CSA.
  • MCSA outperformed existing methods reported in the literature for the tested benchmark functions.

Conclusions:

  • The Modified Cuckoo Search Algorithm (MCSA) effectively improves upon the original CSA.
  • Tournament selection in MCSA enhances solution diversity and increases the probability of finding better results, avoiding premature convergence.