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An integrated cuckoo search optimizer for single and multi-objective optimization problems.

Xiangbo Qi1, Zhonghu Yuan1, Yan Song2

  • 1School of Mechanical Engineering, Shenyang University, Shenyang, China.

Peerj. Computer Science
|April 5, 2021
PubMed
Summary

This study introduces an integrated cuckoo search optimizer (ICSO) and its multi-objective version (MOICSO) to overcome single algorithm limitations. These novel algorithms demonstrate superior performance in optimization tasks compared to existing methods.

Keywords:
Cuckoo search algorithmDifferential evolutionIntegrated cuckoo search algorithmMeta-heuristic

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristic Computing

Background:

  • Single optimization algorithms often exhibit limitations.
  • Integrating diverse biological-inspired strategies can enhance algorithmic performance.
  • The Cuckoo Search (CS) algorithm is a popular metaheuristic.

Purpose of the Study:

  • To propose an integrated cuckoo search optimizer (ICSO) for single-objective problems.
  • To introduce a multi-objective version, MOICSO.
  • To evaluate the effectiveness of integrating multiple search strategies.

Main Methods:

  • Development of the Integrated Cuckoo Search Optimizer (ICSO).
  • Proposal of the Multi-Objective Integrated Cuckoo Search Optimizer (MOICSO).
  • Benchmarking algorithms using a suite of standard test functions.

Main Results:

  • ICSO and MOICSO were benchmarked against various test functions.
  • Comprehensive analysis confirmed the effectiveness of the integrated approach.
  • The proposed algorithms demonstrated superior performance compared to recent methods.

Conclusions:

  • The integration of multiple search strategies in ICSO and MOICSO effectively addresses the shortcomings of single algorithms.
  • The proposed algorithms show significant performance advantages in optimization.
  • This integrated mechanism offers a promising direction for advanced optimization techniques.