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An enhanced parrot optimizer with multiple strategies for wireless sensor network node deployment.

Li Lan1, Zhang Qi2

  • 1Dazhou Vocational and Technical College, Dazhou, 635000, China.

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Summary

The Enhanced parrot Optimizer (EPO) significantly improves upon the original parrot Optimizer (PO) by addressing convergence issues and enhancing exploration. This novel algorithm demonstrates superior performance in complex optimization tasks and real-world applications like Wireless Sensor Network deployment.

Keywords:
Alert updating strategyCEC2017Chaotic sequenceDifferential evolutionNonlinear decreasing factorParrot optimizerT-distribution mutation strategyWireless sensor network

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Area of Science:

  • Computational Intelligence
  • Metaheuristic Optimization Algorithms
  • Swarm Intelligence

Background:

  • Original parrot Optimizer (PO) suffers from limitations including poor initial population diversity, premature convergence, slow convergence speed, and lack of an effective restart mechanism.
  • Addressing these limitations is crucial for developing robust and efficient optimization algorithms applicable to complex problems.

Purpose of the Study:

  • To introduce and evaluate the Enhanced parrot Optimizer (EPO), a novel metaheuristic algorithm designed to overcome the shortcomings of the original PO.
  • To enhance global exploration, escape local optima, balance exploration-exploitation, and incorporate intelligent restart mechanisms.

Main Methods:

  • EPO integrates Cubic chaotic mapping for initial population generation, a risk-aware alert-contraction mechanism for escaping local optima, a nonlinear decay factor for exploration-exploitation balance, a dual-layer intelligent judgment system for diversity preservation, and a hybrid local restart mechanism.
  • Performance was evaluated on the CEC2017 benchmark suite across 30, 50, and 100 dimensions, with comparisons against eleven state-of-the-art algorithms.
  • Statistical validation using Wilcoxon signed-rank tests at α=0.05 was performed.

Main Results:

  • EPO achieved the best mean ranking across all dimensionalities (2.31 at 30D, 2.03 at 50D, 1.83 at 100D), consistently outperforming competitors in solution accuracy, convergence speed, and stability.
  • Statistically significant improvements were confirmed, with EPO outperforming competitors in 81.2% (30D), 86.2% (50D), and 91.6% (100D) of cases.
  • Application to Wireless Sensor Network (WSN) node deployment showed significant coverage improvements, e.g., from 0.7765 to 0.8284 (+6.7%) with 20 nodes.

Conclusions:

  • The Enhanced parrot Optimizer (EPO) represents a significant advancement in parrot-inspired optimization techniques.
  • EPO offers a powerful and reliable tool for solving complex optimization problems, demonstrating superior performance over existing methods.
  • The algorithm's effectiveness is validated through rigorous benchmark testing and successful application to a real-world WSN optimization problem.