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A novel multi objective energy efficient clustering optimization scheme based on heuristic intelligence for wireless

Chuchu Rao1, Mingqi Kan2, Peng Zhou3,4

  • 1School of Mechanical and Electrical Engineering, Quzhou College of Technology, Quzhou, 324000, China.

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|October 22, 2025
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Summary

A new Multi-Objective Butterfly Clustering Optimization routing Algorithm (MBCO) enhances energy efficiency in Wireless Sensor Networks (WSNs). MBCO significantly reduces energy consumption and extends network lifespan, improving overall performance.

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Butterfly optimization algorithmCluster routingEnergy efficiencyMulti-objective optimizationNetwork lifetimeWireless sensor networks

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless Sensor Networks (WSNs) are vital for IoT but face energy constraints.
  • Limited node energy is a critical challenge impacting WSN longevity and performance.
  • Existing routing algorithms struggle to balance energy efficiency with network demands.

Purpose of the Study:

  • To propose a novel routing algorithm for WSNs that significantly improves energy efficiency.
  • To address the core challenge of limited and irreplaceable energy in sensor nodes.
  • To enhance the overall performance and lifespan of large-scale WSN deployments.

Main Methods:

  • Developed the Multi-Objective Butterfly Clustering Optimization routing Algorithm (MBCO).
  • Integrated butterfly foraging behavior with dynamic clustering for optimized cluster head selection.
  • Implemented adaptive weight clustering based on node density and residual energy for load balancing.
  • Introduced a hybrid intra-cluster data fusion strategy and cross-cluster coordination mechanism.

Main Results:

  • MBCO reduced energy consumption by 6.69 J compared to existing methods.
  • Extended network useful life by 83.05 rounds.
  • Increased packet delivery rate by 5.1% and decreased communication delay by 67.34 ms.
  • Demonstrated significant improvements in energy efficiency while maintaining Quality of Service (QoS).

Conclusions:

  • MBCO offers a new paradigm for energy-efficient routing in large-scale WSNs.
  • The algorithm effectively balances energy consumption, network lifespan, and data transmission performance.
  • MBCO provides a viable solution to the persistent energy efficiency challenge in WSN research.