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A self-adaptive attraction and repulsion-based naked mole-rat algorithm for energy-efficient mobile wireless sensor

Supreet Singh1,2, Urvinder Singh1, Nitin Mittal3

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Area of Science:

  • Optimization Algorithms
  • Swarm Intelligence
  • Wireless Sensor Networks

Background:

  • The Naked Mole-Rat Algorithm (NMRA) is a swarm intelligence algorithm inspired by mole rat mating behaviors, used for optimization problems.
  • Original NMRA faces challenges with local optima stagnation and slow convergence towards global optimal solutions.

Purpose of the Study:

  • To introduce an enhanced NMRA, termed Attraction and Repulsion strategy based NMRA (ARNMRA), with self-adaptive properties to address NMRA's limitations.
  • To evaluate ARNMRA's performance on numerical benchmark problems and its application in mobile wireless sensor networks (MWSNs).

Main Methods:

  • ARNMRA incorporates an attraction and repulsion strategy to generate new solutions and a self-adaptive mating factor using a simulated annealing-based mutation operator.
  • Performance was assessed using CEC 2005, CEC 2019, and CEC 2020 numerical benchmark datasets.
  • ARNMRA was applied to data gathering in MWSNs, selecting cluster heads based on mobility, residual energy, and connection time.

Main Results:

  • ARNMRA demonstrated superior performance compared to several established algorithms on CEC 2005 benchmark problems.
  • Experimental results confirmed ARNMRA's improved performance over the original NMRA on CEC 2019 and CEC 2020 datasets.
  • Statistical tests (rank-sum and Friedman) validated the experimental findings, confirming ARNMRA's effectiveness.

Conclusions:

  • ARNMRA effectively overcomes local optima stagnation and improves convergence rates in optimization tasks.
  • The proposed ARNMRA protocol enhances network lifetime and energy efficiency in MWSNs by optimizing data gathering strategies.
  • The study highlights the importance of dynamic node characteristics in designing MWSN data-gathering protocols.