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Multi-objective optimization for 3D heterogeneous WSN deployment using an enhanced Genghis Khan shark algorithm.

Essam H Houssein1,2, Ibrahim E Ibrahim3, Yaser M Wazery4

  • 1Faculty of Computers and Information, Minia University, Minia, Egypt. essam.halim@mu.edu.eg.

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Summary
This summary is machine-generated.

This study introduces the Enhanced Multi-Objective Genghis Khan Shark Optimizer (EnMOGKSO) for optimizing heterogeneous 3D wireless sensor networks (WSNs). EnMOGKSO effectively balances coverage, connectivity, and cost, outperforming existing methods in complex deployment scenarios.

Keywords:
ConnectivityMulti-objectiveOptimizationWireless sensor networkWireless sensor network deployment

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Area of Science:

  • Computer Science
  • Engineering
  • Optimization Algorithms

Background:

  • Heterogeneous 3D wireless sensor network (WSN) deployment faces challenges in balancing sensing coverage, communication connectivity, and deployment cost.
  • Coupled K-coverage and C-connectivity constraints create a complex optimization landscape where traditional multi-objective methods struggle with diversity and feasibility.

Purpose of the Study:

  • To formulate the heterogeneous 3D WSN deployment problem as a constrained multi-objective problem.
  • To propose a novel optimization algorithm, the Enhanced Multi-Objective Genghis Khan Shark Optimizer (EnMOGKSO), to address the limitations of existing methods.

Main Methods:

  • Formulated the problem as a constrained multi-objective optimization task.
  • Developed EnMOGKSO integrating leader-pursuit dynamics with dual archive-guided selection, bounded external archive diversity control, and feasibility-first environmental selection.
  • Evaluated performance on the Congress on Evolutionary Computation (CEC) 2020 benchmark suite and in a simulated heterogeneous 3D WSN deployment scenario.

Main Results:

  • EnMOGKSO achieved superior performance on the CEC 2020 suite, obtaining the best Friedman mean ranks for hypervolume (HV) and inverted generational distance (IGD).
  • In 3D WSN deployment, EnMOGKSO significantly improved coverage and connectivity values compared to baseline methods, maintaining stable deployment costs.
  • Statistical analysis confirmed significant improvements over competing algorithms.

Conclusions:

  • EnMOGKSO demonstrates a robust convergence-diversity balance and effective feasibility-aware search for constrained multi-objective problems.
  • The proposed algorithm offers practical applicability for 3D monitoring tasks in industrial facilities, smart buildings, and environmental sensing.