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

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.