Joint Light-Sensitive Balanced Butterfly Optimizer for Solving the NLO and NCO Problems of WSN for Environmental...

Area of Science:

• Computer Science
• Artificial Intelligence
• Optimization Algorithms

Background:

• Existing swarm intelligence (SI) optimization algorithms exhibit low accuracy for node localization optimization (NLO) and node coverage optimization (NCO) problems.
• Wireless Sensor Networks (WSNs) require efficient optimization for applications like environmental monitoring.

Purpose of the Study:

• To propose a novel Balanced Butterfly Optimizer (BBO) that enhances SI algorithm performance.
• To address the limitations of existing algorithms in NLO and NCO problems.

Main Methods:

• The BBO algorithm integrates both smell-sensitive (global search) and light-sensitive (local search) characteristics of butterflies.
• The algorithm's performance was validated using twenty-three benchmark functions.
• BBO was compared against state-of-the-art SI algorithms: PSO, DE, GWO, ABO, BOA, HHO, and AO.

Main Results:

• The proposed BBO demonstrates superior performance compared to other SI algorithms.
• BBO exhibits enhanced global search capabilities and strong stability.
• The BBO algorithm achieved effective results when applied to NLO and NCO problems in WSNs for environmental monitoring.

Conclusions:

• The Balanced Butterfly Optimizer (BBO) offers a significant improvement over existing SI algorithms for optimization tasks.
• BBO provides a robust and stable solution for NLO and NCO in WSNs, particularly for environmental monitoring.