An improved beetle antennae search algorithm and its application in coverage of wireless sensor networks.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Wireless Sensor Networks

Background:

• The standard beetle antennae search (BAS) algorithm suffers from low convergence accuracy and a tendency to get trapped in local optima.
• Addressing these limitations is crucial for applying swarm intelligence algorithms to complex optimization problems.

Purpose of the Study:

• To introduce ABSAS-CS-GSA, an enhanced beetle antennae search algorithm designed to overcome the convergence and local optima issues of the original BAS.
• To evaluate the performance of ABSAS-CS-GSA on benchmark functions and its application to wireless sensor network coverage optimization.

Main Methods:

• Incorporated dynamic step-size adjustment based on initial population for improved accuracy and convergence.
• Integrated the golden sine algorithm for position updates to enhance search diversity and speed.
• Implemented a population disturbance strategy using vertical and horizontal crossing to prevent local optima entrapment.

Main Results:

• ABSAS-CS-GSA demonstrated superior performance compared to standard BAS, its variants, and two classic swarm intelligence algorithms on 12 benchmark functions.
• Simulations showed significant improvements in optimal and average coverage metrics for wireless sensor networks (30 and 50 nodes) using ABSAS-CS-GSA compared to seven other algorithms.

Conclusions:

• The proposed ABSAS-CS-GSA algorithm effectively addresses the shortcomings of the original BAS, offering enhanced accuracy and robustness.
• ABSAS-CS-GSA shows strong potential for solving wireless sensor network coverage optimization problems, delivering superior results.