Fuzzybased Metaheuristics Artificial Bee Colony Optimization Approach to Enhance the Performance of Wireless Networks

Abstract

There are many different types of continuous optimization issues that can be solved using the
Artificial Bee Colony (ABC) meta-heuristic. However, it cannot be utilized to address discrete
difficulties in Wireless Sensor Networks (WSNs), such as sensor node clustering. In this research,
describe a variation of a bio-inspired method based on the Artificial Bee Colony (ABC) for
optimizing Fuzzy Adaptive Differential Evolution (FADE). ABC is a metaheuristic approach
inspired by the natural activity of bees that may be utilized to solve optimization challenges. First,
the standard ABC is tried with the optimization of a fuzzy adaptive controller. Second, a
modification to the original technique is provided by using fuzzy logic to dynamically adjust the
algorithm's major parameter values during execution. Third, the suggested adaptation of the ABC
algorithm with the fuzzy approach is applied to optimize benchmark control issues. The findings
demonstrate that the suggested FADE technique outperforms in comparison to the existing
LEACH-C and PSO. In each scenario, the technique gives enhanced results in the throughput,
energy consumption, and in-network stability with a set number of nodes.