Hybrid Manta Ray Foraging Algorithm with Cuckoo Search for Global Optimization and Three-Dimensional Wireless Sensor...

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristics

Background:

• Metaheuristic algorithms are crucial for solving complex optimization problems.
• Existing algorithms often face challenges with convergence speed and solution precision.
• Hybridization of algorithms can potentially overcome limitations of individual methods.

Purpose of the Study:

• To propose a novel hybrid algorithm, AMRFOCS, combining Manta Ray Foraging Optimization (MRFO) and Cuckoo Search (CS).
• To enhance the search capabilities and performance of metaheuristic optimization.
• To evaluate the algorithm's effectiveness on benchmark functions and a real-world WSN coverage problem.

Main Methods:

• Developed the AMRFOCS algorithm using quantum bit Bloch spherical coordinate coding for initialization.
• Incorporated a dynamic disturbance factor to balance exploration and exploitation.
• Integrated the cuckoo's nesting strategy and Levy flight for improved search efficiency.
• Tested AMRFOCS on CEC2017 and CEC2020 benchmark functions and a 3D WSN coverage problem.

Main Results:

• AMRFOCS demonstrated superior convergence rate and optimization precision on benchmark functions.
• Statistical tests (Wilcoxon signed-rank, Friedman) confirmed the algorithm's stability and superiority.
• Applied to the 3D WSN coverage problem, AMRFOCS reduced sensor node redundancy and increased coverage speed and quality.

Conclusions:

• The proposed AMRFOCS algorithm offers significant improvements over existing metaheuristic approaches.
• AMRFOCS provides an effective and practical deployment scheme for 3D WSN coverage.
• The hybrid approach shows promise for advancing optimization techniques in computational intelligence.