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Application of Improved Manta Ray Foraging Optimization Algorithm in Coverage Optimization of Wireless Sensor

Fang Zhu¹, Wenhao Wang¹, Shan Li¹

¹School of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China.

Computational Intelligence and Neuroscience

|July 11, 2022

Summary

This summary is machine-generated.

This study introduces an enhanced manta ray foraging optimization (MRFO) algorithm using Latin hypercube sampling and group learning. The improved MRFO algorithm demonstrates faster convergence and better optimization accuracy for complex problems and wireless sensor networks.

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Area of Science:

Computational Intelligence
Optimization Algorithms
Swarm Intelligence

Background:

The standard manta ray foraging optimization (MRFO) algorithm suffers from slow convergence and susceptibility to local optima.
Addressing these limitations is crucial for applying MRFO to complex real-world optimization tasks.

Purpose of the Study:

To propose an improved manta ray foraging optimization (MRFO) algorithm that enhances convergence speed and global search capability.
To evaluate the efficacy of the enhanced MRFO algorithm on standard test functions and a wireless sensor network (WSN) coverage optimization problem.

Main Methods:

Introduced Latin hypercube sampling (LHS) for diverse population initialization.
Incorporated Levy flight and adaptive t-distribution mutation for improved exploration and avoidance of local optima.
Implemented a group learning strategy with leader and follower groups, utilizing differential evolution for enhanced population quality.

Main Results:

Comparative tests on 15 standard functions in low and high dimensions showed significant improvements in convergence speed and accuracy over the original MRFO algorithm.
Application to wireless sensor network (WSN) coverage optimization resulted in approximately 3% increased network coverage and more reasonable node distribution.

Conclusions:

The proposed enhanced MRFO algorithm effectively overcomes the shortcomings of the original MRFO, offering superior performance in both benchmark testing and practical WSN applications.
The integration of LHS, Levy flight, adaptive mutation, and group learning provides a robust framework for advanced metaheuristic optimization.