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Manta Ray Foraging Optimization Algorithm: Modifications and Applications
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DOI：10.1109/ACCESS.2023.3276264.png)
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Abstract
En 中文
The novel metaheuristic manta ray foraging optimization (MRFO) algorithm is based on the smart conduct of manta rays. The MRFO algorithm is a newly developed swarm-based metaheuristic approach that emulates the supportive conduct performed by manta rays in search of food. The MRFO algorithm efficiently resolves several optimization difficulties in various domains due to its ability to provide an equilibrium between global and local searches during the search procedure, resulting in nearly optimal results. Thus, researchers have developed several variants of MRFO since its introduction. This paper provides an in-depth examination of recent MRFO research. First, the paper introduces the natural inspiration context of MRFO and its conceptual optimization framework, and then MRFO modifications, hybridizations, and applications across different domains are discussed. Finally, a meta-analysis of the developments of the MRFO is presented along with the possible future research directions. This study can be useful for researchers and practitioners in optimization, engineering design, machine learning, scheduling, image processing, and other fields.
Keywords:
Metaheuristics
Particle swarm optimization
Sociology
Machine learning algorithms
Information and communication technology
Genetic algorithms
Cyclones
Global search
local search
manta ray foraging optimization (MRFO)
metaheuristic
optimization
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
Organization
Cited Papers
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