Mud Ring Algorithm: A New Meta-Heuristic Optimization Algorithm for Solving Mathematical and Engineering Challenges

This paper proposes a new meta-heuristic optimization algorithm, namely Mud Ring Algorithm (MRA) that mimics the mud ring feeding behaviour of bottlenose dolphins in the Atlantic coast of Florida. The inspiration of MRA is mainly based on the foraging behaviour of bottlenose dolphins and their mud r...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access Vol. 10; pp. 50448 - 50466
Main Authors: Desuky, Abeer S., Cifci, Mehmet Akif, Kausar, Samina, Hussain, Sadiq, El Bakrawy, Lamiaa M.
Format: Journal Article
Language:English
Published: Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
3-bar truss design challenge
Algorithms
Benchmark testing
Benchmarks
Dolphins
Dolphins & porpoises
Fish
Heuristic
Heuristic methods
Mathematical analysis
meta-heuristic
Mud
nature-inspired algorithms
Optimization
Optimization algorithm
Optimization algorithms
Particle swarm optimization
pressure vessel design challenge
Pressure vessels
Springs
swarm intelligence
tension/compression spring design challenge
welded beam design challenge
Welding
ISSN:2169-3536, 2169-3536
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper proposes a new meta-heuristic optimization algorithm, namely Mud Ring Algorithm (MRA) that mimics the mud ring feeding behaviour of bottlenose dolphins in the Atlantic coast of Florida. The inspiration of MRA is mainly based on the foraging behaviour of bottlenose dolphins and their mud ring feeding strategy. This strategy is applied by dolphins to trap fish via creating a plume by a single dolphin moving his tail swiftly in the sand and swims around the group of fish. The fishes become disoriented and jump over the surface only to find the waiting mouths of dolphins. MRA optimization algorithm mathematically simulates this feeding strategy and proves its optimization effectiveness through a comprehensive comparison with other meta-heuristic algorithms. Twenty-nine benchmark functions and four commonly used benchmark engineering challenges are used in the comparison. The statistical comparisons and results prove that the proposed MRA has the superiority in dealing with these optimization problems and can obtain the best solutions than other meta-heuristic optimizers.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3173401