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Flamingo Search Algorithm: A New Swarm Intelligence Optimization Algorithm
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DOI：10.1109/ACCESS.2021.3090512.png)
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Abstract
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This paper presents a new swarm intelligence optimization algorithm: Flamingo Search Algorithm (FSA), which is inspired by the migratory and foraging behavior of flamingos. A mathematical model of flamingo behavior is built so that FSA has the global exploration and local exploitation capabilities required for an optimization algorithm. Three sets of experiments based on 68 test functions are designed to evaluate the convergence speed, optimization-seeking accuracy, stability, running time, and global search capability of FSA. The effect of different input parameters on the search results of FSA is then discussed, and the optimal parameter selection interval is summarized. In addition, nine test functions are selected to visualize the trajectory of the flamingo population during the search. The test results of the above designs all indicate that FSA is superior to other algorithms in solving optimization problems. Finally, three kinds of simulation experiments, which are push-pull circuit problem, path planning problem and network intrusion detection system, are designed to test the practicability of FSA. The code used for the main experiment in this article can be obtained from website https://github.com/18280426650/FSA.
Keywords:
Optimization
Statistics
Sociology
Search problems
Particle swarm optimization
Convergence
Birds
Flamingo search algorithm (FSA)
bio-inspired algorithms
optimization problems
intrusion detection system
intelligence optimization algorithms
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