Abstract
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.
Highlights
The optimization of specific parameters in engineering problems has been a hot topic of research in related fields
1) Convergence accuracy analysis: As shown in Table 3, the average value of the optimal solutions found by Flamingo Search Algorithm (FSA) on the test functions F1–F4 and F8–F9 is better than the results of the other four algorithms
To address the migratory and foraging behavior of flamingos, this paper develops relevant mathematical models that enable FSA to satisfy the global exploration and local exploitation capabilities required from an optimization algorithm
Summary
The optimization of specific parameters in engineering problems has been a hot topic of research in related fields. Many new swarm intelligence optimization algorithms have been proposed, such as Ant Colony Optimization (ACO) [7][8], Particle Swarm Optimization (PSO) [9][10], Cuckoo Search (CS) [11][12], Grey Wolf Optimization (GWO) [13][14], Whale Optimization Algorithm (WOA) [15], Tunicate Swarm Algorithm (TSA) [16], Sparrow Search Algorithm (SSA), and so on [17] All these algorithms have the advantages of high applicability, low parameters, and high global search capability. NFL theorem states that researchers need to come up with new intelligent algorithms to solve optimization problems in different application areas.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
