Abstract
Swarm-based models mimic the collective behavior shown in insects or animals. To date, several algorithms have been proposed by researchers to solve a wide range of complex optimization problems. This paper presents an improved version of follow the leader (iFTL) algorithm that imitates the behavioral movement of a sheep within the flock. The work presented in this paper mathematically models this foraging behavior to realize the process of optimization. The COmparing Continuous Optimisers (COCO) experimental framework is used for performance evaluation with twenty-four noiseless and thirty noisy benchmark functions. After that, it has been compared with fourteen well-presented optimization algorithms in different dimensions. The results generated show that iFTL outperformed all compared optimization algorithms and outranked in all dimensions. The iFTL algorithm is also tested on twenty-four standard benchmark function and compared with eight well-known optimization algorithms to benchmark its performance. Further, the efficacy of the proposed algorithm is tested on 10, 37, 52, 72, 120, 200, 224, and 942 bar truss design problems. Finally, the results generated by truss design problems are compared with other meta-heuristics algorithms to validate the performance of the proposed algorithm. The obtained results reveal that iFTL is efficient and stable than other state-of-the-art algorithms.
Published Version
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.
