The obtained results reveal that iFTL is efficient and stable than other state-of-the-art algorithms. Finally, the results generated by truss design problems are compared with other meta-heuristics algorithms to validate the performance of the proposed algorithm. Further, the efficacy of the proposed algorithm is tested on 10, 37, 52, 72, 120, 200, 224, and 942 bar truss design problems. The iFTL algorithm is also tested on twenty-four standard benchmark function and compared with eight well-known optimization algorithms to benchmark its performance. The results generated show that iFTL outperformed all compared optimization algorithms and outranked in all dimensions. After that, it has been compared with fourteen well-presented optimization algorithms in different dimensions. The COmparing Continuous Optimisers (COCO) experimental framework is used for performance evaluation with twenty-four noiseless and thirty noisy benchmark functions. The work presented in this paper mathematically models this foraging behavior to realize the process of optimization. This paper presents an improved version of follow the leader (iFTL) algorithm that imitates the behavioral movement of a sheep within the flock. To date, several algorithms have been proposed by researchers to solve a wide range of complex optimization problems. Swarm-based models mimic the collective behavior shown in insects or animals. The paper culminates with a discussion of the limitations and directions for future studies. environment, robotic device design, mission planning and decision making, communication, operators and metadata and swarm level knowledge) that are interconnected.
The ontology contains six knowledge domains (i.e. As such, this paper proposes a social spider-inspired ontology for the coordination of robotic swarm devices based on the behaviours observed in social spider swarms. However, it is imperative to understand and formalize the principles underpinning the coordination of swarms of robotic devices for this to happen. In light of the evidence from multiple biological swarms, developers of robotic systems are increasingly moving away from creating individualistic robots towards robotic swarms that can work collectively towards a common goal. One notable example is the social spider swarm, which coordinates its activities to achieve numerous goals (e.g.
Collective behaviour is known to empower many biological species to achieve remarkable results through distributed actions.
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