Florist Optimization Algorithm: A Novel Human-inspired Metaheuristic for Solving Optimization Problems

Abstract

In this study, a novel metaheuristic algorithm, the Florist Optimization Algorithm (FOA), is introduced and developed, inspired by the social interactions between florists and customers within a flower shop. The theoretical foundations and core concepts underlying FOA are first elucidated, followed by a mathematical modeling of the population update process. The initial stage involves the random initialization of population members’ positions within the search space. Subsequently, the population update proceeds iteratively through three well-defined strategies: (1) recommending the best flower model, (2) offering pre-arranged flowers available in the shop, and (3) preparing customized flower orders according to individual customer preferences. Analyses of population diversity and the exploration–exploitation dynamics indicate that FOA possesses a high capability to manage the balance between exploration and exploitation, while maintaining and controlling population diversity effectively. The algorithm’s performance was evaluated on a set of 23 standard benchmark functions and compared against nine well-established metaheuristic algorithms. The results demonstrate that FOA, by leveraging intelligent exploration–exploitation management mechanisms, consistently delivers superior and stable performance relative to its competitors. Simulation results further reveal that FOA achieved first-rank optimization in 22 out of 23 benchmark functions (95.6%), underscoring its exceptional ability to rapidly identify promising regions and avoid local minima. These findings highlight that FOA, with its high convergence speed, stability, and reproducibility, can serve as an effective and reliable optimization tool for solving complex problems in both fundamental scientific research and real-world practical applications.