Akademik Veri Yönetim Sistemi
Metaheuristic Optimization Algorithms: Optimizers, Analysis, and Applications, Elsevier, ss.193-203, 2024
Manta ray foraging optimization (MRFO), a new bioinspired optimization strategy, is introduced and presented as a novel algorithm that gives an additional optimization approach for addressing real-world engineering difficulties. The clever behavior of manta rays inspired this program. To design an effective optimization paradigm for dealing with a wide range of optimization problems, this chapter mimics three different manta ray foraging behaviors: chain foraging, cyclone foraging, and somersault foraging. On eight real-world engineering design scenarios and benchmark optimization functions, MRFO’s performance is compared to those of other cutting-edge optimizers. The benchmark function comparison findings show that MRFO is considerably superior than its competitors. Additionally, real-world engineering applications highlight the method’s benefits. This chapter is a review of the MRFO algorithm papers that have been published. Twenty research papers are analyzed and categorized based on the implementation area in which the MRFO algorithm is used (neural networks, feature selection, and data clustering). The MRFO algorithm’s main procedure is presented. Future researchers can use the data collected in this survey as baseline information on the MRFO and MRFO applications.