Binary bat algorithm
By S. Mirjalili, Xin-She Yang. Neural computing & applications (Print), 2013
657 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Biomedical Engineering
View Paper: https://www.semanticscholar.org/paper/e4cababf593d818d50f836edd7ac871d7b55f08a
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Ali Asghar Heidari
- Seyedali Mirjalili
- Ibrahim Aljarah
- Hossam Faris
- Tansel Dökeroğlu
- Majdi M. Mafarja
- Arpan Kumar Kar
- Leandro dos S. Coelho
In Biomedical Engineering
- Gai-ge Wang
- Arpan Kumar Kar
- Leandro dos S. Coelho
- Seyedali Mirjalili
- Suash Deb
- Aboul Ella Otifey Hassanien
- Majdi M. Mafarja
- Seyedeh Zahra Mirjalili
Citations in the corpus
- Biogeography-based optimisation with chaos (2014, neutral): “The former group is suitable for benchmarking exploration, whereas the latter group is use to examine exploitation [36, 37, 39].”
- Elephant Herding Optimization (2015, background): “…by this phenomenon, ACO [10, 11] was proposed by Dorigo et al. Recently, more effective swarm intelligence algorithms have been proposed, such as artificial bee colony (ABC) [12, 13], cuckoo search (CS) [14-20], bat algorithm (BA) [21-24], firefly algorithm (FA) [25-28], animal migration…”
- Bio inspired computing - A review of algorithms and scope of applications (2016, methodology): “While most of the application of the bat algorithm is in the continuous problem domain, a binary version of the algorithm was proposed to address discrete decision making (Mirjalili et al., 2014).”
- An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems (2018, methodology): “I For this purpose, the binary bGWO [74], BGSA [70], and BBA [75] algorithms are considered here to verify the performance of the proposed BSSA_S3_CP technique.”
- Binary ant lion approaches for feature selection (2016, methodology): “The proposed feature selection methods are benchmarked with particle swarm optimizer (PSO) [35], genetic algorithms (GAs) [36], continuous ALO, and binary bat algorithm (BBA) [37] for evaluation.”
- Evolutionary population dynamics and grey wolf optimizer (2014, methodology): “Similarly to other works in the literature, we employ several test functions to benchmark the performance of the proposed algorithm [47–50].”