Crossref Citations
This article has been cited by the following publications. This list is generated based on data provided by Crossref.
Iqbal, Shahrear
Bari, Md. Faizul
and
Rahman, M. Sohel
2010.
A novel ACO technique for fast and near optimal solutions for the Multi-dimensional Multi-choice Knapsack Problem.
p.
33.
Iqbal, Shahrear
Bari, Md. Faizul
and
Rahman, M. Sohel
2010.
Swarm Intelligence.
Vol. 6234,
Issue. ,
p.
312.
Zahran, E. G.
Arafa, A. A.
Saleh, H. I.
and
Dessouky, M. I.
2018.
Biogeography Based Optimization Algorithm for Efficient RFID Reader Deployment.
p.
454.
Zouari, Wiem
Alaya, Ines
and
Tagina, Moncef
2018.
A Comparative Study of a Hybrid Ant Colony Algorithm MMACS for the Strongly Correlated Knapsack Problem
.
Advances in Science, Technology and Engineering Systems Journal
,
Vol. 3,
Issue. 6,
p.
1.
Fernandez-Fraga, S. M.
Aceves-Fernandez, M. A.
Pedraza-Ortega, J. C.
and
Tovar-Arriaga, S.
2018.
Feature Extraction of EEG Signal upon BCI Systems Based on Steady-State Visual Evoked Potentials Using the Ant Colony Optimization Algorithm.
Discrete Dynamics in Nature and Society,
Vol. 2018,
Issue. ,
p.
1.
Chen, Chi-Chung
and
Liu, Yi-Ting
2018.
Enhanced Ant Colony Optimization with Dynamic Mutation and Ad Hoc Initialization for Improving the Design of TSK-Type Fuzzy System.
Computational Intelligence and Neuroscience,
Vol. 2018,
Issue. ,
p.
1.
Gao, Yangjun
Zhang, Fengming
Zhao, Yu
and
Li, Chao
2018.
Quantum-Inspired Wolf Pack Algorithm to Solve the 0-1 Knapsack Problem.
Mathematical Problems in Engineering,
Vol. 2018,
Issue. ,
p.
1.
Nguyen, Tien Thanh
Luong, Anh Vu
Van Nguyen, Thi Minh
Ha, Trong Sy
Liew, Alan Wee-Chung
and
McCall, John
2019.
Simultaneous meta-data and meta-classifier selection in multiple classifier system.
p.
39.
Yu, Jing
Xing, Lining
Tan, Xu
Ren, Teng
and
Li, Zhenping
2019.
Doctor-Patient Combined Matching Problem and its Solving Algorithms.
IEEE Access,
Vol. 7,
Issue. ,
p.
177723.
Gupta, Abhranil
2021.
Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning.
p.
254.
Yu, Jing
Xing, Lining
and
Tan, Xu
2021.
The new treatment mode research of hepatitis B based on ant colony algorithm.
Journal of Combinatorial Optimization,
Vol. 42,
Issue. 4,
p.
740.
Kumar, Alok
Lekhraj
Singh, Safalata
and
Kumar, Anoj
2021.
Grey wolf optimizer and other metaheuristic optimization techniques with image processing as their applications: a review.
IOP Conference Series: Materials Science and Engineering,
Vol. 1136,
Issue. 1,
p.
012053.
Bhandari, Mohan
Panday, Subash
Bhatta, Chandra Prakash
and
Panday, Sanjeeb Prasad
2022.
Image Steganography Approach Based Ant Colony Optimization with Triangular Chaotic Map.
p.
429.
Zahran, E G
Arafa, A A
Saleh, H I
and
Dessouky, M I
2022.
Effective Hybridization of Biogeography Based Optimization and Simulated Annealing.
Journal of Physics: Conference Series,
Vol. 2304,
Issue. 1,
p.
012013.
N., Sathyanarayana
and
Narasimhamurthy, Anand M.
2022.
Vehicle Type Classification Using Hybrid Features and a Deep Neural Network.
International Journal of Applied Metaheuristic Computing,
Vol. 13,
Issue. 1,
p.
1.
Tan, Shi Hao
Chuah, Joon Huang
Chow, Chee-Onn
and
Kanesan, Jeevan
2023.
Spatially Recalibrated Convolutional Neural Network for Vehicle Type Recognition.
IEEE Access,
Vol. 11,
Issue. ,
p.
142525.
Sathyanarayana N.
and
Narasimhamurthy, Anand M.
2023.
Vehicle Type Classification Using Hybrid Features and a Deep Neural Network.
International Journal of Software Innovation,
Vol. 10,
Issue. 1,
p.
1.
Prado‐Rodríguez, Roberto
González, Patricia
Banga, Julio R.
and
Doallo, Ramón
2024.
Improved cooperative Ant Colony Optimization for the solution of binary combinatorial optimization applications.
Expert Systems,
Vol. 41,
Issue. 8,
Dua, Aman
Chhabra, Rishika
and
Sinha, Deepankar
2024.
Design of service network for containerized export by multimodal transportation: with quality concept.
Benchmarking: An International Journal,
Vol. 31,
Issue. 1,
p.
220.
Dorighello, Renato Sellaro
Delgado, Myriam Regattieri
Lüders, Ricardo
and
Pigatto, Daniel Fernando
2024.
ACO-Pruning for Deep Neural Networks: A Case Study in CNNs.
p.
1895.