An Improved Bat Algorithm with Grey Wolf Optimizer for Solving Continuous Optimization Problems
Subject Areas : Machine Learningnarges jafari 1 , Farhad Soleimanian Gharehchopogh 2
1 - Department of Computer Engineering, Urmia branch, Islamic Azad University, Urmia, Iran
2 - Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, IRAN
Keywords:
Abstract :
1. Abedi, M. and F.S. Gharehchopogh, An improved opposition based learning firefly algorithm with dragonfly algorithm for solving continuous optimization problems. Intelligent Data Analysis, 2020. 24(2): p. 309-338.
2. Gharehchopogh, F.S. and H. Gholizadeh, A comprehensive survey: Whale Optimization Algorithm and its applications. Swarm and Evolutionary Computation, 2019. 48: p. 1-24.
3. Shayanfar, H. and F.S. Gharehchopogh, Farmland fertility: A new metaheuristic algorithm for solving continuous optimization problems. Applied Soft Computing, 2018. 71: p. 728-746.
4. Amjad, S.S.G., Farhad, A Novel Hybrid Approach for Email Spam Detection based on Scatter Search Algorithm and K-Nearest Neighbors. Journal of Advances in Computer Engineering and Technology, 2019. 5(3): p. 169-178.
5. Khanalni, S. and F.S. Gharehchopogh, A New Approach for Text Documents Classification with Invasive Weed Optimization and Naive Bayes Classifier. Journal of Advances in Computer Engineering and Technology, 2018. 4(3): p. 31-40.
6. Allahverdipour, A. and F. Soleimanian Gharehchopogh, An Improved K-Nearest Neighbor with Crow Search Algorithm for Feature Selection in Text Documents Classification. Journal of Advances in Computer Research, 2018. 9(2): p. 37-48.
7. Majidpour, H. and F. Soleimanian Gharehchopogh, An Improved Flower Pollination Algorithm with AdaBoost Algorithm for Feature Selection in Text Documents Classification. Journal of Advances in Computer Research, 2018. 9(1): p. 29-40.
8. Gharehchopogh, F.S., S.R. Khaze, and I. Maleki, A new approach in bloggers classification with hybrid of k-nearest neighbor and artificial neural network algorithms. Indian Journal of Science and technology, 2015. 8(3): p. 237.
9. Geem, Z.W., J.H. Kim, and G.V. Loganathan, A new heuristic optimization algorithm: harmony search. simulation, 2001. 76(2): p. 60-68.
10. Rabani, H. and F. Soleimanian Gharehchopogh, An Optimized Firefly Algorithm based on Cellular Learning Automata for Community Detection in Social Networks. Journal of Advances in Computer Research, 2019. 10(3): p. 13-30.
11. Gharehchopogh, F.S., H. Shayanfar, and H. Gholizadeh, A comprehensive survey on symbiotic organisms search algorithms. Artificial Intelligence Review, 2019: p. 1-48.
12. Osmani, A., J.B. Mohasefi, and F.S. Gharehchopogh, Sentiment Classification Using Two Effective Optimization Methods Derived From The Artificial Bee Colony Optimization And Imperialist Competitive Algorithm. The Computer Journal, 2020.
13. Hasanluo, M. and F. Soleimanian Gharehchopogh, Software cost estimation by a new hybrid model of particle swarm optimization and k-nearest neighbor algorithms. Journal of Electrical and Computer Engineering Innovations, 2016. 4(1): p. 49-55.
14. Holland, J.H., Genetic algorithms. Scientific american, 1992. 267(1): p. 66-73.
15. Kennedy, J., Particle swarm optimization, in Encyclopedia of machine learning. 2011, Springer. p. 760-766.
16. Storn, R. and K. Price, Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 1997. 11(4): p. 341-359.
17. Karaboga, D., An idea based on honey bee swarm for numerical optimization. 2005, Technical report-tr06, Erciyes university, engineering faculty, computer engineering department.
18. Yang, X.-S., Firefly algorithm. Nature-inspired metaheuristic algorithms, 2008. 20: p. 79-90.
19. Yang, X.-S., A new metaheuristic bat-inspired algorithm, in Nature inspired cooperative strategies for optimization (NICSO 2010). 2010, Springer. p. 65-74.
20. Mirjalili, S., S.M. Mirjalili, and A. Lewis, Grey wolf optimizer. Advances in engineering software, 2014. 69: p. 46-61.
21. Zhang, J.W. and G.G. Wang. Image matching using a bat algorithm with mutation. in Applied Mechanics and Materials. 2012. Trans Tech Publ.
22. Saha, S.K., et al., A new design method using opposition-based BAT algorithm for IIR system identification problem. International Journal of Bio-Inspired Computation, 2013. 5(2): p. 99-132.
23. Yılmaz, S., E.U. Kucuksille, and Y. Cengiz, Modified bat algorithm. Elektronika ir Elektrotechnika, 2014. 20(2): p. 71-78.
24. Li, L. and Y. Zhou, A novel complex-valued bat algorithm. Neural Computing and Applications, 2014. 25(6): p. 1369-1381.
25. Cai, X., et al., Bat algorithm with Gaussian walk. International Journal of Bio-Inspired Computation, 2014. 6(3): p. 166-174.
26. Dao, T.-K., et al., Compact bat algorithm, in Intelligent Data analysis and its Applications, Volume II. 2014, Springer. p. 57-68.
27. Fister, I., S. Fong, and J. Brest, A novel hybrid self-adaptive bat algorithm. The Scientific World Journal, 2014. 2014.
28. Jun, L., L. Liheng, and W. Xianyi, A double-subpopulation variant of the bat algorithm. Applied Mathematics and Computation, 2015. 263: p. 361-377.
29. Wang, G.-G., H.E. Chu, and S. Mirjalili, Three-dimensional path planning for UCAV using an improved bat algorithm. Aerospace Science and Technology, 2016. 49: p. 231-238.
30. Liu, Q., et al., A novel hybrid bat algorithm for solving continuous optimization problems. Applied Soft Computing, 2018. 73: p. 67-82.
31. Chakri, A., et al., New directional bat algorithm for continuous optimization problems. Expert Systems with Applications, 2017. 69: p. 159-175.
32. Purkait, G., et al., An Improved Bio-inspired BAT Algorithm for Optimization, in Progress in Advanced Computing and Intelligent Engineering. 2019, Springer. p. 241-248.
33. Asghari Agcheh Dizaj, S. and F. Soleimanian Gharehchopogh, A New Approach to Software Cost Estimation by Improving Genetic Algorithm with Bat Algorithm. Journal of Computer & Robotics, 2018. 11(2): p. 17-30.
