In this paper, two algorithms first, considered to be one of hybrid algorithms. And it is algorithm represents an Invasive Weed Optimization (IWO). This algorithm is a random numerical algorithm and the second algorithm repre- senting the Bat Optimization Algorithm (BA). This algorithm is one of the swarm intelligence algorithms in smart optimization. The Invasive Weed Optimization Algorithm is inspired by nature, as the weeds have colonial behavior and were introduced in 2006 by Mehrabian and Lucas. Invasive weeds pose a serious threat to crops due to their adaptability and pose a threat to the overall planting cycle. In the Invasive Weed Optimization Algorithm, the behavior of these weeds was analyzed and implemented. The Bat algorithm, which is called a swarm intelligence algorithm, was used to achieve the goal and the best solution. The algorithm was designed by Yang in 2010 and taking advantage of the intelligence of the squadrons is to avoid falling into local solutions so the new hybridization process between the previous algorithms Invasive Weed Optimization (IWO) and Bat Optimization Algorithm (BA) and we will symbolize the new algorithm IWO-BA Comparing the hybrid algorithm suggested with the original algorithms the results were excellent. In most research functions, the optimal solution has been found.
Cite this paper
Qasim, W. A. and Mitras, B. A. (2020). A Suggestion Algorithm Instituted on Invasive Weed Optimization Algorithm and Bat Optimization Algorithm. Open Access Library Journal, 7, e6438. doi: http://dx.doi.org/10.4236/oalib.1106438.
Yaseen, H.T., Mitras, B.A. and Khidhir, A.S.M. (2018) Hybrid Invasive Weed Optimization Algorithm with Chicken Swarm Optimization Algorithm to Solve Global Optimization Problems. International Journal of Computer Networks and Communications Security, 6, 173-181.
Blum, C., Roli, A. and Sampels, M. (2008) Hybrid Metaheuristics: An Emerging Approach to Optimization. Vol. 114, Springer, Berlin.
https://doi.org/10.1007/978-3-540-78295-7
Mehrabian, A.R. and Lucas, C. (2006) A Novel Numerical Optimization Algorithm Inspired from Weed Colonization. Ecological Informatics, 1, 355-366.
https://doi.org/10.1016/j.ecoinf.2006.07.003
Zhao, Y., Leng, L., Qian, Z. and Wang, W. (2016) A Discrete Hybrid Invasive Weed Optimization Algorithm for the Capacitated Vehicle Routing Problem. Procedia Computer Science, 91, 978-987. https://doi.org/10.1016/j.procs.2016.07.127
Yang, X.S. (2011) Bat Algorithm for Multi-Objective Optimisation. International Journal of Bio-Inspired Computation, 3, 267-274.
https://doi.org/10.1504/IJBIC.2011.042259
Kie？kowicz, K. and Grela, D. (2016) Modified Bat Algorithm for Nonlinear Optimization. IJCSNS International Journal of Computer Science and Network Security, 16, 46-50.
Simmons, J.A., Howell, D.J. and Suga, N. (1975) Information Content of Bat Sonar Echoes: Recent Research on Echolocation in Bats Identifies Some of the Kinds of Information Conveyed by Echoes of Their Sonar Sounds. American Scientist, 63, 204-215.
Surlykke, A., Futtrup, V. and Tougaard, J. (2003) Prey-Capture Success Revealed by Echolocation Signals in Pipistrelle Bats (Pipistrellus pygmaeus). Journal of Experimental Biology, 206, 93-104. https://doi.org/10.1242/jeb.00049
Ghose, K., Horiuchi, T.K., Krishnaprasad, P.S. and Moss, C.F. (2006) Echolocating Bats Use a Nearly Time-Optimal Strategy to Intercept Prey. PLoS Biology, 4, e108.
https://doi.org/10.1371/journal.pbio.0040108
Vogler, B. and Neuweiler, G. (1983) Echolocation in the Noctule (Nyctalus noctula) and Horseshoe Bat (Rhinolophus ferrumequinum). Journal of Comparative Physiology A: Neuroethology, Sensory, Neural, and Behavioral Physiology, 152, 421-432.
https://doi.org/10.1007/BF00606247
Oliver, J. (2013) Improved Docking of Polypeptides with Glide. Journal of Chemical Information and Modeling, 53, 1689-1699. https://doi.org/10.1021/ci400128m
Yang, X.S. (2010) A New Metaheuristic Bat-Inspired Algorithm. Studies in Computational Intelligence, 284, 65-74. https://doi.org/10.1007/978-3-642-12538-6_6
