Water Distribution Network Design using Hybrid Self-adaptive Multi-population Elitist Pollination Intelligence (HSAMPEPI) Jaya Algorithm
An optimization model to minimize the cost of designing water distribution network is presented in this study. The model was formulated to reduce the cost coefficient in a plumbing system. A new hybrid method of optimization was constructed by combining the search abilities of Jaya-based algorithm and pollination intelligence algorithm, and was used to solve the designed model. The model was implemented by obtaining geometrical information of a water distribution network layout stationed at Gaa Odota, Ilorin, Kwara State, Nigeria. Result obtained from the model showed a significant reduction in the cost coefficient compared to that of the study area.
M. Pilar, G. Adela and L. A. Jose, Water distribution network optimization using a modified genetic algorithm, Water Resources Research 35(11) (1999), 3467-3473. https://doi.org/10.1029/1999WR900167
M. Idel, I. Joaquin, P. Rafael and M.T. Michael, Particle swarm optimization applied to the design of water supply, Computer & Mathematics with Application 56 (2008), 769-776. https://doi.org/10.1016/j.camwa.2008.02.006
J. Saldarriaga, D. Paez, P. Cuero and N. Leon, Optimal design of water distribution networks using mock open tree topology, World Environmental and Water Resources Congress (2013), 869-880. https://doi.org/10.1061/9780784412947.083
Douglas F. Surco, Thelma P. B. Vecchi, Mauro A. S. S. Ravagnani, Optimization of water distribution networks using a modified particle swarm optimization algorithm, Water Supply 18(2) (2018), 660-678. https://doi.org/10.2166/ws.2017.148
R. Storn and K. Price, Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization 11 (1997), 341-359. https://doi.org/10.1023/A:1008202821328
X. S. Yang, Nature Inspired Metaheuristic Algorithm, Luniver Press, Cambridge, United Kingdom, 2008.
Z. Bayraktar, M. Komurcu and D. H. Werner, Wind Driven Optimization (WDO): A novel nature-inspired optimization algorithm and its application to electromagnetic, 2010 IEEE Antennas and Propagation Society International Symposium, Toronto, Canada, 2010, pp. 1-4. https://doi.org/10.1109/APS.2010.5562213
Y. Shi, An optimization algorithm based on brainstorming process, International Journal of Swarm Intelligence Research 2(4) (2011), 35-62. https://doi.org/10.4018/ijsir.2011100103
X. S. Yang, Flower pollination algorithm for global optimization, Unconventional Computation and Natural Computation, Lecture Notes in Computer Science, vol. 7445, Springer, Berlin, Heidelberg, 2012, pp. 240-249. https://doi.org/10.1007/978-3-642-32894-7_27
A. Askarzadeh, A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm, Computers and Structures 169 (2016), 1-12. https://doi.org/10.1016/j.compstruc.2016.03.001
S. Hr. Aghay Kaboli, J. Selvaraj and N. A. Rahim, Rainfall optimization: A population based algorithm for solving constrained optimization problems, Journal of Computational Science 19 (2017), 31-42. https://doi.org/10.1016/j.jocs.2016.12.010
R. V. Rao, Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems, Int. J. Ind. Eng. Comput. 7(1) (2016), 19-34. https://doi.org/10.5267/j.ijiec.2015.8.004
R. V. Rao and V. K. Patel, An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems, Int. J. Ind. Eng. Comput. 3(4) (2012), 535-560. https://doi.org/10.5267/j.ijiec.2012.03.007
R. V. Rao and A. Saroj, An elitism-based self-adaptive multi-population Jaya algorithm and its applications, Soft Comput. 23 (2019), 4383–4406. https://doi.org/10.1007/s00500-018-3095-z
R. V. Rao, Jaya: An Advanced Optimization Algorithm and its Engineering Applications, Springer International Publishing, Switzerland, 2019.
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