A Modified Water Cycle Algorithm: An Opposition Based Meta-Heuristic Optimization to Solve Real World Engineering Problems

Year 2024, Volume: 37 Issue: 3, 1215 - 1234, 01.09.2024

Monalisa Datta , Dıpu Sarkar , Soumyabrata Das

https://doi.org/10.35378/gujs.1294832

Abstract

This paper proposes the Opposition based learning on a latest recent population based Water Cycle Algorithm on different benchmark constraint optimization techniques. Water cycle is a Hydrological based technique which works on better search location of the stream and river that flows to the sea which works on certain control parameters that will be defined initially and obtain the population matrix. With the help of the application of the opposition learning opposite search will be made to receive the better search location to find the better fitness value and avoid the premature convergence and get best convergence rate. This Proposed Opposition based Water Cycle Algorithm is implemented and tested on fifteen benchmark problems mentioning the fitness value as well as the constraints value. The convergence plot using a comparative study between Water Cycle Algorithm and Opposition based Water Cycle Algorithm, the proposed method had proved to obtain the best result and superior for the problems on to which it had implemented. The ANOVA test result is shown for the statistical analysis of the data obtained.

Keywords

Problems on benchmark, Water cycle algorithm, Optimization design, Opposition learning, ANOVA test

References

• [1] Dorigo, M., and Stutzle, T., “Ant Colony Optimization”, The MIT Press Cambridge, London, UK, (2004).
• [2] Haupt, R., and Haupt, S., “Practical Genetic Algorithms”, John Wiley & Sons, Hoboken, NJ, USA, (2004).
• [3] Kramer, O., “Genetic Algorithm Essentials”, Springer International Publishing AG, Berlin, Germany, (2017).
• [4] Lazinica, A., “Particle swarm optimization”, In-Tech, Rijeka, Croatia, (2009).
• [5] Raj, A., Bhattacharya, B., “Optimal Placement of TCSC and SVC for reactive power planning using Whale Optimization algorithm”, Swarm and Evolutionary Computation BASE DATA, (2018).
• [6] Esander, H., Sadollah, A., Bahreininejad, A., Hamdi, M., “Water cycle Algorithm-a novel meta-heuristic optimization method for solving constrained engineering optimization problems”, Elsevier Computer and Structures, 110: 151-166, (2012).
• [7] Kudkelwar, S., Sarkar, D., “Online Implementation of time augmentation of over-current relay coordination using water cycle algorithm”, SN Applied Science Springer Nature Journal, (2019).
• [8] Ramapriya, B., “Profit maximization and optimal bidding strategies of gencos in Electricity market using self-Adaptive Differential Solution”, International Journal on Electrical and Electronics and Informatics, 8(4): 753-761, (2016).
• [9] He, S., Wu,Q. H., and Saunders , J. R., “Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behaviour”, In IEEE Transactions on Evolutionary Computation, 13: 973–990, (2009).
• [10] Reddy, S., Panwar, L. K., Kumar, R., Panigrahi, B.K., “Binary Fireworks Algorithm for Profit Based Unit Commitment (PBUC) Problem”, International Journal of Electrical Power & Energy Systems, 83: 270–282, (2016).
• [11] Manisha, H., YK, A., Thakur, N., Siddiqui, A. S., “Advanced hybrid intelligent model and algorithm for de-regulated electricity market”, Australian Journal of Electrical and Electronics Engineering Taylor & Francis, 1-11, (2020).
• [12] Aggarwal, R., Mallick, R.K., and Choudhury, A. R., “Bidding Strategies for GENCOS In Pool Based DAEM Using GWO Method”, International Journal of Scientific & Technology Research, 9(1): 3229-3235, (2020).
• [13] Tizhoosh, HR., “Opposition-based learning: a new scheme for machine intelligence. In International conference on computational intelligence for modelling”, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC'06), 695-701, (2005). DOI: 10.1109/CIMCA.2005.1631345
• [14] Jabeen, H., Jalil, Z., Baig, AR., “Opposition based initialization in particle swarm optimization”, (O-PSO). In Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers, Jul 8, 2047-2052, (2009).
• [15] Shehab, M., Abualigah, L., “Opposition-based learning multi-verse optimizer with disruption operator for optimization problems”, Soft Computing. Nov; 26(21): 11669-11693, (2022).
• [16] Elgamal, Z.M., Yasin, N.M., Sabri, A.Q., Sihwail, R., Tubishat, M., Jarrah, H., “Improved equilibrium optimization algorithm using elite opposition-based learning and new local search strategy for feature selection in medical datasets”, Computation, Jun 10, 9(6): 68, (2021).
• [17] Alamri, H.S., Alsariera, Y.A., Zamli, K.Z., “Opposition-based whale optimization algorithm”, Advanced Science Letters, Oct 1, 24(10): 7461-7464, (2018).
• [18] Korashy, A., Kamel, S., Houssein, E.H., Jurado, F., Hashim, F.A., “Development and application of evaporation rate water cycle algorithm for optimal coordination of directional overcurrent relays”, Expert Systems with Applications, December 15, 185: 115538, (2021).
• [19] Dhargupta, S., Ghosh, M., Mirjalili, S., Sarkar, R., “Selective opposition based grey wolf optimization”, Expert Systems with Applications, Aug 1, 151: 113389, (2020).
• [20] Zhang, Z., Xu, Z., Luan, S., Li, X., Sun, Y., “Opposition-based ant colony optimization algorithm for the traveling salesman problem”, Mathematics, September 24; 8(10): 1650, (2020).
• [21] Al-Fakih A.M., Algamal, Z.Y., Qasim, M.K., “An improved opposition-based crow search algorithm for biodegradable material classification”, SAR and QSAR in Environmental Research, May 4, 33(5): 403-415, (2022).
• [22] Zhang, G., Cheng, J., Gheorghe, M., and Meng, Q., “A hybrid approach based on differential evolution and tissue membrane systems for solving constrained manufacturing parameter optimization problems”, Applied Soft Computing, 13(3): 1528-1542, (2013).
• [23] Rao, R.V., Savsani, V.J., and Vakharia, D.P., “Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems”, Computer-Aided design, 43(3): 303-315, (2011).
• [24] Akay, B., and Karaboga, D., “Artificial bee colony algorithm for large-scale problems and engineering design optimization”, Journal of Intelligent Manufacturing, 23: 1001-1014, (2012).
• [25] Huang, F.Z., Wang, L., and He, Q., “An effective co-evolutionary differential evolution for constrained optimization”, Applied Mathematics and computation, 186(1): 340-356, (2007).
• [26] David, D.C.N., and Stephen, C.E.A., “Cost Minimization of Welded Beam Design Problem using Non-traditional optimization through Matlab and validation through analyses simulation”, Int J MechEngTechnol IJMET, 9(8): 180-192, (2018).
• [27] Hassan, S., Kumar, K., Raj, C.D., and Sridhar, K., “Design and optimisation of pressure vessel using metaheuristic approach”, In Applied Mechanics and Materials, 401-406, Trans Tech Publications Ltd, (2014).
• [28] Satyanarayana, M., Charyulu, T.N., Ramanamurthy Naidu, S.C.V., and Appala Naidu, G., “Optimization of spring weight using genetic algorithm”, International Journal of Engineering Research & Technology (IJERT), 1(9): 1-10, (2012).
• [29] Lin, M.H., Tsai, J.F., Hu, N.Z., and Chang, S.C., “Design optimization of a speed reducer using deterministic techniques”, Mathematical Problems in Engineering, (2013).
• [30] Malcolm, J.D., Roth, A., Radic, M., Martin-Ramiro, P., Oillarburu, J., Orus, R., and Mugel, S., “Multi-disk clutch optimization using quantum annealing”, arXiv preprint arXiv:2208.05916 (2022).
• [31] Dandagwhal, R.D., and Kalyankar, V.D., “Design optimization of rolling element bearings using advanced optimization technique”, Arabian Journal for Science and Engineering, 44(9): 7407-7422, (2019).
• [32] YILDIRIM, A.E., and Karci, A., “September. Application of three bar truss problem among engineering design optimization problems using artificial atom algorithm”, In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP) 1-5, IEEE (2018).
• [33] Fauzi, H., and Batool, U., “A three-bar truss design using single-solution simulated Kalman filter optimizer”, Mekatronika, 1(2): 98-102, (2019).
• [34] Rao, R.V., and Waghmare, G.G., “A new optimization algorithm for solving complex constrained design optimization problems”, Engineering Optimization, 49(1): 60-83, (2017).
• [35] Rao, R.V., and Waghmare, G.G., “Complex constrained design optimisation using an elitist teaching-learning-based optimisation algorithm”, International Journal of Metaheuristics, 3(1): 81-102, (2014).
• [36] Liao, T.W., “Two hybrid differential evolution algorithms for engineering design optimization”, Applied Soft Computing, 10(4): 1188-1199, (2010).
• [37] Sadollah, A., Bahreininejad, A., Eskandar, H., and Hamdi, M., “Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems”, Applied Soft Computing, 13(5): 2592-2612, (2013).
• [38] Canayaz, M., and Karci, A., “Cricket behaviour-based evolutionary computation technique in solving engineering optimization problems”, Applied Intelligence, 44: 362-376, (2016).
• [39] Wang, Y., Wang, P., Zhang, J., Cui, Z., Cai, X., Zhang, W., and Chen, J., “A novel bat algorithm with multiple strategies coupling for numerical optimization”, Mathematics, 7(2): 135, (2019).
• [40] Gandomi, A.H., Yang, X.S., and Alavi, A.H., “Cuckoo search algorithm: a meta-heuristic approach to solve structural optimization problems”, Engineering with Computers, 29: 17-35, (2013).
• [41] Ray, T., and Saini, P., “Engineering design optimization using a swarm with an intelligent information sharing among individuals”, Engineering Optimization, 33(6): 735-748, (2001).