Application and comparison of NSGA-II and MOPSO in multi-objective optimization of water resources systems

Adeyemo, J.A., 2011. Reservoir operation using multiobjective evolutionary algorithm - A review. Asian Journal of Scientific Research, 4, 1, 16-27. DOI: 10.3923/ajsr.2011.10.3923/ajsr.2011Open DOISearch in Google Scholar

Ajibola, A.S., Adewumi, A.O., 2014. Review of population based meta-heuristics in multi-objective optimization problems. International Journal of Computing, Communications & Instrumentation Engineering (IJCCIE), 1, 1, 126-128.Search in Google Scholar

Baltar, A.M., Fontane, D.G., 2006a. A Multi-objective Particle Swarm Optimization Model for Reservoir Operations and Planning. In: Proceedings of Joint International Conference on Computing and Decision Making in Civil and Building Engineering, 14-16 June 2006, Montréal-Canada.Search in Google Scholar

Baltar, A.M., Fontane, D.G., 2006b. A generalized multiobjective particle swarm optimization solver for spreadsheet models: application to water quality. In: Proceedings of Hydrology Days, March 2006, Fort Collins, Colorado, USA, 1-12.Search in Google Scholar

Bianchi, L. Dorigo, M., Gambardella, L.M., Gutjahr, W.J., 2009. A survey on meta-heuristics for stochastic combinatorial optimization. Natural Computing: An International Journal, 8, 2, 239-287.10.1007/s11047-008-9098-4Search in Google Scholar

Blum, C., Roli, A., 2003. Meta-heuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys, 35, 3, 268-308.10.1145/937503.937505Open DOISearch in Google Scholar

Chang, L.C., Chang, F.J., 2009. Multi-objective evolutionary algorithm for operating parallel reservoir system. Journal of Hydrology, 377, 12-20.10.1016/j.jhydrol.2009.07.061Search in Google Scholar

Chen, S.W., Chiang, T.C., 2014. Evolutionary many-objective optimization by MO-NSGA-II with enhanced mating selection. In: Proceedings of IEEE World Congress on Computational Intelligence (WCCI), pp. 1397-1404.10.1109/CEC.2014.6900400Search in Google Scholar

Eberhart, R., Kennedy, J., 1995. A new optimizer using particle swarm theory. In: Proceedings of IEEE Sixth International Symposium on Micro Machine and Human Science, 4-6 October 1995, Japan.Search in Google Scholar

Fallah-Mehdipour, E., Bozorghaddad, O., Marino, M.A., 2011. MOPSO algorithm and its application in multipurpose multireservoir operations. Journal of Hydroinformatics, 13, 4, 794-811.10.2166/hydro.2010.105Open DOISearch in Google Scholar

Ishibuchi, H., Tsukamoto, N., Nojima, Y., 2008. Evolutionary many-objective optimization: A short review. In: Proceedings of IEEE Congress on Evolutionary Computation, 1-6 June 2008, Hong Kong.10.1109/UKSIM.2008.13Search in Google Scholar

Kalyanmoy, D., Jain, H., 2012. Handling many-objective problems using an improved NSGA-II procedure. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC) 2012.Search in Google Scholar

Kalyanmoy, D., Agrawal, S., Pratap, A., Meyarivan, T., 2002. A fast and elitist multi-objective genetic algorithm: NSGAII. IEEE Transactions on Evolutionary Computation, 6, 2, 182-197.10.1109/4235.996017Open DOISearch in Google Scholar

Kennedy, J., Eberhart, R.C., 1995. Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Network, 27 November to 1 December 1995, Perth, WA.Search in Google Scholar

Konak, A., Coit, D., Smith, E., 2006. Multi-objective optimization using genetic algorithms, a tutorial. Reliability Engineering and System Safety, 91, 9, 92-107.10.1016/j.ress.2005.11.018Search in Google Scholar

Kumar, V., Minz, S., 2014. Multi-objective particle swarm optimization: an introduction. Smart Computing Review, 4, 5, 335-353. Malekmohammadi, B., Zahraie, B., Kerachian, R., 2011. Ranking solution of multi-objective reservoir operation optimization models using multi-criteria decision analysis. Expert Systems with Applications, 38, 7851-7863.10.1016/j.eswa.2010.12.119Search in Google Scholar

Moore, J., Chapman, R., 1999. Application of particle swarm to multi-objective optimization. Technical report. Department of Computer Science and Software Engineering, Auburn University, Auburn, Alabama, USA.Search in Google Scholar

Ostadrahimi, L., Mariño, M.A., Afshar, A., 2011. Multireservoir operation rules: Multi-swarm PSO-based optimization approach. Water Resource Management, 26, 407-427.10.1007/s11269-011-9924-9Search in Google Scholar

Patil, D.D., Dangewar, B.D., 2014. Multi-objective particle swarm optimization (MOPSO) based on Pareto dominance approach. International Journal of Computer Applications. 107, 4, 13-15.10.5120/18738-9983Search in Google Scholar

Reddy, M.J., Kumar, D.N., 2007. Multi-objective particle swarm optimization for generating optimal trade-offs in reservoir operation. Hydrological Processes, 21, 21, 2897-2909.10.1002/hyp.6507Open DOISearch in Google Scholar

Reddy, M.J., Kumar, D.N., 2009. Performance evaluation of elitist-mutated multi-objective particle swarm optimization for integrated water resources management. Journal of Hydroinformatics, 11, 1, 79-88.10.2166/hydro.2009.042Open DOISearch in Google Scholar

Rio, G.L., D’Souza, K., Sekaran, C.H., Kandasamy, A., 2010. Improved NSGA-II based on a novel ranking scheme. Journal of Computing, 2, 2, 91-95.Search in Google Scholar

Schaffer, J.D., 1985. Multiple objective optimization with vector evaluated genetic algorithms. In: Proceedings of the 1st International Conference on Genetic Algorithm and their applications, 2 April 1985, Hillsdale, NJ, USA.Search in Google Scholar

Shuai, W., Xiaohui, L., Xiaomin, H., 2012. Multi-objective optimization of reservoir ood dispatch based on MOPSO algorithm, In: Proceedings of 8th International Conference on Natural Computation, 29-31 May 2012, China.Search in Google Scholar

Srinivas, N., Kalyanmoy, D., 1994. Multi-objective optimization using non-dominated sorting in genetic algorithms. Evolutionary Computation, 2, 3, 221-248.10.1162/evco.1994.2.3.221Open DOISearch in Google Scholar

Yang, J., 2012. A new particle swarm optimization Aalgorithm to hierarchy multi-objective optimization problems and its application in optimal operation of hydropower stations. Journal of Computers, 7, 8, 2039-2046.10.4304/jcp.7.8.2039-2046Search in Google Scholar

Yusoff, Y., Ngadiman, M.S., Zain, A.M., 2011. Overview of NSGA-II for optimizing machining process parameters. Journal of Procedia Engineering, 15, 3978-3983.10.1016/j.proeng.2011.08.745Search in Google Scholar