Extended multi-objective firefly algorithm for hydropower energy generation

Classical methods have severe limitations (such as being trapped in local optima, and the curse of dimensionality) to solve optimization problems. Evolutionary or meta-heuristic algorithms are currently favored as the tools of choice for tackling such complex non-linear reservoir operations. This paper evaluates the performance of an extended multi-objective developed firefly algorithm (MODFA). The MODFA script code was developed using the MATLAB programming language and was applied in MATLAB to optimize hydropower generation by a three-reservoir system in Iran. The two objectives used in the present study are the maximization of the reliability of hydropower generation and the minimization of the vulnerability to generation defficits of the three-reservoir system. Optimal Paretos (OPs) obtained with the MODFA are compared with those obtained with the multi-objective genetic algorithm (MOGA) and the multi-objective firefly algorithm (MOFA) for different levels of performance thresholds (50%, 75%, and 100%). The case study results demonstrate that the MODFA is superior to the MOGA and MOFA for calculating proper OPs with distinct solutions and a wide distribution of solutions. This study9s results show that the MODFA solves multi-objective multi-reservoir operation system with the purpose of hydropower generation that are highly nonlinear that classical methods cannot solve.

[1]  George Kuczera,et al.  Efficient multi-objective optimization methods for computationally intensive urban water resources models , 2015 .

[2]  Omid Bozorg-Haddad,et al.  Modified Firefly Algorithm for Solving Multireservoir Operation in Continuous and Discrete Domains , 2016 .

[3]  Miguel A. Mariño,et al.  Application of the Firefly Algorithm to Optimal Operation of Reservoirs with the Purpose of Irrigation Supply and Hydropower Production , 2016 .

[4]  Omid Bozorg-Haddad,et al.  Intermittent Urban Water Supply with Protection of Consumers’ Welfare , 2016 .

[5]  Abolfazl Akbarpour,et al.  Sustainable groundwater modeling using single- and multi-objective optimization algorithms , 2017 .

[6]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[7]  Jason R. Schott.  Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization. , 1995 .

[8]  D. Nagesh Kumar,et al.  Multi‐objective particle swarm optimization for generating optimal trade‐offs in reservoir operation , 2007 .

[9]  Eckart Zitzler,et al.  HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization , 2011, Evolutionary Computation.

[10]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[11]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[12]  Joshua D. Knowles,et al.  Bounded archiving using the lebesgue measure , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[13]  Nicola Beume,et al.  SMS-EMOA: Multiobjective selection based on dominated hypervolume , 2007, Eur. J. Oper. Res..

[14]  Kyung K. Choi,et al.  Structural Sensitivity Analysis and Optimization 1: Linear Systems , 2005 .

[15]  D. Nagesh Kumar,et al.  Optimal Reservoir Operation Using Multi-Objective Evolutionary Algorithm , 2006 .

[16]  Taesoon Kim,et al.  Application of multi-objective genetic algorithms to multireservoir system optimization in the Han River basin , 2006 .

[17]  Luiz Gustavo Martins Vieira,et al.  Optimization of Hydrocyclone Performance Using Multi-Objective Firefly Colony Algorithm , 2013 .

[18]  H. Loáiciga,et al.  Hydropower Reservoir Management Under Climate Change: The Karoon Reservoir System , 2015, Water Resources Management.

[19]  Xin-She Yang,et al.  Firefly Algorithms for Multimodal Optimization , 2009, SAGA.

[20]  Ye Tian,et al.  An Efficient Approach to Nondominated Sorting for Evolutionary Multiobjective Optimization , 2015, IEEE Transactions on Evolutionary Computation.

[21]  Fang-Fang Li,et al.  Incorporating ecological adaptation in a multi-objective optimization for the Three Gorges Reservoir , 2016 .

[22]  Li-Chiu Chang,et al.  Multi-objective evolutionary algorithm for operating parallel reservoir system , 2009 .

[23]  Dimitri Solomatine,et al.  A Novel Nested Dynamic Programming (nDP) Algorithm For Multipurpose Reservoir Optimization , 2015 .

[24]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[25]  Omid Bozorg Haddad,et al.  Application of the SVR-NSGAII to Hydrograph Routing in Open Channels , 2016 .

[26]  Omid Bozorg-Haddad,et al.  Multiobjective Design of Water-Quality Monitoring Networks in River-Reservoir Systems , 2017 .

[27]  Huifeng Zhang,et al.  Optimal operation of multi-reservoir system by multi-elite guide particle swarm optimization , 2013 .

[28]  Xiaodong Li,et al.  A Non-dominated Sorting Particle Swarm Optimizer for Multiobjective Optimization , 2003, GECCO.

[29]  Xin-She Yang,et al.  Multiobjective firefly algorithm for continuous optimization , 2012, Engineering with Computers.

[30]  Lothar Thiele,et al.  Quality Assessment of Pareto Set Approximations , 2008, Multiobjective Optimization.

[31]  Yunlong Zhu,et al.  An Improved Firefly Algorithm with Adaptive Strategies , 2012 .

[32]  Byran J. Smucker,et al.  On using the hypervolume indicator to compare Pareto fronts: Applications to multi-criteria optimal experimental design , 2015 .

[33]  Chun-Wei Tsai,et al.  A non-dominated sorting firefly algorithm for multi-objective optimization , 2014, 2014 14th International Conference on Intelligent Systems Design and Applications.

[34]  Rong Zhang,et al.  Multi-objective calibration of the physically based, spatially distributed SHETRAN hydrological model , 2016 .

[35]  Nicola Beume,et al.  An EMO Algorithm Using the Hypervolume Measure as Selection Criterion , 2005, EMO.

[36]  Eckart Zitzler,et al.  Indicator-Based Selection in Multiobjective Search , 2004, PPSN.