Multi-objective Cultured Differential Evolution for Generating Optimal Trade-offs in Reservoir Flood Control Operation

Reservoir flood control operation (RFCO) is a complex problem because it needs to consider multiple objectives and a large number of constraints. Traditional methods usually convert multiple objectives into a single objective to solve, using weighted methods or constrained methods. In this paper, a new approach named multi-objective cultured differential evolution (MOCDE) is proposed to deal with RFCO. MOCDE takes cultural algorithm as its framework and adopts differential evolution (DE) in its population space. Considering the features of DE and multi-objective optimization, three knowledge structures are defined in belief space to improve the searching efficiency of MOCDE. MOCDE is first tested on several benchmark problems and compared with some well known multi-objective optimization algorithms. On achieving satisfactory performance for test problems, MOCDE is applied to a case study of RFCO. It is found that MOCDE provides decision makers many alternative non-dominated schemes with uniform coverage and convergence to true Pareto optimal solutions in a short time. The results obtained show that MOCDE can be a viable alternative for generating optimal trade-offs in reservoir multi-objective flood control operation.

[1]  Yanbin Yuan,et al.  An enhanced differential evolution algorithm for daily optimal hydro generation scheduling , 2008, Comput. Math. Appl..

[2]  M. Janga Reddy,et al.  Multiobjective Differential Evolution with Application to Reservoir System Optimization , 2007 .

[3]  Robert G. Reynolds,et al.  Knowledge-based solution to dynamic optimization problems using cultural algorithms , 2001 .

[4]  Olcay I. Unver,et al.  Model for real-time optimal flood control operation of a reservoir system , 1990 .

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

[6]  R. Storn,et al.  On the usage of differential evolution for function optimization , 1996, Proceedings of North American Fuzzy Information Processing.

[7]  Sharad K. Jain,et al.  Optimal Operation of a Multi-Purpose Reservoir Using Neuro-Fuzzy Technique , 2009 .

[8]  Darrell G. Fontane,et al.  Use of Multiobjective Particle Swarm Optimization in Water Resources Management , 2008 .

[9]  H. Abbass,et al.  PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[10]  Taesoon Kim,et al.  Multireservoir system optimization in the Han River basin using multi‐objective genetic algorithms , 2006 .

[11]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[12]  J. Windsor.  Optimization model for the operation of flood control systems , 1973 .

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

[14]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

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

[16]  Gert A. Schultz,et al.  Developing optimal operating rules for flood protection reservoirs , 1976 .

[17]  Robert G. Reynolds,et al.  Cultural swarms: modeling the impact of culture on social interaction and problem solving , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[18]  N. Chakraborty,et al.  Short-term combined economic emission scheduling of hydrothermal power systems with cascaded reservoirs using differential evolution , 2009 .

[19]  Arthur C. Sanderson,et al.  Pareto-based multi-objective differential evolution , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[20]  R. Reynolds,et al.  Using knowledge-based evolutionary computation to solve nonlinear constraint optimization problems: a cultural algorithm approach , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[21]  Li-Chiu Chang,et al.  Guiding rational reservoir flood operation using penalty-type genetic algorithm , 2008 .

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

[23]  Nien-Sheng Hsu,et al.  Multireservoir Flood-Control Optimization with Neural-Based Linear Channel Level Routing Under Tidal Effects , 2008 .

[24]  N. Madavan.  Multiobjective optimization using a Pareto differential evolution approach , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[25]  Mohammad Karamouz,et al.  Development of Optimization Schemes for Floodplain Management; A Case Study , 2009 .

[26]  C. Coello,et al.  Cultured differential evolution for constrained optimization , 2006 .

[27]  William W.-G. Yeh,et al.  A diversified multiobjective GA for optimizing reservoir rule curves , 2007 .

[28]  Weiyi Qian,et al.  Adaptive differential evolution algorithm for multiobjective optimization problems , 2008, Appl. Math. Comput..

[29]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[30]  K. Srinivasa Raju,et al.  Optimal Reservoir Operation for Flood Control Using Folded Dynamic Programming , 2010 .

[31]  D. Kumar,et al.  Folded Dynamic Programming for Optimal Operation of Multireservoir System , 2003 .

[32]  Bogdan Filipic,et al.  DEMO: Differential Evolution for Multiobjective Optimization , 2005, EMO.