A diversified multiobjective GA for optimizing reservoir rule curves

Abstract

The paper develops an efficient macro-evolutionary multiobjective genetic algorithm (MMGA) for optimizing the rule curves of a multi-purpose reservoir system in Taiwan. Macro-evolution is a new kind of high-level species evolution that can avoid premature convergence that may arise during the selection process of conventional GAs. MMGA enriches the capabilities of GA to handle multiobjective problems by diversifying the solution set. Simulation results using a benchmark test problem indicate that the proposed MMGA yields better-spread solutions and converges closer to the true Pareto frontier than the nondominated sorting genetic algorithm-II (NSGA-II). When applied to a real case study, MMGA is able to generate uniformly spread solutions for a two-objective problem involving water supply and hydropower generation. Results of this work indicate that the proposed MMGA is highly competitive and provides a viable alternative to solve multiobjective optimization problems for water resources planning and management.