Abstract
Reservoir optimal operation (ROO) needs to coordinate various profit-making objectives, which is a typical multiobjective optimization problem (MOP) with complex constraints. With the development of multiobjective evolutionary algorithms (MOEAs) in the past decades, more and more research has focused on MOEAs to solve MOP. Considering that multiobjective ROO is also a typical multi-stage Markov decision-making problem, this paper introduces the application of multiobjective dynamic programming (MODP) for multiobjective ROO in detail. On this basis, an improved MODP with selection mechanism of non-dominated solutions based on reference lines (MODP-BRL) is proposed to improve the convergence efficiency of MODP. The experimental results show that the proposed MODP-BRL is a reliable and effective tool in solving multiobjective ROO. In addition, MODP-BRL has better performance in convergence effect and efficiency in comparison experiments with NSGAII, NSGAIII, and SPEA2. It is noteworthy that MODP and MODP-BRL are very sensitive to the discrete step. With the decrease of the discrete step (the higher the discrete precision), the computing time increases nonlinearly. The appropriate discrete step of the state variable is key presets to balance the superiority and computational efficiency of non-dominated solutions with the application of MODP and MODP-BRL.
Highlights
Reservoir operation optimization (ROO) facilitates flood control, agriculture irrigation, hydropower generating and shipping [1], [2], which serves human by optimizing benefit through meeting societal demand [3]
Various multiobjective evolutionary algorithms (MOEAs) based on Pareto dominance have been proposed like Multiobjective Genetic Algorithm (MOGA) [5], [11], the Improved Strength Pareto Evolutionary Algorithm (SPEA2) [12], Nondominated Sorting Genetic Algorithm II (NSGAII) [13]–[15], Multiobjective Particle Swarm Optimization (MOPSO) [6], [16], Multiobjective Evolutionary Algorithm based on Decomposition
multiobjective dynamic programming (MODP)-BRL performs better on average number of dominated solutions (ANDS) and inverted generational distance (IGD) than improved MODP (IMODP), which fully demonstrates that MODP-BRL is reliable and effective tool in solving problems
Summary
Reservoir operation optimization (ROO) facilitates flood control, agriculture irrigation, hydropower generating and shipping [1], [2], which serves human by optimizing benefit through meeting societal demand [3] These structures and their catchments need an efficient and stable solving tool to handle their complexity in terms of non-linearity, multimodal, conflicting objectives and multiple constraints. Daellenbach & Dekluyver [29] proposed the multiobjective DP (MODP), which retains all the Pareto-optimal solutions based on non-dominated policy in recursive computation, resulting in an exponentially increasing computational burden with the length of study horizon. A multiobjective dynamic programming based on the reference lines (MODP-BRL) is proposed to solve ROO of Three Gorges Reservoir (TGR) with two objectives. Major contributions are outlined as follows: 1) The multiobjective dynamic programming (MODP) based on principle of Pareto-optimality is introduced in detail, which extended single-objective DP with non-dominated policy to solve MOP.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
