Study on reservoir optimal operation based on coupled adaptive ε constraint and multi strategy improved Pelican algorithm

Abstract

The optimal operation of reservoir groups is a strongly constrained, multi-stage, and high-dimensional optimization problem. In response to this issue, this article couples the standard Pelican optimization algorithm with adaptive ε constraint methods, and further improves the optimization performance of the algorithm by initializing the population with a good point set, reverse differential evolution, and optimal individual t-distribution perturbation strategy. Based on this, an improved Pelican algorithm coupled with adaptive ε constraint method is proposed (ε-IPOA). The performance of the algorithm was tested through 24 constraint testing functions to find the optimal ability and solve constraint optimization problems. The results showed that the algorithm has strong optimization ability and stable performance. In this paper, we select Sanmenxia and Xiaolangdi reservoirs as the research objects, establish the maximum peak-cutting model of terrace reservoirs, apply the ε-IPOA algorithm to solve the model, and compare it with the ε-POA (Pelican algorithm coupled with adaptive ε constraint method) and ε-DE (Differential Evolution Algorithm) algorithms, the results indicate that ε. The peak flow rate of the Huayuankou control point solved by the IPOA algorithm is 12,319 m3/s, which is much lower than the safe overflow flow rate of 22,000 m3/s at the Huayuankou control point, with a peak shaving rate of 44%, and other algorithms do not find effective solutions meeting the constraint conditions. This paper provides a new idea for solving the problem of flood control optimal operation of cascade reservoirs.

Research on hybrid reservoir scheduling optimization based on improved walrus optimization algorithm with coupling adaptive ε constraint and multi-strategy optimization

“…Although intelligent algorithms have been widely used in multiple fields and their powerful search capabilities are impressive, the issue of excessive randomness cannot be ignored. During the solution process, intelligent algorithms are prone to falling into local optimal solutions, leading to instability in the solution results, which may not be suitable for some application scenarios with high requirements for accuracy and stability 26 .…”

“…To overcome these limitations, this paper further explores other possible optimization strategies and techniques to achieve better results in solving the problem of reservoir flood control optimization scheduling. Literature 26 proposes an innovative adaptive method based on the ε-constraint. This method prevents the algorithm from falling into the dilemma of locally optimal solutions by optimizing constraint handling techniques and adaptively setting ε values.…”