Application of HSMAAOA Algorithm in Flood Control Optimal Operation of Reservoir Groups

Abstract

The joint flood control operation of reservoir groups is a complex engineering problem with a large number of constraints and interdependent decision variables. Its solution has the characteristics of strong constraint, multi-stage, nonlinearity, and high dimension. In order to solve this problem, this paper proposes a hybrid slime mold and arithmetic optimization algorithm (HSMAAOA) combining stochastic reverse learning. Since ancient times, harnessing the Yellow River has been a major event for the Chinese nation to rejuvenate the country and secure the country. Today, flood risk is still the greatest threat to the Yellow River basin. This paper chooses five reservoirs in the middle and lower reaches of the Yellow River as the research object, takes the water level of each reservoir in each period as the decision variable, and takes the peak clipping of Huayuankou control point as the objective to build an optimization model. Then, HSMAAOA is used to solve the problem, and the results are compared with those of the slime mold algorithm (SMA) and particle swarm optimization (PSO). The peak clipping rates of the three algorithms are 52.9% (HSMAAOA), 48.69% (SMA), and 47.55% (PSO), respectively. The results show that the HSMAAOA algorithm is better than other algorithms. This paper provides a new idea to solve the problem of the optimal operation of reservoir flood controls.

Multiprocessor Fair Scheduling Based on an Improved Slime Mold Algorithm

“…In addition, the opposite learning approach adopts the idea of obtaining reverse solutions from the initial population. By adding reverse solutions, it is possible to further boost population variety [30], enhancing the search capability of the algorithm. Therefore, in this study, after applying the Bernoulli mapping to the population, the opposite learning approach was employed.…”

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

“…Some nature-inspired optimization algorithms have been widely used in large-scale reservoir optimization models 20 , providing effective approaches for solving water resources system planning problems. He et al 21 proposed an Improved Sparrow Search Algorithm (ISSA) that integrates Cauchy mutation and reverse learning strategies to solve the joint scheduling problem of the Sanmenxia Reservoir and the Xiaolangdi Reservoir on the mainstream of the Yellow River; Wang et al 22 proposed a Yin-Yang Firefly Algorithm (YYFA) based on Cauchy mutation, which determines the initial position of fireflies through a Good Node Set (GNS) strategy to ensure the spatial representativeness of the firefly population; Cheng 23 successfully applied the chaotic genetic algorithm to the scheduling of hydropower station reservoirs, demonstrating superior performance with significantly better convergence speed compared to dynamic programming and standard genetic algorithms; He et al 24 developed a slime mold algorithm combined with random reverse learning (HSMAAOA), which has also been applied to reservoir flood control scheduling, for the flood control scheduling of mixed reservoir groups. Hu Hexuan 25 proposed a Q-learning algorithm combined with a penalty function.…”

Optimization of Cascade Reservoir Operation for Power Generation, Based on an Improved Lightning Search Algorithm

“…Mixing different algorithms could help overcome the shortcomings of a single algorithm and solve problems more efficiently [23]. The improved algorithms of these algorithms are hybrid whale optimization algorithm [30], hybrid algorithm of invasive weed optimization and cuckoo search algorithm [23], hybrid slime mold and arithmetic optimization algorithm [31], and so on. These are widely used in reservoir optimal operation problems, including short-term [32,33], medium-and long-term optimal operation [34][35][36].…”