Spark-based parallel dynamic programming and particle swarm optimization via cloud computing for a large-scale reservoir system

TL;DR: In this article , a projection pursuit (PP) model improved by the sparrow search algorithm (SSA) was proposed to measure the key drivers of resilience, and the driving mechanism of resilience was analyzed in combination with the logarithmic mean divisia index (LMDI).

TL;DR: Wang et al. as mentioned in this paper presented a state-of-the-art large-scale multi-objective evolutionary algorithm (LSMOEA), called the weight optimization framework (WOF) with Non-dominated Sorting Genetic Algorithm II (NSGAII) optimizer, to alleviate the problem, and improve its performance by determining applicable grouping mechanism based on inflow features.

TL;DR: This study applies Sobol' sensitivity analysis to optimize reservoir operations, revealing that removing insensitive water levels and prioritizing sensitive ones enhances search efficiency, solution quality, and operational speed, crucial for fine-tuning multi-objective reservoir operations.

TL;DR: In this paper , the authors proposed an improved version of the progressive optimality algorithm known as the dipole optimization procedure (DOP) for optimizing cascade reservoir operations, which uses a dynamic variable decoupling strategy to obtain search directions that are more targeted than those of the POA so as to improve the quality of the solutions.

TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.

TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.

TL;DR: A new parameter, called inertia weight, is introduced into the original particle swarm optimizer, which resembles a school of flying birds since it adjusts its flying according to its own flying experience and its companions' flying experience.

TL;DR: This paper analyzes a particle's trajectory as it moves in discrete time, then progresses to the view of it in continuous time, leading to a generalized model of the algorithm, containing a set of coefficients to control the system's convergence tendencies.