algorithm (NSGA-III), which effectively solved the high-dimensional multi-objective optimization problem, and is one of the most advanced methods at present. The results have been published in IEEE Trans.on EC (
In Neural networks, especially deep learning models have demonstrated remarkable success in various tasks such as image recognition, natural language processing and speech synthesis. However, the increased complexity of these models comes with a trade-off. Graph theory provides a framework for modeling ...
They introduce a two-stage policy that combines Fourier analysis with a confidence bound鈥揵ased learning procedure. This innovative approach allows the algorithm to adapt to time-varying mean rewards that follow a periodic pattern. The first stage estimates the periods of all decision-making arm...
Crystal structure prediction is a long-standing challenge in condensed matter and chemical science. Here we report a machine-learning approach for crystal structure prediction, in which a graph network (GN) is employed to establish a correlation model between the crystal structure and formation enthalp...
The biologically inspired metaheuristic algorithm obtains the optimal solution by simulating the living habits or behavior characteristics of creatures in nature. It has been widely used in many fields. A new bio-inspired algorithm, Aphids Optimization A
The Whale Optimization Algorithm (WOA) is a nature-inspired algorithm that mimics the hunting pattern of humpback whales. It involves three main steps: encircling prey, bubble-net attack, and searching for prey. In the algorithm, the location of the optimal solution is initially unknown and is ...
To overcome these limitations, this paper proposes a novel contribution to GJO based on a new technique, namely the fast random opposition-based learning Golden Jackal Optimization algorithm (FROBL-GJO). The FROBL technique is mainly inspired by opposition-based learning (OBL) and random ...
Parameter values are internally normalized to [0; 1] range and, to stay in this range, are wrapped in a special manner before each function evaluation. The method uses an alike of a probabilistic state-automata (by means of "selectors") to switch between algorithm flow-paths, depending on ...
To overcome the disadvantages of premature convergence and easy trapping into local optimum solutions, this paper proposes an improved particle swarm optimization algorithm (named NDWPSO algorithm) based on multiple hybrid strategies. Firstly, the elite opposition-based learning method is utilized to init...
The challenges and future development of energy storage systems are briefly described, and the research results of energy storage system optimization methods are summarized. This paper summarizes the application of swarm intelligence optimization algorithm in photovoltaic energy storage systems, including ...