Chaoshun Li

Bio: Chaoshun Li is an academic researcher from Huazhong University of Science and Technology. The author has contributed to research in topics: Wind speed & Prediction interval. The author has an hindex of 32, co-authored 122 publications receiving 3132 citations. Previous affiliations of Chaoshun Li include Wuhan University.

Short-Term Wind Speed Interval Prediction Based on Ensemble GRU Model

Abstract: Wind speed interval prediction is playing an increasingly important role in wind power production. The intermittent and fluctuant characteristics of wind power make high-quality prediction interval challenging. In this paper, a novel hybrid model based on a gated recurrent unit neural network and variational mode decomposition is proposed for wind speed interval prediction. Initially, variational mode decomposition is employed to decompose the complex wind speed time series into simplified modes. Interval prediction model and a point prediction model based on a gated recurrent unit neural network are designed to conduct interval prediction in primary mode and point prediction in rest modes, respectively, before the composition and construction of the prediction interval. Then, an error prediction model based on a gated recurrent unit neural network is proposed to enhance the model performance by error correction. Eight cases from two wind fields are used to test and verify the proposed method. The results indicate that the proposed method is a highly qualified method that has a much higher prediction interval coverage probability and narrower prediction interval width.

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Abstract: Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms

Abstract: In this paper, the algorithmic concepts of the Cuckoo-search (CK), Particle swarm optimization (PSO), Differential evolution (DE) and Artificial bee colony (ABC) algorithms have been analyzed. The numerical optimization problem solving successes of the mentioned algorithms have also been compared statistically by testing over 50 different benchmark functions. Empirical results reveal that the problem solving success of the CK algorithm is very close to the DE algorithm. The run-time complexity and the required function-evaluation number for acquiring global minimizer by the DE algorithm is generally smaller than the comparison algorithms. The performances of the CK and PSO algorithms are statistically closer to the performance of the DE algorithm than the ABC algorithm. The CK and DE algorithms supply more robust and precise results than the PSO and ABC algorithms.

Grey wolf optimizer: a review of recent variants and applications

Abstract: Grey wolf optimizer (GWO) is one of recent metaheuristics swarm intelligence methods. It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods: it has very few parameters, and no derivation information is required in the initial search. Also it is simple, easy to use, flexible, scalable, and has a special capability to strike the right balance between the exploration and exploitation during the search which leads to favourable convergence. Therefore, the GWO has recently gained a very big research interest with tremendous audiences from several domains in a very short time. Thus, in this review paper, several research publications using GWO have been overviewed and summarized. Initially, an introductory information about GWO is provided which illustrates the natural foundation context and its related optimization conceptual framework. The main operations of GWO are procedurally discussed, and the theoretical foundation is described. Furthermore, the recent versions of GWO are discussed in detail which are categorized into modified, hybridized and paralleled versions. The main applications of GWO are also thoroughly described. The applications belong to the domains of global optimization, power engineering, bioinformatics, environmental applications, machine learning, networking and image processing, etc. The open source software of GWO is also provided. The review paper is ended by providing a summary conclusion of the main foundation of GWO and suggests several possible future directions that can be further investigated.