Jiujun Cheng

TL;DR: Six learning algorithms including biogeography-based optimization, particle swarm optimization, genetic algorithm, ant colony optimization, evolutionary strategy, and population-based incremental learning are used to train a new dendritic neuron model (DNM) and are suggested to make DNM more powerful in solving classification, approximation, and prediction problems.

TL;DR: This work aims to provide a review of the routing protocols in the Internet of Vehicles from routing algorithms to their evaluation approaches, and provides five different taxonomies of routing protocols.

TL;DR: A novel JADE variant is presented by incorporating chaotic local search (CLS) mechanisms into JADE to alleviate this problem and has a superior performance in comparison with JADE and some other state-of-the-art optimization algorithms.

TL;DR: Experimental results based on different scales of DLRP instances demonstrate that the clustering algorithm can significantly improve the performance of KACO in terms of the qualities and robustness of solutions.

TL;DR: A state-of-the-art optimization method, namely, directional permutation differential evolution algorithm (DPDE), to tackle the parameter estimation of several kinds of solar PV models, and extensive comparative results show that DPDE outperforms its peers in terms of the solution accuracy.