[HTML][HTML] Artificial neural networks based optimization techniques: A review
In the last few years, intensive research has been done to enhance artificial intelligence (AI)
using optimization techniques. In this paper, we present an extensive review of artificial …
using optimization techniques. In this paper, we present an extensive review of artificial …
Chemical reaction optimization: a tutorial
Abstract Chemical Reaction Optimization (CRO) is a recently established metaheuristics for
optimization, inspired by the nature of chemical reactions. A chemical reaction is a natural …
optimization, inspired by the nature of chemical reactions. A chemical reaction is a natural …
How effective is the Grey Wolf optimizer in training multi-layer perceptrons
S Mirjalili - Applied intelligence, 2015 - Springer
This paper employs the recently proposed Grey Wolf Optimizer (GWO) for training Multi-
Layer Perceptron (MLP) for the first time. Eight standard datasets including five classification …
Layer Perceptron (MLP) for the first time. Eight standard datasets including five classification …
Classification of underwater acoustical dataset using neural network trained by Chimp Optimization Algorithm
Due to the variability of the radiated signal of the underwater targets, the classification of the
underwater acoustical dataset is a challenging problem in the real world application. In this …
underwater acoustical dataset is a challenging problem in the real world application. In this …
Real-coded chemical reaction optimization
Optimization problems can generally be classified as continuous and discrete, based on the
nature of the solution space. A recently developed chemical-reaction-inspired metaheuristic …
nature of the solution space. A recently developed chemical-reaction-inspired metaheuristic …
Chemical reaction optimization with greedy strategy for the 0–1 knapsack problem
The 0–1 knapsack problem (KP01) is a well-known combinatorial optimization problem. It is
an NP-hard problem which plays important roles in computing theory and in many real life …
an NP-hard problem which plays important roles in computing theory and in many real life …
Review of meta-heuristic optimization based artificial neural networks and its applications
D Devikanniga, K Vetrivel… - Journal of Physics …, 2019 - iopscience.iop.org
There are several meta-heuristic optimization algorithms developed on inspiration from
nature. Artificial neural network proves to be efficient among other machine learning …
nature. Artificial neural network proves to be efficient among other machine learning …
Electricity price forecast using combinatorial neural network trained by a new stochastic search method
O Abedinia, N Amjady, M Shafie-Khah… - Energy Conversion and …, 2015 - Elsevier
Electricity price forecast is key information for successful operation of electricity market
participants. However, the time series of electricity price has nonlinear, non-stationary and …
participants. However, the time series of electricity price has nonlinear, non-stationary and …
Key factors influencing the kinetics of tetra-n-butylammonium bromide hydrate formation as a cold storage and transport material
Cold energy storage by semi-clathrate hydrates is highly advantageous as they have large
latent heat and appropriate phase change temperatures for cooling applications. The …
latent heat and appropriate phase change temperatures for cooling applications. The …
Efficient optimization of convolutional neural networks using particle swarm optimization
T Yamasaki, T Honma, K Aizawa - 2017 IEEE third …, 2017 - ieeexplore.ieee.org
This work presents methods to automatically find optimal parameter settings for
convolutional neural networks (CNNs) by using an evolutionary algorithm called particle …
convolutional neural networks (CNNs) by using an evolutionary algorithm called particle …