scholarly journals An Extended Artificial Neural Network Assisted Hybrid Harris Hawks and Whale Optimizer to Find Optimal Solution for Engineering Design Problems

2020 ◽  
Vol 8 (6) ◽  
pp. 4843-4855
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Search Space ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Whale Optimization ◽  
Engineering Problems ◽  
Artificial Neural

The algorithms that have been developed recently have decorous behavior to solve and find optimum solution to various optimization problems in search space. Withal such calculations stuck in issues nearby quest space for compelled engineering problems. In succession to achieve an optimal solution a hybrid algorithmic approach is proffered. Artificial Neural Network (ANN) is considered as better solution for the known outputs. A hybrid variant of applying ANN on Harris Hawks and Whale Optimization Algorithm (ANNHHOWOA) is proposed to achieve effective solution for engineering problems. The effectiveness of proposed algorithm is tested for various nonlinear, non-convex and standard engineering problems and to approve consequences of proposed algorithm standard benchmarks and multidisciplinary design problems have been considered. The validation endorsed that the results shown by ANNHHOWOA showed much better results than individual ANN, HHO and WOA and its effectiveness on multidisciplinary engineering problems.

scholarly journals An efficient quantum multiverse optimization algorithm for solving optimization problems

2020 ◽  
Vol 9 (1) ◽  
pp. 27
Author(s):  
Samira Sarvari ◽  
Nor Fazlida Mohd. Sani ◽  
Zurina Mohd Hanapi ◽  
Mohd Taufik Abdullah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection System ◽  
Quantum Interference ◽  
Optimization Problems ◽  
Detection System ◽  
Optimal Solution ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<p>Due to the recent trend of technologies to use the network-based systems, detecting them from threats become a crucial issue. Detecting unknown or modified attacks is one of the recent challenges in the field of intrusion detection system (IDS). In this research, a new algorithm called quantum multiverse optimization (QMVO) is investigated and combined with an artificial neural network (ANN) to develop advanced detection approaches for an IDS. QMVO algorithm depends on adopting a quantum representation of the quantum interference and operators in the multiverse optimization to obtain the optimal solution. The QMVO algorithm determining the neural network weights based on the kernel function, which can improve the accuracy and then optimize the training part of the artificial neural network. It is demonstrated 99.98% accuracy with experimental results that the proposed QMVO is significantly improved optimization compared with multiverse optimizer (MVO) algorithms.</p>

A review of the artificial neural network surrogate modeling in aerodynamic design

2019 ◽  
Vol 233 (16) ◽  
pp. 5863-5872 ◽  
Author(s):  
Gang Sun ◽  
Shuyue Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Design Space ◽  
Optimal Solution ◽  
Surrogate Modeling ◽  
Aerodynamic Design ◽  
Hidden Information ◽  
Data Manipulation ◽  
Artificial Neural ◽  
Selection Of

Artificial neural network surrogate modeling with its economic computational consumption and accurate generalization capabilities offers a feasible approach to aerodynamic design in the field of rapid investigation of design space and optimal solution searching. This paper reviews the basic principle of artificial neural network surrogate modeling in terms of data treatment and configuration setup. A discussion of artificial neural network surrogate modeling is held on different objectives in aerodynamic design applications, various patterns of realization via cutting-edge data technique in numerous optimizations, selection of network topology and types, and other measures for improving modeling. Then, new frontiers of modern artificial neural network surrogate modeling are reviewed with regard to exploiting the hidden information for bringing new perspectives to optimization by exploring new data form and patterns, e.g. quick provision of candidates of better aerodynamic performance via accumulated database instead of random seeding, and envisions of more physical understanding being injected to the data manipulation.

scholarly journals An Author Gender Detection Method Using Whale Optimization Algorithm and Artificial Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 48428-48437 ◽  
Author(s):  
Fatemeh Safara ◽  
Amin Salih Mohammed ◽  
Moayad Yousif Potrus ◽  
Saqib Ali ◽  
Quan Thanh Tho ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Algorithm ◽  
Detection Method ◽  
Whale Optimization Algorithm ◽  
Whale Optimization ◽  
Artificial Neural ◽  
Gender Detection

scholarly journals Artificial neural network for solving multi-parameter optimization problems

2021 ◽  
Vol 2092 (1) ◽  
pp. 012013
Author(s):  
Krivorotko Olga ◽  
Liu Shuang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Problems ◽  
Three Dimensional ◽  
Test Functions ◽  
Backpropagation Method ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Quality Of Approximation ◽  
And Function

Abstract An artificial neural network (ANN) is a mathematical or computational model that simulates the structure and function of biological neural networks used to evaluate or approximate functions at given points. After developing the training algorithm, the resulting model will be used to solve image recognition problems, control problems, optimization, etc. In the process of ANN training, the algorithm of backpropagation is used in the case of convex optimization functions. The article is analyzed test functions for experiments and also study the effect of the number of ANN layers on the quality of approximation in cases one-, two- and three-dimensional. The backpropagation method is improved during the experiments with the help of adaptive gradient, as a result of which more accurate approximations of the functions are obtained. This article also presents the numerical results of test functions.

Evaluation of the Use of Artificial Neural Networks in Solar Energy

2021 ◽  
pp. 182-210
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Set Theory ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Pv System ◽  
Solar Insolation ◽  
Pv Panel ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Artificial neural network (ANN) is initially used to forecast the solar insolation level and followed by the particle swarm optimisation (PSO) to optimise the power generation of the PV system based on the solar insolation level, cell temperature, efficiency of PV panel, and output voltage requirements. Genetic algorithm is a general-purpose optimization algorithm that is distinguished from conventional optimization techniques by the use of concepts of population genetics to guide the optimization search. Tabu search algorithm is a conceptually simple and an elegant iterative technique for finding good solutions to optimization problems. Simulated annealing algorithms appeared as a promising heuristic algorithm for handling the combinatorial optimization problems. Fuzzy logic algorithms set theory can be considered as a generation of the classical set theory. The artificial neural network (ANN)-based solar insolation forecast has shown satisfactory results with minimal error, and the generated PV power can be optimised significantly with the aids of the PSO algorithm.

Hybrid Weights-Utility and Taguchi Method for Multi-Objective Optimization Problems

2015 ◽  
Vol 764-765 ◽  
pp. 305-308
Author(s):  
Kuang Hung Hsien ◽  
Shyh Chour Huang
Keyword(s):  
Taguchi Method ◽  
Engineering Design ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Multi Objective Optimization ◽  
Design Problems ◽  
Multi Objective ◽  
Utility Concept ◽  
Engineering Design Problems

In this paper, hybrid weights-utility and Taguchi method is proposed to solve multi-objective optimization problems. The new method combines the Taguchi method and the weights-utility concept. The weights of the objective function and overall utility values are very important for the weights-utility, and must be set correctly in order to obtain an optimal solution. Application of this method to engineering design problems is illustrated with the aid of one case study, and the result shows that the weights-utlity method is able to handle multi-objective optimization problems, with an optimal solution which better meets the demand of multi-objective optimization problems than the utility concept does.

Sign in / Sign up

Export Citation Format

Share Document