[HTML][HTML] Leveraging UAVs to enable dynamic and smart aerial infrastructure for ITS and smart cities: an overview
Micro-unmanned aerial vehicles (UAVs), also known as drones, have been recognized as
an emerging technology offering a plethora of applications touching various aspects of our …
an emerging technology offering a plethora of applications touching various aspects of our …
Soft computing in business: exploring current research and outlining future research directions
Purpose The primary aim of this study is to detail the use of soft computing techniques in
business and management research. Its objectives are as follows: to conduct a …
business and management research. Its objectives are as follows: to conduct a …
Learning-aided evolution for optimization
Learning and optimization are the two essential abilities of human beings for problem
solving. Similarly, computer scientists have made great efforts to design artificial neural …
solving. Similarly, computer scientists have made great efforts to design artificial neural …
A bi-objective knowledge transfer framework for evolutionary many-task optimization
Many-task problem (MaTOP) is a kind of challenging multitask optimization problem with
more than three tasks. Two significant issues in solving MaTOPs are measuring intertask …
more than three tasks. Two significant issues in solving MaTOPs are measuring intertask …
Block-level knowledge transfer for evolutionary multitask optimization
Evolutionary multitask optimization is an emerging research topic that aims to solve multiple
tasks simultaneously. A general challenge in solving multitask optimization problems …
tasks simultaneously. A general challenge in solving multitask optimization problems …
Orthogonal transfer for multitask optimization
Knowledge transfer (KT) plays a key role in multitask optimization. However, most of the
existing KT methods still face two challenges. First, the tasks may commonly have different …
existing KT methods still face two challenges. First, the tasks may commonly have different …
Scale adaptive fitness evaluation‐based particle swarm optimisation for hyperparameter and architecture optimisation in neural networks and deep learning
Research into automatically searching for an optimal neural network (NN) by optimisation
algorithms is a significant research topic in deep learning and artificial intelligence …
algorithms is a significant research topic in deep learning and artificial intelligence …
Knowledge learning for evolutionary computation
Evolutionary computation (EC) is a kind of meta-heuristic algorithm that takes inspiration
from natural evolution and swarm intelligence behaviors. In the EC algorithm, there is a …
from natural evolution and swarm intelligence behaviors. In the EC algorithm, there is a …
Gene targeting differential evolution: A simple and efficient method for large scale optimization
Large-scale optimization problems (LSOPs) are challenging because the algorithm is
difficult in balancing too many dimensions and in escaping from trapped bottleneck …
difficult in balancing too many dimensions and in escaping from trapped bottleneck …
Transferable adaptive differential evolution for many-task optimization
The evolutionary multitask optimization (EMTO) algorithm is a promising approach to solve
many-task optimization problems (MaTOPs), in which similarity measurement and …
many-task optimization problems (MaTOPs), in which similarity measurement and …