Particle swarm optimization: A comprehensive survey
Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms
in the literature. Although the original PSO has shown good optimization performance, it still …
in the literature. Although the original PSO has shown good optimization performance, it still …
Modelling and optimal energy management for battery energy storage systems in renewable energy systems: A review
Abstract Incorporating Battery Energy Storage Systems (BESS) into renewable energy
systems offers clear potential benefits, but management approaches that optimally operate …
systems offers clear potential benefits, but management approaches that optimally operate …
INFO: An efficient optimization algorithm based on weighted mean of vectors
This study presents the analysis and principle of an innovative optimizer named weIghted
meaN oF vectOrs (INFO) to optimize different problems. INFO is a modified weight mean …
meaN oF vectOrs (INFO) to optimize different problems. INFO is a modified weight mean …
Artificial intelligence applied to battery research: hype or reality?
This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to
battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily …
battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily …
[HTML][HTML] Population size in particle swarm optimization
AP Piotrowski, JJ Napiorkowski… - Swarm and Evolutionary …, 2020 - Elsevier
Abstract Particle Swarm Optimization (PSO) is among the most universally applied
population-based metaheuristic optimization algorithms. PSO has been successfully used in …
population-based metaheuristic optimization algorithms. PSO has been successfully used in …
ASRO-DIO: Active subspace random optimization based depth inertial odometry
High-dimensional nonlinear state estimation is at the heart of inertial-aided navigation
systems (INS). Traditional methods usually rely on good initialization and find difficulty in …
systems (INS). Traditional methods usually rely on good initialization and find difficulty in …
A new network forensic framework based on deep learning for Internet of Things networks: A particle deep framework
With the prevalence of Internet of Things (IoT) systems, inconspicuous everyday household
devices are connected to the Internet, providing automation and real-time services to their …
devices are connected to the Internet, providing automation and real-time services to their …
Advanced metaheuristic optimization techniques in applications of deep neural networks: a review
Deep neural networks (DNNs) have evolved as a beneficial machine learning method that
has been successfully used in various applications. Currently, DNN is a superior technique …
has been successfully used in various applications. Currently, DNN is a superior technique …
Particle swarm optimisation: a historical review up to the current developments
The Particle Swarm Optimisation (PSO) algorithm was inspired by the social and biological
behaviour of bird flocks searching for food sources. In this nature-based algorithm …
behaviour of bird flocks searching for food sources. In this nature-based algorithm …
A powerful meta-heuristic search algorithm for solving global optimization and real-world solar photovoltaic parameter estimation problems
The teaching-learning-based artificial bee colony (TLABC) is a new hybrid swarm-based
metaheuristic search algorithm. It combines the exploitation of the teaching learning-based …
metaheuristic search algorithm. It combines the exploitation of the teaching learning-based …