A comprehensive review of swarm optimization algorithms
Many swarm optimization algorithms have been introduced since the early 60's,
Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms …
Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms …
Optimization techniques to enhance the performance of induction motor drives: A review
MA Hannan, JA Ali, A Mohamed, A Hussain - Renewable and Sustainable …, 2018 - Elsevier
Induction motor (IM) drives, specifically the three-phase IMs, are a nonlinear system that are
difficult to explain theoretically because of their sudden changes in load or speed conditions …
difficult to explain theoretically because of their sudden changes in load or speed conditions …
Aquila optimizer: a novel meta-heuristic optimization algorithm
This paper proposes a novel population-based optimization method, called Aquila Optimizer
(AO), which is inspired by the Aquila's behaviors in nature during the process of catching the …
(AO), which is inspired by the Aquila's behaviors in nature during the process of catching the …
MEALPY: An open-source library for latest meta-heuristic algorithms in Python
N Van Thieu, S Mirjalili - Journal of Systems Architecture, 2023 - Elsevier
Meta-heuristic algorithms are becoming more prevalent and have been widely applied in
various fields. There are numerous reasons for the success of such techniques in both …
various fields. There are numerous reasons for the success of such techniques in both …
Oxide‐based solid‐state batteries: a perspective on composite cathode architecture
The garnet‐type phase Li7La3Zr2O12 (LLZO) attracts significant attention as an oxide solid
electrolyte to enable safe and robust solid‐state batteries (SSBs) with potentially high …
electrolyte to enable safe and robust solid‐state batteries (SSBs) with potentially high …
Lightning search algorithm
This paper introduces a novel metaheuristic optimization method called the lightning search
algorithm (LSA) to solve constraint optimization problems. It is based on the natural …
algorithm (LSA) to solve constraint optimization problems. It is based on the natural …
An efficient surrogate-assisted hybrid optimization algorithm for expensive optimization problems
Surrogate-assisted evolutionary algorithms (SAEAs) are potential approaches to solve
computationally expensive optimization problems. The critical idea of SAEAs is to combine …
computationally expensive optimization problems. The critical idea of SAEAs is to combine …
Entropy based segmentation of tumor from brain MR images–a study with teaching learning based optimization
V Rajinikanth, SC Satapathy, SL Fernandes… - Pattern Recognition …, 2017 - Elsevier
Image processing plays an important role in various medical applications to support the
computerized disease examination. Brain tumor, such as glioma is one of the life threatening …
computerized disease examination. Brain tumor, such as glioma is one of the life threatening …
[HTML][HTML] A new method for parameter extraction of solar photovoltaic models using gaining–sharing knowledge based algorithm
G Xiong, L Li, AW Mohamed, X Yuan, J Zhang - Energy Reports, 2021 - Elsevier
For the solar photovoltaic (PV) system to operate efficiently, it is necessary to effectively
establish an equivalent model of PV cell and extract the relevant unknown model …
establish an equivalent model of PV cell and extract the relevant unknown model …
A comprehensive technique for optimal allocation of distributed energy resources in radial distribution systems
Distributed generation (DG) is a better alternative to meet power demand near the load
centers than centralized power generation. Optimal placement and sizing of DGs plays a …
centers than centralized power generation. Optimal placement and sizing of DGs plays a …