An Improved Grey Wolf Optimization Algorithm with Variable Weights.
Zheng-Ming Gao,Juan Zhao +1 more
TL;DR: An improved grey wolf optimization algorithm with variable weights (VW-GWO) is proposed, which works better than the standard GWO, the ant lion optimization (ALO), the particle swarm optimization (PSO) algorithm, and the bat algorithm.
read more
Abstract: With a hypothesis that the social hierarchy of the grey wolves would be also followed in their searching positions, an improved grey wolf optimization (GWO) algorithm with variable weights (VW-GWO) is proposed. And to reduce the probability of being trapped in local optima, a new governing equation of the controlling parameter is also proposed. Simulation experiments are carried out, and comparisons are made. Results show that the proposed VW-GWO algorithm works better than the standard GWO, the ant lion optimization (ALO), the particle swarm optimization (PSO) algorithm, and the bat algorithm (BA). The novel VW-GWO algorithm is also verified in high-dimensional problems.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO, BAT and COA algorithms
Abdul-Lateef Balogun,Fatemeh Rezaie,Quoc Bao Pham,Ljubomir Gigović,Siniša Drobnjak,Yusuf A. Aina,Mahdi Panahi,Shamsudeen Temitope Yekeen,Saro Lee +8 more
TL;DR: Multiple hybrid machine-learning models were developed to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models to confirm the ability of metaheuristic algorithms to improve model performance.
139
Evolving CNN-LSTM Models for Time Series Prediction Using Enhanced Grey Wolf Optimizer
TL;DR: An enhanced Grey Wolf Optimizer (GWO) for designing the evolving Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) networks for time series analysis offers better representational capacities to not only capture the vital feature interactions, but also encapsulate the sophisticated dependencies in complex temporal contexts for undertaking time-series tasks.
FOX: a FOX-inspired optimization algorithm
TL;DR: In this paper , the authors proposed a novel nature-inspired optimization algorithm called the Fox Optimizer (FOX) which mimics the foraging behavior of foxes in nature when hunting preys.
GWO: a review and applications
TL;DR: This article presents a state of the art review of the GWO algorithm, its progress, and applications in more complex real-world problem-solving.
87
References
•Book
Adaptation in natural and artificial systems
John H. Holland
- 01 Jan 1975
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Equation of state calculations by fast computing machines
TL;DR: In this article, a modified Monte Carlo integration over configuration space is used to investigate the properties of a two-dimensional rigid-sphere system with a set of interacting individual molecules, and the results are compared to free volume equations of state and a four-term virial coefficient expansion.
Grey Wolf Optimizer
TL;DR: The results of the classical engineering design problems and real application prove that the proposed GWO algorithm is applicable to challenging problems with unknown search spaces.
15K
No free lunch theorems for optimization
TL;DR: A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving and a number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class.
A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms
TL;DR: The basics are discussed and a survey of a complete set of nonparametric procedures developed to perform both pairwise and multiple comparisons, for multi-problem analysis are given.
4.9K