A Comprehensive Survey on Artificial Electric Field Algorithm: Theories and Applications
The artificial electric field algorithm (AEFA) is a population-based metaheuristic optimization
algorithm. It is inspired by the electrostatic field theory and fundamental laws of physics. The …
algorithm. It is inspired by the electrostatic field theory and fundamental laws of physics. The …
A competitive and collaborative-based multilevel hierarchical artificial electric field algorithm for global optimization
Competitive and collaborative strategies and topologies are among the most essential
concepts and greatly influence the optimization ability of population-based optimization …
concepts and greatly influence the optimization ability of population-based optimization …
An archive-based self-adaptive artificial electric field algorithm with orthogonal initialization for real-parameter optimization problems
In this article, a series of learning strategies are proposed to enhance the optimization ability
of the artificial electric field algorithm. Orthogonal learning is an important mathematical tool …
of the artificial electric field algorithm. Orthogonal learning is an important mathematical tool …
Solving non-linear fixed-charge transportation problems using nature inspired non-linear particle swarm optimization algorithm
D Rani - Applied Soft Computing, 2023 - Elsevier
The main objective of this study is to formulate and solve the model of non-linear fixed-
charge transportation problem (NFCTP), which is one of the NP-hard problems. This type of …
charge transportation problem (NFCTP), which is one of the NP-hard problems. This type of …
Competitive Swarm Optimizer: A decade survey
Since its inception in 2014, the Competitive Swarm Optimizer (CSO) has emerged as a
significant advancement in the field of swarm intelligence, particularly in addressing large …
significant advancement in the field of swarm intelligence, particularly in addressing large …
A multi-agent optimization algorithm and its application to training multilayer perceptron models
The optimal parameter values in a feed-forward neural network model play an important role
in determining the efficiency and significance of the trained model. In this paper, we propose …
in determining the efficiency and significance of the trained model. In this paper, we propose …
Surrogate-assisted fully-informed particle swarm optimization for high-dimensional expensive optimization
Abstract Surrogate-Assisted Evolutionary Algorithms (SAEAs) have been proven to be
powerful optimization tools for tackling Expensive Optimization Problems (EOPs) where a …
powerful optimization tools for tackling Expensive Optimization Problems (EOPs) where a …
U-AEFA: Online and offline learning-based unified artificial electric field algorithm for real parameter optimization
Optimization problems in real-world scenarios require algorithms that effectively balance
exploration and exploitation to avoid local optima and achieve global solutions. To address …
exploration and exploitation to avoid local optima and achieve global solutions. To address …
Improved multi-strategy adaptive Grey Wolf Optimization for practical engineering applications and high-dimensional problem solving
M Yu, J Xu, W Liang, Y Qiu, S Bao, L Tang - Artificial Intelligence Review, 2024 - Springer
Abstract The Grey Wolf Optimization (GWO) is a highly effective meta-heuristic algorithm
leveraging swarm intelligence to tackle real-world optimization problems. However, when …
leveraging swarm intelligence to tackle real-world optimization problems. However, when …
Stability and agent dynamics of artificial electric field algorithm
Abstract The Artificial Electric Field Algorithm (AEFA) is a recently developed optimization
algorithm inspired by the principles of electrostatic force and the law of motion. It operates as …
algorithm inspired by the principles of electrostatic force and the law of motion. It operates as …