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Found 8 papers, 6 papers with code
Clustering in Dynamic Environments: A Framework for Benchmark Dataset Generation With Heterogeneous Changes
Clustering in dynamic environments is of increasing importance, with broad applications ranging from real-time data analysis and online unsupervised learning to dynamic facility location problems.
Semantic-Preserving Feature Partitioning for Multi-View Ensemble Learning
Addressing these challenges, multi-view ensemble learning (MEL) has emerged as a transformative approach, with feature partitioning (FP) playing a pivotal role in constructing artificial views for MEL.
GNBG: A Generalized and Configurable Benchmark Generator for Continuous Numerical Optimization
To effectively assess, validate, and compare optimization algorithms, it is crucial to use a benchmark test suite that encompasses a diverse range of problem instances with various characteristics.
GNBG-Generated Test Suite for Box-Constrained Numerical Global Optimization
This document introduces a set of 24 box-constrained numerical global optimization problem instances, systematically constructed using the Generalized Numerical Benchmark Generator (GNBG).
Evolutionary Dynamic Optimization Laboratory: A MATLAB Optimization Platform for Education and Experimentation in Dynamic Environments
In this paper, to assist researchers in performing experiments and comparing their algorithms against several EDOAs, we develop an open-source MATLAB platform for EDOAs, called Evolutionary Dynamic Optimization LABoratory (EDOLAB).
Generating Large-scale Dynamic Optimization Problem Instances Using the Generalized Moving Peaks Benchmark
This document describes the generalized moving peaks benchmark (GMPB) and how it can be used to generate problem instances for continuous large-scale dynamic optimization problems.
Competition on Dynamic Optimization Problems Generated by Generalized Moving Peaks Benchmark (GMPB)
This document introduces the Generalized Moving Peaks Benchmark (GMPB), a tool for generating continuous dynamic optimization problem instances that is used for the CEC 2024 Competition on Dynamic Optimization.
A Review of the Family of Artificial Fish Swarm Algorithms: Recent Advances and Applications
Owing to a number of salient properties, which include flexibility, fast convergence, and insensitivity to the initial parameter settings, the family of AFSA has emerged as an effective Swarm Intelligence (SI) methodology that has been widely applied to solve real-world optimization problems.
