3 citations · 9 across the 8 of their papers we have counts for
8 papers
ATLAS: Scaffold-Free Algorithm Synthesis by LLMs via Embedding-Guided Quality-Diversity Search
Danial Yazdani, Mohammad Nabi Omidvar, Yuan Sun +2
Most LLM-based automated algorithm design methods optimize a designated component within a human-specified scaffold, fixing overall organization and component interactions. We pres…
PORTAL: Controllable Landscape Generator for Continuous Optimization-Part I: Framework
Danial Yazdani, Mai Peng, Delaram Yazdani +6
Benchmarking is central to optimization research, yet existing test suites for continuous optimization remain limited: classical collections are fixed and rigid, while previous gen…
Clustering in Dynamic Environments: A Framework for Benchmark Dataset Generation With Heterogeneous Changes
Danial Yazdani, Juergen Branke, Mohammad Sadegh Khorshidi +4
Clustering in dynamic environments is of increasing importance, with broad applications ranging from real-time data analysis and online unsupervised learning to dynamic facility lo…
GNBG-Generated Test Suite for Box-Constrained Numerical Global Optimization
Amir H. Gandomi, Danial Yazdani, Mohammad Nabi Omidvar +1
This document introduces a set of 24 box-constrained numerical global optimization problem instances, systematically constructed using the Generalized Numerical Benchmark Generator…
A Generalized and Configurable Benchmark Generator for Continuous Unconstrained Numerical Optimization
Amir H. Gandomi, Mohammad Nabi Omidvar, Rohit Salgotra +1
As optimization challenges continue to evolve, so too must our tools and understanding. To effectively assess, validate, and compare optimization algorithms, it is crucial to use a…
Variable Functioning and Its Application to Large Scale Steel Frame Design Optimization
Amir H Gandomi, Kalyanmoy Deb, Ronald C Averill +2
To solve complex real-world problems, heuristics and concept-based approaches can be used in order to incorporate information into the problem. In this study, a concept-based appro…
Generating Large-scale Dynamic Optimization Problem Instances Using the Generalized Moving Peaks Benchmark
Mohammad Nabi Omidvar, Danial Yazdani, Juergen Branke +3
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)
Danial Yazdani, Michalis Mavrovouniotis, Changhe Li +6
The Generalized Moving Peaks Benchmark (GMPB) is a tool for generating continuous dynamic optimization problem instances with controllable dynamic and morphological characteristics…