8 citations · 17 across the 4 of their papers we have counts for
4 papers
Benchmarking Continuous Dynamic Multi-Objective Optimization: Survey and Generalized Test Suite
Chang Shao, Qi Zhao, Nana Pu +3
The field of Dynamic Multi-Objective Optimization (DMOO) has witnessed a surge of interest from both academia and industry, as numerous time-evolving real-world applications can be…
Distributed Evolution Strategies with Multi-Level Learning for Large-Scale Black-Box Optimization
Qiqi Duan, Chang Shao, Guochen Zhou +3
In the post-Moore era, main performance gains of black-box optimizers are increasingly depending on parallelism, especially for large-scale optimization (LSO). Here we propose to p…
Cooperative Coevolution for Non-Separable Large-Scale Black-Box Optimization: Convergence Analyses and Distributed Accelerations
Qiqi Duan, Chang Shao, Guochen Zhou +3
Given the ubiquity of non-separable optimization problems in real worlds, in this paper we analyze and extend the large-scale version of the well-known cooperative coevolution (CC)…
PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization
Qiqi Duan, Guochen Zhou, Chang Shao +7
In this paper, we present an open-source pure-Python library called PyPop7 for black-box optimization (BBO). As population-based methods (e.g., evolutionary algorithms, swarm intel…