Yuhui Shi

A comprehensive survey of brain storm optimization algorithms

2017

The development, implementation, variant, and future directions of a new swarm intelligence algorithm, brain storm optimization (BSO) algorithm, are comprehensively surveyed. Brain storm optimization algorithm is a new and promising swarm intelligence algorithm, which simulates …

mBSO: A Multi-Population Brain Storm Optimization for Multimodal Dynamic Optimization Problems

2019

Brain Storm Optimization (BSO), which is an effective swarm intelligence method inspired by the human brainstorming process, has shown promising results in solving static optimization problems. However, The search spaces of many real-world problems change …

BSOGCN: Brain Storm Optimization Graph Convolutional Networks Based Heterogeneous Information Networks Embedding

2020

Recently, Graph Convolutional Networks (GCNs) have shown great potential in the field of graph embedding. They map the nodes of the graph into the low dimensional vectors by aggregating the neighbor nodes' features information. However, …

Distributed evolution strategies for large-scale optimization

2022 · Proceedings of the Genetic and Evolutionary Computation Conference Companion

As their underlying models are becoming larger and data-driven, an increasing number of modern real-world applications can be mathematically formulated as large-scale continuous optimization. In this paper, we propose a distributed evolution strategy (DES) for …

Automated Similarity Metric Generation for Recommendation

2024 · arXiv (Cornell University)

The embedding-based architecture has become the dominant approach in modern recommender systems, mapping users and items into a compact vector space. It then employs predefined similarity metrics, such as the inner product, to calculate similarity …

Decentralized Collaborative Learning with Adaptive Reference Data for On-Device POI Recommendation

2024

In Location-based Social Networks (LBSNs), Point-of-Interest (POI) recommendation helps users discover interesting places. There is a trend to move from the conventional cloud-based model to on-device recommendations for privacy protection and reduced server reliance. Due …

Distributed Evolution Strategies With Multi-Level Learning for Large-Scale Black-Box Optimization

2024 · IEEE Transactions on Parallel and Distributed Systems

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 parallelize the well-established covariance matrix adaptation evolution strategy (CMA-ES) and in …