arXiv subjects

Min-Rong Chen

Publications and source records attributed to Min-Rong Chen.

3 recordsLinked to original sources

arXiv2026

A Large-Scale Sparse Multiobjective Optimization Algorithm Based on Optimal Performance Scores

Large-scale sparse multiobjective optimization problems (LSSMOPs) involve a large number of decision variables and Pareto optimal solutions with only a few nonzero variables. However, as the number of decision variables grows, it becomes increasingly challenging to accurately identify the nonzero variables, and optimization performance is adversely affected. To address these issues, this paper proposes an evolutionary algorithm for LSSMOPs. Specifically, we propose a new initialization method capable of generating scores that accurately reflect the importance of variables, and an initial mask vector template that can locate nonzero variables. This leads to the generation of a high-quality initial population. Additionally, this paper introduces a new strategy to calculate the mutation probability for each variable and a novel optimization for real variables based on the Pareto-guided normal distribution, enabling the population to avoid being trapped in local optima and quickly converge to the global optimum. Experimental results from eight benchmark problems and three real-world applications demonstrate that the proposed algorithm achieves superior performance compared with state-of-the-art algorithms.

cs.NE↗

arXiv2025

A Universal Framework for Large-Scale Multi-Objective Optimization Based on Particle Drift and Diffusion

Large-scale multi-objective optimization poses challenges to existing evolutionary algorithms in maintaining the performances of convergence and diversity because of high dimensional decision variables. Inspired by the motion of particles in physics, we propose a universal framework for large-scale multi-objective optimization based on particle drift and diffusion to solve these challenges in this paper. This framework innovatively divides the optimization process into three sub-stages: two coarse-tuning sub-stages and one fine-tuning sub-stage. Different strategies of drift-diffusion operations are performed on the guiding solutions according to the current sub-stage, ingeniously simulating the movement of particles under diverse environmental conditions. Finally, representative evolutionary algorithms are embedded into the proposed framework, and their effectiveness are evaluated through comparative experiments on various large-scale multi-objective problems with 1000 to 5000 decision variables. Moreover, comparative algorithms are conducted on neural network training problems to validate the effectiveness of the proposed framework in the practical problems. The experimental results demonstrate that the framework proposed in this paper significantly enhances the performance of convergence and diversity of MOEAs, and improves the computational efficiency of algorithms in solving large-scale multi-objective optimization problems.

cs.NE↗

arXiv2024

Enabling Privacy-Preserving and Publicly Auditable Federated Learning

Federated learning (FL) has attracted widespread attention because it supports the joint training of models by multiple participants without moving private dataset. However, there are still many security issues in FL that deserve discussion. In this paper, we consider three major issues: 1) how to ensure that the training process can be publicly audited by any third party; 2) how to avoid the influence of malicious participants on training; 3) how to ensure that private gradients and models are not leaked to third parties. Many solutions have been proposed to address these issues, while solving the above three problems simultaneously is seldom considered. In this paper, we propose a publicly auditable and privacy-preserving federated learning scheme that is resistant to malicious participants uploading gradients with wrong directions and enables anyone to audit and verify the correctness of the training process. In particular, we design a robust aggregation algorithm capable of detecting gradients with wrong directions from malicious participants. Then, we design a random vector generation algorithm and combine it with zero sharing and blockchain technologies to make the joint training process publicly auditable, meaning anyone can verify the correctness of the training. Finally, we conduct a series of experiments, and the experimental results show that the model generated by the protocol is comparable in accuracy to the original FL approach while keeping security advantages.

cs.CR↗