11 citations · 14 across the 29 of their papers we have counts for
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MuEvo: LLM-Driven Evolution of Multi-Heuristic Ensemble
Haoze Lv, Ning Lu, Shengcai Liu +2
Large language model-based automated heuristic design (LLM-AHD) has shown strong potential in discovering effective heuristics for combinatorial optimization problems. However, exi…
Large Language Model-Driven Cooperative Operator Ensemble Evolution for Permutation Flow Shop Scheduling
Rui Xu, Yufan Liao, Haoze Lv +3
The permutation flow shop scheduling problem (PFSP) is a classical NP-hard combinatorial optimization problem in intelligent manufacturing. In practice, PFSP is commonly addressed…
General-Purpose Co-Evolutionary Construction of Parallel Algorithm Portfolios for Multi-Objective Binary Optimization
Zhiyuan Wang, Shengcai Liu, Shaofeng Zhang +1
Despite recent progress in constructing generalizable parallel algorithm portfolios (PAPs), no general-purpose approach is yet available for multi-objective binary optimization pro…
Multi-Objective Evolutionary Optimization of Chance-Constrained Multiple-Choice Knapsack Problems with Implicit Probability Distributions
Xuanfeng Li, Shengcai Liu, Wenjie Chen +2
The multiple-choice knapsack problem (MCKP) is a classic combinatorial optimization with wide practical applications. This paper investigates a significant yet underexplored extens…
A Novel Population Initialization Method via Adaptive Experience Transfer for General-Purpose Binary Evolutionary Optimization
Zhiyuan Wang, Shengcai Liu, Shaofeng Zhang +1
Evolutionary Algorithms (EAs) are widely used general-purpose optimization methods due to their domain independence. However, under a limited number of function evaluations (#FEs),…
Knowledge-Guided Memetic Algorithm for Capacitated Arc Routing Problems with Time-Dependent Service Costs
Qingya Li, Shengcai Liu, Wenjie Chen +3
The capacitated arc routing problem with time-dependent service costs (CARPTDSC) is a challenging combinatorial optimization problem that arises from winter gritting applications.…
LLM-Driven Instance-Specific Heuristic Generation and Selection
Shaofeng Zhang, Shengcai Liu, Ning Lu +4
Combinatorial optimization problems are widely encountered in real-world applications. A critical research challenge lies in designing high-quality heuristic algorithms that effici…
Memetic Search for Green Vehicle Routing Problem with Private Capacitated Refueling Stations
Rui Xu, Xing Fan, Shengcai Liu +2
The green vehicle routing problem with private capacitated alternative fuel stations (GrVRP-PCAFS) extends the traditional green vehicle routing problem by considering capacitated…
Cascaded Large-Scale TSP Solving with Unified Neural Guidance: Bridging Local and Population-based Search
Haoze Lv, Wenjie Chen, Zhiyuan Wang +1
The traveling salesman problem (TSP) is a fundamental NP-hard optimization problem. Over the past decades, traditional heuristic methods have achieved substantial success in solvin…
Evolving Generalizable Parallel Algorithm Portfolios for Binary Optimization Problems via Domain-Agnostic Instance Generation
Zhiyuan Wang, Shengcai Liu, Peng Yang +1
Generalization is the core objective when training optimizers from data. However, limited training instances often constrain the generalization capability of the trained optimizers…
Large Language Models as Evolutionary Optimizers
Shengcai Liu, Caishun Chen, Xinghua Qu +2
Evolutionary algorithms (EAs) have achieved remarkable success in tackling complex combinatorial optimization problems. However, EAs often demand carefully-designed operators with…