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Yufeng Wang

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#edge computing Oct 2026

A Novel Parallel Approach With Index Optimization for Truss Maintenance

The <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="bai-ieq1-3702362.gif"/></alternatives></inline-formula>-truss is a type of cohesive subgraph where each edge is contained in at least <inline-formula><tex-math notation="LaTeX">$k-2$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>k</mml:mi><mml:mo>-</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="bai-ieq2-3702362.gif"/></alternatives></inline-formula> triangles within the subgraph, and it is commonly used in community search and dense subgraph discovery. Although <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="bai-ieq3-3702362.gif"/></alternatives></inline-formula>-trusses can be computed in polynomial time, obtaining all <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="bai-ieq4-3702362.gif"/></alternatives></inline-formula>-trusses of a dynamic graph that evolves over time through edge insertions and deletions remains computationally expensive. To address this challenge, prior studies proposed truss maintenance approaches that update the affected <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="bai-ieq5-3702362.gif"/></alternatives></inline-formula>-trusses when the dynamic graph changes. However, existing approaches only utilize one tool of parallelism or auxiliary indexes due to latent conflicts, which restricts their overall performance. To resolve this limitation, we propose a novel parallel approach with index optimization that effectively resolves the conflicts between parallelism and index maintenance. Specifically, we first establish a novel truss maintenance theory based on auxiliary indexes, which ensures the feasibility and correctness of our maintenance solution. Second, we design two effective edge partitioning strategies to improve the parallelism of the proposed algorithms. Finally, we implement two efficient parallel algorithms that cooperate with auxiliary indexes to reduce maintenance costs. Extensive experiments on real-world graphs demonstrate that our methods outperform all baseline approaches by up to one order of magnitude.

Wen Bai, Yufeng Wang, K. Zheng et al. · 0 citations
Open access Aug 2026

A Hybrid Particle Swarm Optimization and Differential Evolution Algorithm with Adaptive Population and Dynamic Parameter Allocation

Traditional particle swarm optimization (PSO) easily falls into premature convergence, while differential evolution (DE) is highly sensitive to fixed control parameters. Existing PSO-DE hybrid frameworks suffer from static population sizes and insufficient cross-population information exchange. This paper proposes PSO-DE-ADP, a hybrid optimizer with sinusoidal adaptive parameters, elite-guided mutation, ring neighborhood-weighted PSO and fitness-driven dynamic dual-population allocation. Four complementary mechanisms are integrated: (i) sine-wave perturbation superimposed on linear decay adaptively adjusts PSO inertia weight, acceleration factors and DE scaling/crossover coefficients to balance search stages; (ii) global elite individuals are embedded into DE mutation to reduce blind random search; (iii) ring topology with weighted learning realizes bidirectional information interaction between PSO and DE subpopulations; (iv) the proportion of PSO/DE individuals is dynamically adjusted according to elite ratio to allocate computing resources. Experiments adopt the CEC2017 30-dimensional benchmark with 30 test functions covering unimodal, multimodal, hybrid and composite landscapes. Compared with 8 state-of-the-art metaheuristics, PSO-DE-ADP achieves the lowest Friedman rank (1.08 vs. 2.23–4.90 for PSO variants; 1.53 vs. 2.07–5.00 for non-PSO algorithms). Ablation tests prove each component significantly boosts accuracy; The algorithm only costs 0.172 s average runtime, superior to all competitors. Statistical Wilcoxon and Friedman tests verify its significant superiority. Future work extends this method to multi-objective, constrained and real engineering optimization tasks.

Yao-Pei Wang, Yufeng Wang, Ke Liu · 0 citations

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