The paper introduces a dual‑perspective explainability framework for Particle Swarm Optimization (PSO). From a landscape viewpoint, it uses Exploratory Landscape Analysis (ELA) and machine‑learning classifiers to predict topology‑specific hyperparameters for unseen problems. From an algorithmic viewpoint, it incorporates IOHxplainer and Search Trajectory Networks (STN) with new metrics—Connectivity Density, Fragmentation Score, and Search Efficiency—to visualize and quantify PSO’s search organization and transition effectiveness across 24 benchmark functions and multiple topologies.
By Nitin Gupta, Bapi Dutta, Anupam Yadav
arXiv:2607. 11913v1 Announce Type: cross Abstract: Recent advancements in agentic AI have increasingly moved toward graph-based methods, driven by the demand for explainable, human-centered, and non-linear reasoning workflows.
By Ali Kohan, Mohamad Roshanzamir, Roohallah Alizadehsani, Seyedali Mirjalili
arXiv:2509. 06108v2 Announce Type: replace-cross Abstract: Graph drawing concerns the algorithmic visualization of graphs.
By Timo Brand, Henry F\"orster, Stephen Kobourov, Daniel Kohrt, Robin Schukrafft, Markus Wallinger, Johannes Zink
arXiv:2606. 09100v1 Announce Type: cross Abstract: Community detection is a fundamental problem in the analysis of complex networks.
By Shahin Momenzadeh, Rojiar Pir Mohammadiani
arXiv:2606. 10086v1 Announce Type: new Abstract: This paper develops a theory of exploratory adaptation under AI-assisted optimization.
By Balaraju Battu
The paper evaluates the Tabular Prior-data Fitted Network (TabPFN) as a surrogate model in surrogate‑assisted evolutionary algorithms (SAEAs) for expensive optimization problems. Through extensive experiments in both offline and online settings across a range of problem types—including single‑objective, multi‑objective, constrained, combinatorial, mixed‑variable, and engineering tasks—the study finds that TabPFN’s effectiveness varies strongly with the problem characteristics. The authors conclude that TabPFN should be used selectively, with customized model management and algorithm design tailored to data availability, landscape complexity, and search‑space properties.
By Lu Han, Jin Wang, Yuchen Li, Haoran Gu, Shulei Liu, Ziyang Shi, Wenao Lu, Handing Wang
arXiv:2606. 02863v1 Announce Type: new Abstract: AI-Driven Research Systems (ADRS) -- systems coupling LLMs with automated evaluation to discover algorithms, proofs, and designs -- are being optimized and adopted across domains, but the tools to analyze them have not kept pace.
By Marquita Ellis, Paul Castro
arXiv:2606. 05956v1 Announce Type: new Abstract: Bidirectional heuristic search can potentially reduce search effort for problems amenable to backward search.
By Tzur Shubi, Ariel Felner, Solomon Eyal Shimony, Shahaf S. Shperberg
arXiv:2606. 18267v1 Announce Type: cross Abstract: Benchmarking shortest-path algorithms is commonly based on aggregate performance over heterogeneous graph sets, which limits insight into how different search paradigms react to instance structure.
By Maryam Gholami Shiri, Ivana Krminac, Marko Djukanovi\'c, Sa\v{s}o D\v{z}eroski, Eva Tuba, Tome Eftimov
arXiv:2609.23547v1 Announce Type: new
Abstract: Graph prompt learning enables parameter-efficient adaptation of frozen Graph Neural Networks to downstream tasks through lightweight prompt parameters....
By Xiangyu Wang, Shuo Wang, Ruiyi Fang, Zhao Kang
arXiv:2608.28627v1 Announce Type: new
Abstract: Designing high-performance tactical wireless networks under realistic operational constraints gives rise to challenging combinatorial optimization prob...
By Wissem Ahmed Zaid, Alain Hertz, Defeng Liu
The paper introduces LLM-EBG, an evolutionary framework that uses a large language model as a generative operator to automatically create optimization benchmarks. By generating unconstrained single-objective continuous minimization problems expressed as mathematical formulas, the framework can produce benchmarks that consistently favor a target algorithm over a comparison algorithm in over 80% of trials. Landscape analysis shows that these generated problems exhibit distinct geometric traits, such as sensitivity to variable scaling, reflecting the search behaviors of different optimization methods.
By Yuhiro Ono, Tomohiro Harada, Yukiya Miura