26 papers · ranked by Valyu relevance
Stefan Ivić, Siniša Družeta, Luka Grbčić
—Benchmarking optimization algorithms is fundamental for the advancement of computational intelligence. However, widely adopted artificial test suites exhibit limited correspondence with the diversity and complexity of real-world engineering optimization tasks. This paper presents a new benchmark suite comprising 231…
Anna V. Kononova, Bas van Stein, Olaf Mersmann, Thomas Bäck + 13 more
``` Anna V. Kononova 1 , Niki van Stein 1 , Olaf Mersmann 2 , Thomas Bäck 1 , Thomas Bartz-Beielstein 3 , Tobias Glasmachers 4 , Michael Hellwig 5 , Sebastian Krey 6 , Jakub Kůdela 7 , Boris Naujoks 3 , Leonard Papenmeier 8 , Elena Raponi 1 , Quentin Renau 9 , Jeroen Rook10 , Lennart Schäpermeier 8 Diederick…
Authors not listed
Recent advances in generative artificial intelligence have enabled in silico molecular design to become a powerful approach for exploring chemical space toward specific design goals across various domains. However, in actual design workflows, determining the appropriate generation conditions, including generative…
Nicholas Santavas, Kareem Eissa, Patrycja Cieplicka, Piotr Florek + 5 more
Enterprise LLM deployment faces a critical scalability challenge: organizations must optimize models systematically to scale AI initiatives within constrained compute budgets, yet the specialized expertise required for manual optimization remains a niche and scarce skillset. This challenge is particularly evident in…
Yuhiro Ono, Tomohiro Harada, Yukiya Miura
Optimization benchmarks play a fundamental role in assessing algorithm performance; however, existing artificial benchmarks often fail to capture the diversity and irregularity of real-world problem structures, while benchmarks derived from real-world problems are costly and difficult to construct. To address these…
Melih Peker, Ozcan Ozturk
Selecting a good set of optimization flags requires extensive effort and expert input. While most of the prior research considers using static, spatial, or dynamic features, some of the latest research directly applied deep neural networks to source code. We combined the static features, spatial features, and deep…
Elliot Xie, Lingxin Cheng, Yujia Cai, Jack Shireman + 1 more
Performance bottlenecks in widely used genomics and bioinformatics software present a substantial and growing burden as biological datasets continue to increase in size and number. Relieving these bottlenecks relies largely on expert manual optimization and therefore remains difficult to scale. Here we present…
Andrea Polo-Rodríguez, David R. Penas, Julio R. Banga
Parameter estimation is a central challenge in systems biology, particularly for large dynamic models described by nonlinear ordinary differential equations (ODEs). These global optimization problems exhibit landscapes which are topologically heterogeneous, often exhibiting a pathological mixture of stiff, smooth…
Stephan Grein, David R. Penas, Daniel Weindl, Polina Lakrisenko + 2 more
Dynamic models are central to the computational life sciences but typically contain unknown parameters that must be inferred from experimental data. High-throughput measurements have made this task increasingly challenging, yielding high-dimensional search spaces and non-convex objectives with many local optima. This…
Tuğrul Yağbasan, Ömür Akyazı, Hayati Türe, Bekir Dizdaroğlu + 2 more
Biomimetic optimizers are increasingly used to solve complex engineering problems, yet their performance depends strongly on how effectively they preserve diversity while maintaining selection pressure toward promising regions. In this study, the Starfish Optimization Algorithm (SFOA) is enhanced through…
Ruyi Dong, Ran Cui, Zhennao Cai, Ali Asghar Heidari + 4 more
Kernel Search Optimization (KSO) is characterized by insufficient accuracy in local search, which makes it difficult to achieve local optimization. Therefore, this paper proposes a Large Local Search Kernel Search Optimization (LLSKSO) to enhance the local optimization ability. LLSKSO achieves the performance…
Filippo Schiavio, Lubomír Bulej, Walter Binder
Developers often use microbenchmarks to choose the most performant implementation of a method or a class. On the Java Virtual Machine (JVM), this is commonly done using the Java Microbenchmark Harness (JMH) which addresses common pitfalls of measuring code performance on the JVM. However, even using JMH guidelines…
Mohammad Zaeed, Tanzima Z. Islam, Vladimir Indic
Large language models (LLMs) show promise for automated code optimization. However, without performance context, they struggle to produce correct and effective code transformations. Existing performance tools can identify bottlenecks but stop short of generating actionable code changes. Consequently, performance…
Ali Asghari, Mohammadhossein Mohammadi, Heming Jia
Metaheuristic algorithms are widely used to find optimal or near-optimal solutions for complex problems by taking inspiration from natural behaviors and processes. Although many different methods have been developed, a common problem in many of them is maintaining a good balance between exploration and exploitation and…
Mohammed Jameel, Hana Merah, Alaa M. Abd El-latif, Tareq M. Al-shami + 2 more
This paper presents a robust multi-objective optimization approach-the multi-objective starfish optimization algorithm (MOSFOA)-designed to address complex challenges in engineering design and optimal power flow analysis. As an advanced extension of the starfish optimization algorithm (SFOA), MOSFOA leverages…
Sunde Wang, Beilei Yin, Pu Wang, Zihao Cheng + 1 more
To address the inherent limitations of the traditional Animated Oat Optimization Algorithm (AOO), including poor uniformity of initial random population distribution and insufficient dynamic balance between global exploration and local exploitation, this paper proposes an Enhanced Animated Oat Optimization Algorithm…
Assem F. Alabu-Husain, Mostafa A. ElBahloul, Mahmoud M. Saafan, Eman M. El-Gendy
This study introduces the Pray Optimization Algorithm (POA), a novel metaheuristic inspired by the procedural rituals of Islamic pray, designed to solve complex engineering and robotic manipulator problems. The mathematical model is structured into three distinct phases: Phase I simulates searching for a suitable…
Authors not listed
The performance of Pt-based oxygen reduction reaction (ORR) catalysts in proton exchange membrane fuel cells (PEMFCs) arises from complex interactions among Pt nanoparticles, carbon supports, and proton-conductive ionomer. Membrane electrode assembly (MEA) testing is the industrial benchmark for catalyst evaluation but…
Authors not listed
We report a new charge model and a new general small molecule force field. Here, we address the development and benchmarking of both the Open Force Field (OpenFF) AshGC charge model, as well as the Sage 2.3.0 small molecule force field for drug-like molecules. AshGC is a graph neural network-based method for efficient…
Qing Shao
We benchmark six numerical precision configurations for ESM-2 protein language models across throughput, memory footprint and predictive accuracy, on two workloads with sharply different characteristics: bulk embedding extraction and deep mutational scanning (DMS) variant-effect scoring. Accuracy is evaluated on the…
Authors not listed
Incorporating prior domain knowledge into Bayesian optimization (BO) remains difficult for statistical methods, which also typically suffer from limited interpretability. Large language models (LLMs) offer complementary strengths in reasoning and knowledge integration, but it remains unclear when and how they improve…
Jie Feng, Mingdong He, Lei Jin, Hui Dou + 1 more
Software systems often expose a large number of configurable parameters to satisfy diverse application requirements and deployment scenarios. Given the intricate dependencies between parameters, manually finding a well-performing configuration is a daunting task even for experienced operators. Most existing automatic…
Niklas Neubrand, Timo Rachel, Tim Litwin, Jens Timmer + 2 more
Systems biology strives to unravel the complex dynamics of cellular processes, often with the help of ordinary differential equations (ODEs). However, the sparsity of measured data and the strong non-linearity of common ODEs introduce severe numerical problems in typical modeling tasks. This gave rise to the…
Authors not listed
Physics-based coarse-grained (CG) models are widely used in (bio)molecular simulations, yet their parameterization remains challenging and labor-intensive. In this work, we demonstrate how recently developed gradient-based optimization methods can substantially accelerate the refinement of CG force field (FF)…
Authors not listed
Finding the most stable adsorption geometry of a flexible molecule on a catalytic surface remains a key challenge due to the high dimensionality and ruggedness of the potential energy surface. We present a Gradient-Enhanced Genetic Algorithm (GE-GA) for the global optimization of adsorbate–surface configurations…
Authors not listed
Continuous manufacturing processes offer significant advantages over batch processes, including easier scalability, reduced costs, lower raw material and solvent consumption, and improved energy efficiency. A robust techno-economic assessment is therefore essential to evaluate and facilitate the adoption of such…