26 papers · ranked by Valyu relevance
Echevarrieta, Judith, Arza, Etor + 4 more
In certain real-world optimization scenarios, practitioners are not interested in solving multiple problems but rather in finding the best solution to a single, specific problem. When the computational budget is large relative to the cost of evaluating a candidate solution, multiple heuristic alternatives can be tried…
Natasha Mhatre, Daniel Robert
Insects have small brains and heuristics or ‘rules of thumb’ are proposed here to be a good model for how insects optimize the objects they make and use. Generally, heuristics are thought to increase the speed of decision making by reducing the computational resources needed for making decisions. By corollary…
Shaofeng Zhang, Shengcai Liu, Ning Lü, Jiahao Wu + 3 more
Combinatorial optimization problems are widely encountered in real-world applications. Designing high-quality heuristic algorithms that efficiently approximate optimal solutions within reasonable time is a critical research challenge. In recent years, many works have explored integrating Large Language Models (LLMs)…
Lijuan Wang, Yuze Wang, Chen Qiu, Liwei Xiao + 2 more
Protein sequence design for tailored functional properties is a fundamental task in protein engineering, with critical applications in drug discovery and therapeutic development. Efficient navigation of the combinatorial vastness of protein sequence space to identify functional variants remains a formidable challenge.…
Chao Shang, Ting-ting Zhou, Shuai Liu
In this article, a modified version of the Sine Cosine algorithm (MSCA) is proposed to solve the optimization problem. Based on the Sine Cosine algorithm (SCA), the position update formula of SCA is redefined to increase the convergence speed, then the Levy random walk mutation strategy is adopted to improve the…
Alina Selega, Kieran R. Campbell
Many practical applications require optimization of multiple, computationally expensive, and possibly competing objectives that are well-suited for multi-objective Bayesian optimization (MOBO) procedures. However, for many types of biomedical data, measures of data analysis workflow success are often heuristic and…
Matthew J. Craven, John R. Woodward
> Abstract. In previous work, we developed a single Evolutionary Algorithm (EA) to solve random instances of the Anshel-Anshel-Goldfeld (AAG) key exchange protocol over polycyclic groups. The EA consisted of six simple heuristics which manipulated strings. The present work extends this by exploring the use of…
Neil M. Dundon, Jaron T. Colas, Neil Garrett, Viktoriya Babenko + 5 more
Heuristics can inform human decision making in complex environments through a reduction of computational requirements (accuracy-resource trade-off) and a robustness to overparameterisation (less-is-more). However, tasks capturing the efficiency of heuristics typically ignore action proficiency in determining rewards.…
Pooria Namyar, Behnaz Arzani, Ryan Beckett, Santiago Segarra + 4 more
'Himanshu Raj' 'Umesh Krishnaswamy' 'Ramesh Govindan' 'Srikanth Kandula'] Abstract– Production systems use heuristics because they are faster or scale better than their optimal counterparts. Yet, practitioners are often unaware of the performance gap between a heuristic and the optimum or between two heuristics in…
Wilson Siringoringo, Andrew M. Connor, Nick Clements, Nicholas A Alexander
'Nicholas A Alexander'] > Minimum Cost Polygon Overlay (MCPO) is a unique two-dimensional optimization problem that involves the task of covering a polygon shaped area with a series of rectangular shaped panels. This has a number of applications in the construction industry. This work examines the MCPO problem in order…
Delong Guo, Huajuan Huang, Yinggao Yue
The Honey Badger Algorithm (HBA) is a recently proposed metaheuristic optimization algorithm inspired by the foraging behavior of honey badgers. The search mechanism of this algorithm is divided into two phases: a mining phase and a honey-seeking phase, effectively emulating the processes of exploration and…
Grecia C. Duque-Gimenez, Daniel F. Zambrano-Gutierrez, Maricela Rodriguez-Nieto, Jorge Luis Menchaca + 4 more
'Maricela Rodriguez-Nieto' 'Jorge Luis Menchaca' 'Jorge M. Cruz-Duarte' 'Diana G. Zárate-Triviño' 'Juan Gabriel Avina-Cervantes' 'José Carlos Ortiz-Bayliss'] Understanding the viscoelastic properties of cells is essential for studying their mechanical behavior and identifying disease-related biomechanical markers. This…
Ashish B. George, Kirill S. Korolev
Assembling optimal microbial communities is key for various applications in biofuel production, agriculture, and human health. Finding the optimal community is challenging because the number of possible communities grows exponentially with the number of species, and so an exhaustive search cannot be performed even for…
Matheus Sant’Ana Lima, Seyedali Mirjalili
Distributed Systems architectures are becoming the standard computational model for processing and transportation of information, especially for Cloud Computing environments. The increase in demand for application processing and data management from enterprise and end-user workloads continues to move from a single-node…
Kenneth Sörensen, Marc Sevaux, Fred Glover
Even though people have used heuristics throughout history, and the human brain is equipped with a formidable heuristic engine to solve an enormous array of challenging optimization problems, the scientific study of heuristics (and, by extension, metaheuristics) is a relatively young endeavour. It is not an…
Adithya Sagar, Rachel LeCover, Christine Shoemaker, Jeffrey Varner
Mathematical modeling is a powerful tool to analyze, and ultimately design biochemical networks. However, the estimation of the parameters that appear in biochemical models is a significant challenge. Parameter estimation typically involves expensive function evaluations and noisy data, making it difficult to quickly…
Daniel Karapetyan
Combinatorial optimization is widely applied in a number of areas nowadays. Unfortunately, many combinatorial optimization problems are NPhard which usually means that they are unsolvable in practice. However, it is often unnecessary to have an exact solution. In this case one may use heuristic approach to obtain a…
Authors not listed
Background: Pharmaceutical batch production faces significant scheduling challenges due to operational uncertainties including equipment failures, yield variability, and demand fluctuations. While scheduling heuristics are widely used in practice, their comparative performance under varying uncertainty conditions…
Martin Gonzalez, Jose J. López-Espín, Juan Aparicio, El-Ghazali Talbi + 1 more
'El-Ghazali Talbi' 'Nicholas Higham'] Mixed Integer Linear Programs (MILPs) are usually NP-hard mathematical programming problems, which present difficulties to obtain optimal solutions in a reasonable time for large scale models. Nowadays, metaheuristics are one of the potential tools for solving this type of problems…
Yifan Wu, Aron Walsh, Alex Ganose
What is the minimum number of experiments, or calculations, required to find an optimal solution? Relevant chemical problems range from identifying a compound with target functionality within a given phase space to controlling materials synthesis and device fabrication conditions. A common feature in this application…
Authors not listed
Background: Pharmaceutical batch scheduling in multi-reactor configurations presents complex optimization challenges under operational uncertainty, yet limited research addresses how parallel processing capacity affects heuristic performance and predictive modeling. Objectives: This study investigated scheduling…
Giuseppe Lancia, Paolo Vidoni
sub-quadratic time Authors: ['Giuseppe Lancia' 'Paolo Vidoni'] We describe an exact algorithm for finding the best 2-OPT move which, experimentally, was observed to be much faster than the standard quadratic approach. To analyze its average-case complexity, we introduce a family of heuristic procedures and discuss…
Authors not listed
Automated chemistry platforms hold the potential to enable large-scale organic synthesis campaigns, such as producing a library of compounds for biological evaluation. The efficiency of such platforms will depend on the schedule according to which the synthesis operations are executed. In this work, we study the…
Riley Hickman, Priyansh Parakh, Austin Cheng, Qianxiang Ai + 3 more
Experiment planning algorithms are a required component of autonomous platforms for scientific discovery. Selecting a suitable optimization algorithm for a novel application is an important yet difficult choice a researcher has to make based on past empirical performance on similar tasks. To facilitate the evaluation…
Michael Freitas Gustavo, Toon Verstraelen
In this work we explore the properties which make many real-life global optimization problems extremely difficult to handle, and some of the common techniques used in literature to address them. We then introduce a general optimization management tool called GloMPO (Globally Managed Parallel Optimization) to help…
Riley Hickman, Matteo Aldeghi, Alán Aspuru-Guzik
Model-based optimization strategies, such as Bayesian optimization (BO), have been deployed across the natural sciences in design and discovery campaigns due to their sample efficiency and flexibility. The combination of such strategies with automated laboratory equipment and/or high-performance computing in a…