25 papers · ranked by Valyu relevance
Hadi Bayzidi, Siamak Talatahari, Meysam Saraee, Charles-Philippe Lamarche
'Charles-Philippe Lamarche'] In this paper, a new metaheuristic optimization algorithm, called social network search (SNS), is employed for solving mixed continuous/discrete engineering optimization problems. The SNS algorithm mimics the social network user's efforts to gain more popularity by modeling the decision…
Hongmei Bai, Taosuo Wu, Jianfu Luo, Na Ta + 1 more
This paper proposes a multi-strategy improved pied kingfisher optimizer (MSIPKO), a novel metaheuristic algorithm designed to address constrained optimization problems (COPs). COPs are widely encountered in engineering and industrial applications and are characterized by complex constraints that restrict the feasible…
Vijay K. Garg
—We present a method to design parallel algorithms for constrained combinatorial optimization problems. Our method solves and generalizes many classical combinatorial optimization problems including the stable marriage problem, the shortest path problem and the market clearing price problem. These three problems are…
Cyril Cayron
Constrained optimization problems are classically solved with the help of the Lagrange multipliers and the Lagrangian function. However, the disadvantage of this approach is that it artificially increases the dimensionality of the problem. Here, we show that the determinant of the Jacobian of the problem (function to…
Iman Rahimi, Amir H. Gandomi, Mohammad Reza Nikoo, Mohsen Mousavi + 1 more
'Fang Chen'] Many real-world optimization problems, particularly engineering ones, involve constraints that make finding a feasible solution challenging. Numerous researchers have investigated this challenge for constrained single- and multi-objective optimization problems. In particular, this work extends the boundary…
Stefano Fioravanzo, Giovanni Iacca
Constrained optimization problems are often characterized by multiple constraints that, in the practice, must be satisfied with different tolerance levels. While some constraints are hard and as such must be satisfied with zero-tolerance, others may be soft, such that nonzero violations are acceptable. Here, we…
Andre KY Low, Flore Mekki-Berrada, Aleksandr Ostudin, Jiaxun Xie + 7 more
The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces. To reach target properties efficiently, these platforms are increasingly paired with intelligent experimental design. When solving optimization problems, Bayesian-based optimizers are often…
Giovanni Lavezzi, Kidus Guye, Marco Ciarcià
In this paper we propose a set of guidelines to select a solver for the solution of nonlinear programming problems. With this in mind, we present a comparison of the convergence performances of commonly used solvers for both unconstrained and constrained nonlinear programming problems. The comparison involves accuracy…
Mourad Naidji, Alla Eddine Toubal Maamar, Mohamed Ilyas Rahal, Saad Mekhilef + 2 more
Optimal Power Flow (OPF) is a highly nonlinear and constrained optimization problem that seeks optimal operating conditions while ensuring secure and efficient power system operation. Although metaheuristic algorithms have demonstrated strong global search capability for OPF, their performance is often limited by…
Charles Audet, Théo Denorme, Youssef Diouane, Sébastien Le Digabel + 1 more
This work introduces ADS-PB, an extension of the Adaptive Direct Search (ADS) framework for solving constrained blackbox optimization problems. With ADS, iterates progress without relying on mesh structures or sufficient decrease conditions on the objective function value. Unlike the extreme barrier approach used in…
K. H. Benjamin Leung, Nasrin Yousefi, Timothy C. Y. Chan, Ahmed M. Bayoumi
We have provided an overview of how to implement constrained optimization with 2 health-related examples. Although our examples were stylized for ease of exposition, the level of model complexity is comparable to many published examples. Readers of this tutorial can readily apply these principles to real-world…
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…
Ishaan R. Kale, Anand J. Kulkarni, Efrén Mezura‐Montes
Several Artificial Intelligence based heuristic and metaheuristic algorithms have been developed so far. These algorithms have shown their superiority towards solving complex problems from different domains. However, it is necessary to critically validate these algorithms for solving real-world constrained optimization…
Sébastien Le Digabel, Stefan M. Wild
The types of constraints encountered in black-box and simulationbased optimization problems differ significantly from those treated in nonlinear programming. We introduce a characterization of constraints to address this situation. We provide formal definitions for several constraint classes and present illustrative…
Authors not listed
Experimental design plays an important role in efficiently acquiring informative data for system characterization and deriving robust conclusions under resource limitations. Recent advancements in high-throughput experimentation coupled with machine learning have notably improved experimental procedures. While Bayesian…
St. Elmo Wilken, Mathieu Besançon, Miroslav Kratochvíl, Chilperic Armel Foko Kuate + 3 more
Metabolic models are typically characterized by a large number of parameters. Traditionally, metabolic control analysis is applied to differential equation-based models to investigate the sensitivity of predictions to parameters. A corresponding theory for constraint-based models is lacking, due to their formulation as…
István Kolossváry, Woody Sherman
Conformational sampling of complex biomolecules is an emerging frontier in drug discovery. Indeed, advances in lab-based structural biology and related computational approaches like AlphaFold have made great strides in obtaining static protein structures. However, biology is in constant motion and many important…
Ankur Sinha, Pekka Malo, Kalyanmoy Deb
In this paper, we propose a procedure for designing controlled test problems for single-objective bilevel optimization. The construction procedure is flexible and allows its user to control the different complexities that are to be included in the test problems independently of each other. In addition to properties…
Nenad Kostić, Nenad Petrović, Vesna Marjanović, Ružica R. Nikolić + 5 more
'Janusz Szmidla' 'Nenad Marjanović' 'Robert Ulewicz' 'Enrique Casarejos' 'Giovanni Garcea'] This research aims to show the effects of adding cardinality constraints to limit the number of different cross-sections used in simultaneous sizing and shape optimization of truss structures. The optimal solutions for sizing…
Richard D. Paul, Johann F. Jadebeck, Anton Stratmann, Wolfgang Wiechert + 1 more
Effective collaboration between developers of Bayesian inference methods and users is key to advance our quantitative understanding of biosystems. We here present hopsy, a versatile open source platform designed to provide convenient access to powerful Markov chain Monte Carlo sampling algorithms tailored to models…
Wolfram Liebermeister
Cells need to make an efficient use of metabolites, proteins, energy, membrane space, and time, and resource allocation is also an important aspect of metabolism. How, for example, should cells distribute their protein budget between different cellular functions, e.g. different metabolic pathways, to maximise growth?…
Authors not listed
Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…
Fabian Fröhlich, Peter K. Sorger
Ordinary differential equation (ODE) models are widely used to describe biochemical processes, since they effectively represent mass action kinetics. Optimization-based calibration of ODE models on experimental data can be challenging, even for low-dimensional problems. However, reliable model calibration is a…
Jesse A Sharp, Kevin Burrage, Matthew J Simpson
Optimal control theory provides insight into complex resource allocation decisions. The forward-backward sweep method (FBSM) is an iterative technique commonly implemented to solve two-point boundary value problems (TPBVPs) arising from the application of Pontryagin’s Maximum Principle (PMP) in optimal control. In this…
Authors not listed
Solving optimization problems, especially for nonlinear and constrained systems, is a challenge. Decades of specialized algorithms have been developed for general and special cases of root finding, minimization (including constraints), for parameter estimation, and mapping connected spaces. These approaches typically…