25 papers · ranked by Valyu relevance
Bar Light
These lecture notes are derived from a graduate-level course in dynamic optimization, offering an introduction to techniques and models extensively used in management science, economics, operations research, engineering, and computer science. The course emphasizes the theoretical underpinnings of discrete-time dynamic…
Abdennour Boulesnane
Evolutionary Computation (EC) has emerged as a powerful field of Artificial Intelligence, inspired by nature's mechanisms of gradual development. However, EC approaches often face challenges such as stagnation, diversity loss, computational complexity, population initialization, and premature convergence. To overcome…
Kai Gong, Tong Lu, Xu Wang, Xinggao Liu
Cytotoxic chemotherapy and immune checkpoint inhibitors (ICIs) have transformed the management of advanced cancers, yet durable responses remain restricted to subsets of patients and strongly depend on the tumor immune microenvironment (TIME). Distinct “hot” and “cold” TIMEs differ in pre-existing effector T-cell…
Sanjai Pathak, Ashish Mani, Mayank Sharma, Amlan Chatterjee
Optimization Authors: ['Sanjai Pathak' 'Ashish Mani' 'Mayank Sharma' 'Amlan Chatterjee'] Abstract – Many real-world problems are dynamic optimization problems that are unknown beforehand. In practice, unpredictable events such as the arrival of new jobs, due date changes, and reservation cancellations, changes in…
Jonas Skackauskas, Tatiana Kalganova
Currently available dynamic optimization strategies for Ant Colony Optimization (ACO) algorithm offer a trade-off of slower algorithm convergence or significant penalty to solution quality after each dynamic change occurs. This paper proposes a discrete dynamic optimization strategy called Ant Colony Optimization (ACO)…
Daniel Herring, Michael Kirley, Xin Yao
Dynamic multi-objective optimization problems (DMOPs) are widely accepted to be more challenging than stationary problems due to the time-dependent nature of the objective functions and/or constraints. Evaluation of purpose-built algorithms for DMOPs is often performed on narrow selections of dynamic instances with…
Bestoun S. Ahmed
—The rapidly changing landscapes of modern optimization problems require algorithms that can be adapted in real-time. This paper introduces an Adaptive Metaheuristic Framework (AMF) designed for dynamic environments. It is capable of intelligently adapting to changes in the problem parameters. The AMF combines a…
GilHwan Kim, Fabrizio Sergi
Human-in-the-loop optimization (HILO) is an established method for identifying subject-specific optimal controllers for performance augmentation. For HILO algorithms to be useful in rehabilitation, however, the optimization algorithm may need to account for how the human response changes over time in response to…
Fei Wu, Wanliang Wang, Jiacheng Chen, Zheng Wang
The dynamic multi-objective optimization problem is a common problem in real life, which is characterized by conflicting objectives, the Pareto frontier (PF) and Pareto solution set (PS) will follow the changing environment. There are various dynamic multi-objective algorithms have been suggested to solve such…
Nicolás Caselli, Ricardo Soto, Broderick Crawford, Sergio Valdivia + 3 more
'Elizabeth Chicata' 'Rodrigo Olivares' 'Heming Jia'] In the optimization field, the ability to efficiently tackle complex and high-dimensional problems remains a persistent challenge. Metaheuristic algorithms, with a particular emphasis on their autonomous variants, are emerging as promising tools to overcome this…
Shao Chang, Qi Zhao, Nana Pu, Shi Cheng + 2 more
a Australian Artificial Intelligence Institute (AAII), Faculty of Engineering and Information Technology, University of Technology Sydney, 15 Broadway, Ultimo, Sydney, 2007, NSW, Australia b School of Big Data and Artificial Intelligence, Chengdu Technological University, Chengdu, 611730, Sichuan, China c School of…
GilHwan Kim, Haider A. Chishty, Fabrizio Sergi
Human-in-the-loop (HIL) optimization is a control paradigm used for tuning the control parameters of human-interacting devices while accounting for variability among individuals. A limitation of state-of-the-art HIL optimization algorithms such as Bayesian Optimization (BO) is that they assume that the relationship…
GilHwan Kim, Haider A. Chishty, Fabrizio Sergi
Human-in-the-loop (HIL) optimization is a control paradigm used for tuning the control parameters of human-interacting devices while accounting for variability among individuals. A limitation of state-of-the-art HIL optimization algorithms such as Bayesian Optimization (BO) is that they assume that the relationship…
GilHwan Kim, Fabrizio Sergi
Human-in-the-Loop Optimization (HILO) has demonstrated efficacy in achieving a plethora of assistive or augmentative effects. However, conventional optimizers such as Bayesian optimization (BO) do not account for the non-stationary aspects of the human-robot system, and may thus be limited in the domains of…
Megha Varshney, Pravesh Kumar, Musrrat Ali, Yonis Gulzar + 3 more
'Ameer Hamza Khan' 'Shuai Li' 'Danish Hussain'] One of the most important tasks in handling real-world global optimization problems is to achieve a balance between exploration and exploitation in any nature-inspired optimization method. As a result, the search agents of an algorithm constantly strive to investigate the…
Ignacio Tapia García, Cristóbal Torrealba, Ricardo Luna, José Ricardo Pérez-Correa + 1 more
Dynamic Flux Balance Analysis (DFBA) enables simulation of microbial culture dynamics under changing environmental conditions, but remains computationally expensive for tasks such as parameter calibration and fermentation optimization when applied using genome-scale metabolic models (GEMs). To address this challenge…
Fereshteh Vaezi Jezeie, Seyed Jafar Sadjadi, Ahmad Makui, Seyedali Mirjalili
'Seyedali Mirjalili'] Portfolio optimization is one of the most important issues in financial markets. In this regard, the more realistic are assumptions and conditions of modelling to portfolio optimization into financial markets, the more reliable results will be obtained. This paper studies the knapsack-based…
Yujie Chen, Guangyu Wang, Baichuan Yin, Chongyun Ma + 2 more
Real-world optimisation problems are increasingly high-dimensional, nonlinear and constrained. The No Free Lunch theorem implies that no single optimiser dominates across all problem classes, making domain-specific metaheuristics indispensable. Yet mainstream population-based methods often converge prematurely and fail…
Peter Sagmeister, Lukas Melnizky, Jason Williams, C. Oliver Kappe
In modern pharmaceutical research, the demand for expeditious development of synthetic routes to active pharmaceutical ingredients (APIs) has led to a paradigm shift towards data-rich process development. Conventional methodologies en-compass prolonged timelines for reaction and analytical model developments. Both…
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…
Authors not listed
Deriving versatile and robust mechanistic models from experimental data is a key challenge in engineering and natural sciences. This is especially true in chemical reaction engineering, where reactor manufacturers and operators increasingly pursue the development and maintenance of digital twins that rely on frequent…
Robert Arbon, Yanchen Zhu, Antonia S. J. S. Mey
Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins, including protein folding. With all statistical and machine learning (ML) models choices must be made about the modeling pipeline that cannot be directly learned from the data. These choices, or…
AKHIL SHAJAN, Madushanka Manathunga, Andreas Goetz, Kenneth Merz
Based on a series of energy minimizations with starting structures obtained from the Baker test set of 30 organic molecules, a comparison is made between various open-source geometry optimization codes that are interfaced with the open-source QUantum Interaction Computational Kernel (QUICK) program for gradient and…
Pablo Quijano Velasco, Kedar Hippalgaonkar, Balamurugan Ramalingam
The discovery of optimal conditions of chemical reactions is a labor-intensive, time-consuming task that requires exploring a high-dimensional parametric space. Historically the optimization of chemical reactions has been performed by manual experimentation guided by human intuition and Design of Experiments where one…
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…