26 papers · ranked by Valyu relevance
Xin‐She Yang
Many problems in science and engineering are optimization problems, which may require sophisticated optimization techniques to solve. Nature-inspired algorithms are a class of metaheuristic algorithms for optimization, and some algorithms or variants are often developed by hybridization. Benchmarking is also important…
Rohit Salgotra, Amanjot Kaur Lamba, Dhruv Talwar, Dhairya Gulati + 1 more
'Amir H. Gandomi'] This paper proposes a novel multi-hybrid algorithm named DHPN, using the best-known properties of dwarf mongoose algorithm (DMA), honey badger algorithm (HBA), prairie dog optimizer (PDO), cuckoo search (CS), grey wolf optimizer (GWO) and naked mole rat algorithm (NMRA). It follows an iterative…
Urbańczyk, Piotr, Urbanczyk, Aleksandra + 5 more
The goal of this paper is twofold. First, it explores hybrid evolutionary-swarm metaheuristics that combine the features of PSO and GA in a sequential, parallel and consecutive manner in comparison with their standard basic form: Genetic Algorithm and Particle Swarm Optimization. The algorithms were tested on a set of…
Pankaj Sharma, Rohit Salgotra, Saravanakumar Raju, Mohamed Abouhawwash + 1 more
'Mohamed Abouhawwash' 'S. S. Askar'] This paper presents a study to enhance the performance of a recently introduced naked mole-rat algorithm (NMRA), by local optima avoidance, and better exploration as well as exploitation properties. A new set of algorithms, namely Prairie dog optimization algorithm, INFO, and…
Neha Majhi, Rajashree Mishra
Background Traditional optimization methods often struggle to balance global exploration and local refinement, particularly in complex real-world problems. To address this challenge, we introduce a novel hybrid optimization strategy that integrates the Nelder-Mead (NM) technique and the Genetic Algorithm (GA), named…
Aswathi Malanthara, Ishaan R. Kale
Knapsack Problems Authors: ['Aswathi Malanthara' 'Ishaan R. Kale'] This paper addresses the challenges faced by algorithms, such as the Firefly Algorithm (FA) and the Genetic Algorithm (GA), in constrained optimization problems. While both algorithms perform well for unconstrained problems, their effectiveness…
Authors not listed
We present a unified theoretical framework that classifies and analyzes quantum enhancement strategies for classical algorithms, establishing design paradigms that systematically combine quantum subroutines with classical procedures. The theory identifies four fundamental enhancement mechanisms: quantum search…
Panagiotis Aivaliotis-Apostolopoulos, Dimitrios Loukidis, Seyedali Mirjalili
'Seyedali Mirjalili'] Particle swarm optimization and genetic algorithms are two classes of popular heuristic algorithms that are frequently used for solving complex multi-dimensional mathematical optimization problems, each one with its one advantages and shortcomings. Particle swarm optimization is known to favor…
Azad A. Ameen, Tarik A. Rashid, Shavan Askar
Child drawing development optimization (CDDO) is a recent example of a metaheuristic algorithm. The motive for inventing this method is children's learning behavior and cognitive development, with the golden ratio being employed to optimize the aesthetic value of their artwork. Unfortunately, CDDO suffers from low…
Abubakr S. Issa, Yossra H. Ali, Tarik A. Rashid
— Feature selection can be defined as one of the pre-processing steps that decrease the dimensionality of a dataset by identifying the most significant attributes while also boosting the accuracy of classification. For solving feature selection problems, this study presents a hybrid binary version of the Harris Hawks…
Sayed Sayeed Ahmad, Rashmi Rani, Ihab Wattar, Meghna Sharma + 3 more
'Sanjiv Sharma' 'Rajit Nair' 'Basant Tiwari'] Recommender systems are chiefly renowned for their applicability in e-commerce sites and social media. For system optimization, this work introduces a method of behaviour pattern mining to analyze the person's mental stability. With the utilization of the sequential pattern…
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…
Jing-Kai Fang, Yue-Feng Lin, Jun-Han Huang, Yibo Chen + 9 more
Computational biology holds immense promise as a domain that can leverage quantum advantages due to its involvement in a wide range of challenging computational tasks. Researchers have recently explored the applications of quantum computing in genome assembly implementation. However, the issue of repetitive sequences…
Maryam T. Abdulkhaleq, Tarik A. Rashid, Abeer Alsadoon, Bryar A. Hassan + 12 more
'Bryar A. Hassan' 'Mokhtar Mohammadi' 'Jaza M. Abdullah' 'Amit Chhabra' 'Sazan L. Ali' 'Rawshan Nuree Othman' 'Hadil A. Hasan' 'Sara Afshari Azad' 'Naz A. Mahmood' 'Sivan S. Abdalrahman' 'Hezha O. Rasul' 'Nebojša Bačanin' 'S. Vimal'] Maryam T. Abdulkhaleq1 , Tarik A. Rashid1 , Abeer Alsadoon2,3, Bryar A. Hassan4 …
Yang Li, Anjun Ma, Yizhong Wang, Cankun Wang + 4 more
We present an algorithm, STREAM, for enhancer-driven gene regulatory network (eGRN) inference from transcriptome and chromatin accessibility profiled in the same cells. The algorithm improves the prediction accuracy of relations among transcription factors (TFs), enhancers, and genes by two new ideas: (i) a Steiner…
Carlos Martínez, Facundo Rocha Calvette, Marielle Péré, Mauricio Barrientos + 1 more
A hybrid model combines a mechanistic model, described by ordinary differential equations, with a data-driven component, such as neural networks. This strategy leverages both the physical knowledge of the system and the predictive power of machine learning methods, and it has been applied to a variety of bioprocesses.…
José Pinto, Mykaella Mestre, Rafael S. Costa, Gerald Striedner + 1 more
Numerous studies have reported the use of hybrid semiparametric systems that combine shallow neural networks with mechanistic models for bioprocess modeling. Here we revisit the general bioreactor hybrid modeling problem and introduce some of the most recent deep learning techniques. The single layer networks were…
Emanuel Vega, José Lemus-Romani, Ricardo Soto, Broderick Crawford + 4 more
Population-based metaheuristics can be seen as a set of agents that smartly explore the space of solutions of a given optimization problem. These agents are commonly governed by movement operators that decide how the exploration is driven. Although metaheuristics have successfully been used for more than 20 years…
Nándor Bándi, Noémi Gaskó, Bilal Alatas
This article introduces a new hybrid hyper-heuristic framework that deals with single-objective continuous optimization problems. This approach employs a nested Markov chain on the base level in the search for the best-performing operators and their sequences and simulated annealing on the hyperlevel, which evolves the…
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…
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…
Rashmi Seneviratne, Michael Rappolt, Lars Jeuken, Paul Beales
Lipids and block copolymers can individually self-assemble into vesicles, each with their own particular benefits and limitations. Combining polymers with lipids allows for further optimisation of the vesicle membranes for bionanotechnology applications. Here, POPC lipid is mixed with poly(1,2-butadiene-block-ethylene…
Authors not listed
Artificial intelligence (AI) is reshaping chemical engineering. Still, its role in safety-critical operations is limited because we rarely see tools that link physical models with data-driven methods. This study brings together three elements: physics-constrained neural networks, uncertainty quantification, and a…
Jonas Verhellen
Computer-assisted design of small molecules has experienced a resurgence in academic and indus- trial interest due to the widespread use of data-driven techniques such as deep generative models. While the ability to generate molecules that fulfill required chemical properties is encouraging, the use of deep learning…
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)…
Authors not listed
Bioprocess engineering has incorporated effective AI applications in recent years that consist of traditional approaches to training models on relevant data to then analyze and predict new and unseen data. The missing component has been the ability to process mixed data from an assortment of dissimilar information…