25 papers · ranked by Valyu relevance
Jörg Stork, A. E. Eiben, Thomas Bartz–Beielstein
Surrogate-based optimization, nature-inspired metaheuristics, and hybrid combinations have become state of the art in algorithm design for solving real-world optimization problems. Still, it is difficult for practitioners to get an overview that explains their advantages in comparison to a large number of available…
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…
Hassan A. Bashir, R.S. Neville
This material represents the opinion of the author only and does not necessarily represent the opinion of the School or the University. While every attempt has been made to ensure the accuracy of this publication, neither the author, the School or the University can accept liability for mistakes that may exist. Please…
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…
Gutha Jaya Krishna, Vadlamani Ravi
In management, business, economics, science, engineering, and research domains, Large Scale Global Optimization (LSGO) plays a predominant and vital role. Though LSGO is applied in many of the application domains, it is a very troublesome and a perverse task. The Congress on Evolutionary Computation (CEC) began an LSGO…
Seyed Hamid Reza Pasandideh, Soheyl Khalilpourazari
This paper presents a novel hybrid algorithm named Since Cosine Crow Search Algorithm. To propose the SCCSA, two novel algorithms are considered including Crow Search Algorithm (CSA) and Since Cosine Algorithm (SCA). The advantages of the two algorithms are considered and utilize to design an efficient hybrid algorithm…
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…
Xiang Li, Mohammad Reza Bonyadi, Zbigniew Michalewicz, Luigi Barone
This paper presents a hybrid evolutionary algorithm to deal with the wheat blending problem. The unique constraints of this problem make many existing algorithms fail: either they do not generate acceptable results or they are not able to complete optimization within the required time. The proposed algorithm starts…
Jaya Shankar Tumuluru, Richard McCulloch, Wijitha Senadeera
Optimization is a crucial step in the analysis of experimental results. Deterministic methods only converge on local optimums and require exponentially more time as dimensionality increases. Stochastic algorithms are capable of efficiently searching the domain space; however convergence is not guaranteed. This article…
Ali Jamali, M. M. A. Hashem, Md. Bazlar Rahman
– For small number of equations, systems of linear (and sometimes nonlinear) equations can be solved by simple classical techniques. However, for large number of systems of linear (or nonlinear) equations, solutions using classical method become arduous. On the other hand evolutionary algorithms have mostly been used…
Andrea Villagra, Enrique Alba, Guillermo Leguizamón
This work presents the results of a new methodology for hybridizing metaheuristics. By first locating the active components (parts) of one algorithm and then inserting them into second one, we can build efficient and accurate optimization, search, and learning algorithms. This gives a concrete way of constructing new…
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 …
Harini Narayanan, Mariano Nicolas Cruz Bournazou, Gonzalo Guillén-Gosálbez, Alessandro Butté
Mathematical models used for the representation of (bio)-chemical processes can be grouped into two broad paradigms: white-box or mechanistic models, completely based on knowledge or black-box data-driven models based on patterns observed in data. However, in the past two-decade, hybrid modeling that explores the…
Authors not listed
The use of hybrid models, combing mechanistic and machine learning (ML), has emerged as a promising approach, contributing to the development of Industry 4.0. This work presents a hybrid model that forecasts minibioreactor (MBR) production runs of mammalian cell culture recombinant for monoclonal antibodies (mAbs)…
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.…
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…
Atefe Darabi, Zheming An, Muhammad Ali Al-Radhawi, William Cho + 2 more
This work explores the integration of machine learning (ML) and mechanistic models (MM). While ML has demonstrated remarkable success in data-driven modeling across engineering, biology, and other scientific fields, MM remain essential for their interpretability and capacity to extrapolate beyond observed conditions…
Roel Dobbe, Claire J. Tomlin
The advent of biological data of increasingly higher resolution in space and time has triggered the use of dynamic models to explain and predict the evolution of biological systems over space and time. Computer-aided system modeling and analysis in biology has led to many new discoveries and explanations that would…
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…
Martín Gutiérrez, Yerko Ortiz, Javier Carrión
Metaheuristic procedures (MH) have been a trend driving Artificial Intelligence (AI) researchers for the past 50 years. A variety of tools and applications (not only in Computer Science) stem from these techniques. Also, MH frequently rely on evolution, a trademark process involved in cell colony growth. Generally, MH…
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…
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…
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…