25 papers · ranked by Valyu relevance
Guangwei Liu, Zhiqing Guo, Wei Liu, Bo Cao + 3 more
'Chunguang Wang' 'Aytaç Altan'] This paper proposes a novel hybrid algorithm, named Multi-Strategy Hybrid Harris Hawks Tunicate Swarm Optimization Algorithm (MSHHOTSA). The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy…
Jiahao Chen, Jiaxin Li, Deqian Zheng, Yan Zhang + 4 more
'Jianjun Han' 'Manxing Wang' 'Runmei Zhao'] Title: ABSTRACT Grain stored for long periods is highly susceptible to localized condensation, mold growth, and insect infestations, leading to significant storage losses. These issues are particularly acute in large-capacity bungalow warehouses, where food security concerns…
Shadab Khan, Yash Veer Singh, Prasant Singh Yadav, Vishnu Sharma + 4 more
'Chia-Chen Lin' 'Ki-Hyun Jung' 'Nirvana Meratnia' 'Enrico Meli'] Energy efficiency is important for underwater sensor networks. Designing such networks is challenging due to underwater environmental traits that hinder network lifespan extension. Unlike terrestrial protocols, underwater settings require novel protocols…
Vanisree Chandran, Prabhujit Mohapatra
Over the last few decades, a number of prominent meta-heuristic algorithms have been put forth to address complex optimization problems. However, there is a critical need to enhance these existing meta-heuristics by employing a variety of evolutionary techniques to tackle the emerging challenges in engineering…
Ebubekir Kaya, Ceren Baştemur Kaya, Emre Bendeş, Sema Atasever + 5 more
'Başak Öztürk' 'Bilgin Yazlık' 'Gang Hu' 'Weiguo Zhao' 'Zhenxing Zhang'] One of the most used artificial intelligence techniques for maximum power point tracking is artificial neural networks. In order to achieve successful results in maximum power point tracking, the training process of artificial neural networks is…
Essam H. Houssein, Mohamed Hossam Abdel Gafar, Naglaa Fawzy, Ahmed Y. Sayed
'Ahmed Y. Sayed'] In this study, a novel hybrid metaheuristic algorithm, termed (BES-GO), is proposed for solving benchmark structural design optimization problems, including welded beam design, three-bar truss system optimization, minimizing vertical deflection in an I-beam, optimizing the cost of tubular columns, and…
Shadab Khan, Yash Veer Singh, Pushpendra Singh, Ram Sewak Singh
In today's environment, electronics technology is growing rapidly because of the availability of the numerous and latest devices which can be deployed for monitoring and controlling the various healthcare systems. Due to the limitations of such devices, there is a dire need to optimize the utilization of the devices.…
Ngaiming Kwok
In the article (Kudela, 2022), experimental demonstrations indicated that two Bio-/Natureinspired optimization algorithms (BNIOAs)—Sooty Tern Optimization Algorithm (STOA) and Tunicate Swarm Algorithm (TSA)—exhibit a zero-bias, leading to the conclusion that the claims made in the original papers were overstated. In…
Charly Empereur-mot, Luca Pesce, Davide Bochicchio, Claudio Perego + 1 more
We present Swarm-CG, a versatile software for the automatic parametrization of bonded parameters in coarse-grained (CG) models. By coupling state-of-the-art metaheuristics to Boltzmann inversion, Swarm-CG performs accurate parametrization of bonded terms in CG models composed of up to 200 pseudoatoms within 4h-24h on…
Karthik Reddy Kanjula, Sai Meghana Kolla
This paper presents the development of a distributed application that facilitates the understanding and application of swarm intelligence in solving optimization problems. The platform comprises a search space of customizable random particles, allowing users to tailor the solution to their specific needs. By leveraging…
Charly Empereur-mot, Luca Pesce, Davide Bochicchio, Claudio Perego + 1 more
We present Swarm-CG, a versatile software for the automatic parametrization of bonded parameters incoarse-grained (CG) models. By coupling state-of-the-art metaheuristics to Boltzmann inversion, Swarm-CG performs accurate parametrization of bonded terms in CG models composed of up to 200 pseudoatomswithin 4h-24h on…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Thounaojam Chinglemba, Soujanyo Biswas, Debashish Malakar, Vivek Meena + 2 more
'Vivek Meena' 'Debojyoti Sarkar' 'Anupam Biswas'] > Abstract. With the rapid upliftment of technology, there has emerged a dire need to 'fine-tune' or 'optimize' certain processes, software, models or structures, with utmost accuracy and efficiency. Optimization algorithms are preferred over other methods of…
Utku Köse, Ahmet Arslan
In this paper, the idea of a new artificial intelligence based optimization algorithm, which is inspired from the nature of vortex, has been provided briefly. As also a bio-inspired computation algorithm, the idea is generally focused on a typical vortex flow / behavior in nature and inspires from some dynamics that…
David Vadnais, Michael Middleton, Oluwatosin Oluwadare
The three-dimensional (3D) structure of chromatin has a massive effect on its function. Because of this, it is desirable to have an understanding of the 3D structural organization of chromatin. To gain greater insight into the spatial organization of chromosomes and genomes and the functions they perform, chromosome…
Kwok Pui Choi, Enzio Hai Hong Kam, Tze Leung Lai, Xin Tong + 1 more
'Weng Kee Wong'] Nature-inspired swarm-based algorithms have been widely applied to tackle high-dimensional and complex optimization problems across many disciplines. They are general purpose optimization algorithms, easy to use and implement, flexible and assumption-free. A common drawback of these algorithms is…
David L. Krongauz, Teddy Lazebnik
Collective motion (CM) takes many forms in nature; schools of fish, flocks of birds, and swarms of locusts to name a few. Commonly, during CM the individuals of the group avoid collisions. These CM and collision avoidance (CA) behaviors are based on input from the environment such as smell, air pressure, and vision…
Videh Seksaria
The swarm intelligence of animals is a natural paradigm to apply to optimization problems. Ant colony, bee colony, firefly and bat algorithms are amongst those that have been demonstrated to efficiently to optimize complex constraints. This paper proposes the new Sparkling Squid Algorithm (SSA) for multimodal…
Leo Cazenille, Nicolas Bredeche, Nathanael Aubert-Kato
We are interested in programming a swarm of molecular robots that can perform self-assembly to form specific shapes at a specific location. Programming such robot swarms is challenging for two reasons. First, the goal is to optimize both the parameters and the structure of chemical reaction networks. Second, the search…
Arjun Chandrasekhar, Deborah M. Gordon, Saket Navlakha
We study how the arboreal turtle ant (Cephalotes goniodontus) solves a fundamental computing problem: maintaining a trail network and finding alternative paths to route around broken links in the network. Turtle ants form a routing backbone of foraging trails linking several nests together. This species travels only in…
Bertrand Ngansop, Stefan Götz, M. Eckl
—Particle swarm optimization (PSO) is attracting an ever-growing attention and more than ever it has found many application areas for many challenging optimization problems. It is, however, a known fact that PSO has a severe drawback in the update of its global best (gbest) particle, which has a crucial role of guiding…
David L. Krongauz, Amir Ayali, Gal A. Kaminka
Naturally occurring collective motion is a fascinating phenomenon in which swarming individuals aggregate and coordinate their motion. Many theoretical models of swarming assume idealized, perfect perceptual capabilities, and ignore the underlying perception processes, particularly for agents relying on visual…
Adam Erskine, J. Michael Herrmann
Particle Swarm Optimisation (PSO) makes use of a dynamical system for solving a search task. Instead of adding search biases in order to improve performance in certain problems, we aim to remove algorithm-induced scales by controlling the swarm with a mechanism that is scale-free except possibly for a suppression of…
Authors not listed
Swarm intelligence-based algorithms have proven successful in exploring a potential energy surfaces (PESs) of chemical systems. One of the major limitations to recent implementations of these algorithms is that they have been implemented serially. To overcome this limitation we present our asynchronously parallel…
Authors not listed
With the ever-increasing demand for atomistic structures representative of real-life systems as well as the ad-vent of exascale computers, it has now become necessary and possible to use advanced global optimization (GO) techniques to intelligently sample the potential energy surface (PES). Given the previous studies…