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1. Protein Tertiary Structure Prediction via SVD and PSO Sampling

   https://doi.org/10.1007/978-3-319-78723-7_18  

   Álvarez Ó., Fernández-Martínez J.L. ( March 2018 , Lecture notes in computer science)

   We discuss the use of the Singular Value Decomposition as a model reduction technique in Protein Tertiary Structure prediction, alongside to the uncertainty analysis associated to the tertiary protein predictions via Particle Swarm Optimization (PSO). The algorithm presented in this paper corresponds to the category of the decoy-based modelling, since it first finds a good protein model located in the low energy region of the protein energy landscape, that is used to establish a three-dimensional space where the free-energy optimization and search is performed via an exploratory version of PSO. The ultimate goal of this algorithm is to get a representative sample of the protein backbone structure and the alternate states in an energy region equivalent or lower than the one corresponding to the protein model that is used to establish the expansion (model reduction), obtaining as result other protein structures that are closer to the native structure and a measure of the uncertainty in the protein tertiary protein reconstruction. The strength of this methodology is that it is simple and fast, and serves to alleviate the ill-posed character of the protein structure prediction problem, which is very highly dimensional, improving the results when it is performed in a good protein model of the low energy region. To prove this fact numerically we present the results of the application of the SVD-PSO algorithm to a set of proteins of the CASP competition whose native’s structures are known. 

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2. Prediction of Protein Tertiary Structure via Regularized Template Classification Techniques

   https://doi.org/10.3390/molecules25112467  

   Álvarez-Machancoses, Óscar ; Fernández-Martínez, Juan Luis ; Kloczkowski, Andrzej ( June 2020 , Molecules)

   We discuss the use of the regularized linear discriminant analysis (LDA) as a model reduction technique combined with particle swarm optimization (PSO) in protein tertiary structure prediction, followed by structure refinement based on singular value decomposition (SVD) and PSO. The algorithm presented in this paper corresponds to the category of template-based modeling. The algorithm performs a preselection of protein templates before constructing a lower dimensional subspace via a regularized LDA. The protein coordinates in the reduced spaced are sampled using a highly explorative optimization algorithm, regressive–regressive PSO (RR-PSO). The obtained structure is then projected onto a reduced space via singular value decomposition and further optimized via RR-PSO to carry out a structure refinement. The final structures are similar to those predicted by best structure prediction tools, such as Rossetta and Zhang servers. The main advantage of our methodology is that alleviates the ill-posed character of protein structure prediction problems related to high dimensional optimization. It is also capable of sampling a wide range of conformational space due to the application of a regularized linear discriminant analysis, which allows us to expand the differences over a reduced basis set. 

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3. Applying Different Wide-Area Response-Based Controls to Different Contingencies in Power Systems

   https://doi.org/10.1109/PECI51586.2021.9435251  

   Iranmanesh, Shahrzad ; Rovnyak, Steven M. ( April 2021 , 2021 IEEE Power and Energy Conference at Illinois (PECI))

   null (Ed.)

   Electrical disturbances in the power system can threaten stability. One-shot control is an effective method for stabilizing some events. In this paper, predetermined amounts of loads are increased or decreased around the network. Determining the amounts of loads, and the location for shedding is crucial. This paper is completed in two different sections. First, finding the effective control combinations, and second, finding an algorithm for applying different control combinations to different contingencies in real time. The particle swarm optimization (PSO) algorithm is used to find the effective control combinations. Next, decision trees (DT) are trained to assess the benefits of applying each of the three most effective control combinations found by PSO method. The DT outputs are combined into an algorithm for selecting the best control in real time. Finally, the algorithm is evaluated using a test set of contingencies. The results reveal a 46% improvement in comparison to previous studies. 

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4. A Simple Dynamic Controller for Emulating Human Balance Control

   McNeal, J Stu ; Hunt, Alexander ( August 2023 , Springer, Cham.)

   This paper presents a biologically inspired control system developed for maintaining balance in a simulated human atop an oscillating platform. This work advances our previous research by adapting a human balance controller to an inverted pendulum and controlled by linear-Hill muscle models. To expedite neuron/synapse parameter value selection, we employ a novel two-stage process that pairs a previously developed analytic method with particle swarm optimization. Using the parameter values found analytically as inputs for particle swarm optimization (PSO), we take advantage of the benefits of each method while avoiding their pitfalls. Our results show that PSO optimization allowed improved balance control from modest (<10%) changes to the synaptic parameters. The improved performance was accompanied by muscle coactivations, however, and further refinement is needed to better align overall behavior of the neural controller with biological systems. 

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5. A Schedule of Duties in the Cloud Space Using a Modified Salp Swarm Algorithm

   Jamali, H ; Shill, PC ; Feil-Seifer, D ; Harris, FC ; Dascalu, SM ( October 2023 , Internet of Things. Advances in Information and Communication Technology)

   Cloud computing is a concept introduced in the information technology era, with the main components being the grid, distributed, and valuable computing. The cloud is being developed continuously and, naturally, comes up with many challenges, one of which is scheduling. A schedule or timeline is a mechanism used to optimize the time for performing a duty or set of duties. A scheduling process is accountable for choosing the best resources for performing a duty. The main goal of a scheduling algorithm is to improve the efficiency and quality of the service while at the same time ensuring the acceptability and effectiveness of the targets. The task scheduling problem is one of the most important NP-hard issues in the cloud domain and, so far, many techniques have been proposed as solutions, including using genetic algorithms (GAs), particle swarm optimization, (PSO), and ant colony optimization (ACO). To address this problem, in this paper one of the collective intelligence algorithms, called the Salp Swarm Algorithm (SSA), has been expanded, improved, and applied. The performance of the proposed algorithm has been compared with that of GAs, PSO, continuous ACO, and the basic SSA. The results show that our algorithm has generally higher performance than the other algorithms. For example, compared to the basic SSA, the proposed method has an average reduction of approximately 21% in makespan. 

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