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13 pages, 718 KiB

Open AccessArticle

Application of Optimization Algorithms in Voter Service Module Allocation

by Edgar Jardón, Marcelo Romero and José-Raymundo Marcial-Romero

Information 2025, 16(6), 506; https://doi.org/10.3390/info16060506 - 18 Jun 2025

Viewed by 299

Abstract

Allocation models are essential tools for optimally distributing client requests across multiple services under defined restrictions and objective functions. This study evaluates several heuristics to address an allocation problem involving young individuals reaching voting age. A five-step methodology was implemented: defining variables, executing [...] Read more.

Allocation models are essential tools for optimally distributing client requests across multiple services under defined restrictions and objective functions. This study evaluates several heuristics to address an allocation problem involving young individuals reaching voting age. A five-step methodology was implemented: defining variables, executing heuristics, compiling results, evaluating outcomes, and selecting the most effective heuristic. Using experimental data from the Mexican National Electoral Institute (INE), the study focuses on 88,107 individuals aged 17–18 in the 16 municipalities of the Toluca Valley, who can access any of the 10 INE service modules. Six heuristics were analyzed in sequence: genetic algorithm, ant colony optimization, local search, tabu search, simulated annealing, and greedy algorithm. The results indicate that genetic algorithm significantly reduces the processing time when used as the initial heuristic. Furthermore, given the current capacity of the 10 INE modules, serving the entire target population would require nine working days. These findings align with principles of spatial justice and highlight the practical efficiency of heuristic-based solutions in administrative resource allocation. The main contribution of this study is the development and evaluation of a hybrid heuristic framework for allocating INE modules, demonstrating that combining multiple heuristics—with a genetic algorithm as the initial phase—significantly improves solution quality and computational efficiency. Full article

(This article belongs to the Special Issue Optimization Algorithms and Their Applications)

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21 pages, 608 KiB

Open AccessArticle

A Machine Learning-Assisted Automation System for Optimizing Session Preparation Time in Digital Audio Workstations

by Bogdan Moroșanu, Marian Negru, Georgian Nicolae, Horia Sebastian Ioniță and Constantin Paleologu

Information 2025, 16(6), 494; https://doi.org/10.3390/info16060494 - 13 Jun 2025

Viewed by 448

Abstract

Modern audio production workflows often require significant manual effort during the initial session preparation phase, including track labeling, format standardization, and gain staging. This paper presents a rule-based and Machine Learning-assisted automation system designed to minimize the time required for these tasks in [...] Read more.

Modern audio production workflows often require significant manual effort during the initial session preparation phase, including track labeling, format standardization, and gain staging. This paper presents a rule-based and Machine Learning-assisted automation system designed to minimize the time required for these tasks in Digital Audio Workstations (DAWs). The system automatically detects and labels audio tracks, identifies and eliminates redundant fake stereo channels, merges double-tracked instruments into stereo pairs, standardizes sample rate and bit rate across all tracks, and applies initial gain staging using target loudness values derived from a Genetic Algorithm (GA)-based system, which optimizes gain levels for individual track types based on engineer preferences and instrument characteristics. By replacing manual setup processes with automated decision-making methods informed by Machine Learning (ML) and rule-based heuristics, the system reduces session preparation time by up to 70% in typical multitrack audio projects. The proposed approach highlights how practical automation, combined with lightweight Neural Network (NN) models, can optimize workflow efficiency in real-world music production environments. Full article

(This article belongs to the Special Issue Optimization Algorithms and Their Applications)

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23 pages, 3846 KiB

Open AccessArticle

Efficient Context-Preserving Encoding and Decoding of Compositional Structures Using Sparse Binary Representations

by Roman Malits and Avi Mendelson

Information 2025, 16(5), 343; https://doi.org/10.3390/info16050343 - 24 Apr 2025

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Abstract

Despite their unprecedented success, artificial neural networks suffer extreme opacity and weakness in learning general knowledge from limited experience. Some argue that the key to overcoming those limitations in artificial neural networks is efficiently combining continuity with compositionality principles. While it is unknown [...] Read more.

Despite their unprecedented success, artificial neural networks suffer extreme opacity and weakness in learning general knowledge from limited experience. Some argue that the key to overcoming those limitations in artificial neural networks is efficiently combining continuity with compositionality principles. While it is unknown how the brain encodes and decodes information in a way that enables both rapid responses and complex processing, there is evidence that the neocortex employs sparse distributed representations for this task. This is an active area of research. This work deals with one of the challenges in this field related to encoding and decoding nested compositional structures, which are essential for representing complex real-world concepts. One of the algorithms in this field is called context-dependent thinning (CDT). A distinguishing feature of CDT relative to other methods is that the CDT-encoded vector remains similar to each component input and combinations of similar inputs. In this work, we propose a novel encoding method termed CPSE, based on CDT ideas. In addition, we propose a novel decoding method termed CPSD, based on triadic memory. The proposed algorithms extend CDT by allowing both encoding and decoding of information, including the composition order. In addition, the proposed algorithms allow to optimize the amount of compute and memory needed to achieve the desired encoding/decoding performance. Full article

(This article belongs to the Special Issue Optimization Algorithms and Their Applications)

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30 pages, 16764 KiB

Open AccessArticle

Design of a Device for Optimizing Burden Distribution in a Blast Furnace Hopper

by Gabriele Degrassi, Lucia Parussini, Marco Boscolo, Elio Padoano, Carlo Poloni, Nicola Petronelli and Vincenzo Dimastromatteo

Information 2025, 16(5), 337; https://doi.org/10.3390/info16050337 - 22 Apr 2025

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Abstract

The coke and ore are stacked alternately in layers inside the blast furnace. The capability of the charging system to distribute them in the desired manner and with optimum strata thickness is crucial for the efficiency and high-performance operation of the blast furnace [...] Read more.

The coke and ore are stacked alternately in layers inside the blast furnace. The capability of the charging system to distribute them in the desired manner and with optimum strata thickness is crucial for the efficiency and high-performance operation of the blast furnace itself. The objective of this work is the optimization of the charging equipment of a specific blast furnace. This blast furnace consists of a hopper, a single bell and a deflector inserted in the hopper under the conveyor belt. The focus is the search for a deflector geometry capable of distributing the material as evenly as possible in the hopper in order to ensure the effective disposal of the material released in the blast furnace. This search was performed by coupling the discrete element method with a multi-strategy and self-adapting optimization algorithm. The numerical results were qualitatively validated with a laboratory-scale model. Low cost and the simplicity of operation and maintenance are the strengths of the proposed charging system. Moreover, the methodological approach can be extended to other applications and contexts, such as chemical, pharmaceutical and food processing industries. This is especially true when complex material release conditions necessitate achieving bulk material distribution requirements in containers, silos, hoppers or similar components. Full article

(This article belongs to the Special Issue Optimization Algorithms and Their Applications)

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13 pages, 850 KiB

Open AccessArticle

Improving Physically Unclonable Functions’ Performance Using Second-Order Compensated Measurement

by Jorge Fernández-Aragón, Guillermo Diez-Señorans, Miguel Garcia-Bosque, Raúl Aparicio-Téllez, Gabriel López-Pinar and Santiago Celma

Information 2025, 16(3), 166; https://doi.org/10.3390/info16030166 - 21 Feb 2025

Viewed by 593

Abstract

In this paper, we study the performance of second-order compensated measurement to generate a multi-bit response in physically unclonable functions (PUFs). The proposed technique is based on a novel second-order compensated measurement generating multiple bits instead of a single bit provided by the [...] Read more.

In this paper, we study the performance of second-order compensated measurement to generate a multi-bit response in physically unclonable functions (PUFs). The proposed technique is based on a novel second-order compensated measurement generating multiple bits instead of a single bit provided by the conventional compensated measurement. A PUF based on this technique has been proposed and implemented in 40 Artix-7 FPGAs, and its uniqueness and reproducibility have been compared to those of another PUF using the compensated measurement technique. In addition, we demonstrate that the best trade-off between identifiability and computation time performance is obtained when using only two bits. At the same time, the good performance of the technique has been demonstrated, improving the identifiability of a ring oscillator PUF (RO-PUF) between 70 and 90% compared to a RO-PUF that uses conventional compensated measurement. In particular, equal error rates (EER) of the order of EER∼

10^{- 16}

can be achieved by combining the sign bit with another bit extracted using the proposed technique; and up to EER∼

10^{- 19}

by using one more extra bit. In addition, the high reliability of the responses generated by this technique against possible temperature and voltage variations has been proved. These results show how this new technique improves the performance of the PUF in terms of identifiability, so it can be effectively used for device identification purposes. Full article

(This article belongs to the Special Issue Optimization Algorithms and Their Applications)

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21 pages, 1072 KiB

Open AccessArticle

Community Detection Using Deep Learning: Combining Variational Graph Autoencoders with Leiden and K-Truss Techniques

by Jyotika Hariom Patil, Petros Potikas, William B. Andreopoulos and Katerina Potika

Information 2024, 15(9), 568; https://doi.org/10.3390/info15090568 - 16 Sep 2024

Viewed by 2324

Abstract

Deep learning struggles with unsupervised tasks like community detection in networks. This work proposes the Enhanced Community Detection with Structural Information VGAE (VGAE-ECF) method, a method that enhances variational graph autoencoders (VGAEs) for community detection in large networks. It incorporates community structure information [...] Read more.

Deep learning struggles with unsupervised tasks like community detection in networks. This work proposes the Enhanced Community Detection with Structural Information VGAE (VGAE-ECF) method, a method that enhances variational graph autoencoders (VGAEs) for community detection in large networks. It incorporates community structure information and edge weights alongside traditional network data. This combined input leads to improved latent representations for community identification via K-means clustering. We perform experiments and show that our method works better than previous approaches of community-aware VGAEs. Full article

(This article belongs to the Special Issue Optimization Algorithms and Their Applications)

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19 pages, 373 KiB

Open AccessArticle

A New Integer Model for Selecting Students at Higher Education Institutions: Preparatory Classes of Engineers as Case Study

by Soufyane Majdoub, Chakir Loqman and Jaouad Boumhidi

Information 2024, 15(9), 529; https://doi.org/10.3390/info15090529 - 2 Sep 2024

Cited by 3 | Viewed by 1396

Abstract

This study addresses the challenge of selecting outstanding students at higher education institutions under multiple constraints. We propose a novel integer programming solution to manage this process, formulating it as a constrained assignment problem with a maximization objective function. This function prioritizes the [...] Read more.

This study addresses the challenge of selecting outstanding students at higher education institutions under multiple constraints. We propose a novel integer programming solution to manage this process, formulating it as a constrained assignment problem with a maximization objective function. This function prioritizes the fair selection of students while respecting criteria such as academic qualifications, required skills, and student preferences. The goal is to develop a decision support system that efficiently selects qualified students at higher education institutions within a reasonable time. The model was tested using real data from Moroccan preparatory classes, achieving important assignment rates across all student categories. Results demonstrate significance in execution time, fulfillment of student choices, and prioritization of outstanding students. This approach offers a flexible, efficient solution for managing academic merit-based selections, optimizing resource utilization, and enhancing fairness in the selection process. Full article

(This article belongs to the Special Issue Optimization Algorithms and Their Applications)

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19 pages, 404 KiB

Open AccessArticle

A New Algorithm Framework for the Influence Maximization Problem Using Graph Clustering

by Agostinho Agra and Jose Maria Samuco

Information 2024, 15(2), 112; https://doi.org/10.3390/info15020112 - 14 Feb 2024

Cited by 2 | Viewed by 2450

Abstract

Given a social network modelled by a graph, the goal of the influence maximization problem is to find k vertices that maximize the number of active vertices through a process of diffusion. For this diffusion, the linear threshold model is considered. A new [...] Read more.

Given a social network modelled by a graph, the goal of the influence maximization problem is to find k vertices that maximize the number of active vertices through a process of diffusion. For this diffusion, the linear threshold model is considered. A new algorithm, called ClusterGreedy, is proposed to solve the influence maximization problem. The ClusterGreedy algorithm creates a partition of the original set of nodes into small subsets (the clusters), applies the SimpleGreedy algorithm to the subgraphs induced by each subset of nodes, and obtains the seed set from a combination of the seed set of each cluster by solving an integer linear program. This algorithm is further improved by exploring the submodularity property of the diffusion function. Experimental results show that the ClusterGreedy algorithm provides, on average, higher influence spread and lower running times than the SimpleGreedy algorithm on Watts–Strogatz random graphs. Full article

(This article belongs to the Special Issue Optimization Algorithms and Their Applications)

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Review

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19 pages, 2302 KiB

Open AccessReview

Solutions to Address the Low-Capacity Utilization Issue in Singapore’s Precast Industry

by Chen Chen and Robert Tiong

Information 2024, 15(8), 458; https://doi.org/10.3390/info15080458 - 1 Aug 2024

Viewed by 1942

Abstract

Singapore has established six Integrated Construction and Prefabrication Hubs with the goal of meeting ambitious productivity targets and building a resilient precast supply chain by 2024. These factories are equipped with high levels of mechanization and automation. However, they are currently operating far [...] Read more.

Singapore has established six Integrated Construction and Prefabrication Hubs with the goal of meeting ambitious productivity targets and building a resilient precast supply chain by 2024. These factories are equipped with high levels of mechanization and automation. However, they are currently operating far below their designed capacity due to a storage bottleneck. In land-scarce Singapore, finding large spaces for precast storage is a challenge. One possible solution is to implement a just-in-time approach. To achieve this, a systematic approach is required to plan, monitor, and control the entire supply chain effectively, utilizing various strategies, methods, and tools. This paper aims to conduct a comprehensive literature review in related areas, believing that knowledge transfer is a faster way to develop solutions to new problems. The main idea of the proposed solution is to implement an integrated supply chain system model with a central decision-maker. It is recommended that the factories take a more active role in decision-making. Establishing this integrated system relies on trust and information sharing, which can be facilitated by cutting-edge digital technologies. The results of this paper will provide valuable insights for future research aimed at completely solving this issue. Full article

(This article belongs to the Special Issue Optimization Algorithms and Their Applications)

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