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Algorithms
Special Issues
2024 and 2025 Selected Papers from Algorithms Editorial Board Members
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2024 and 2025 Selected Papers from Algorithms Editorial Board Members

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1966

Special Issue Editor

Prof. Dr. Frank Werner

E-Mail Website
Guest Editor
Faculty of Mathematics, Otto-von-Guericke-University, P.O. Box 4120, D-39016 Magdeburg, Germany
Interests: scheduling; development of exact and approximate algorithms; stability investigations; discrete optimization; scheduling with interval processing times; complex investigations for scheduling problems; train scheduling; graph theory; logistics; supply chains; packing; simulation; applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

I am pleased to announce the fourth edition of a Special Issue in Algorithms that is quite different from our typical Special Issues and will mainly focus on either selected areas of research or special techniques. Being creative in many ways, with this Special Issue, Algorithms is compiling a collection of papers submitted exclusively by its Editorial Board Members (EBMs) covering different areas of algorithms and their applications. The main idea behind this Special Issue is to turn the tables and allow our readers to be the judges of our board members.

Prof. Dr. Frank Werner
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • optimization
  • metaheuristics
  • evolutionary algorithms
  • neural networks
  • machine learning

Related Special Issue

Published Papers (3 papers)

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Research

15 pages, 724 KiB  
Article
Automated Personalized Loudness Control for Multi-Track Recordings
by Bogdan Moroșanu, Marian Negru and Constantin Paleologu
Algorithms 2024, 17(6), 228; https://doi.org/10.3390/a17060228 - 24 May 2024
Viewed by 239
Abstract
This paper presents a novel approach to automated music mixing, focusing on the optimization of loudness control in multi-track recordings. By taking into consideration the complexity and artistic nature of traditional mixing processes, we introduce a personalized multi-track leveling method using two types [...] Read more.
This paper presents a novel approach to automated music mixing, focusing on the optimization of loudness control in multi-track recordings. By taking into consideration the complexity and artistic nature of traditional mixing processes, we introduce a personalized multi-track leveling method using two types of approaches: a customized genetic algorithm and a neural network-based method. Our method tackles common challenges encountered by audio professionals during prolonged mixing sessions, where consistency can decrease as a result of fatigue. Our algorithm serves as a ‘virtual assistant’ to consistently uphold the initial mixing objectives, hence assuring consistent quality throughout the process. In addition, our system automates the repetitive elements of the mixing process, resulting in a substantial reduction in production time. This enables engineers to dedicate their attention to more innovative and intricate jobs. Our experimental framework involves 20 diverse songs and 10 sound engineers possessing a wide range of expertise, offering a useful perspective on the adaptability and effectiveness of our method in real-world scenarios. The results demonstrate the capacity of the algorithms to mimic decision-making, achieving an optimal balance in the mix that resonates with the emotional and technical aspects of music production. Full article
(This article belongs to the Special Issue 2024 and 2025 Selected Papers from Algorithms Editorial Board Members)
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16 pages, 292 KiB  
Article
The Knapsack Problem with Conflict Pair Constraints on Bipartite Graphs and Extensions
by Abraham P. Punnen and Jasdeep Dhahan
Algorithms 2024, 17(5), 219; https://doi.org/10.3390/a17050219 - 18 May 2024
Viewed by 331
Abstract
In this paper, we study the knapsack problem with conflict pair constraints. After a thorough literature survey on the topic, our study focuses on the special case of bipartite conflict graphs. For complete bipartite (multipartite) conflict graphs, the problem is shown to be [...] Read more.
In this paper, we study the knapsack problem with conflict pair constraints. After a thorough literature survey on the topic, our study focuses on the special case of bipartite conflict graphs. For complete bipartite (multipartite) conflict graphs, the problem is shown to be NP-hard but solvable in pseudo-polynomial time, and it admits an FPTAS. Extensions of these results to more general classes of graphs are also presented. Further, a class of integer programming models for the general knapsack problem with conflict pair constraints is presented, which generalizes and unifies the existing formulations. The strength of the LP relaxations of these formulations is analyzed, and we discuss different ways to tighten them. Experimental comparisons of these models are also presented to assess their relative strengths. This analysis disclosed various strong and weak points of different formulations of the problem and their relationships to different types of problem data. This information can be used in designing special purpose algorithms for KPCC involving a learning component. Full article
(This article belongs to the Special Issue 2024 and 2025 Selected Papers from Algorithms Editorial Board Members)
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19 pages, 589 KiB  
Article
Efficient Algorithm for Proportional Lumpability and Its Application to Selfish Mining in Public Blockchains
by Carla Piazza, Sabina Rossi and Daria Smuseva
Algorithms 2024, 17(4), 159; https://doi.org/10.3390/a17040159 - 15 Apr 2024
Viewed by 1008
Abstract
This paper explores the concept of proportional lumpability as an extension of the original definition of lumpability, addressing the challenges posed by the state space explosion problem in computing performance indices for large stochastic models. Lumpability traditionally relies on state aggregation techniques and [...] Read more.
This paper explores the concept of proportional lumpability as an extension of the original definition of lumpability, addressing the challenges posed by the state space explosion problem in computing performance indices for large stochastic models. Lumpability traditionally relies on state aggregation techniques and is applicable to Markov chains demonstrating structural regularity. Proportional lumpability extends this idea, proposing that the transition rates of a Markov chain can be modified by certain factors, resulting in a lumpable new Markov chain. This concept facilitates the derivation of precise performance indices for the original process. This paper establishes the well-defined nature of the problem of computing the coarsest proportional lumpability that refines a given initial partition, ensuring a unique solution exists. Additionally, a polynomial time algorithm is introduced to solve this problem, offering valuable insights into both the concept of proportional lumpability and the broader realm of partition refinement techniques. The effectiveness of proportional lumpability is demonstrated through a case study that consists of designing a model to investigate selfish mining behaviors on public blockchains. This research contributes to a better understanding of efficient approaches for handling large stochastic models and highlights the practical applicability of proportional lumpability in deriving exact performance indices. Full article
(This article belongs to the Special Issue 2024 and 2025 Selected Papers from Algorithms Editorial Board Members)
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