Search for Topics:
Title/Keyword
Journal
Submission Status
Category
 
 
Topics
Distributed Optimization for Control, 2nd Edition
Submit your Manuscript Submit your Abstract Propose a Topic

Topic Menu

► Topic Menu

Topic Editors

School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth 6845, Australia
Dr. Lingyun Wang
College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
share announcement

Distributed Optimization for Control, 2nd Edition

Abstract submission deadline
20 March 2026
Manuscript submission deadline
20 May 2026
Viewed by
2336

Topic Information

Dear Colleagues,

This Topic is a continuation of the previous successful Topic “Distributed Optimization for Control”.

Distributed control and optimization have become a major concern in recent years due to an increase in industrial applications, such as multivehicle mobility, smart grid operations, and intelligent transportation management. Each agent of a networked system often only has access to its own private local features and a local perspective of the network topology. Each agent must adopt an optimal strategy in a local sense to attain the overall maximum performance. The interaction of the agents can generate an ability to find the optimal solution that is beyond each agent’s competence. Distributed optimization for control offers valuable mathematical tools for determining the best control strategies and choices for networked agents.

The current topical collection welcomes high-quality contributions in distributed control and optimization over networks, decentralized algorithms, and their practical applications.

Topics of interest:

  • Distributed control over networks;
  • Distributed methods for optimization in networks;
  • Distributed optimization algorithms for control;
  • Game theory to distributed control;
  • Robust distributed optimization;
  • Stochastic distributed optimization;
  • Computational algorithms for distributed optimization and control;
  • New distributed optimization and control techniques in smart grids, transportation, social networks, etc.

Dr. Honglei Xu
Dr. Lingyun Wang
Topic Editors

Keywords

  • distributed optimization
  • distributed control
  • control over networks
  • agent networks
  • decentralized algorithms

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci 		2.5 5.3 2011 18.4 Days CHF 2400 Submit
Designs
designs 		- 3.9 2017 21.7 Days CHF 1600 Submit
Electronics
electronics 		2.6 5.3 2012 16.4 Days CHF 2400 Submit
Energies
energies 		3.0 6.2 2008 16.8 Days CHF 2600 Submit
Mathematics
mathematics 		2.3 4.0 2013 18.3 Days CHF 2600 Submit

Preprints.org is a multidisciplinary platform offering a preprint service designed to facilitate the early sharing of your research. It supports and empowers your research journey from the very beginning.

MDPI Topics is collaborating with Preprints.org and has established a direct connection between MDPI journals and the platform. Authors are encouraged to take advantage of this opportunity by posting their preprints at Preprints.org prior to publication:

  1. Share your research immediately: disseminate your ideas prior to publication and establish priority for your work.
  2. Safeguard your intellectual contribution: Protect your ideas with a time-stamped preprint that serves as proof of your research timeline.
  3. Boost visibility and impact: Increase the reach and influence of your research by making it accessible to a global audience.
  4. Gain early feedback: Receive valuable input and insights from peers before submitting to a journal.
  5. Ensure broad indexing: Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (3 papers)

Order results
Result details
Journals
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
12 pages, 1144 KiB  
Article
Optimized Green Unrelated Parallel Machine Scheduling Problem Subject to Preventive Maintenance
by Najat Almasarwah
Designs 2025, 9(2), 26; https://doi.org/10.3390/designs9020026 - 25 Feb 2025
Viewed by 387
Abstract
Manufacturing areas typically conduct machine maintenance to prevent early failures and to ensure a safe working environment and efficient production. In this study, the green unrelated parallel machine scheduling problem (GUPMSP) is studied. Besides preventive maintenance, machine availability and non-preemption are considered. A [...] Read more.
Manufacturing areas typically conduct machine maintenance to prevent early failures and to ensure a safe working environment and efficient production. In this study, the green unrelated parallel machine scheduling problem (GUPMSP) is studied. Besides preventive maintenance, machine availability and non-preemption are considered. A globally optimal solution (mathematical model) and local optimal solution (a modified Moore heuristic algorithm) are used to optimize the number of products returned early in the GUPMSP. Three datasets, namely, a most favorable case, an average case, and a least favorable case, are created to test the performance of the two solutions’ approaches. The results demonstrate the ability of the mathematical model to dominate the results of the modified Moore’s algorithm in the tested datasets. However, optimizing the number of products returned early in the UPMSP with preventive maintenance reduces costs as a step to support the concept of sustainability and enhance efficiency. Full article
(This article belongs to the Topic Distributed Optimization for Control, 2nd Edition)
►▼ Show Figures

Figure 1

22 pages, 1474 KiB  
Article
A New Method for the Exact Controllability of Linear Parabolic Equations
by Inmaculada Gayte Delgado and Irene Marín-Gayte
Mathematics 2025, 13(3), 344; https://doi.org/10.3390/math13030344 - 22 Jan 2025
Viewed by 609
Abstract
This work solves the exact controllability to zero in the final time for a linear parabolic problem when the control only acts in a part of the spatial domain. Specifically, it is proved, by compactness arguments, the existence of a partially distributed control. [...] Read more.
This work solves the exact controllability to zero in the final time for a linear parabolic problem when the control only acts in a part of the spatial domain. Specifically, it is proved, by compactness arguments, the existence of a partially distributed control. The lack of regularity in the problem prevents the use of standard techniques in this field, that is, Carleman’s inequalities. Controlling a parabolic equation when the diffusion is discontinuous and only acts in a part of the domain is interesting, for example, as in the spreading of a brain tumor. The proof is based on a new maximum principle in the final time; in a linear parabolic equation, with a right-hand side that changes sign in a certain way, and an initial datum of a constant sign, the solution at the final time has the same sign as the initial datum. As a consequence of the exact control result, we prove a unique continuation theorem when the data are not regular. Full article
(This article belongs to the Topic Distributed Optimization for Control, 2nd Edition)
►▼ Show Figures

Figure 1

27 pages, 3862 KiB  
Article
Agent-Based Intelligent Fuzzy Traffic Signal Control System for Multiple Road Intersection Systems
by Tamrat D. Chala and László T. Kóczy
Mathematics 2025, 13(1), 124; https://doi.org/10.3390/math13010124 - 31 Dec 2024
Viewed by 921
Abstract
Traffic congestion at a single intersection can propagate and thus affect adjacent intersections as well, potentially resulting in prolonged gridlock across an entire urban area. Despite numerous research efforts aimed at developing intelligent traffic signal control systems, urban areas continue to experience traffic [...] Read more.
Traffic congestion at a single intersection can propagate and thus affect adjacent intersections as well, potentially resulting in prolonged gridlock across an entire urban area. Despite numerous research efforts aimed at developing intelligent traffic signal control systems, urban areas continue to experience traffic congestion. This paper presents a novel agent-based fuzzy traffic control system for multiple road intersections. The proposed system is designed to operate in a decentralized manner, with each intersection having its own agent (fuzzy controller) functioning concurrently. The intelligent fuzzy controller of the system can recognize emergency vehicles, assess the queue length and waiting time of vehicles, measure the distance of vehicles from intersections, and consider the cumulated waiting times of short vehicle queues. Two distinct types of agent-based intelligent fuzzy traffic control systems were implemented for comparison: one involving collaboration between an agent and its immediate neighboring agent(s) (where one intersection exchanges traffic data with its immediate neighboring intersection(s)), and the other implementing a non-collaborative agent-based intelligent fuzzy traffic control system (where the individual intersection has no direct communication). Following the experimental simulations, the results were compared with those of existing intelligent fuzzy traffic control systems that lack any module to calculate the distance of the vehicles from the intersection. The results demonstrated that the proposed agent-based system of controllers exhibited superior performance compared with the existing fuzzy controllers in terms of indicators such as average waiting time, fuel consumption, and CO2 emissions. For instance, the proposed system reduced the average waiting time of vehicles at an intersection by 48.65% compared with the existing three-stage intelligent fuzzy traffic control system. In addition, a comparison was conducted between non-collaborating and collaborating agent-based intelligent fuzzy traffic control systems, where collaboration achieved better results than the non-collaborating system. In the simulation experiments, an interesting new feature emerged: despite any direct communication missing at multiple intersections, green waves evolved with time. This emergent feature suggests that fuzzy controllers have the potential to evolve and adapt to traffic complexity issues in urban environments when operating in an autonomous agent-based mode. This study demonstrates that agent-based fuzzy controllers can effectively communicate with one another to share traffic data and improve the overall system performance. Full article
(This article belongs to the Topic Distributed Optimization for Control, 2nd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop