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Metaheuristic Algorithms–an Effective Way to Optimize the Behaviour of Dynamical...
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Metaheuristic Algorithms–an Effective Way to Optimize the Behaviour of Dynamical Systems

A special issue of Automation (ISSN 2673-4052).

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 8371

Special Issue Editor

Prof. Dr. Viorel Minzu

E-Mail Website
Guest Editor
Control and Electrical Engineering Department, “Dunarea de Jos” University of Galati, 800008 Galati, Romania
Interests: control systems; computational intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, Automation that integrates Artificial Intelligence techniques is the new paradigm of processes control. A very large number of papers describe applications and theoretical studies that use artificial intelligence techniques for all the aspects that involve nonlinearities or imprecise, incomplete, uncertain knowledge. That is why the Metaheuristic Algorithms (MAs), such as Genetic Algorithm, Simulated Annealing, Particle Swarm Optimization, Ant Colony Systems, and many others, are good options to solve optimization problems that involve a big computational complexity.

Smart sensors have onboard capabilities to tackle self-diagnostics, self-identification, and self-adaptation tasks that sometimes involve difficult optimization problems. Optimizing sensor deployment for multi-sensor systems, human activity recognition, data fusion models are also known applications where the MAs can be useful. Lately, the new "structure as a sensor" paradigm can entail multicriteria optimization problems with multimodal objective functions. Structural health monitoring uses a dense array of sensors that can lead to such a problem.

Especially the ability to cope with nonlinearities and the lack of smoothness properties renders the MAs' use an effective and at hand approach. Moreover, the implementation can be made straightforwardly, and a near-optimal solution is always available.

For the confirmed scholars working in this domain, this special issue will be a good opportunity to present the different facets of using the MAs: approaches, ideas, new techniques, and applications. In the same time, the lecturers that are newcomers in this framework will have an instructive image concerning the MAs.

This special issue is focused more on automation. Papers focus on sensors may choose our joint Special Issue in Sensors (ISSN 1424-8220, IF 3.275).

Prof. Dr. Viorel Minzu
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. Automation is an international peer-reviewed open access quarterly 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 1000 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

  • Improvement of the generic MAs using new techniques
  • Optimal control problem using MAs
  • Model predictive control using MAs
  • Receding horizon control using MAs
  • MAs in industrial processes
  • Optimization using MAs in health care systems
  • Planning and scheduling optimization
  • Multi-objective modelling and optimization
  • Real-time applications using MAs

Published Papers (2 papers)

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Research

21 pages, 2263 KiB  
Article
Optimal Control Systems Using Evolutionary Algorithm-Control Input Range Estimation
by Viorel Mînzu and Iulian Arama
Automation 2022, 3(1), 95-115; https://doi.org/10.3390/automation3010005 - 28 Jan 2022
Cited by 7 | Viewed by 3133
Abstract
The closed-loop optimal control systems using the receding horizon control (RHC) structure make predictions based on a process model (PM) to calculate the current control output. In many applications, the optimal prediction over the current prediction horizon is calculated using a metaheuristic algorithm, [...] Read more.
The closed-loop optimal control systems using the receding horizon control (RHC) structure make predictions based on a process model (PM) to calculate the current control output. In many applications, the optimal prediction over the current prediction horizon is calculated using a metaheuristic algorithm, such as an evolutionary algorithm (EA). The EAs, as other population-based metaheuristics, have large computational complexity. When integrated into the controller, the EA is carried out at each sampling moment and subjected to a time constraint: the execution time should be smaller than the sampling period. This paper proposes a software module integrated into the controller, called at each sampling moment. The module estimates using the PM integration the future process states, over a short time horizon, for different control input values covering the given technological interval. Only a narrower interval is selected for a ‘good’ evolution of the process, based on the so-called ‘state quality criterion’. The controller will consider only a shrunk control output range for the current sampling period. EA will search for its best prediction inside a smaller domain that does not cause the convergence to be affected. Simulations prove that the computational complexity of the controller will decrease. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms–an Effective Way to Optimize the Behaviour of Dynamical Systems)
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18 pages, 2256 KiB  
Article
Optimal Control Implementation with Terminal Penalty Using Metaheuristic Algorithms
by Viorel Minzu
Automation 2020, 1(1), 48-65; https://doi.org/10.3390/automation1010004 - 15 Oct 2020
Cited by 5 | Viewed by 4056
Abstract
Optimal control problems can be solved by a metaheuristic based algorithm (MbA) that yields an open-loop solution. The receding horizon control mechanism can integrate an MbA to produce a closed-loop solution. When the performance index includes a term depending on the final state [...] Read more.
Optimal control problems can be solved by a metaheuristic based algorithm (MbA) that yields an open-loop solution. The receding horizon control mechanism can integrate an MbA to produce a closed-loop solution. When the performance index includes a term depending on the final state (terminal penalty), the prediction’s time possibly surpasses a sampling period. This paper aims to avoid predicting the terminal penalty. The sequence of the best solution’s state variables becomes a reference trajectory; this one is used by a tracking structure that includes the real process, a process model (PM) and a tracking controller (TC). The reference trajectory must be followed up as much as possible by the real trajectory. The TC makes a one-step-ahead prediction and calculates the control inputs through a minimization procedure. Therefore the terminal penalty’s calculation is avoided. An example of a tracking structure is presented. The TC may also use an MbA for its minimization procedure. The implementation is presented in two versions: using a simulated annealing algorithm and an evolutionary algorithm. The simulations have proved that the proposed approach is realistic. The tracking structure does or does not work well, depending on the PM’s accuracy in reproducing the real process. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms–an Effective Way to Optimize the Behaviour of Dynamical Systems)
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