Special Issue "New Frontiers for Optimal Control Applications"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Systems".

Deadline for manuscript submissions: 1 May 2020.

Special Issue Editors

Prof. Paolo Di Giamberardino
E-Mail Website
Guest Editor
Dept. of Computer, Control and Management Engineering, Sapienza University of Rome, 00185 Roma, Italy
Interests: discrete time and sampled nonlinear dynamics; sensors networks and distributed actuators; modeling and control of viruses spread; optimal control; applications
Prof. Daniela Iacoviello
E-Mail Website
Guest Editor
Dept. of Computer, Control and Management Engineering, Sapienza University of Rome, 00185 Roma, Italy
Interests: optimal control; epidemic modeling; image analysis

Special Issue Information

Dear Colleagues,

Optimal control theory represents a powerful instrument to determine the best strategy to modify the behavior of a system satisfying operative constraints. While the classical theory is well established, the variety of applications requires continuous improvements in the methodologies and in the consequent numerical implementations.

With the recent improved interest in some modern research fields, optimal control techniques are increasing the range of their effective application, spanning from aerospace to automotive, from process control to fault detection, from traffic control to energy distribution, extending their capabilities to communications, economics, social sciences, life sciences, and human health.

The heterogeneous scenario in which optimal control strategies can be fruitfully adopted, involving various modeling techniques as well as different mathematical tools, brings forth the necessity of introducing new potentialities in the classical theory, thus also producing interesting improvements in the theoretical framework, in the numerical solutions, and in the implementation techniques.

The purpose of this Special Issue is to present the latest developments in optimal control, both from a methodological and application point of view, with particular attention to the new research and development areas in which optimal control techniques can provide promising solutions.

Original research as well as reviews papers are welcome.

Topics of interest include all the modern applications in which optimal control approaches are suitably and successfully introduced.

Prof. Paolo Di Giamberardino
Prof. Daniela Iacoviello
Guest Editors

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 papers will be 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. Information 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 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.

Published Papers (2 papers)

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Research

Open AccessArticle
A Parameter-Free Outlier Detection Algorithm Based on Dataset Optimization Method
Information 2020, 11(1), 26; https://doi.org/10.3390/info11010026 - 31 Dec 2019
Abstract
Recently, outlier detection has widespread applications in different areas. The task is to identify outliers in the dataset and extract potential information. The existing outlier detection algorithms mainly do not solve the problems of parameter selection and high computational cost, which leaves enough [...] Read more.
Recently, outlier detection has widespread applications in different areas. The task is to identify outliers in the dataset and extract potential information. The existing outlier detection algorithms mainly do not solve the problems of parameter selection and high computational cost, which leaves enough room for further improvements. To solve the above problems, our paper proposes a parameter-free outlier detection algorithm based on dataset optimization method. Firstly, we propose a dataset optimization method (DOM), which initializes the original dataset in which density is greater than a specific threshold. In this method, we propose the concepts of partition function (P) and threshold function (T). Secondly, we establish a parameter-free outlier detection method. Similarly, we propose the concept of the number of residual neighbors, as the number of residual neighbors and the size of data clusters are used as the basis of outlier detection to obtain a more accurate outlier set. Finally, extensive experiments are carried out on a variety of datasets and experimental results show that our method performs well in terms of the efficiency of outlier detection and time complexity. Full article
(This article belongs to the Special Issue New Frontiers for Optimal Control Applications)
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Open AccessArticle
Grey Wolf Algorithm and Multi-Objective Model for the Manycast RSA Problem in EONs
Information 2019, 10(12), 398; https://doi.org/10.3390/info10120398 - 17 Dec 2019
Abstract
Manycast routing and spectrum assignment (RSA) in elastic optical networks (EONs) has become a hot research field. In this paper, the mathematical model and high efficient algorithm to solve this challenging problem in EONs is investigated. First, a multi-objective optimization model, which minimizes [...] Read more.
Manycast routing and spectrum assignment (RSA) in elastic optical networks (EONs) has become a hot research field. In this paper, the mathematical model and high efficient algorithm to solve this challenging problem in EONs is investigated. First, a multi-objective optimization model, which minimizes network power consumption, the total occupied spectrum, and the maximum index of used frequency spectrum, is established. To handle this multi-objective optimization model, we integrate these three objectives into one by using a weighted sum strategy. To make the population distributed on the search domain uniformly, a uniform design method was developed. Based on this, an improved grey wolf optimization method (IGWO), which was inspired by PSO (Particle Swarm Optimization, PSO) and DE (Differential Evolution, DE), is proposed to solve the maximum model efficiently. To demonstrate high performance of the designed algorithm, a series of experiments are conducted using several different experimental scenes. Experimental results indicate that the proposed algorithm can obtain better results than the compared algorithm. Full article
(This article belongs to the Special Issue New Frontiers for Optimal Control Applications)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Applications of nonlinear programming to the optimization of fractionated protocols in cancer radiotherapy
Authors: A. Bertuzzi, F. Conte, F. Papa, C. Sinisgalli
Abstract: The aim of fractionated radiotherapy treatment is to maximize the overall tumor damage, preserving the surrounding healthy tissue and allowing, at the same time, its repair while hindering the effects of tumor repopulation. Methods for identifying optimal fractionation protocols can highly contribute to improve the outcome of cancer radiation treatment and have been object of many studies in recent years. In this paper, we review the main results of our work in the context of the optimization of cancer radiotherapy protocol, and we evidence the connections with other literature studies in the same framework. We applied classical nonlinear programming tools to derive optimal dose-time-fractionation schemes in external beam radiation therapy for fast and slowly proliferating tumors.
A general optimization problem in terms of size and number of the dose fractions is formulated, using the linear quadratic (LQ) model to describe the radiation response of tumor and surrounding normal tissue. Exponential repopulation and sublethal damage due to incomplete repair are included. Additional constraints are set to limit the daily fraction size. The general problem is then decomposed into two subproblems solved in cascade, analytically and numerically, respectively.
The first subproblem leads to the determination of an analytical expression of the optimal sizes of the fractional doses for a fixed, but arbitrary number of fractions. The optimal solution of this subproblem has been given as a function of the problem parameters, in particular of the tumor type represented by the radiosensitivity ratio a/b.
The second subproblem is solved by means of numerical simulations allowing to find the optimal number of dose fractions, as well as the optimal treatment time. We also proved the existence of a finite upper bound for the optimal number of fractions, providing its expression in terms of the model parameters. We investigated the behaviour of the optimal solution for different tumor classes and for wide variations of the tumor parameters, thereby recognizing the crucial role of the tumor radiosensitivity ratio in determining the optimality of hypo or equi-fractionated treatments. Comparisons with recent results of both theoretical and clinical literature are presented.

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