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

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 17147

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 sensors. Papers focus on Automation may choose our joint Special Issue in Automation (ISSN 2673-4052).

Prof. Dr. Viorel Minzu
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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
  • Sensor applications using MAs
  • Optimizing sensor deployment for multi-sensor systems
  • Structural health monitoring
  • Model Predictive Control using MAs
  • Human activity recognition
  • 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

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Published Papers (6 papers)

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Research

14 pages, 757 KiB  
Article
Industrial Soft Sensor Optimized by Improved PSO: A Deep Representation-Learning Approach
by Alcemy Gabriel Vitor Severino, Jean Mário Moreira de Lima and Fábio Meneghetti Ugulino de Araújo
Sensors 2022, 22(18), 6887; https://doi.org/10.3390/s22186887 - 13 Sep 2022
Cited by 6 | Viewed by 2040
Abstract
Soft sensors based on deep learning approaches are growing in popularity due to their ability to extract high-level features from training, improving soft sensors’ performance. In the training process of such a deep model, the set of hyperparameters is critical to archive generalization [...] Read more.
Soft sensors based on deep learning approaches are growing in popularity due to their ability to extract high-level features from training, improving soft sensors’ performance. In the training process of such a deep model, the set of hyperparameters is critical to archive generalization and reliability. However, choosing the training hyperparameters is a complex task. Usually, a random approach defines the set of hyperparameters, which may not be adequate regarding the high number of sets and the soft sensing purposes. This work proposes the RB-PSOSAE, a Representation-Based Particle Swarm Optimization with a modified evaluation function to optimize the hyperparameter set of a Stacked AutoEncoder-based soft sensor. The evaluation function considers the mean square error (MSE) of validation and the representation of the features extracted through mutual information (MI) analysis in the pre-training step. By doing this, the RB-PSOSAE computes hyperparameters capable of supporting the training process to generate models with improved generalization and relevant hidden features. As a result, the proposed method can generate more than 16.4% improvement in RMSE compared to another standard PSO-based method and, in some cases, more than 50% improvement compared to traditional methods applied to the same real-world nonlinear industrial process. Thus, the results demonstrate better prediction performance than traditional and state-of-the-art methods. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms–an Effective Way to Optimize the Behaviour of the Dynamical System)
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26 pages, 562 KiB  
Article
A New Frequency Analysis Operator for Population Improvement in Genetic Algorithms to Solve the Job Shop Scheduling Problem
by Monique Simplicio Viana, Rodrigo Colnago Contreras and Orides Morandin Junior
Sensors 2022, 22(12), 4561; https://doi.org/10.3390/s22124561 - 17 Jun 2022
Cited by 12 | Viewed by 1996
Abstract
Job Shop Scheduling is currently one of the most addressed planning and scheduling optimization problems in the field. Due to its complexity, as it belongs to the NP-Hard class of problems, meta-heuristics are one of the most commonly used approaches in its resolution, [...] Read more.
Job Shop Scheduling is currently one of the most addressed planning and scheduling optimization problems in the field. Due to its complexity, as it belongs to the NP-Hard class of problems, meta-heuristics are one of the most commonly used approaches in its resolution, with Genetic Algorithms being one of the most effective methods in this category. However, it is well known that this meta-heuristic is affected by phenomena that worsen the quality of its population, such as premature convergence and population concentration in regions of local optima. To circumvent these difficulties, we propose, in this work, the use of a guidance operator responsible for modifying ill-adapted individuals using genetic material from well-adapted individuals. We also propose, in this paper, a new method of determining the genetic quality of individuals using genetic frequency analysis. Our method is evaluated over a wide range of modern GAs and considers two case studies defined by well-established JSSP benchmarks in the literature. The results show that the use of the proposed operator assists in managing individuals with poor fitness values, which improves the population quality of the algorithms and, consequently, leads to obtaining better results in the solution of JSSP instances. Finally, the use of the proposed operator in the most elaborate GA-like method in the literature was able to reduce its mean relative error from 1.395% to 0.755%, representing an improvement of 45.88%. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms–an Effective Way to Optimize the Behaviour of the Dynamical System)
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20 pages, 506 KiB  
Article
A Population-Based Iterated Greedy Algorithm for Maximizing Sensor Network Lifetime
by Salim Bouamama, Christian Blum and Pedro Pinacho-Davidson 
Sensors 2022, 22(5), 1804; https://doi.org/10.3390/s22051804 - 24 Feb 2022
Cited by 5 | Viewed by 2129
Abstract
Finding dominating sets in graphs is very important in the context of numerous real-world applications, especially in the area of wireless sensor networks. This is because network lifetime in wireless sensor networks can be prolonged by assigning sensors to disjoint dominating node sets. [...] Read more.
Finding dominating sets in graphs is very important in the context of numerous real-world applications, especially in the area of wireless sensor networks. This is because network lifetime in wireless sensor networks can be prolonged by assigning sensors to disjoint dominating node sets. The nodes of these sets are then used by a sleep–wake cycling mechanism in a sequential way; that is, at any moment in time, only the nodes from exactly one of these sets are switched on while the others are switched off. This paper presents a population-based iterated greedy algorithm for solving a weighted version of the maximum disjoint dominating sets problem for energy conservation purposes in wireless sensor networks. Our approach is compared to the ILP solver, CPLEX, which is an existing local search technique, and to our earlier greedy algorithm. This is performed through its application to 640 random graphs from the literature and to 300 newly generated random geometric graphs. The results show that our algorithm significantly outperforms the competitors. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms–an Effective Way to Optimize the Behaviour of the Dynamical System)
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26 pages, 3368 KiB  
Article
An Arithmetic-Trigonometric Optimization Algorithm with Application for Control of Real-Time Pressure Process Plant
by P. Arun Mozhi Devan, Fawnizu Azmadi Hussin, Rosdiazli B. Ibrahim, Kishore Bingi, M. Nagarajapandian and Maher Assaad
Sensors 2022, 22(2), 617; https://doi.org/10.3390/s22020617 - 13 Jan 2022
Cited by 31 | Viewed by 4017
Abstract
This paper proposes a novel hybrid arithmetic–trigonometric optimization algorithm (ATOA) using different trigonometric functions for complex and continuously evolving real-time problems. The proposed algorithm adopts different trigonometric functions, namely sin, cos, and tan, with the conventional sine cosine algorithm (SCA) and arithmetic optimization [...] Read more.
This paper proposes a novel hybrid arithmetic–trigonometric optimization algorithm (ATOA) using different trigonometric functions for complex and continuously evolving real-time problems. The proposed algorithm adopts different trigonometric functions, namely sin, cos, and tan, with the conventional sine cosine algorithm (SCA) and arithmetic optimization algorithm (AOA) to improve the convergence rate and optimal search area in the exploration and exploitation phases. The proposed algorithm is simulated with 33 distinct optimization test problems consisting of multiple dimensions to showcase the effectiveness of ATOA. Furthermore, the different variants of the ATOA optimization technique are used to obtain the controller parameters for the real-time pressure process plant to investigate its performance. The obtained results have shown a remarkable performance improvement compared with the existing algorithms. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms–an Effective Way to Optimize the Behaviour of the Dynamical System)
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28 pages, 2156 KiB  
Article
Control of Microalgae Growth in Artificially Lighted Photobioreactors Using Metaheuristic-Based Predictions
by Viorel Minzu, George Ifrim and Iulian Arama
Sensors 2021, 21(23), 8065; https://doi.org/10.3390/s21238065 - 2 Dec 2021
Cited by 8 | Viewed by 2498
Abstract
A metaheuristic algorithm can be a realistic solution when optimal control problems require a significant computational effort. The problem stated in this work concerns the optimal control of microalgae growth in an artificially lighted photobioreactor working in batch mode. The process and the [...] Read more.
A metaheuristic algorithm can be a realistic solution when optimal control problems require a significant computational effort. The problem stated in this work concerns the optimal control of microalgae growth in an artificially lighted photobioreactor working in batch mode. The process and the dynamic model are very well known and have been validated in previous papers. The control solution is a closed-loop structure whose controller generates predicted control sequences. An efficient way to make optimal predictions is to use a metaheuristic algorithm, the particle swarm optimization algorithm. Even if this metaheuristic is efficient in treating predictions with a very large prediction horizon, the main objective of this paper is to find a tool to reduce the controller’s computational complexity. We propose a soft sensor that gives information used to reduce the interval where the control input’s values are placed in each sampling period. The sensor is based on measurement of the biomass concentration and numerical integration of the process model. The returned information concerns the specific growth rate of microalgae and the biomass yield on light energy. Algorithms, which can be used in real-time implementation, are proposed for all modules involved in the simulation series. Details concerning the implementation of the closed loop, controller, and soft sensor are presented. The simulation results prove that the soft sensor leads to a significant decrease in computational complexity. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms–an Effective Way to Optimize the Behaviour of the Dynamical System)
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21 pages, 13402 KiB  
Article
A Study of Distributed Earth Observation Satellites Mission Scheduling Method Based on Game-Negotiation Mechanism
by Lihao Liu, Zhenghong Dong, Haoxiang Su and Dingzhan Yu
Sensors 2021, 21(19), 6660; https://doi.org/10.3390/s21196660 - 7 Oct 2021
Cited by 6 | Viewed by 2582
Abstract
While monolithic giant earth observation satellites still have obvious advantages in regularity and accuracy, distributed satellite systems are providing increased flexibility, enhanced robustness, and improved responsiveness to structural and environmental changes. Due to increased system size and more complex applications, traditional centralized methods [...] Read more.
While monolithic giant earth observation satellites still have obvious advantages in regularity and accuracy, distributed satellite systems are providing increased flexibility, enhanced robustness, and improved responsiveness to structural and environmental changes. Due to increased system size and more complex applications, traditional centralized methods have difficulty in integrated management and rapid response needs of distributed systems. Aiming to efficient missions scheduling in distributed earth observation satellite systems, this paper addresses the problem through a networked game model based on a game-negotiation mechanism. In this model, each satellite is viewed as a “rational” player who continuously updates its own “action” through cooperation with neighbors until a Nash Equilibria is reached. To handle static and dynamic scheduling problems while cooperating with a distributed mission scheduling algorithm, we present an adaptive particle swarm optimization algorithm and adaptive tabu-search algorithm, respectively. Experimental results show that the proposed method can flexibly handle situations of different scales in static scheduling, and the performance of the algorithm will not decrease significantly as the problem scale increases; dynamic scheduling can be well accomplished with high observation payoff while maintaining the stability of the initial plan, which demonstrates the advantages of the proposed methods. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms–an Effective Way to Optimize the Behaviour of the Dynamical System)
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