Mathematics

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13 pages, 608 KiB

Open AccessArticle

Admissible Perturbation of Demicontractive Operators within Ant Algorithms for Medical Images Edge Detection

by Cristina Ticala, Ioana Zelina and Camelia-M. Pintea

Mathematics 2020, 8(6), 1040; https://doi.org/10.3390/math8061040 - 26 Jun 2020

Cited by 9 | Viewed by 2602

Nowadays, demicontractive operators in terms of admissible perturbation are used to solve difficult tasks. The current research uses several demicontractive operators in order to enhance the quality of the edge detection results when using ant-based algorithms. Two new operators are introduced,

χ

-operator [...] Read more.

Nowadays, demicontractive operators in terms of admissible perturbation are used to solve difficult tasks. The current research uses several demicontractive operators in order to enhance the quality of the edge detection results when using ant-based algorithms. Two new operators are introduced,

χ

-operator and

K H

-operator, the latter one is a Krasnoselskij admissible perturbation of a demicontractive operator. In order to test the efficiency of the new operators, a comparison is made with a trigonometric operator. Ant Colony Optimization (ACO) is the solver chosen for the images edge detection problem. Demicontractive operators in terms of admissible perturbation are used during the construction phase of the matrix of ants artificial pheromone, namely the edge information of an image. The conclusions of statistical analysis on the results shows a positive influence of proposed operators for image edge detection of medical images. Full article

(This article belongs to the Special Issue Computational Intelligence)

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29 pages, 3194 KiB

Open AccessArticle

An Advanced Learning-Based Multiple Model Control Supervisor for Pumping Stations in a Smart Water Distribution System

by Alexandru Predescu, Ciprian-Octavian Truică, Elena-Simona Apostol, Mariana Mocanu and Ciprian Lupu

Mathematics 2020, 8(6), 887; https://doi.org/10.3390/math8060887 - 1 Jun 2020

Cited by 19 | Viewed by 3759

Abstract

Water distribution is fundamental to modern society, and there are many associated challenges in the context of large metropolitan areas. A multi-domain approach is required for designing modern solutions for the existing infrastructure, including control and monitoring systems, data science and Machine Learning. [...] Read more.

Water distribution is fundamental to modern society, and there are many associated challenges in the context of large metropolitan areas. A multi-domain approach is required for designing modern solutions for the existing infrastructure, including control and monitoring systems, data science and Machine Learning. Considering the large scale water distribution networks in metropolitan areas, machine and deep learning algorithms can provide improved adaptability for control applications. This paper presents a monitoring and control machine learning-based architecture for a smart water distribution system. Automated test scenarios and learning methods are proposed and designed to predict the network configuration for a modern implementation of a multiple model control supervisor with increased adaptability to changing operating conditions. The high-level processing and components for smart water distribution systems are supported by the smart meters, providing real-time data, push-based and decoupled software architectures and reactive programming. Full article

(This article belongs to the Special Issue Computational Intelligence)

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11 pages, 333 KiB

Open AccessFeature PaperArticle

On the Selective Vehicle Routing Problem

by Cosmin Sabo, Petrică C. Pop and Andrei Horvat-Marc

Mathematics 2020, 8(5), 771; https://doi.org/10.3390/math8050771 - 12 May 2020

Cited by 14 | Viewed by 3414

Abstract

The Generalized Vehicle Routing Problem (GVRP) is an extension of the classical Vehicle Routing Problem (VRP), in which we are looking for an optimal set of delivery or collection routes from a given depot to a number of customers divided into predefined, mutually [...] Read more.

The Generalized Vehicle Routing Problem (GVRP) is an extension of the classical Vehicle Routing Problem (VRP), in which we are looking for an optimal set of delivery or collection routes from a given depot to a number of customers divided into predefined, mutually exclusive, and exhaustive clusters, visiting exactly one customer from each cluster and fulfilling the capacity restrictions. This paper deals with a more generic version of the GVRP, introduced recently and called Selective Vehicle Routing Problem (SVRP). This problem generalizes the GVRP in the sense that the customers are divided into clusters, but they may belong to one or more clusters. The aim of this work is to describe a novel mixed integer programming based mathematical model of the SVRP. To validate the consistency of the novel mathematical model, a comparison between the proposed model and the existing models from literature is performed, on the existing benchmark instances for SVRP and on a set of additional benchmark instances used in the case of GVRP and adapted for SVRP. The proposed model showed better results against the existing models. Full article

(This article belongs to the Special Issue Computational Intelligence)

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20 pages, 994 KiB

Open AccessArticle

On the Resilience of Ant Algorithms. Experiment with Adapted MMAS on TSP

by Elena Nechita, Gloria Cerasela Crişan, Laszlo Barna Iantovics and Yitong Huang

Mathematics 2020, 8(5), 752; https://doi.org/10.3390/math8050752 - 9 May 2020

Cited by 4 | Viewed by 2648

Abstract

This paper focuses on the resilience of a nature-inspired class of algorithms. The issues related to resilience fall under a very wide umbrella. The uncertainties that we face in the world require the need of resilient systems in all domains. Software resilience is [...] Read more.

This paper focuses on the resilience of a nature-inspired class of algorithms. The issues related to resilience fall under a very wide umbrella. The uncertainties that we face in the world require the need of resilient systems in all domains. Software resilience is certainly of critical importance, due to the presence of software applications which are embedded in numerous operational and strategic systems. For Ant Colony Optimization (ACO), one of the most successful heuristic methods inspired by the communication processes in entomology, performance and convergence issues have been intensively studied by the scientific community. Our approach addresses the resilience of MAX–MIN Ant System (MMAS), one of the most efficient ACO algorithms, when studied in relation with Traveling Salesman Problem (TSP). We introduce a set of parameters that allow the management of real-life situations, such as imprecise or missing data and disturbances in the regular computing process. Several metrics are involved, and a statistical analysis is performed. The resilience of the adapted MMAS is analyzed and discussed. A broad outline on future research directions is given in connection with new trends concerning the design of resilient systems. Full article

(This article belongs to the Special Issue Computational Intelligence)

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26 pages, 1908 KiB

Open AccessArticle

Comparison of Three Computational Approaches for Tree Crop Irrigation Decision Support

by Panagiotis Christias, Ioannis N. Daliakopoulos, Thrassyvoulos Manios and Mariana Mocanu

Mathematics 2020, 8(5), 717; https://doi.org/10.3390/math8050717 - 3 May 2020

Cited by 5 | Viewed by 2826

Abstract

This paper explores methodologies for developing intelligent automated decision systems for complex processes that contain uncertainties, thus requiring computational intelligence. Irrigation decision support systems (IDSS) promise to increase water efficiency while sustaining crop yields. Here, we explored methodologies for developing intelligent IDSS that [...] Read more.

This paper explores methodologies for developing intelligent automated decision systems for complex processes that contain uncertainties, thus requiring computational intelligence. Irrigation decision support systems (IDSS) promise to increase water efficiency while sustaining crop yields. Here, we explored methodologies for developing intelligent IDSS that exploit statistical, measured, and simulated data. A simple and a fuzzy multicriteria approach as well as a Decision Tree based system were analyzed. The methodologies were applied in a sample of olive tree farms of Heraklion in the island of Crete, Greece, where water resources are scarce and crop management is generally empirical. The objective is to support decision for optimal financial profit through high yield while conserving water resources through optimal irrigation schemes under various (or uncertain) intrinsic and extrinsic conditions. Crop irrigation requirements are modelled using the FAO-56 equation. The results demonstrate that the decision support based on probabilistic and fuzzy approaches point to strategies with low amounts and careful distributed water irrigation strategies. The decision tree shows that decision can be optimized by examining coexisting factors. We conclude that irrigation-based decisions can be highly assisted by methods such as decision trees given the right choice of attributes while keeping focus on the financial balance between cost and revenue. Full article

(This article belongs to the Special Issue Computational Intelligence)

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20 pages, 2610 KiB

Open AccessFeature PaperArticle

An Efficient Hybrid Genetic Approach for Solving the Two-Stage Supply Chain Network Design Problem with Fixed Costs

by Ovidiu Cosma, Petrică C. Pop and Cosmin Sabo

Mathematics 2020, 8(5), 712; https://doi.org/10.3390/math8050712 - 3 May 2020

Cited by 8 | Viewed by 3137

Abstract

This paper deals with a complex optimization problem, more specifically the two-stage transportation problem with fixed costs. In our investigated transportation problem, we are modeling a distribution network in a two-stage supply chain. The considered two-stage supply chain includes manufacturers, distribution centers, and [...] Read more.

This paper deals with a complex optimization problem, more specifically the two-stage transportation problem with fixed costs. In our investigated transportation problem, we are modeling a distribution network in a two-stage supply chain. The considered two-stage supply chain includes manufacturers, distribution centers, and customers, and its principal feature is that in addition to the variable transportation costs, we have fixed costs for the opening of the distribution centers, as well as associated with the routes. In this paper, we describe a different approach for solving the problem, which is an effective hybrid genetic algorithm. Our proposed hybrid genetic algorithm is constructed to fit the challenges of the investigated supply chain network design problem, and it is achieved by incorporating a linear programming optimization problem within the framework of a genetic algorithm. Our achieved computational results are compared with the existing solution approaches on a set of 150 benchmark instances from the literature and on a set of 50 new randomly generated instances of larger sizes. The outputs proved that we have developed a very competitive approach as compared to the methods that one can find in the literature. Full article

(This article belongs to the Special Issue Computational Intelligence)

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19 pages, 1704 KiB

Open AccessArticle

Innovative Platform for Designing Hybrid Collaborative & Context-Aware Data Mining Scenarios

by Anca Avram, Oliviu Matei, Camelia Pintea and Carmen Anton

Mathematics 2020, 8(5), 684; https://doi.org/10.3390/math8050684 - 1 May 2020

Cited by 6 | Viewed by 2391

Abstract

The process of knowledge discovery involves nowadays a major number of techniques. Context-Aware Data Mining (CADM) and Collaborative Data Mining (CDM) are some of the recent ones. the current research proposes a new hybrid and efficient tool to design prediction models called Scenarios [...] Read more.

The process of knowledge discovery involves nowadays a major number of techniques. Context-Aware Data Mining (CADM) and Collaborative Data Mining (CDM) are some of the recent ones. the current research proposes a new hybrid and efficient tool to design prediction models called Scenarios Platform-Collaborative & Context-Aware Data Mining (SP-CCADM). Both CADM and CDM approaches are included in the new platform in a flexible manner; SP-CCADM allows the setting and testing of multiple configurable scenarios related to data mining at once. The introduced platform was successfully tested and validated on real life scenarios, providing better results than each standalone technique—CADM and CDM. Nevertheless, SP-CCADM was validated with various machine learning algorithms—k-Nearest Neighbour (k-NN), Deep Learning (DL), Gradient Boosted Trees (GBT) and Decision Trees (DT). SP-CCADM makes a step forward when confronting complex data, properly approaching data contexts and collaboration between data. Numerical experiments and statistics illustrate in detail the potential of the proposed platform. Full article

(This article belongs to the Special Issue Computational Intelligence)

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22 pages, 1507 KiB

Open AccessArticle

A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem

by José García, Paola Moraga, Matias Valenzuela and Hernan Pinto

Mathematics 2020, 8(4), 507; https://doi.org/10.3390/math8040507 - 2 Apr 2020

Cited by 22 | Viewed by 3136

Abstract

This article proposes a hybrid algorithm that makes use of the db-scan unsupervised learning technique to obtain binary versions of continuous swarm intelligence algorithms. These binary versions are then applied to large instances of the well-known multidimensional knapsack problem. The contribution of the [...] Read more.

This article proposes a hybrid algorithm that makes use of the db-scan unsupervised learning technique to obtain binary versions of continuous swarm intelligence algorithms. These binary versions are then applied to large instances of the well-known multidimensional knapsack problem. The contribution of the db-scan operator to the binarization process is systematically studied. For this, two random operators are built that serve as a baseline for comparison. Once the contribution is established, the db-scan operator is compared with two other binarization methods that have satisfactorily solved the multidimensional knapsack problem. The first method uses the unsupervised learning technique k-means as a binarization method. The second makes use of transfer functions as a mechanism to generate binary versions. The results show that the hybrid algorithm using db-scan produces more consistent results compared to transfer function (TF) and random operators. Full article

(This article belongs to the Special Issue Computational Intelligence)

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14 pages, 2274 KiB

Open AccessArticle

Using Machine Learning Classifiers to Recognize the Mixture Control Chart Patterns for a Multiple-Input Multiple-Output Process

by Yuehjen E. Shao and Yu-Ting Hu

Mathematics 2020, 8(1), 102; https://doi.org/10.3390/math8010102 - 7 Jan 2020

Cited by 12 | Viewed by 3102

Abstract

A statistical process control (SPC) chart is one of the most important techniques for monitoring a process. Typically, a certain root cause or a disturbance in a process would result in the presence of a systematic control chart pattern (CCP). Consequently, the effective [...] Read more.

A statistical process control (SPC) chart is one of the most important techniques for monitoring a process. Typically, a certain root cause or a disturbance in a process would result in the presence of a systematic control chart pattern (CCP). Consequently, the effective recognition of CCPs has received considerable attention in recent years for their potential use in improving process quality. However, most studies have focused on the recognition of CCPs for SPC applications alone. Specifically, even though numerous studies have addressed the increased use of the SPC and engineering process control (EPC) mechanisms, very little research has discussed the recognition of CCPs for multiple-input multiple-output (MIMO) systems. It is much more difficult to recognize the CCPs of an MIMO system since two or more disturbances are simultaneously involved in the process. The purpose of this study is thus to propose several machine learning (ML) classifiers to overcome the difficulties in recognizing CCPs in MIMO systems. Because of their efficient and fast algorithms and effective classification performance, the considered ML classifiers include an artificial neural network (ANN), support vector machine (SVM), extreme learning machine (ELM), and multivariate adaptive regression splines (MARS). Furthermore, one problem may arise due to the existence of embedded mixture CCPs (MCCPs) in MIMO systems. In contrast to using typical process outputs alone in a classifier, this study employs both process outputs and EPC compensation to ensure the effectiveness of CCP recognition. Experimental results reveal that the proposed classifiers are able to effectively recognize MCCPs for MIMO systems. Full article

(This article belongs to the Special Issue Computational Intelligence)

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