Study of genetic algorithm for optimization problems

Dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáThis work consists in to explore the Genetic Algorithms to solve non-linear optimization problems. The aim of this work is to study and develop strategies in order to improve the performance of the Genetic Algorithm that can be applied to solve several optimization problems, as time schedule, costs minimization, among others. For this, the behavior of a traditional Genetic Algorithm was observed and the acquired information was used to propose variations of this algorithm. Thereby, a new approach for the selection operator was proposed, considering the abilities of population individuals to generate offspring. In addition, a Genetic Algorithm that uses dynamic operators rates, controlled by the amplitude and the standard deviation of the population, is also proposed. Together with this algorithm, a new stopping criterion is also proposed. This criterion uses population and the problem information to identify the stopping point. The strategies proposed are validated by twelve benchmark optimization functions, defined in the literature for testing optimization algorithms. The dynamic rate algorithm results were compared with a fixed rate Genetic Algorithm and with the defaultMatlab Genetic Algorithm, and in both cases, the proposed algorithm presented excellent results, for all considered functions, which demonstrates the robustness of the algorithm for solving several optimization problems.Este trabalho consiste em explorar o Algoritmo Genético para resolução de problemas de otimização não-linear. O objetivo deste trabalho é estudar e desenvolver estratégias para melhorar o desempenho do Algoritmo Genético que possa ser aplicado para resolução de problemas de otimização variados, como escalonamento de horários, minimização de custos, entre outros. Para isso, foi observado o comportamento usual do Algoritmo Genético e as informações adquiridas foram usadas para propor variações deste algoritmo. Assim, uma nova abordagem para o operador de seleção é proposta, considerando a habilidade dos indivíduos da população em gerar descendentes. Além disso, também é proposto um Algoritmo Genético que utiliza taxas dinâmicas nos operadores, controladas pela amplitude e desvio padrão da população. Juntamente com este algoritmo, um novo critério de paragem também é proposto. Este critério utiliza informações da população e do problema de otimização para determinar o local de paragem. As estratégias propostas são validadas por doze funções de teste, definidas na literatura para teste de algoritmos de otimização. Os resultados do algoritmo de taxas dinâmicas foram comparados com um Algoritmo Genético de taxas fixas e com o Algoritmo Genético padrão disponível no Matlab, e em ambos os casos o algoritmo proposto apresentou excelentes resultados, para todas as funções consideradas, o que demonstra a robustez do método para resolução de problemas de otimização variados

A comprehensive data analysis of e-bike mobility and greenhouse gas emissions in a higher education community: IPBike study of case

Sustainable mobility is a goal for several countries. This kind of mobility depends not only on personal motivation but also on government actions. Encouraging people, particularly children and younger, to raise awareness of the importance and benefits of using sustainable transport, like bicycles, is crucial to developing a sustainable society. This work presents the IPBike project, a Portuguese project applied at the Polytechnic Institute of Bragança. This project aims to promote the use of sustainable transport in the academic community through a rental bike program. This paper presents the results of the over three years of the IPBike project, as well as the user’s impressions and suggestions to improve the project. Moreover, a greenhouse gas emission reduction is estimated, comparing the displacement using bikes or only by cars. In general, the results achieved are promising to promote sustainable cities and plan the future since the bikes of the project are constantly rented and the user’s positive feedback, which makes the IPBikes a popular community asset. Moreover, the results demonstrate a bike-sharing program’s capacity to impact people’s lives, improve their health, reduce travel expenses, and impact society and the environment. Besides, according to the estimates, the IPBikes provided a reduction of 7% per year in the greenhouse gases emitted by the users, which means a significant reduction all over the project implantation.This work has been supported by FCT-Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/05757/2020,UIDP/05757/2020 and POSEUR-01- 1407-FC-000010 and SusTEC (LA/P/0007/2021). Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021.info:eu-repo/semantics/publishedVersio

Hybrid approaches to optimization and machine learning methods: a systematic literature review

Notably, real problems are increasingly complex and require sophisticated models and algorithms capable of quickly dealing with large data sets and finding optimal solutions. However, there is no perfect method or algorithm; all of them have some limitations that can be mitigated or eliminated by combining the skills of different methodologies. In this way, it is expected to develop hybrid algorithms that can take advantage of the potential and particularities of each method (optimization and machine learning) to integrate methodologies and make them more efficient. This paper presents an extensive systematic and bibliometric literature review on hybrid methods involving optimization and machine learning techniques for clustering and classification. It aims to identify the potential of methods and algorithms to overcome the difficulties of one or both methodologies when combined. After the description of optimization and machine learning methods, a numerical overview of the works published since 1970 is presented. Moreover, an in-depth state-of-art review over the last three years is presented. Furthermore, a SWOT analysis of the ten most cited algorithms of the collected database is performed, investigating the strengths and weaknesses of the pure algorithms and detaching the opportunities and threats that have been explored with hybrid methods. Thus, with this investigation, it was possible to highlight the most notable works and discoveries involving hybrid methods in terms of clustering and classification and also point out the difficulties of the pure methods and algorithms that can be strengthened through the inspirations of other methodologies; they are hybrid methods.Open access funding provided by FCT|FCCN (b-on). This work has been supported by FCT— Fundação para a Ciência e Tecnologia within the R &D Units Project Scope: UIDB/00319/2020. Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021 The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/ MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021).info:eu-repo/semantics/publishedVersio

Hybrid approaches to optimization and machine learning methods

This paper conducts a comprehensive literature review concerning hybrid techniques that combine optimization and machine learning approaches for clustering and classification problems. The aim is to identify the potential benefits of integrating these methods to address challenges in both fields. The paper outlines optimization and machine learning methods and provides a quantitative overview of publications since 1970. Additionally, it offers a detailed review of recent advancements in the last three years. The study includes a SWOT analysis of the top ten most cited algorithms from the collected database, examining their strengths and weaknesses as well as uncovering opportunities and threats explored through hybrid approaches. Through this research, the study highlights significant findings in the realm of hybrid methods for clustering and classification, showcasing how such integrations can enhance the shortcomings of individual techniques.info:eu-repo/semantics/publishedVersio

Optimum sensors allocation for a forest fires monitoring system

Every year forest fires destroy millions of hectares of land worldwide. Detecting forest fire ignition in the early stages is fundamental to avoid forest fires catastrophes. In this approach, Wireless Sensor Network is explored to develop a monitoring system to send alert to authorities when a fire ignition is detected. The study of sensors allocation is essential in this type of monitoring system since its performance is directly related to the position of the sensors, which also defines the coverage region. In this paper, a mathematical model is proposed to solve the sensor allocation problem. This model considers the sensor coverage limitation, the distance, and the forest density interference in the sensor reach. A Genetic Algorithm is implemented to solve the optimisation model and minimise the forest fire hazard. The results obtained are promising since the algorithm could allocate the sensor avoiding overlaps and minimising the total fire hazard value for both regions considered.This research received no external funding.info:eu-repo/semantics/publishedVersio

Hybrid approaches to optimization and machine learning methods

This paper conducts a comprehensive literature review concerning hybrid techniques that combine optimization and machine learning approaches for clustering and classification problems. The aim is to identify the potential benefits of integrating these methods to address challenges in both fields. The paper outlines optimization and machine learning methods and provides a quantitative overview of publications since 1970. Additionally, it offers a detailed review of recent advancements in the last three years. The study includes a SWOT analysis of the top ten most cited algorithms from the collected database, examining their strengths and weaknesses as well as uncovering opportunities and threats explored through hybrid approaches. Through this research, the study highlights significant findings in the realm of hybrid methods for clustering and classification, showcasing how such integrations can enhance the shortcomings of individual techniques.The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020), Algoritmi (UIDB/00319/2020) and SusTEC (LA/P/0007/2021). Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021

A collaborative multi-objective approach for clustering task based on distance measures and clustering validity indices

First Online: 28 December 2023Clustering algorithm has the task of classifying a set of elements so that the elements within the same group are as similar as possible and, in the same way, that the elements of different groups (clusters) are as different as possible. This paper presents the Multi-objective Clustering Algorithm (MCA) combined with the NSGA-II, based on two intra- and three inter-clustering measures, combined 2-to-2, to define the optimal number of clusters and classify the elements among these clusters. As the NSGA-II is a multi-objective algorithm, the results are presented as a Pareto front in terms of the two measures considered in the objective functions. Moreover, a procedure named Cluster Collaborative Indices Procedure (CCIP) is proposed, which aims to analyze and compare the Pareto front solutions generated by different criteria (Elbow, Davies-Bouldin, Calinski-Harabasz, CS, and Dumn indices) in a collaborative way. The most appropriate solution is suggested for the decision-maker to support their final choice, considering all solutions provided by the measured combination. The methodology was tested in a benchmark dataset and also in a real dataset, and in both cases, the results were satisfactory to define the optimal number of clusters and to classify the elements of the dataset.This work has been supported by FCT Fundação para a Ciência e Tecnologia within the R &D Units Project Scope UIDB/00319/2020, UIDB/05757/2020, UIDP/05757/2020 and Erasmus Plus KA2 within the project 2021-1-PT01-KA220-HED-000023288. Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021

Sensor allocation in a forest fire monitoring system: a bi-objective approach

Forests worldwide have been suffering from fires damages, provoking incalculable losses in fauna and flora, economic losses, people and animals’ deaths, among other problems. To avoid forest fires catastrophes, it is fundamental to develop innovative operations, such as a forest fire monitoring system. This work concentrates efforts on defining the optimum sensor allocation in a forest fires monitoring system based on a wireless sensor network. Thus, a bi-objective mathematical model is developed to solve the problem, in which the first objective consists of minimising the forest fire hazard of a given forest region, and the second objective refers to the sensors spreading into this region. The developed mathematical model was solved by genetic algorithm and the results demonstrated that the methodology was capable of presenting suitable solutions for the problem.This work has been supported by Fundação La Caixa and FCT—Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/05757/2020 and by SAFe Project through PROMOVE—Fundação La Caixa.info:eu-repo/semantics/publishedVersio

Dataset of mathematics learning and assessment of higher education students using the MathE platform

Higher education institutions are promoting the adoption of innovative methodologies and instructional approaches to engage and promote personalized learning paths to their students. Several strategies based on gamification, artificial intelligence, and data mining are adopted to create an interactive educational setting centred around students. Within this personalized learning environment, there is a notable boost in student engagement and enhanced educational outcomes. The MathE platform, an online educational system introduced in 2019, is specifically crafted to support students tackling difficulties in comprehending higher-education-level mathematics or those aspiring to deepen their understanding of diverse mathematical topics - all at their own pace. The MathE platform provides multiple-choice questions, categorized under topics and subtopics, aligning with the content taught in higher education courses. Accessible to students worldwide, the platform enables them to train their mathematical skills through these resources. When the students log in to the training area of the platform, they choose a topic to study and specify whether they prefer basic or advanced questions. The platform then selects a set of seven multiple choice questions from the available ones under the chosen topic and generates a test for the student. After completing and submitting the test, the answers are recorded and stored on the platform. This paper describes the data stored in the MathE platform, focusing on the 9546 answers to 833 ques- tions, provided by 372 students from 8 countries who use the platform to practice their skills using the questions (and other resources) available on the platform. The information in this paper will help research about active learning tools to support the improvement of future education, especially at higher educational level. Furthermore, these data are valu- able for understanding student learning patterns, assessing platform efficacy, gaining a global perspective on mathemat- ics education, and contributing to the advancement of active learning tools for higher education. (c) 2024 Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)This work has been supported by FCT Fundacao para a Ciencia e Tecnologia within the R\& D Units Project Scope UIDB/05757/2020 , UIDP/05757/2020 , SusTEC LA/P/0007/2021 and Erasmus Plus KA2 within the project 2021-1-PT01-KA220-HED-000023288 . Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021 .info:eu-repo/semantics/publishedVersio

Data analysis techniques applied to the mathE database

MathE is an international online platform that aims to provide a resource for in-class support as well as an alternative instrument to teach and study mathematics. This work focuses on the investigations of the students’ behavior when answering the training questions available in the platform. In order to draw conclusions about the value of the platform, the ways in which the students use it and what are the most wanted mathematical topics, thus deepening the knowledge about the difficulties faced by the users and finding how to make the platform more efficient, the data collected since the it was launched (3 years ago) is analyzed through the use of data mining and machine learning techniques. In a first moment, a general analysis was performed in order to identify the students’ behavior as well as the topics that require reorganization; it was followed by a second iteration, according to the students’ country of origin, in order to identify the existence of differences in the behavior of students from distinct countries. The results point out that the advanced level of the platform’s questions is not adequate and that the questions should be reorganized in order to ensure a more consistent support for the students’ learning process. Besides, with this analysis it was possible to identify the topics that require more attention through the addition of more questions. Furthermore, it was not possible to identify significant disparities in the students behavior in what concerns the students’ country of origin.This work has been supported by FCT Fundação para a Ciência e Tecnologia within the R&D Units Project Scope UIDB/00319/2020, UIDB/05757/2020 and Erasmus Plus KA2 within the project 2021-1-PT01-KA220-HED-000023288. Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021.info:eu-repo/semantics/publishedVersio