Online Convex Optimization in Dynamic Environments

This paper introduces the optimization of a quantum control application, the so-called molecular alignment problem, subject to a dynamic environment. Given the relative simplicity of optimized pulse-shapes in the low-intensity variant of the problem, versus the high complexity of the optimized pulse-shapes in the high-intensity case, a dynamic-intensity environment is simulated in a noise-free calculation. Specific evolution strategies, natural candidates for optimization in dynamic environments, are applied to this task. The calculations reveal the evolution of the pulse-shapes and their underlying evolving structures, that allow a complete physical interpretation. The combination of an optimization in a dynamic environment with the examination of the intermediate optimized solutions offers a sharper physics view of the problem, and accomplishes a fruitful interdisciplinary study.

Formulating the solution as an optimisation problem has proven to be effective in developing solutions to many real world problems. We generally obtain the best possible solution using these methods. In this work, the traffic scheduling problem has been formulated as a waiting time minimisation problem, and appropriate cost functions have been developed, in pursuit of finding the optimal solution. A first-in, first-out queuing model is used, with the vehicles arriving in a Poisson process, and the service time being exponentially distributed. The key feature of this model is that it adapts to varying service and arrival rates of the lanes. These rates are forecast using a neural network model, and appear in the objective function. It was observed that the use of the neural network greatly improved the robustness of the model. Although the model has been developed for a four lane, two way architecture, it can be generalised to any architecture. Results have been analysed by comparing the proposed method to a proportional time distribution. It is shown that the proposed model performs relatively well, when there is rapid variation in the arrival and service rates.

Online learning models are rapidly emerging. By tracking the relevant learning behaviors of students in the learning process, certain suggestions can be provided for learners’ study guidance. In this paper, the improved K-Means algorithm and behavioral data from students’ online learning are used to cluster their learning status. In addition, the self-attention mechanism is introduced on the basis of TCN, and the network structure for dealing with the language habit classification problem is constructed as a whole by combining it with the Meier filter bank alternative network. Finally, TCNSA is used to learn and classify pronunciation habits, and the two networks are trained together to form an end-to-end recognition method. The classification effectiveness of the method was finally verified on different datasets, and the classification accuracy of Region 5 language habits increased from 42 to 49, and the classification accuracy of the remaining regions also increased compared to that of the TCN. It comprehensively reflects the performance of the TCNSA model in distinguishing language habits. The method proposed in this paper helps to provide targeted online business English learning guidance to students from different regions, with different learning behaviors, and different language habits.

Optimisation in dynamic environments is a very active and important area which tackles problems that change with time (as most real-world problems do). The possibility to use a new centralised cooperative strategy based on trajectory methods (tabu search) for solving Dynamic Optimisation Problems (DOPs) was previously introduced showing good results against state of the art methods like the Particle Swarm Optimisation (PSO) variant with multiple swarms and different types of particles. The analysis of this previous work are further extended here by exploring more possibilities for the cooperation rules used in the strategy. The results show that different classes of cooperation can lead to quite different results, some of them greatly outperforming the previous ones.

A prediction mechanism for Memetic Algorithm is presented in this paper. The Predictive Memetic Algorithm (PMA) uses a nonlinear regression method to estimate the parameters used by the algorithm to obtain good solutions in a dynamic and stochastic environment. The algorithm is applied to nonlinear data sets and performance is compared with genetic and simulated annealing algorithms. When compared with the existing methods, the proposed method generates a relatively small error difference after prediction thereby proving its superior performance. A dynamic stochastic environment is used for experimentation, so as to determine the efficacy of the algorithm on non-stationary problem environments.

Conventional evolutionary algorithms operate in a fixed search space with limiting parameter range, which is often predefined via a priori knowledge or trial and error in order to `guess' a suitable region comprising the global optimal solution. This requirement is hard, if not impossible, to fulfil in many real-world optimization problems since there is often no clue of where the desired solutions are located in these problems. Thus, this paper proposes an inductive---deductive learning approach for single- and multi-objective evolutionary optimization. The method is capable of directing evolution towards more promising search regions even if these regions are outside the initial predefined space. For problems where the global optimum is included in the initial search space, it is capable of shrinking the search space dynamically for better resolution in genetic representation to facilitate the evolutionary search towards more accurate optimal solutions. Validation results based on benchmark optimization problems show that the proposed inductive---deductive learning is capable of handling different fitness landscapes as well as distributing nondominated solutions uniformly along the final trade-offs in multi-objective optimization, even if there exist many local optima in a high-dimensional search space or the global optimum is outside the predefined search region.

The landscape of higher education is continuously evolving, with online learning emerging as a prominent educational modality. This cross-sectional study, conducted at the Holy Spirit University of Kaslik (USEK) in Lebanon from January to October 2023, aimed to assess Lebanese students’ perceptions regarding the effectiveness of online learning as a mode of education and to develop strategies and recommendations for enhancing online learning in Lebanon. Using a convenience-sampling method, we collected 309 responses, focusing on educators’ preparedness, student satisfaction, adaptability in online learning, and preferences for future learning modalities. The results highlighted a pressing demand for innovative course design strategies that promote engagement and interactivity, with 41.4% of respondents expressing this need. Additionally, 34.6% of learners emphasized the need for accessible technical support. Furthermore, 38.8% of participants underscored the necessity of comprehensive training programs for educators and learners in navigating online learning environments effectively. Moreover, the preference for online or hybrid learning models (55%) emphasized the importance of flexibility while preserving the value of in-person interactions. The study also highlighted the potential of educational advances to further enhance online learning environments. These technologies could provide opportunities for personalized learning experiences, tailoring course content, delivery methods, and assessments to meet the diverse needs of students, thus fostering a more adaptable and inclusive learning ecosystem. These insights are crucial for refining online education strategies and addressing the evolving needs of students in Lebanon.

In order to promote high-quality online teaching and learning of Chinese colleges and universities during the COVID-19 pandemic. This study conducted an online questionnaire survey on college students in different universities in China, and analyzed the data collected from 155 valid questionnaires to gain an in-depth understanding of the status and the specific needs of online learning during the epidemic. According to the survey results, an online learning model during the epidemic was constructed based on mixed teaching theory and generative teaching theory, and the model for the proposed upgrade teaching and learning quality of implementation strategies.

Covid 19 cases in Indonesia have been published since early March 2020. These cases have continued to increase significantly, spreading to almost all provinces in Indonesia including Aceh Province. In fact, Aceh province is the province with the highest increase in coronavirus deaths in mid-October. As conditions deteriorate, the government continues to implement policy reforms to suppress the spread of Covid-19, including policies in the education world using online methods. Online learning is an educational innovation that uses elements of information technology as a means of support. This study aims to answer the question, how do online communication and learning at universities in Aceh during the Covid-19 pandemic? The theory used in this research is the theory of technological determinism. This research uses qualitative methods with descriptive analytical presentation. The research showed that Aceh universities used many online learning models such as the Zoom application, Google Classroom, WhatsApp and others. This is related to the interest of the application users. Online learning guidelines also have advantages and disadvantages with students and lecturers studying at universities in Aceh.

Learning activities were carried out online or the Online Learning Model (OLM) in the era of the Covid-19 pandemic. Every student who participates in OLM is trained in critical thinking and regularly improves the solving of a mathematical problem. This research used qualitative research with a descriptive approach. Researchers were interested in identifying critical thinking standards of analysis at the Novice Performance Level with the Online Learning Model (OLM) in Geometry Lectures in solving geometric problems based on students' Critical thinking skills with analysis indicators. Subjects were taken based on the assignment score on the material. Subjects were grouped into 3 abilities, namely low ability (Re), medium ability (Se), and high ability (Ti). The number of subjects was three people who were taken from each group at each level. Data were collected using 2 mutually supporting instruments, namely the test instrument and the interview instrument. The validation used was expert validation and content validation. The results showed that online learning activities or the Online Learning Model (OLM) can be identified that subjects with low abilities fulfill all indicators in the analysis stage, while not meeting all indicators for moderate and high student abilities because the Novice performance level is in accordance with its understanding, namely the lowest level where the subject had not been able to solve the given problem at all.

The Covid-19 pandemic has impacted various sectors, including the education sector. This study aimed to determine Polbangtan YoMa students’ readiness to follow online learning during the Covid-19 pandemic. This research is survey research. The subjects of this study were 158 Polbangtan YoMa students. The instrument used for data collection was a questionnaire. Data were analyzed using descriptive statistics. This study results are: (1) the aspects of facilities and infrastructure get the percentage of 66%, meaning that Polbangtan YoMa students are ready to follow the learning from the aspect of facilities and infrastructure; (2) the technological literacy aspect gets the percentage of 65%; meaning the Polbangtan YoMa students are ready to follow the learning from the aspect of their technological literacy abilities; (3) the quality aspect of online interactions gets the percentage 35%; meaning that Polbangtan YoMa students are not ready to take part in learning from the aspect of online interaction quality capabilities; (4) aspects of the benefits of online learning get the average sroce percentage of 52%, meaning that Polbangtan YoMa students agree that online learning will be helpful; (5) aspects of assistance services get the percentage of 67%, meaning that Polbangtan YoMa students entirely agree that assistance services help in online learning; and (6) aspects of online learning prospects get the average score percentage of 57%, meaning that Polbangtan YoMa students somewhat agree that online learning provides online learning prospects, thus Polbangtan YoMa students are ready to do online learning with an average proportion of 57%. The online learning model used at Polbangtan YoMa is asynchronous learning using e-learning, WhatsApp, and Google Classroom.

Educational hub refers to centres of excellence in higher education and research whose aims are to provide high-quality education for both national and international students to enhance the competitiveness of the country. These educational hubs provide an opportunity for knowledge exchanges and innovation in local regions through education and training. In response to the COVID-19 pandemic, rapid shifts were made towards online learning in education around the world. Although the lockdown is over, remote learning will likely play an increasingly prominent role in education. The adoption of scaled remote learning during the pandemic provided evidence of the importance of online learning. They offer an insight into global society, helping prepare students for an increasingly interconnected world by facilitating links between different regions. Educational hubs can be tied to distance learning and are successful in attracting international students when offering a combination of distance learning methods and innovative programs. This paper examines the phenomenon of educational hubs in higher education for international education through online learning with digital technology. New opportunities for online and distance learning within the definition of educational hubs are analyzed, and three online and blended learning models that reflect the development of educational hubs based on COVID-19 conditions of education are offered. In addition, the successful cases and experiences of distance learning hubs in China and Iran in recent years are described.

The government's appeal regarding the Covid-19 pandemic to carry out online learning through video conferences, digital documents and other online means is not without problems. Lack of knowledge about media use, especially the use of Information and Communication Technology, is the biggest problem. The program organized to solve these obstacles is to carry out online learning mentoring activities. The mentoring program began by holding an online Forum Group Discussion to obtain information on the needs and constraints of teachers in conducting online learning. The action continued with filling out questionnaires about the implementation of online learning to get information on online learning models carried out by teachers. From the questionnaire, ten teachers were identified who later became participants in learning assistance. Scheduled mentoring is carried out online for three days with the Zoom material for Video Conference, the use of the classroom for online classes, and google forms to create simple quizzes. Unscheduled mentoring is carried out from before the training until all teachers can implement the online learning model provided. The results of the mentoring show that these activities are beneficial and have been implemented in the online learning process. The Zoom application is the most widely used platform in online learning. The problem faced by the participants were related to data connections and internet network infrastructure.

Online learning changes the ways of learning. Learning has been shifted from traditional face-to-face teacher-student knowledge transfer to online learning with diversified learning pattern. To understand the learning experiences of online learning, a qualitative study with in-depth interview was conducted. The data was analysed by developing the thematic maps for thematic analysis. The final four themes categorized were pedagogy, learning, objective and technology. With the identified themes, the learning experiences of online learning were explored. An important finding was that the role of technology became more important and the role of teacher became less prominent. With the advanced technology which enabled diversified learning in the online environment, it was inevitable that some roles of teaching have been shifted from the teacher to the system. However, the role of technology cannot be truly reflected in the current online learning models. In this paper, the importance of pedagogical-driven element is again proven. Furthermore, the element of technical-enabled is found as essential in contemporary online learning. As a result, a new online learning model, Pedagogy-driven, Learner-Centred, Objective-Oriented, Technology-Enabled (PLOT) model, was proposed, as a model for contemporary online learning.

In the real world, many optimization problems are dynamic. This requires an optimization algorithm to not only find the global optimal solution under a specific environment but also to track the trajectory of the changing optima over dynamic environments. To address this requirement, this paper investigates a clustering particle swarm optimizer (PSO) for dynamic optimization problems. This algorithm employs a hierarchical clustering method to locate and track multiple peaks. A fast local search method is also introduced to search optimal solutions in a promising subregion found by the clustering method. Experimental study is conducted based on the moving peaks benchmark to test the performance of the clustering PSO in comparison with several state-of-the-art algorithms from the literature. The experimental results show the efficiency of the clustering PSO for locating and tracking multiple optima in dynamic environments in comparison with other particle swarm optimization models based on the multiswarm method.