Self-organizing Learning Algorithm for Multidimensional Non-linear Optimization Applications

The computational efficiency of the quantum particle swarm optimization (QPSO) is significantly higher than that of the traditional particle swarm optimization when solving the parameters of the optimization problem model. However, the exploration and exploitation abilities of the QPSO are weak, based on the historical best position and the global best position. Enlightened by the multi-stage search strategies of marine predators algorithm, we propose a novel hybrid quantum particle swarm optimization algorithm with marine predators (HMPQPSO) in this paper. The evolutionary process of the algorithm is divided two stages: firstly, the Brownian motion of the predator is introduced to the exploration. The randomness and uniformity of which can expand the solution space of particles; secondly, the Levy motion strategy and dynamic parameter strategy are used to update the position, which can accelerate the convergence of the algorithm and enhance the diversity of the algorithm. Meanwhile, both fish aggregation devices(FADs) and opposition-based learning strategy incorporated are used to increase the diversity of the population and prevent the phenomenon of premature particle aggregation. The algorithm is applied to distinct types of CEC2017 benchmark test functions and four multidimensional nonlinear structure design optimization problems, as compared to other recent algorithms. The results demonstrate that the convergence speed and accuracy of HMPQPSO are notably superior to that of other algorithms.

In this paper, we present a concept of a transistor level implementation of the Particle Swarm Optimization (PSO) algorithm that belongs to the group of unsupervised learning algorithms aimed at the design of artificial neural networks (ANNs). The algorithm exhibits an ability to search for an optimal solution in a multidimensional data space, in which many sub-optimal solutions may exist. The ANN that operates in accordance with the PSO algorithm is composed of a set of cooperating particles (agents) that explore an input data space and communicate information on the best found solution to other particles. The PSO algorithm is usually implemented in software. We in our investigations focus on its transistor level realization. Such an approach enables parallel data processing, in which the overall data rate only moderately depends on the number of particles. Most of the operations and components of such implemented PSO algorithm may be reused considering our former CMOS realizations of other self-organizing learning algorithms. This allowed us to assess main parameters of the PSO.

With the Internet of Things paradigm, the data generated by the rapidly increasing number of connected devices lead to new possibilities, such as using machine learning for activity recognition in smart environments. However, it also introduces several challenges. The sensors of different devices might be of different types, making the fusion of data non-trivial. Moreover, the devices are often mobile, resulting in that data from a particular sensor is not always available, i.e. there is a need to handle data from a dynamic set of sensors. From a machine learning perspective, the data from the sensors arrives in a streaming fashion, i.e., online learning, as compared to many learning problems where a static dataset is assumed. Machine learning is in many cases a good approach for classification problems, but the performance is often linked to the quality of the data. Having a good data set to train a model can be an issue in general, due to the often costly process of annotating the data. With dynamic and heterogeneous data, annotation can be even more problematic, because of the ever-changing environment. This means that there might not be any, or a very small amount of, annotated data to train the model on at the start of learning, often referred to as the cold start problem. To be able to handle these issues, adaptive systems are needed. With adaptive we mean that the model is not static over time, but is updated if there for instance is a change in the environment. By including human-in-the-loop during the learning process, which we refer to as interactive machine learning, the input from users can be utilized to build the model. The type of input used is typically annotations of the data, i.e. user input in the form of correctly labelled data points. Generally, it is assumed that the user always provides correct labels in accordance with the chosen interactive learning strategy. In many real-world applications these assumptions are not realistic however, as users might provide incorrect labels or not provide labels at all in line with the chosen strategy. In this thesis we explore which interactive learning strategies are possible in the given scenario and how they affect performance, as well as the effect of machine learning algorithms on performance. We also study how a user who is not always reliable, i.e. that does not always provide a correct label when expected to, can affect performance. We propose a taxonomy of interactive online machine learning strategies and test how the different strategies affect performance through experiments on multiple datasets. The findings show that the overall best performing interactive learning strategy is one where the user provides labels when previous estimations have been incorrect, but that the best performing machine learning algorithm depends on the problem scenario. The experiments also show that a decreased reliability of the user leads to decreased performance, especially when there is a limited amount of labelled data.

Sub-pixel displacement measurement in Digital Image Correlation (DIC) is an optimization problem in search of a point of the deformed image where has the maximum correlation coefficient value to the point of the reference image. Particle Swarm Optimization (PSO) is a novel swarm intelligence algorithm which has great prospect in multidimensional nonlinear optimization because of its advantages of high convergence rate, global search effectiveness, robustness and having few parameters. In this paper, PSO is first applied into DIC to get sub-pixel displacement fields with our aim of proving the feasibility of this method. Two displacements and four deformation gradients for a subset of DIC can all be taken into PSO, while we only take the first two displacements into calculation in consideration of rigid body translation. With set iterative time 20, an accuracy of 0.005 pixels has been achieved between two computer-simulated images by using basic PSO algorithm. In order to limit positions of particles within small areas and thus reduce iterative calculation, an improved integer pixel displacement search method called Grid Method is put forward for accurate integer pixel displacement measurement. Besides, through empirical study, Inverse Function Decreasing Inertia Weight has been proposed and other parameters of PSO have been discussed concerning DIC correlation coefficient function while we attempt to minimize the iterative calculation. We compare the proposed parameters with commonly used PSO parameters and find that PSO with the proposed parameters has much higher convergence rate. So Inverse Function Decreasing Inertia Weight and the set parameters are recommended for use in the PSO method of DIC.

The eigenimage filter generates a composite image in which a desired feature is segmented from interfering features. The signal-to-noise ratio (SNR) of the eigenimage equals its contrast-to-noise ratio (CNR) and is directly proportional to the dissimilarity between the desired and interfering features. Since image gray levels are analytical functions of magnetic resonance imaging (MRI) parameters, it is possible to maximize this dissimilarity by optimizing these parameters. For optimization, the authors consider four MRI pulse sequences: multiple spin-echo (MSE); spin-echo (SE); inversion recovery (IR); and gradient-echo (GE). The authors use the mathematical expressions for MRI signals along with intrinsic tissue parameters to express the objective function (normalized SNR of the eigenimage) in terms of MRI parameters. The objective function along with a set of diagnostic or instrumental constraints define a multidimensional nonlinear constrained optimization problem, which the authors solve by the fixed point approach. The optimization technique is demonstrated through its application to phantom and brain images. The authors show that the optimal pulse sequence parameters for a sequence of four MSE and one IR images almost doubles the smallest normalized SNR of the brain eigenimages, as compared to the conventional brain protocol.

Microgrids can benefit from multi-objective optimization dispatch in several ways, including reduced operation costs and improved service dependability. While using the traditional power network optimization method to solve power network planning, the algorithm mostly falls into the local optimal solution, rendering the global optimal solution intractable. In this research, multi-objective power grid planning is applied to an eight-bus system using a multi-objective optimization technique, such as reducing distribution network building costs and losses, planning distribution network growth and fixed capacity, and distributed generation (DG) addressing planning. Improvements are made to the quantum particle swarm optimization algorithm so that it can be applied to solving discrete problems. This report also employs a binary-coded quantum particle swarm optimization technique to design the distribution network without DG and runs the debugged program in MATLAB to compare the final optimization results. Finally, MATLAB software is used to simulate the example, and the corresponding planning results are obtained. From the model verification results, it can be observed that the quantum particle swarm optimization algorithm applied in this research can complete the task of power grid planning well under the premise of ensuring the calculation speed in the multi-objective design of a smart grid. Cite this article as: F. Long, et al. “Research on multi-objective optimization of smart grid based on particle swarm optimization,” Electrica, 23(2), 222-230, 2023.

Teaching descriptive texts at the junior high school level, particularly in Grade 7, often encounters challenges in creating an interactive and inclusive learning environment, especially for students with special needs. This study aims to explore and analyze effective interactive learning strategies for teaching descriptive texts, with a particular focus on technology integration, adaptations for special needs students, and comprehensive evaluation methods. Using a literature review approach, this article examines recent sources from educational journals, textbooks, and scholarly publications in the fields of Indonesian language instruction, educational technology, and inclusive education. The discussion addresses fundamental concepts of descriptive texts, interactive learning strategies such as the use of visual and audio media, group activities, language games, and the integration of technology into the learning process. The findings indicate that interactive learning approaches which incorporate technology and address students’ diverse needs significantly enhance descriptive writing skills, creativity, and student engagement. In conclusion, implementing interactive learning strategies in teaching descriptive texts not only improves students' academic abilities but also fosters critical thinking and social skills, ultimately creating a more inclusive and meaningful learning experience for all students.

Artificial intelligence methods such as fuzzy logic and particle swarm optimization (PSO) have been applied to maximum power point tracking (MPPT) for solar panels. The P-V curve of a solar panel exhibits multiple peaks under partial shading condition (PSC) when all modules of a solar panel do not receive the same solar irradiation. Although conventional PSO has been shown to perform well under uniform insolation, it is often unable to find the global maximum power point under PSC. Fuzzy adaptive PSO controllers have been proposed for MPPT. However, the controller became computation-intensive in order to adjust the PSO parameters for each particle. In this paper, fuzzy adaptive PSO-based and conventional PSO-based MPPT are compared and evaluated in the aspect of design and performance. A simple fuzzy adaptive PSO controller for MPPT was designed to reach the global optimal point under PSC and uniform irradiation. The controller combines the advantages of both PSO and fuzzy control. The fuzzy controller dynamically adjusts the PSO parameter to improve the convergence speed and global search capability. Since tuning of the PSO parameter is designed to be common for all particles, it reduced the computation complexity. The fuzzy controller’s rule base is designed to obtain a fast transient response and stable steady state response. Design of the fuzzy adaptive PSO-based MPPT is verified with simulation results using a boost converter. The results are evaluated in comparison to the results using a conventional PSO controller under PSC. Simulation shows the fuzzy adaptive PSO-based MPPT is able to improve the global search process and increase the convergency speed. The comparison indicates the settling time using the fuzzy adaptive PSO-based MPPT is 14% faster under PSC on average and 30% faster under uniform irradiation than the settling time using the conventional PSO. Both the fuzzy adaptive and conventional PSO controllers have similar output power tracking accuracy.

Multiple-response grinding process is usually too complex to optimise, requiring a large number of interacting process variables and responses. Experimentation techniques, such as factorial design, fractional factorial design and Response Surface Methodology (RSM) that may be used for this process are too difficult to implement for production lines involving grinding and other necessary operations. For grinding process involving continuous variable, non-linear and multiple-response optimisation problem, the potential of Tabu Search (TS) strategy needs to be explored either in its original form or its variant. In this paper, integrating Artificial Neural Network (ANN) and composite desirability function with a Modified Tabu Search (MTS) strategy, based on Mahalanobis multivariate distance approach to identify tabu move, with scatter search intensification scheme is proposed for the above-mentioned problem. Computational results show that MTS provides better consistency in terms of sample mean and standard deviation of composite desirability measures than that of real-coded GA.

information from the original image as compared with crisp or hard segmentation methods. In practice, noisy images (even high noise images) are very co mmon. It's very essential and critical to deal with such images to process real-image segmentation and pattern recognition. In this paper, differences of credibilistic clustering algorithm (CCA) and fuzzy c-means algorithm (FCM) in dealing with noisy images are studied and the research shows that in most cases, CCA performs better than FCM in high noise image segmentation. Based on that, a new kind of fuzzy clustering methods is presented. It combines spatial credibilistic clustering algorithm (SCCA) with particle swarm optimization (PSO) and takes full advantages of them. The advantages that come from CCA in noise image segmentation also help in SCCA, and the imposition of spatial information enlarges the advantage. The addition of PSO helps to improve global search performance; thereby the novel methods overcome the drawback of single clustering methods - local optimal solutions. Computational experiments show that the proposed methods give the best segmentation results when compared with FCM, CCA, spatial fuzzy c-means algorithm (SFCM), SCCA and the PSO incorporated versions of FCM, CCA, and SFCM.

In the paper, a necessary and sufficient criterion it provided such that any local optimal solution is also global in a not necessarily differentiable constrained optimization problem. This criterion is compared to others earlier appeared in the literature, which are sufficient but not necessary for a local optimal solution to be global. The importance of the established criterion is illustrated by suitable examples of nonconvex optimization problems presented in the paper.

Particle swarm optimization (PSO) is a recently developed optimization method that has attracted interest of researchers in various areas. PSO has been shown to be effective in solving a variety of complex optimization problems. With properly chosen parameters, PSO can converge to local optima. However, conventional PSO does not have global convergence. Empirical evidences indicate that the PSO algorithm may fail to reach global optimal solutions for complex problems. We propose to combine the branch-and-bound framework with the particle swarm optimization algorithm. With this integrated approach, convergence to global optimal solutions is theoretically guaranteed. We have developed and implemented the BB-PSO algorithm that combines the efficiency of PSO and effectiveness of the branch-and-bound method. The BB-PSO method was tested with a set of standard benchmark optimization problems. Experimental results confirm that BB-PSO is effective in finding global optimal solutions to problems that may cause difficulties for the PSO algorithm.

In this study, a set of different search strategies for locating submarine groundwater discharge (SGD) are investigated. This set includes pre-defined path planning (PPP), adapted random walk (RW), particle swarm optimisation (PSO), inertia Levy-flight (ILF), self-organising-migration-algorithm (SOMA), and bumblebee search algorithm (BB). The influences of self-localisation and communication errors and limited travel distance of the autonomous underwater vehicles (AUVs) on the performance of the proposed algorithms are investigated. This study shows that the proposed search strategies could not outperform the classic search heuristic based on full coverage path planning if all AUVs followed the same search strategy. In this study, the influence of self-localisation and communication errors was investigated. The simulations showed that, based on the median error of the search runs, the performance of SOMA was in the same order of magnitude regardless the strength of the localisation error. Furthermore, it was shown that the performance of BB was highly affected by increasing localisation errors. From the simulations, it was revealed that all the algorithms, except for PSO and SOMA, were unaffected by disturbed communications. Here, the best performance was shown by PPP, followed by BB, SOMA, ILF, PSO, and RW. Furthermore, the influence of the limited travel distances of the AUVs on the search performance was evaluated. It was shown that all the algorithms, except for PSO, were affected by the shorter maximum travel distances of the AUVs. The performance of PPP increased with increasing maximum travel distances. However, for maximum travel distances > 1800 m the median error appeared constant. The effect of shorter travel distances on SOMA was smaller than on PPP. For maximum travel distances < 1200 m, SOMA outperformed all other strategies. In addition, it can be observed that only BB showed better performances for shorter travel distances than for longer ones. On the other hand, with different search strategies for each AUV, the search performance of the whole swarm can be improved by incorporating population-based search strategies such as PSO and SOMA within the PPP scheme. The best performance was achieved for the combination of two AUVs following PPP, while the third AUV utilised PSO. The best fitness of this combination was 15.9. This fitness was 26.4% better than the performance of PPP, which was 20.4 on average. In addition, a novel mechanism for dynamically selecting a search strategy for an AUV is proposed. This mechanism is based on fuzzy logic. This dynamic approach is able to perform at least as well as PPP and SOMA for different travel distances of AUVs. However, due to the better adaptation to the current situation, the overall performance, calculated based on the fitness achieved for different maximum travel distances, the proposed dynamic search strategy selection performed 32.8% better than PPP and 34.0% better than SOMA.

The integration of interactive web-based learning media is essential to address challenges in teaching hydrocarbon materials at SMAN 1 Sungai Pinang. Traditional teaching methods, while effective, often lack student engagement, motivation, and interactivity. This study explores the need for digital learning tools to enhance chemistry education. A needs analysis was conducted through surveys and questionnaires to assess student and teacher perceptions regarding existing teaching methods. The validity and reliability of the questionnaire were confirmed to ensure accurate data collection. The study also examined school facilities and academic challenges that impact the implementation of interactive learning. Findings indicate that well-equipped school facilities support the adoption of interactive and digital learning strategies. While traditional methods remain beneficial, a blended learning approach is necessary to optimize student engagement and academic performance. The study also highlights various academic and non-academic challenges faced by students, necessitating further support and development. Regular evaluation of teaching strategies is crucial to adapting to students' evolving needs. The study underscores the importance of interactive web-based learning media in improving teaching effectiveness and student engagement. The findings suggest that supplementing traditional methods with digital tools can enhance comprehension and motivation. This study supports the development of interactive digital learning media to complement traditional chemistry instruction. Implementing blended learning strategies can address current educational challenges, fostering a more engaging and effective learning environment.

Particle swarm optimization (PSO) and Ant Colony Optimization (ACO) are two important methods of stochastic global optimization. PSO has fast global search capability with fast initial speed. But when it is close to the optimal solution, its convergence speed is slow and easy to fall into the local optimal solution. ACO can converge to the optimal path through the accumulation and update of the information with the distributed parallel global search ability. But it has slow solving speed for the lack of initial pheromone at the beginning. In this paper, the hybrid algorithm is proposed in order to use the advantages of both of the two algorithm. PSO is first used to search the global solution. When it maybe fall in local one, ACO is used to complete the search for the optimal solution according to the specific conditions. The experimental results show that the hybrid algorithm has achieved the design target with fast and accurate search.