Optimization Techniques Research Articles

Optimal Allocation of Capacitor Banks and Distributed Generation: A Comparison of Recently Developed Metaheuristic Optimization Techniques on the Real Distribution Networks of ALG-AB-Hassi Sida, Algeria

Recent advancements in renewable energy technologies, alongside changes in utility infrastructure and progressive government policies, have bolstered the integration of renewable-based distributed generation units within distribution systems. This paper introduces the Energy Valley Optimizer, a novel tool designed for the strategic placement of distributed generation units and capacitor banks. This placement is crucial not only for optimizing energy loss and enhancing bus voltage stability but also for promoting sustainable energy use and reducing environmental impact over the long term. By minimizing energy loss and voltage fluctuations, the optimizer contributes to a more sustainable and resilient energy system. It achieves this through the optimal allocation of resources across various load patterns within a 24 h period and is tested on the ALG-AB-Hassi-Sida 157-bus distribution network in South Algeria. Comparative analysis with existing algorithms—such as the Liver Cancer Algorithm, Walrus Optimization Algorithm, and Zebra Optimization Algorithm—demonstrates the superior performance of the Energy Valley Optimizer. It not only enhances technical and economic efficiencies but also significantly lowers the total cost of energy over 24 years, thus supporting sustainable development goals in energy management.

Optimizing renewable energy systems through artificial intelligence: Review and future prospects

The global transition toward sustainable energy sources has prompted a surge in the integration of renewable energy systems (RES) into existing power grids. To improve the efficiency, reliability, and economic viability of these systems, the synergistic application of artificial intelligence (AI) methods has emerged as a promising avenue. This study presents a comprehensive review of the current state of research at the intersection of renewable energy and AI, highlighting key methodologies, challenges, and achievements. It covers a spectrum of AI utilizations in optimizing different facets of RES, including resource assessment, energy forecasting, system monitoring, control strategies, and grid integration. Machine learning algorithms, neural networks, and optimization techniques are explored for their role in complex data sets, enhancing predictive capabilities, and dynamically adapting RES. Furthermore, the study discusses the challenges faced in the implementation of AI in RES, such as data variability, model interpretability, and real-time adaptability. The potential benefits of overcoming these challenges include increased energy yield, reduced operational costs, and improved grid stability. The review concludes with an exploration of prospects and emerging trends in the field. Anticipated advancements in AI, such as explainable AI, reinforcement learning, and edge computing, are discussed in the context of their potential impact on optimizing RES. Additionally, the paper envisions the integration of AI-driven solutions into smart grids, decentralized energy systems, and the development of autonomous energy management systems. This investigation provides important insights into the current landscape of AI applications in RES.

Parametric analysis, modeling and optimization of the process parameters in electric discharge machining of aluminium metal matrix composite

Optimizing electric discharge machining (EDM) for aluminum/SiCp metal matrix composites poses challenges due to intricate machine parameters and process complexity, impacting process economy and elevating product costs. The research aims to find the optimal combination of process parameters which include pulse on-time, pulse current, duty cycle (%), gap voltage, sensitivity and flushing pressure for EDM of Al/SiCp-MMC using a copper electrode for the selected response factors such as material erosion rate and surface roughness, Ra. The experiments were designed using the central composite design of response surface methodology and an advanced optimization technique known as Teaching–learning-based optimization (TLBO), is applied to find the optimal combination of process parameters to obtain maximum material erosion rate subject to the desired range of surface roughness (SR), Ra. The combination of the high pulse on-time (i.e. 150 μs) and high pulse current (i.e. 12A) results in high material removal rate with deep craters on the machined surface clearly visible in SEM images contrasting the minimized surface roughness at lower values of pulse on-time (50 μs) and the pulse current (4A). Pulse on - time (Ton) is found to be the most significant factor for material erosion rate and surface roughness with percentage contribution of 70.86 and 54.9 respectively for optimization of the response. The regression models were developed at 95% confidence level for material removal rate and surface roughness with R2 value of 0.93 and 0.95 respectively signifying high degree of accuracy in predicting the response. Confirmation tests conducted to check the adequacy of the established models revealed that the percentage error between the predicted and experimental responses is found to be within acceptable levels. Electron discharge machining of the aluminium metal matrix composite at the optimized conditions could provide economical aspect in the aerospace and automobile industry.

Mechanical thrombectomy with Embolus Retriever with Interlinked Cages device for medium vessel occlusions: First pass results and safety profile.

Reperfusion of medium vessel occlusions is correlated with good outcomes. However, optimal techniques and medical devices are still unclear. In this study, we sought to evaluate the safety and efficacy of mechanical thrombectomy with Embolus Retriever with Interlinked Cages (ERIC™) retrieval device in patients with acute ischemic stroke due to distal medium vessel occlusions. A retrospective review of the prospectively collected mechanical thrombectomy database revealed 50 patients who had fulfilled the inclusion criteria. The data collected includes clinical patient characteristics, procedural measures, timestamps at each stage, and patient outcomes. Intravenous thrombolytics application, pre- and post-intervention imaging findings, device-related adverse events and any type of intracranial hemorrhage were recorded. There were 25 men (50%) and 25 women (50%) with a median of 67 years (interquartile range (IQR) 41-84). Median presenting NIHSS was 14 (IQR, 3-23). First pass rates were: eTICI3 in 16 cases (32%), eTICI-2c in eight cases (16%), eTICI-2B67 in nine cases (10%), eTICI2B50 in three cases (6%) and mTICI 0-2A in 18 cases (36%). Final recanalization rate of mTICI 2b-3 was 90% and 2c-3 was 70%. In conclusion, the ERIC thrombectomy device offers a high rate of first-pass success along with a favorable safety profile. Larger series and multi-center studies are needed for further investigation.

Multi‐objective optimization of a Fibonacci phyllotaxis micro pin‐fin heat sink

AbstractThere are still significant technical challenges associated with thermal management of electronic devices such as microprocessors. To improve heat dissipation performance of integrated circuits, a new Fibonacci phyllotaxis design of circular micropin fin heat sinks has been developed. To minimize both chip temperature and pumping power, a multi‐objective optimization technique was employed. The effect of design parameters such as phyllotaxis coefficient, pin fin diameter, and pin fin height on response parameters was numerically investigated using the full factorial design of the experiment. Artificial neural network was coupled with MO‐Jaya, to arrive at a Pareto frontier of optimal compromise solutions. The optimal set of design variables were found to be a height of 300 μm, a diameter of 122.6 μm, and a phyllotaxis coefficient of 130 μm with an inlet velocity of coolant 2.263 m/s. The selected optimum design was then investigated numerically, and the outcomes were compared to those predicted by the MO‐Jaya algorithm. The final confirmed response variables were a maximum temperature of 51.6°C and a pumping power of 0.191 W. The results show that the Fibonacci phyllotaxis structure of the micro pin fin heat sink has better heat‐dissipating performance.

Integrated Multi-fidelity Structural Optimization for UAV Wings

The paper explores comprehensive Unmanned Aerial Vehicle (UAV) wing optimization, integrating aerodynamic and structural techniques. A detailed comparison between the base and optimized modules while considering specifications such as composite material orientation, spar and rib material, deformation, stress, strain, safety factor, and weight. The methodology utilizes material changes for wing weight reduction while maintaining structural integrity. The optimized module (Case 7) balances weight reduction, safety, and structural performance, notably shifting from Al 7075 to Al 2024. Structural optimization focuses on changes in Carbon Fiber/Epoxy orientation, leveraging material changes for weight reduction. Constitutive equations and transformation matrices calculate stiffness matrices for the laminate, resulting in a robust wing. This holistic optimization combines low and high-fidelity techniques, addressing UAV wing structural aspects. Outcomes include weight reduction, deformation minimization, fluttering modal deformation and buckling points, an increased factor of safety, and improved strength-to-weight ratio. The research significantly advances aerospace engineering, particularly in UAV design and optimization.

Inferential control strategies using neural soft sensor in a high purity distillation column

High-purity distillation columns are processes in which you want to minimally increase the purity of a key component by separation. These processes are sensitive to disturbances, where small changes in the feed flowrate stream cause drastic changes in product compositions. Furthermore, when one wants to apply traditional control and optimization techniques to these processes, some difficulties have to be faced: the process is generally non-linear and has a long response time, there are many immeasurable disturbances, it is difficult to keep the process in steady-state, and the bottom and top compositions are highly coupled. Therefore, this paper presents a methodology for the development of soft sensors (SS), here in applied to an industrial 1,2-Dichloroethane separation plant. The process was simulated and validated with real data from the industrial plant. In the step second an algorithm based on multivariate statistics was developed for the selection of inputs SS variables. The configuration for training multilayer artificial neural network (ANN's) was optimize, accounting for the ANN's prediction capacity of responses and the rejection of disturbances inserted in the process. Thereafter, a temperature control was implemented to maintain the impurity compositions within specifications but failed due to the small temperature variations in the high purity column. To overcome these difficulty three inferential control strategies (impurity composition estimated by the SS) were implemented: a classic feedback control, a cascade control, and a ratio-cascade control. All control strategies acted to minimize the process disturbances effects. However, the inferential ratio-cascade control shown to be the most robust, because of the lowest integral error criteria value and percentage overshoot.

Chaotic-quasi-oppositional-phasor based multi populations gorilla troop optimizer for optimal power flow solution

This paper proposes an efficient modified Gorilla Troops Optimizer (MGTO) to address the problem of prone to local optima and stagnation possibility in the standard Gorilla Troops Optimizer (GTO). The proposed MGTO is based on multiple strategies including chaotic initialization, quasi-oppositional based learning, phasor motion, and multi-populations strategy. The proposed MGTO is applied to resolve the non-convex single objectives optimal power flow (OPF) problem to reduce the fuel cost without and with vple, the emissions, the fuel cost considering prohibted zones (POZs) and piecewise functions, while for multiple objective cases the fuel cost is optimized with the power losses, the voltage deviation and the emissions. To validate the effectiveness of the proposed optimizer, it is tested on IEEE 30-bus and 57-bus systems, as well as 23 standard benchmark functions and the obtained outcomes are compared with the other seven well-known optimization techniques such as SCSO, GWO, WOA, SCA, PF, BWO and GTO. The simulation results verified that the proposed MGTO provides a remarkable and robust solution for the OPF problem solution compared to the other state-of-the-arts algorithms.

Search engine Performance optimization: methods and techniques

Background With the rapid advancement of information technology, search engine optimisation (SEO) has become crucial for enhancing the visibility and relevance of online content. In this context, the use of cloud platforms like Microsoft Azure is being explored to bolster SEO capabilities. Methods This scientific article offers an in-depth study of search engine optimisation. It explores the different methods and techniques used to improve the performance and efficiency of a search engine, focusing on key aspects such as result relevance, search speed and user experience. The article also presents case studies and concrete examples to illustrate the practical application of optimisation techniques. Results The results demonstrate the importance of optimisation in delivering high quality search results and meeting the increasing demands of users. Conclusions The article addresses the enhancement of search engines through the Microsoft Azure infrastructure and its associated components. It highlights methods such as indexing, semantic analysis, parallel searches, and caching to strengthen the relevance of results, speed up searches, and optimise the user experience. Following the application of these methods, a marked improvement was observed in these areas, thereby showcasing the capability of Microsoft Azure in enhancing search engines. The study sheds light on the implementation and analysis of these Azure-focused techniques, introduces a methodology for assessing their efficacy, and details the specific benefits of each method. Looking forward, the article suggests integrating artificial intelligence to elevate the relevance of results, venturing into other cloud infrastructures to boost performance, and evaluating these methods in specific scenarios, such as multimedia information search. In summary, with Microsoft Azure, the enhancement of search engines appears promising, with increased relevance and a heightened user experience in a rapidly evolving sector.

Task-based automatic keV selection: leveraging routine virtual monoenergetic imaging for dose reduction on clinical photon-counting detector CT * *Portions presented at RSNA 2021 and 2022.

Objective. Photon-counting detector (PCD) CT enables routine virtual-monoenergetic image (VMI) reconstruction. We evaluated the performance of an automatic VMI energy level (keV) selection tool on a clinical PCD-CT system in comparison to an automatic tube potential (kV) selection tool from an energy-integrating-detector (EID) CT system from the same manufacturer. Approach. Four torso-shaped phantoms (20–50 cm width) containing iodine (2, 5, and 10 mg cc−1) and calcium (100 mg cc−1) were scanned on PCD-CT and EID-CT. Dose optimization techniques, task-based VMI energy level and tube-potential selection on PCD-CT (CARE keV) and task-based tube potential selection on EID-CT (CARE kV), were enabled. CT numbers, image noise, and dose-normalized contrast-to-noise ratio (CNRd) were compared. Main results. PCD-CT produced task-specific VMIs at 70, 65, 60, and 55 keV for non-contrast, bone, soft tissue with contrast, and vascular settings, respectively. A 120 kV tube potential was automatically selected on PCD-CT for all scans. In comparison, EID-CT used x-ray tube potentials from 80 to 150 kV based on imaging task and phantom size. PCD-CT achieved consistent dose reduction at 9%, 21% and 39% for bone, soft tissue with contrast, and vascular tasks relative to the non-contrast task, independent of phantom size. On EID-CT, dose reduction factor for contrast tasks relative to the non-contrast task ranged from a 65% decrease (vascular task, 70 kV, 20 cm phantom) to a 21% increase (soft tissue with contrast task, 150 kV, 50 cm phantom) due to size-specific tube potential adaptation. PCD-CT CNRd was equivalent to or higher than those of EID-CT for all tasks and phantom sizes, except for the vascular task with 20 cm phantom, where 70 kV EID-CT CNRd outperformed 55 keV PCD-CT images. Significance. PCD-CT produced more consistent CT numbers compared to EID-CT due to standardized VMI output, which greatly benefits standardization efforts and facilitates radiation dose reduction.

Simulation Learning and Optimization: Methodology and Applications

Due to the rapid advancement of computational power and the Internet of Things (IoT) together with recent developments in digital-twin and cyber-physical systems, the integration of big data analytics techniques (BDAT) (e.g., data mining, machine learning (ML), deep learning, data farming, etc.) into traditional simulation methodologies, along with the enhancement of past simulation optimization techniques, has given rise to an emerging field known as simulation learning and optimization (SLO). Unlike simulation optimization, SLO is a data-driven fusion approach that integrates conventional simulation modeling methodologies, simulation optimization techniques, and learning-based big data analytics (BDA) to enhance the ability to address stochastic and dynamic decision-making problems in the real-world complex system and quickly find the best outcomes for the decision support in both real-time and nonreal-time analyses to achieve the data-driven digital twin capability. Although some literature in the past has mentioned similar applications and concepts, no paper provides a structural explanation and comprehensive review of relevant literature on SLO from both methodological and applied perspectives. Consequently, in the first part of this paper, we conduct a literature review on a novel SLO methodology emerging from the fusion of traditional simulation methodology, simulation optimization algorithms, and BDAT. In the second part of this paper, we review the applications of SLO in various contexts, with detailed discussions on manufacturing, maintenance, redundancy allocation, hybrid energy systems, humanitarian logistics, and healthcare systems. Lastly, we investigate potential research directions for SLO in this new era of big data and artificial intelligence (AI).

Performance and optimization study of selected 4-terminal tandem solar cells

Tandem solar cells owing to their layered structure in which each sub-cell utilizes a certain part of the solar spectrum with reduced thermal losses, are promising applicants to promote the power conversion efficiency beyond the Shockley–Queisser limit of single-junction solar cells. This study delves into the performance and optimization of 4-terminal organic/silicon tandem solar cells through numerical simulations using SCAPS-1D software. The tandem architecture combining organic, perovskite, and silicon materials, shows potential in enhancing light absorption across the solar spectrum with complementary absorption spectra. Through innovative material exploration, optimization techniques are explored to advance the performance boundaries of organic/silicon tandem solar cells. The study employs the Beer–Lambert law to assess the impact of varied physical parameters on tandem solar cell efficiency, aiming to propose optimal configurations. Results indicate a maximum efficiency of 25.86% with P3HT:PC70BM organic active layer (150 nm thickness) and 36.8% with Cs2AgBi0.75Sb0.25Br6 active layer (400 nm thickness) in the studied 4-terminal tandem structures. These findings offer valuable insights into the complex physics of these tandem solar cells, for developing high-performance and commercially practical photovoltaic devices.

Fuzzy analytic hierarchy process based generation management for interconnected power system

Decision makers consistently face the challenge of simultaneously assessing numerous attributes, determining their respective importance, and selecting an appropriate method for calculating their weights. This article addresses the problem of automatic generation control (AGC) in a two area power system (2-APS) by proposing fuzzy analytic hierarchy process (FAHP), an multi-attribute decision-making (MADM) technique, to determine weights for sub-objective functions. The integral-time-absolute-errors (ITAE) of tie-line power fluctuation, frequency deviations and area control errors, are defined as the sub-objectives. Each of these is given a weight by the FAHP method, which then combines them into an single final objective function. This objective function is then used to design a PID controller. To improve the optimization of the objective function, the Jaya optimization algorithm (JOA) is used in conjunction with other optimization techniques such as sine cosine algorithm (SCA), Luus–Jaakola algorithm (LJA), Nelder–Mead simplex algorithm (NMSA), symbiotic organism search algorithm (SOSA) and elephant herding optimization algorithm (EHOA). Six distinct experimental cases are conducted to evaluate the controller’s performance under various load conditions, with data plotted to show responses corresponding to fluctuations in frequency and tie-line exchange. Furthermore, statistical analysis is performed to gain a better understanding of the effectiveness of the JOA-based PID controller. For non-parametric evaluation, Friedman rank test is also used to validate the performance of the proposed JOA-based controller.

Optimal sizing and siting of single tunned passive filter based on jellyfish algorithm

<p class="MsoNormal" style="margin-top: 10pt; text-align: justify;"><span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;">The presence of electrical harmonics in low voltage networks causes many problems for customers, the most important of which is the decrease in the power quality beyond the permissible limits, which leads to a decrease in voltage for long periods, a decrease in power factor and a large losses in the electrical network due to the deformation of the electric waveform of voltage and current. The aim of this paper is to design an optimal passive filter and its optimal location to suppress the harmonic distortion in the distribution system that results from nonlinear loads. This filter will use single objective functions and multi objective functions that will minimize the proposed filter cost, the real power losses, total harmonic distortion "THD" and individual harmonic distortion "IHD". This will be done under the equality constraint of power balance and some inequality constraints such as filter parameters limits, quality factor limits, voltage limits, harmonic distortion limits. The optimal passive filter parameters and location will be calculated using the modern optimization techniques such jellyfish optimization technique. IEEE 33 bus radial system will be used for demonstrating results obtained by the proposed method.</span></p>

Continuous optimization by quantum adaptive distribution search

We introduce the quantum adaptive distribution search (QuADS), a quantum continuous optimization algorithm that integrates Grover adaptive search (GAS) with the covariance matrix adaptation evolution strategy (CMA-ES), a classical technique for continuous optimization. QuADS utilizes the quantum-based search capabilities of GAS and enhances them with the principles of CMA-ES for more efficient optimization. It employs a multivariate normal distribution for the initial state of the quantum search and repeatedly updates it throughout the optimization process. Our numerical simulations show that QuADS outperforms both GAS and CMA-ES. This is achieved through adaptive refinement of the initial state distribution rather than consistently using a uniform state, resulting in fewer oracle calls. This study presents an important step toward exploiting the potential of quantum computing for continuous optimization. Published by the American Physical Society 2024

A Novel Technique for Facial Recognition Based on the GSO-CNN Deep Learning Algorithm

Face recognition is one of the important elements that can be used for securing the facilities, emotion recognition, sentiment exploration, fraud analysis, and traffic pattern analysis. Intelligent face recognition has yielded excellent accuracy in a controlled environment whereas vice versa in an uncontrolled environment. However, conventional methods can no longer satisfy the demand at present due to their low recognition accuracy and restrictions on many occasions. This study proposed an optimal deep learning-based face recognition system that improves the security of the model developed in the IoT cloud environment. Initially, the dataset of images was gathered from the public repository. The captured images are explored using image processing techniques like image preprocessing employing the Gaussian filter technique for removing the noise and smoothing the image. The histogram of oriented gradients (HOGs) is used for the image segmentation. The processed images are preserved at the cloud service layer. Extract features were linked to facial activities using the spatial-temporal interest point (STIP). On the other hand, the extracted feature vectors are investigated using galactic swarm optimization (GSO) techniques that give optimized feature vectors. The necessary features are selected using the gray level co-occurrence matrix (GLCM), which separates the statistical texture features. The GSO output is fed into the deep convolutional neural network (DCNN) that effectively trains the captured face images. This will allow the effectiveness of the GSO-CNN technique to be assessed in terms of recognition accuracy, recall, precision, and error rate.

Variety Breeding Studies on Hesperis isatidea (Boİss.) D.A. German & Al-Shehbaz

Hesperis isatidea (Boiss.) D.A.German & Al-Shehbaz, a member of the Brassicaceae family, represents a distinctive natural species characterized by its fragrant, conspicuous inflorescences, unique flower coloration, and distinct feather-like foliage. The Turkish name of the species is known as “Allı Gelin”. This species is native, exhibiting resilience to drought conditions and requiring minimal care. Cultivation studies on this species were carried out at Erzincan Horticulture Research Institute (Turkey) with project number 106G022 within the scope of TÜBİTAK (Turkish Scientific and Technological Research Council). The research aimed to determine optimal production techniques through generative and vegetative propagation methods. The project, initiated in 2013 and concluded in 2022, focused on developing a cultivar of the Allı Gelin plant using texel selection breeding techniques. Thirteen populations of Allı Gelin from institute's gene pool were utilized. Employing the Texel Selection method, four distinct lines were identified: compact, tightly spread compact, pyramid1and pyramid-2 shaped. Subsequent baby plant tests revealed stability in the tightly spread compact and pyramid-1 lines, leading to their selection for further breeding. Although breeding efforts continued on the compact line, the pyramid-2 line was discontinued due to an inability to attain the desired stability. The successfully tested variants, namely the pyramid-1 and tightly spread compact forms, have been earmarked as candidates for outdoor ornamental plant varieties. Registration procedures for these candidates are underway. These new varieties promise to enrich diversity within the ornamental plants sector, potentially enhancing competitiveness in foreign markets.

Navigating Supply Chain Resilience: A Hybrid Approach to Agri-Food Supplier Selection

Globalization and multinational commerce have increased the dynamism and complexity of supply networks, thereby increasing their susceptibility to disruptions along interconnected supply chains. This study aims to tackle the significant concern of supplier selection disruptions in the Thai agri-food industry as a response to the aforementioned challenges. A novel supplier evaluation system, PROMETHEE II, is suggested; it combines the Fuzzy Analytical Hierarchy Process (FAHP) with inferential statistical techniques. This investigation commences with the identification of critical indicators of risk in the sustainable supply chain via three phases of analysis and 315 surveys of management teams. Exploratory factor analysis (EFA) is utilized to ascertain six supply risk criteria and twenty-three sub-criteria. Following this, the parameters are prioritized by FAHP, whereas four prospective suppliers for an agricultural firm are assessed by PROMETHEE II. By integrating optimization techniques into sensitivity analysis, this hybrid approach improves supplier selection criteria by identifying dependable solutions that are customized to risk scenarios and business objectives. The iterative strategy enhances the resilience of the agri-food supply chain by enabling well-informed decision-making amidst evolving market dynamics and chain risks. In addition, this research helps agricultural and other sectors by providing a systematic approach to selecting low-risk suppliers and delineating critical supply chain risk factors. By bridging complexity and facilitating informed decision-making in supplier selection processes, the results of this study fill a significant void in the academic literature concerning sustainable supply chain risk management.

Human-like Learning in Temporally Structured Environments

Natural environments have correlations at a wide range of timescales. Human cognition is tuned to this temporal structure, as seen by power laws of learning and memory, and by spacing effects whereby the intervals between repeated training data affect how long knowledge is retained. Machine learning is instead dominated by batch iid training or else relatively simple nonstationarity assumptions such as random walks or discrete task sequences. The main contributions of our work are: (1) We develop a Bayesian model formalizing the brain's inductive bias for temporal structure and show our model accounts for key features of human learning and memory. (2) We translate the model into a new gradient-based optimization technique for neural networks that endows them with human-like temporal inductive bias and improves their performance in realistic nonstationary tasks. Our technical approach is founded on Bayesian inference over 1/f noise, a statistical signature of many natural environments with long-range, power law correlations. We derive a new closed-form solution to this problem by treating the state of the environment as a sum of processes on different timescales and applying an extended Kalman filter to learn all timescales jointly. We then derive a variational approximation of this model for training neural networks, which can be used as a drop-in replacement for standard optimizers in arbitrary architectures. Our optimizer decomposes each weight in the network as a sum of subweights with different learning and decay rates and tracks their joint uncertainty. Thus knowledge becomes distributed across timescales, enabling rapid adaptation to task changes while retaining long-term knowledge and avoiding catastrophic interference. Simulations show improved performance in environments with realistic multiscale nonstationarity. Finally, we present simulations showing our model gives essentially parameter-free fits of learning, forgetting, and spacing effects in human data. We then explore the analogue of human spacing effects in a deep net trained in a structured environment where tasks recur at different rates and compare the model's behavioral properties to those of people.

Meta‐heuristic optimization techniques for scheduling in multiple‐input multiple‐output multiple‐access channel system

SummaryIn a highly dense multiple‐input multiple‐output (MIMO) communication system, proper resource allocation is exceedingly challenging. The simultaneous selection of antennas at the base‐station (BS) and scheduling of users in an uplink multiple access channel (MAC) system add up to the signal processing load in the MIMO wireless system. The method chosen for combining the user scheduling and antenna selection must have a very low computational complexity. However, the exhaustive search algorithm (ESA), which achieves the best sum‐rate capacity in a MIMO‐MAC system, has a very high level of complexity. In this paper, we use a metaheuristic‐based optimization algorithms to optimize the sum‐rate capacity in a MAC system with significantly less computational complexity. First, we use the beluga whale optimization (BWO) metaheuristic method to solve the optimization problem. The BWO is a swarm‐based algorithm that draws inspiration from beluga whale behavior. An improved BWO (iBWO) is also presented and is utilized for the same optimization challenge. The proposed iBWO scheme improves the search capability of the algorithm and thereby ensures better exploration and exploitation capabilities of the beluga whales. We compare the performance of these algorithms with a standard differential evolution (DE) algorithm. The simulation results demonstrate that the proposed iBWO is a very competitive meta‐heuristic algorithm for solving optimization problems. The proposed iBWO algorithm achieves near optimal throughput in a MIMO‐MAC system. Also, the computational complexity related to the suggested optimization technique in a MIMO‐MAC system is found to be very less compared with the complexity related to ESA.