Optimization Task Research Articles

Fault-tolerant partition resolvability of cycle with chord.

In the realm of connected networks, distance-based parameters, particularly the partition dimension of graphs, have extensive applications across various fields, including chemistry and computer science. A notable variant of the partition dimension is the fault-tolerant resolving partition, which is critical in computer science for networking, optimization, and navigation tasks. In networking, fault-tolerant partitioning ensures robust communication pathways even in the event of network failures or disruptions. In optimization, it aids in developing efficient algorithms capable of withstanding errors or changes in input data. In navigation systems, fault-tolerant partitioning supports reliable route planning and navigation services under uncertain or dynamic conditions. This paper focuses on the fault-tolerant partition dimension within the specific context of the cycle with chord graphs, exploring its properties and implications for enhancing the robustness and reliability of networked systems.

A modified average-roulette cellular automaton algorithm for optimization tasks

A modified average-roulette cellular automaton algorithm for optimization tasks

A general Bayesian algorithm for the autonomous alignment of beamlines.

Autonomous methods to align beamlines can decrease the amount of time spent on diagnostics, and also uncover better global optima leading to better beam quality. The alignment of these beamlines is a high-dimensional expensive-to-sample optimization problem involving the simultaneous treatment of many optical elements with correlated and nonlinear dynamics. Bayesian optimization is a strategy of efficient global optimization that has proved successful in similar regimes in a wide variety of beamline alignment applications, though it has typically been implemented for particular beamlines and optimization tasks. In this paper, we present a basic formulation of Bayesian inference and Gaussian process models as they relate to multi-objective Bayesian optimization, as well as the practical challenges presented by beamline alignment. We show that the same general implementation of Bayesian optimization with special consideration for beamline alignment can quickly learn the dynamics of particular beamlines in an online fashion through hyperparameter fitting with no prior information. We present the implementation of a concise software framework for beamline alignment and test it on four different optimization problems for experiments on X-ray beamlines at the National Synchrotron Light Source II and the Advanced Light Source, and an electron beam at the Accelerator Test Facility, along with benchmarking on a simulated digital twin. We discuss new applications of the framework, and the potential for a unified approach to beamline alignment at synchrotron facilities.

Enhancing 5G Vehicular Edge Computing Efficiency with the Hungarian Algorithm for Optimal Task Offloading

The rapid advancements in vehicular technologies have enabled modern autonomous vehicles (AVs) to perform complex tasks, such as augmented reality, real-time video surveillance, and automated parking. However, these applications require significant computational resources, which AVs often lack. To address this limitation, Vehicular Edge Computing (VEC) has emerged as a promising solution, allowing AVs to offload computational tasks to nearby vehicles and edge servers. This offloading process, however, is complicated by factors such as high vehicle mobility and intermittent connectivity. In this paper, we propose the Hungarian Algorithm for Task Offloading (HATO), a novel approach designed to optimize the distribution of computational tasks in 5G-enabled VEC systems. HATO leverages 5G’s low-latency, high-bandwidth communication to efficiently allocate tasks across edge servers and nearby vehicles, utilizing the Hungarian algorithm for optimal task assignment. By designating an edge server to gather contextual information from surrounding nodes and compute the best offloading scheme, HATO reduces computational burdens on AVs and minimizes task failures. Through extensive simulations in both urban and highway scenarios, HATO achieved a significant performance improvement, reducing execution time by up to 75.4% compared to existing methods under full 5G coverage in high-density environments. Additionally, HATO demonstrated zero energy constraint violations and achieved the highest task processing reliability, with an offloading success rate of 87.75% in high-density urban areas. These results highlight the potential of HATO to enhance the efficiency and scalability of VEC systems for autonomous vehicles.

Phase-Angle-Encoded Snake Optimization Algorithm for K-Means Clustering

The rapid development of metaheuristic algorithms proves their advantages in optimization. Data clustering, as an optimization problem, faces challenges for high accuracy. The K-means algorithm is traditaaional but has low clustering accuracy. In this paper, the phase-angle-encoded snake optimization algorithm (θ-SO), based on mapping strategy, is proposed for data clustering. The disadvantages of traditional snake optimization include slow convergence speed and poor optimization accuracy. The improved θ-SO uses phase angles for boundary setting and enables efficient adjustments in the phase angle vector to accelerate convergence, while employing a Gaussian distribution strategy to enhance optimization accuracy. The optimization performance of θ-SO is evaluated by CEC2013 datasets and compared with other metaheuristic algorithms. Additionally, its clustering optimization capabilities are tested on Iris, Wine, Seeds, and CMC datasets, using the classification error rate and sum of intra-cluster distances. Experimental results show θ-SO surpasses other algorithms on over 2/3 of CEC2013 test functions, hitting a 90% high-performance mark across all clustering optimization tasks. The method proposed in this paper effectively addresses the issues of data clustering difficulty and low clustering accuracy.

Acoustic structure inverse design and optimization using deep learning

From ancient to modern times, acoustic structures have been employed to manage the spread of acoustic waves. Nevertheless, designing these structures traditionally remains a laborious and computationally intensive iterative process. Recognizing that complex acoustic systems can be effectively analyzed using the lumped-parameter method, we introduce a deep learning model that learns the correlation between the equivalent electrical parameters and the acoustic properties of these structures. As an illustration, we consider the design of multi-order Helmholtz resonators, showing experimentally that our model can predict structures with high precision that closely align with the specified design criteria. Furthermore, our model can seek multiple solutions in conjunction with dimensionality reduction algorithms and support evolutionary algorithms in optimization tasks. Compared to traditional numerical methods, our approach offers greater efficiency, flexibility, and universality. The designed acoustic structures hold broad potential for applications including speech enhancement, sound absorption, and insulation.

Research on hybrid strategy Particle Swarm Optimization algorithm and its applications

The increasing complexity and high-dimensional nature of real-world optimization problems necessitate the development of advanced optimization algorithms. Traditional Particle Swarm Optimization (PSO) often faces challenges such as local optima entrapment and slow convergence, limiting its effectiveness in complex tasks. This paper introduces a novel Hybrid Strategy Particle Swarm Optimization (HSPSO) algorithm, which integrates adaptive weight adjustment, reverse learning, Cauchy mutation, and the Hook-Jeeves strategy to enhance both global and local search capabilities. HSPSO is evaluated using CEC-2005 and CEC-2014 benchmark functions, demonstrating superior performance over standard PSO, Dynamic Adaptive Inertia Weight PSO (DAIW-PSO), Hummingbird Flight patterns PSO (HBF-PSO), Butterfly Optimization Algorithm (BOA), Ant Colony Optimization (ACO), and Firefly Algorithm (FA). Experimental results show that HSPSO achieves optimal results in terms of best fitness, average fitness, and stability. Additionally, HSPSO is applied to feature selection for the UCI Arrhythmia dataset, resulting in a high-accuracy classification model that outperforms traditional methods. These findings establish HSPSO as an effective solution for complex optimization and feature selection tasks.

The complexity of optimizing atomic congestion

Atomic congestion games are a classic topic in network design, routing, and algorithmic game theory, and are capable of modeling congestion and flow optimization tasks in various application areas. While both the price of anarchy for such games as well as the computational complexity of computing their Nash equilibria are by now well-understood, the computational complexity of computing a system-optimal set of strategies—that is, a centrally planned routing that minimizes the average cost of agents—is severely understudied in the literature. We close this gap by identifying the exact boundaries of tractability for the problem through the lens of the parameterized complexity paradigm. After showing that the problem remains highly intractable even on extremely simple networks, we obtain a set of results which demonstrate that the structural parameters which control the computational (in)tractability of the problem are not vertex-separator based in nature (such as, e.g., treewidth), but rather based on edge separators. We conclude by extending our analysis towards the (even more challenging) min-max variant of the problem.

A comparative analysis of intelligent energy modelling approaches for a grid-tied PVT system application

Renewable energy resources are a long-term answer to the current chronic energy dilemma. Solar photovoltaic (PV) systems are the most often used form of a practical solution for power supply and energy management in buildings. A PV-thermal (PVT) system is essential to lowering the demand for grid energy by capturing electrical and thermal energy from sunlight, optimising energy usage and encouraging sustainable energy practices. The most significant difficulty faced by developing countries, such as South Africa, is a lack of power supply to meet demand while limiting grid overload. An alternative source of energy is a critical component. This research compares two intelligent energy modelling methodologies: optimal control scheme-based open loop and model predictive control (MPC) in a PVT application. The primary goal of the modelling is to limit the electricity required from the grid while stressing the system’s energy efficiency and cost-effectiveness. The open loop approach uses mathematical optimisation techniques to establish the best control strategy for the PVT system. The ideal set-points for various system parameters are found by presenting the problem as an optimisation task to reduce grid power usage. The optimal control technique delivers a global optimisation solution based on the system dynamics and constraints. The MPC technique employs a predictive control technique of the PVT system to create optimal control actions in a receding horizon manner. The MPC algorithm anticipates future system behaviour and generates control actions that minimise grid power drawn by iteratively solving an optimisation problem at each time step. This robust online control scheme enables real-time modifications and responses to system disruptions that effectively and dynamically coordinate system behaviour.

U-AEFA: Online and offline learning-based unified artificial electric field algorithm for real parameter optimization

Optimization problems in real-world scenarios require algorithms that effectively balance exploration and exploitation to avoid local optima and achieve global solutions. To address this, we propose a unified artificial electric field algorithm (U-AEFA) that integrates both offline learning (inspired by high-performing metaheuristic algorithms) and online learning (through historical data generated during evolution). U-AEFA introduces a unique three-layer population structure to enhance search efficiency, consisting of the best agent in the first layer, the top-performing agents in the second layer, and the remaining agents in the third layer. Key features of U-AEFA include (i) an effective Coulomb’s constant for improved exploration, (ii) a non-uniform mutation operator to mitigate premature convergence, and (iii) two acceleration coefficients for enhanced performance. These three factors constitute offline learning and have been implemented to improve different design elements of the algorithm. As part of online learning, it employs a difference vector reuse (DVR) strategy to evolve the first-layer agents. The algorithm is evaluated using the CEC 2017 test suite across multiple dimensions (10, 30, 50, 100), where it consistently outperforms seven state-of-the-art algorithms, demonstrating superior accuracy and convergence speed. Moreover, U-AEFA’s robustness is validated on 12 high-dimensional feature selection problems, further highlighting its effectiveness in solving complex optimization tasks. The source code of U-AEFA is available at

Recent applications and advances of African Vultures Optimization Algorithm

The African Vultures Optimization Algorithm (AVOA) is a recently developed meta-heuristic algorithm inspired by the foraging behavior of African vultures in nature. This algorithm has gained attention due to its simplicity, flexibility, and effectiveness in tackling many optimization problems. The significance of this review lies in its comprehensive examination of the AVOA’s development, core principles, and applications. By analyzing 112 studies, this review highlights the algorithm’s versatility and the growing interest in enhancing its performance for real-world optimization challenges. This review methodically explores the evolution of AVOA, investigating proposed improvements that enhance the algorithm’s ability to adapt to various search geometries in optimization problems. Additionally, it introduces the AVOA solver, detailing its functionality and application in different optimization scenarios. The review demonstrates the AVOA’s effectiveness, particularly its unique weighting mechanism, which mimics vulture behavior during the search process. The findings underscore the algorithm’s robustness, ease of use, and lack of dependence on derivative information. The review also critically evaluates the AVOA’s convergence behavior, identifying its strengths and limitations. In conclusion, the study not only consolidates the existing knowledge on AVOA but also proposes directions for future research, including potential adaptations and enhancements to address its limitations. The insights gained from this review offer valuable guidance for researchers and practitioners seeking to apply or improve the AVOA in various optimization tasks.

Transfer learning Bayesian optimization for competitor DNA molecule design for use in diagnostic assays.

With the rise in engineered biomolecular devices, there is an increased need for tailor-made biological sequences. Often, many similar biological sequences need to be made for a specific application meaning numerous, sometimes prohibitively expensive, lab experiments are necessary for their optimization. This paper presents a transfer learning design of experiments workflow to make this development feasible. By combining a transfer learning surrogate model with Bayesian optimization, we show how the total number of experiments can be reduced by sharing information between optimization tasks. We demonstrate the reduction in the number of experiments using data from the development of DNA competitors for use in an amplification-based diagnostic assay. We use cross-validation to compare the predictive accuracy of different transfer learning models, and then compare the performance of the models for both single objective and penalized optimization tasks.

Prospective memory in the developmental age: a systematic review to synthesize the evaluation tools and the main cognitive functions involved.

Prospective memory (PM) is the ability to remember and realize one's intentions in the future; therefore, it is crucial for the daily functioning of children and adolescents and their ability to become independent from caregivers. PM errors can have repercussions during childhood, such as influencing school performance and social relationships. The aim of this systematic review was to synthesize studies analysing PM in children and adolescents (age range: 0-16 years) following PRISMA guidelines. The goal was to outline the most commonly used tasks, offering information on the development of PM, and-through a detailed analysis of the assessment of specific cognitive processes carried out in the primary studies included-providing information on the main cognitive processes involved in PM within this age group. Forty-nine studies were selected that examined PM in children and adolescents with typical development. The studies used many different tasks that can be traced back to eleven different main paradigms to evaluate PM, each structured into a PM and an ongoing task. Older children performed better on PM targets than younger children, suggesting a developmental trajectory of PM that follows a J-shaped function. Children as young as 2 years old exhibited the first signs of PM, while adolescents performed similarly to adults on PM tasks. Several factors are involved in PM development: retrospective memory, executive functions (planning, working memory, inhibitory control, monitoring), attention, metamemory, and motivation. This review May be considered a starting point to summarize the most used tools to evaluate PM in children and adolescents, and to shed light on the primary cognitive functions involved in PM, potentially offering indications to researchers in selecting optimal tasks for measuring PM across different age groups. Additionally, it underscores the importance of developing standardized measures for potential clinical applications.

Enhancing Hopfield network performance for pattern retrieval using sparse recovery algorithm and Parzen estimator

An improved pattern recovery approach that integrates the Hopfield neural network (HNN) with the iterative signal reconstruction and Parzen window-based classification is proposed. The HNN is observed as a form of associative memory network, used for various pattern recognition and optimization tasks. However, when the input pattern is highly damaged with a very limited set of available samples, the Hopfield network fails to perform the retrieval. The convex optimization-based gradient descent algorithm is considered for pattern recovery of damaged inputs in order to provide an improved pattern approximation for further processing within the HNN, enabling successful network performance. Additionally, in the case of grayscale images, the Parzen window approach is used to classify the probability density functions (pdfs) of the training set and to choose those being comparable to the pdf of the input pattern, therefore refining the selection of patterns and providing better convergence to the exact retrieval. The theoretical considerations are verified experimentally, showing the high performance of the proposed approach when only 10 % of the pixels are available for binary patterns and 40 % of pixels for grayscale patterns.

Airfoil Optimization Using Deep Learning Models and Evolutionary Algorithms for the Case Large-Endurance UAVs Design

This article presents the development of the AZTLI-NN network and the evaluation of this network as a set of evolutionary algorithms in airfoil optimization tasks. AZTLI-NN has the characteristic of predicting the aerodynamic coefficients of the airfoils in the form of images (graphs of the aerodynamic coefficients as a function of the angle of attack) from parameter vectors corresponding to the parameterization method CST. This feature allows the network to achieve good performance when generalizing the predictions of the aerodynamic coefficients, being on par with neural networks that have the aerodynamic coefficients encoded in the form of structured data, and has the ability to handle a wide range of usage airfoils in general aviation. In addition, a case of how AZTLI-NN together with an adaptive evolutionary algorithm and population size reduction methods achieve good performance in finding the airfoil that provides the highest possible endurance value is shown, so this work is considered as an option in the early stages of the design for the selection of airfoils in the design of large-endurance UAVs.

Designing an optimal task scheduling and VM placement in the cloud environment with multi-objective constraints using Hybrid Lemurs and Gannet Optimization Algorithm

ABSTRACT An efficient resource utilization method can greatly reduce expenses and unwanted resources. Typical cloud resource planning approaches lack support for the emerging paradigm regarding asset management speed and optimization. The use of cloud computing relies heavily on task planning and allocation of resources. The task scheduling issue is more crucial in arranging and allotting application jobs supplied by customers on Virtual Machines (VM) in a specific manner. The task planning issue needs to be specifically stated to increase scheduling efficiency. The task scheduling in the cloud environment model is developed using optimization techniques. This model intends to optimize both the task scheduling and VM placement over the cloud environment. In this model, a new hybrid-meta-heuristic optimization algorithm is developed named the Hybrid Lemurs-based Gannet Optimization Algorithm (HL-GOA). The multi-objective function is considered with constraints like cost, time, resource utilization, makespan, and throughput. The proposed model is further validated and compared against existing methodologies. The total time required for scheduling and VM placement is 30.23%, 6.25%, 11.76%, and 10.44% reduced than ESO, RSO, LO, and GOA with 2 VMs. The simulation outcomes revealed that the developed model effectively resolved the scheduling and VL placement issues.

Cognitive radio inspired RSMA-MEC under hardware impairments: Performance analysis and optimization

In this paper, we investigate a cognitive radio (CR) inspired rate splitting multiple access (RSMA) aided mobile edge computing (MEC) network, where hardware impairments (HIs) in all transceivers are considered. Given that allowing the secondary user (SU) to share the resource with the primary user (PU) for data offloading does not degrade the PU’s offloading performance, we derive closed-form expressions for the rate-splitting parameter and power allocation coefficient at the SU to maximize its offloading rate. Using the derived parameters, we then derive the successful computation probability (SCP), which is defined as the probability that both the PU and SU can successfully compute their task bits within a given latency budget, into the closed form. To further enhance the performance of the CR inspired RSMA-MEC network with HIs, a SCP maximization-based problem is formulated to jointly optimize the task offloading ratio and task offloading time of both the SU and PU. Leveraging the convex theory, we obtain the optimal solutions in the closed form. Simulation results confirm the following two insights. First, the presence of HIs leads to a decreasing on the SCP and the SCP approaches to a constant which is less than 1 with the increase of the transmit power. Second, with the optimal task offloading ratio and time, the CR inspired RSMA-MEC achieves the highest SCP compared to the existing schemes.

Computing optimal drug dosing regarding efficacy and safety: the enhanced OptiDose method in NONMEM.

Recently, an optimal dosing algorithm (OptiDose) was developed to compute the optimal drug doses for any pharmacometrics model for a given dosing scenario. In the present work, we enhance the OptiDose concept to compute optimal drug dosing with respect to both efficacy and safety targets. Usually, these are not of equal importance, but one is a top priority, that needs to be satisfied, whereas the other is a secondary target and should be achieved as good as possible without failing the top priority target. Mathematically, this leads to state-constrained optimal control problems. In this paper, we elaborate how to set up such problems and transform them into classical unconstrained optimal control problems which can be solved in NONMEM. Three different optimal dosing tasks illustrate the impact of the proposed enhanced OptiDose method.

Urban Air Logistics with Unmanned Aerial Vehicles (UAVs): Double-Chromosome Genetic Task Scheduling with Safe Route Planning

In an efficient aerial package delivery scenario carried out by multiple Unmanned Aerial Vehicles (UAVs), a task allocation problem has to be formulated and solved in order to select the most suitable assignment for each delivery task. This paper presents the development methodology of an evolutionary-based optimization framework designed to tackle a specific formulation of a Drone Delivery Problem (DDP) with charging hubs. The proposed evolutionary-based optimization framework is based on a double-chromosome task encoding logic. The goal of the algorithm is to find optimal (and feasible) UAV task assignments such that (i) the tasks’ due dates are met, (ii) an energy consumption model is minimized, (iii) re-charge tasks are allocated to ensure service persistency, (iv) risk-aware flyable paths are included in the paradigm. Hard and soft constraints are defined such that the optimizer can also tackle very demanding instances of the DDP, such as tens of package delivery tasks with random temporal deadlines. Simulation results show how the algorithm’s development methodology influences the capability of the UAVs to be assigned to different tasks with different temporal constraints. Monte Carlo simulations corroborate the results for two different realistic scenarios in the city of Turin, Italy.

GeQuPI: Quantum Program Improvement with Multi-Objective Genetic Programming

Processing quantum information poses novel challenges regarding the debugging of faulty quantum programs. Notably, the lack of accessible information on intermediate states during quantum processing, renders traditional debugging techniques infeasible. Moreover, even correct quantum programs might not be processable, as current quantum computers are limited in computation capacity. Thus, quantum program developers have to consider trade-offs between accuracy (i.e., probabilistically correct functionality) and computational cost of the proposed solutions. Manually finding sufficiently accurate and efficient solutions is a challenging task, even for quantum computing experts. To tackle these challenges, we propose a quantum program improvement framework for an automated generation of accurate and efficient solutions, coined Genetic Quantum Program Improver (GeQuPI). In particular, we focus on the tasks of debugging and optimization of quantum programs. Our framework uses techniques from quantum information theory and applies multi-objective genetic programming, which can be further hybridized with quantum-aware optimizers. To demonstrate the benefits of GeQuPI, it is applied to 47 quantum programs reused from literature and openly published libraries. The results show that our approach is capable of correcting faulty programs and optimize inefficient ones for the majority of the studied cases, showing average optimizations of 35% with respect to computational cost.