Optimization Algorithm Research Articles

Probabilistic Multi-Robot Task Scheduling for the Antarctic Environments with Crevasses

This paper deals with the problem of multi-robot task scheduling in the Antarctic environments with crevasses. Because the crevasses may cause hazardous situations when robots are operated in the Antarctic environments, robot navigation should be planned to safely avoid the positions of crevasses. However, the positions of the crevasses may be inaccurately measured due to the lack of sensor performance, the asymmetry of sensor data, and the possibility of crevasses drifting irregularly as time passes. To overcome these uncertain and asymmetric problems, this paper proposes a probabilistic multi-robot task scheduling method based on the Nearest Neighbors Test (NNT) algorithm and the probabilistic modeling of the positions of crevasses. The proposed method was tested with a Google map of the Antarctic environments and showed a better performance than the Ant Colony Optimization (ACO) algorithm and the Genetic Algorithm (GA) in the context of total cost and computational time.

A dual opposition learning-based multi-objective Aquila Optimizer for trading-off time-cost-quality-CO2 emissions of generalized construction projects

PurposeMost of the existing time-cost-quality-environmental impact trade-off (TCQET) analysis models have focused on solving a simple project representation without taking typical activity and project characteristics into account. This study aims to present a novel approach called the “hybrid opposition learning-based Aquila Optimizer” (HOLAO) for optimizing TCQET decisions in generalized construction projects.Design/methodology/approachIn this paper, a HOLAO algorithm is designed, incorporating the quasi-opposition-based learning (QOBL) and quasi-reflection-based learning (QRBL) strategies in the initial population and generation jumping phases, respectively. The crowded distance rank (CDR) mechanism is utilized to rank the optimal Pareto-front solutions to assist decision-makers (DMs) in achieving a single compromise solution.FindingsThe efficacy of the proposed methodology is evaluated by examining TCQET problems, involving 69 and 290 activities, respectively. Results indicate that the HOLAO provides competitive solutions for TCQET problems in construction projects. It is observed that the algorithm surpasses multiple objective social group optimization (MOSGO), plain Aquila Optimization (AO), QRBL and QOBL algorithms in terms of both number of function evaluations (NFE) and hypervolume (HV) indicator.Originality/valueThis paper introduces a novel concept called hybrid opposition-based learning (HOL), which incorporates two opposition strategies: QOBL as an explorative opposition and QRBL as an exploitative opposition. Achieving an effective balance between exploration and exploitation is crucial for the success of any algorithm. To this end, QOBL and QRBL are developed to ensure a proper equilibrium between the exploration and exploitation phases of the basic AO algorithm. The third contribution is to provide TCQET resource utilizations (construction plans) to evaluate the impact of these resources on the construction project performance.

Optimization of classification model for electric vehicle charging station placement using dynamic graylag goose algorithm

The study of electric vehicles (EVs) aims to address the critical challenges of promoting widespread adoption. These challenges include EVs’ high upfront costs compared to conventional vehicles, the need for more sufficient charging stations, limitations in battery technology and charging speeds, and concerns about the distance EVs can travel on a single charge. This paper is dedicated to designing an innovative strategy to handle EV charging station arrangement issues in different cities. Our research will support the development of sustainable transportation by intelligently replying to the challenges related to short ranges and long recharging times through the distribution of fast and ultra-fast charge terminals by allocating demand to charging stations while considering the cost variable of traffic congestion. A hybrid combination of Dynamic Greylag Goose Optimization (DGGO) algorithm, as well as a Long Short-Term Memory (LSTM) model, is employed in this approach to determine, in a cost-sensitive way, the location of the parking lots, factoring in the congestion for traffic as a variable. This study examines in detail the experiments on the DGGO + LSTM model performance for the purpose of finding an efficient charging station place. The results show that the DGGO + LSTM model has achieved a stunning accuracy of 0.988,836, more than the other models. This approach shapes our finding’s primary purpose of proposing solutions in terms of EV charging infrastructure optimization that is fully justified to the EV’s wide diffusion and mitigating of the environmental consequences.

Multi-objective parameter design and economic analysis of VSG-controlled hybrid energy systems in islanded grids

Photovoltaic (PV) systems offer cost-effective power solutions for outlying islands but often compromise system stability due to reduced inertia. This study introduces a Virtual Synchronous Generator (VSG) control strategy, integrated with Energy Storage Systems (ESS) and PV, to enhance system inertia. By optimizing coordination between these energy sources, the proposed method mitigates oscillations and improves grid stability. However, PV-VSG systems are generally not favored by energy providers due to the requirement for pre-curtailment of power output. To address this, the paper proposes a parameter design method for VSG control of ESS and PV, utilizing multi-objective genetic algorithm (MOGA) optimization to simultaneously increase the frequency nadir and minimize the settling time after disturbances. Additionally, an adaptive curtailment decision and parameter design method based on artificial neural networks is introduced to enhance the feasibility of PV-VSG systems by reducing PV pre-curtailment and prioritizing PV power release and ESS charging during frequency oscillations. Real data from the Penghu Archipelago in Taiwan are used to build a dynamic model in DIgSILENT, enabling interaction with MOGA. The Value at Risk (VaR) method with dual stochastic variables is employed to assess the allowable PV installed capacity. The results show that when VaR is set at 1%, the proposed PV-VSG method can increase PV penetration by 57.5% compared to scenarios without VSG. Furthermore, compared to traditional PV-VSG methods, the proposed approach achieves a 16.8% increase in PV penetration and reduces annual PV curtailment by 25 MWh. This study also evaluates the economic impact of planners choosing different risk levels, offering valuable insights for grid development in remote or island regions.

Flamingo Search Sailfish Optimizer Based SqueezeNet for Detection of Breast Cancer Using MRI Images

Breast cancer with increased risk in women is identified with Breast Magnetic Resonance Imaging (Breast MRI) and this helps in evaluating treatment therapies. Breast MRI is time time-consuming process that involves the assessment of current imaging. This research work depends on the detection of breast cancer at the earlier stages. Among various cancers, breast cancer in women occurs in larger accounts for almost 30% of estimated cancer cases. In this research, many steps are followed for breast cancer detection like pre-processing, segmentation, augmentation, extraction of features, and cancer detection. Here, the median filter is utilized for pre-processing, as well as segmentation is followed after pre-processing, which is done by Psi-Net. Moreover, the process of augmentation like shearing, translation, and cropping are followed after segmentation. Also, the segmented image tends to process feature extraction, where features like shape features, Completed Local Binary Pattern (CLBP), Pyramid Histogram of Oriented Gradients (PHOG), and statistical features are extracted. Finally, breast cancer is detected using the DL model, SqueezeNet. Here, the newly devised Flamingo Search SailFish Optimizer (FSSFO) is used in training Psi-Net as well as SqueezeNet. Furthermore, FSSFO is the combination of both the Flamingo Search Algorithm (FSA) and SailFish Optimizer (SFO).

Multiobjective optimal TCSC placement using multiobjective grey wolf optimizer for power losses reduction

This study investigates the application of the multiobjective grey wolf optimizer (MOGWO) for optimal placement of thyristor-controlled series compensator (TCSC) to minimize power loss in power systems. Two conflicting objectives are considered: (1) minimizing real and reactive power loss, and (2) minimizing real power loss and TCSC capital cost. The Pareto-optimal method is employed to generate the Pareto front for these objectives. The fuzzy set technique is used to identify the optimal trade-off solution, while the technique for order preference by similarity to the ideal solution suggests multiple optimal solutions catering to diverse utility preferences. Simulations on an IEEE 30 bus test system demonstrate the effectiveness of TCSC placement for power loss minimization using MOGWO. The superiority of MOGWO is confirmed by comparing its results with those obtained from a multiobjective particle swarm optimization algorithm. These findings can assist power system utilities in identifying optimal TCSC locations to maximize their performance.

Comparative Performance Analysis on WEDM Responses for Titanium Matrix Composite Using Novel Multi-objective Optimization Algorithms

Comparative Performance Analysis on WEDM Responses for Titanium Matrix Composite Using Novel Multi-objective Optimization Algorithms

Application of Seagull Optimization Algorithm-BP Neural Network in Oil-Water Two-Phase Flow Pattern Forecasting

With the ongoing increase in global energy demand, the significance of innovations in oil exploration and development technologies is rising, especially in relation to the development of unconventional reservoirs. The application of horizontal wells is becoming increasingly important in this particular situation. However, accurately monitoring and analyzing fluids in horizontal wells remains challenging due to the complex and fluctuating flow patterns of oil-water two-phase flow within the wellbore. Several elements, including well slope angle, flow rate, and water content, are involved. This study aimed to explore and develop an effective method for forecasting flow patterns, improving the precision of the dynamic monitoring of oil-water two-phase flow in horizontal wells. By analyzing the flow patterns in different experimental conditions, a predictive model using the SOA-BP neural network was developed, providing a scientific basis for dynamic monitoring in actual production scenarios. Initially, the simulated experiment for oil-water two-phase flow was carried out at room temperature and pressure utilizing a multiphase flow simulator. An optically transparent wellbore, with a diameter comparable to that of a real downhole well, was utilized, and No. 10 industrial white oil and tap water were employed as the experimental fluids. The experiment considered multiple contributing factors, including different well deviation, total flow, and water cut. The flow characteristics of oil and water were observed via visual monitoring and high-definition video, followed by detailed analysis. After collecting the experimental data, flow regimes for various scenarios were classified based on the established theory of oil-water two-phase flow in horizontal wells; then, detailed flow distribution diagrams were drawn. These data and diagrams presented offer a visual representation of the behavioral patterns exhibited by oil-water two-phase flow under varying situations and form the basis for subsequent model training and testing. Subsequently, based on the experimental data, this study combined the Seagull Optimization Algorithm (SOA) with a BP neural network to effectively learn and predict the experimental data. The SOA optimized the weights and biases of the BP neural network, improving the model’s convergence speed and prediction accuracy. Through rigorous training and testing, an oil-water two-phase flow pattern forecasting model was established, effectively predicting flow patterns under different well deviation, total flow, and water cut conditions. Finally, to validate the efficiency of the established model, a total of 15 data points were chosen from a sample well for validation. By comparing the flow patterns predicted by the model with actual logging data, the results indicate that the model’s accuracy in identifying flow pattern was 86.67%. This demonstrates that the flow pattern prediction model based on the SOA-BP neural network achieved a high level of accuracy under different complicated working conditions. This model effectively fulfills the requirements for dynamic monitoring in actual production. This indicates that the SOA-BP neural network-based flow pattern forecasting method is highly valuable due to its practical application value and provides an efficient technical approach for the development of unconventional reservoirs and the dynamic monitoring of horizontal wells in the future.

Consensus-based economic dispatch for DC microgrids incorporating realistic cyber delays

This paper introduces a distributed control approach for optimizing the operation of islanded DC microgrids (µGs), accounting for cyber delays and network failures. Initially, an integrated network emulation platform is utilized to calculate realistic cyber delays for islanded DC µGs employing IEC 61850 communication standard. To address the instability issues arising from cyber delays, the Artstein-Kwon-Pearson reduction technique is employed to convert the system with cyber delays into one without delays. The Subsequently, a consensus-based generation-cost optimization algorithm is designed for maintaining the economic operation of the µG. This algorithm efficiently equalizes the incremental costs (ICs) of all distributed generators (DGs) in a fully distributed manner, while adhering to the DGs capacity limits. Additionally, a fully distributed voltage regulation control is developed to stabilize µG average voltage, ensuring the balance between generation and demand. The efficacy of the formulated control technique is substantiated through diverse case studies, thereby underscoring its superior operational performance.

Three-Dimensional Path Planning of UAVs for Offshore Rescue Based on a Modified Coati Optimization Algorithm

Three-Dimensional Path Planning of UAVs for Offshore Rescue Based on a Modified Coati Optimization Algorithm

OPTIMIZED DEEP LEARNING FOR CYBER INTRUSION DETECTION AND SECURED COMMUNICATION IN MANET

The popularity of the Mobile Ad-Hoc Network increases with low expense solutions to the real time applications. The dynamic nature, limited centralized system with low bandwidth often susceptible to the security threads and became the hot topic. To protect the MANET system it is essential to implement a secured system. The purpose of this work is to design an intelligent Cyber Security System (CSS) by collecting the threads data from the MANET system. Initially, the data is pre-processed using the Min-Max normalization technique, followed by the feature selection with the newly approached Mother Optimization algorithm (MOA). The feature selection is effectuated for speedy classification of intrusions and prevent the attacks and therein significantly enhances the security. To provide the security to the MANET system the work proposes a novel Adaptive Deep Belief Network (ADBN). This classifies the attacks and normal data and prevents the system from falling for the threat. Simulation is performed in NS2 and analyzed the effectiveness of the proposed work with existing systems. Our proposed work effectively enhances the security of the MANET system and surpasses all the other works.

Fractionation dose optimization facilities the implementation of transmission proton FLASH-RT

Objective.The beam switching time and fractional dose influence the FLASH effect. A single-beam-per-fraction (SBPF) scheme using uniform fractional dose (UFD) has been proposed for FLASH- radiotherapy (FLASH-RT) to eliminate the beam switching time. Based on SBPF schemes, a fractionation dose optimization algorithm is proposed to optimize non-UFD plans to maximize the fractionation effect and dose-dependent FLASH effect.Approach.The UFD plan, containing five 236 MeV transmission proton beams, was optimized for 11 patients with peripheral lung cancer, with each beam delivering a uniform dose of 11 Gy to the target. Meanwhile, the non-UFD plan was optimized using fractionation dose optimization. To compare the two plans, the equivalent dose to 2 Gy (EQD2) for the target and normal tissues was calculated with anα/βratio of 10 and 3, respectively. Both UFD and non-UFD plans ensured that the target received an EQD2 of 96.3 Gy. To investigate the overall improvement in normal tissue sparing with the non-UFD plan, the FLASH-enhanced EQD2 was calculated.Main results.The fractional doses in non-UFD plans ranged between 5.0 Gy and 24.2 Gy. No significant differences were found in EQD22%and EQD298%of targets between UFD and non-UFD plans. However, theD95%of the target in non-UFD plans was significantly reduced by 15.1%. The sparing effect in non-UFD plans was significantly improved. The FLASH-enhanced EQD2meanin normal tissue and ipsilateral lung was significantly reduced by 3.5% and 10.4%, respectively, in non-UFD plans. The overall improvement is attributed to both the FLASH and fractionation effects.Significance.The fractionation dose optimization can address the limitation of multiple-beam FLASH-RT and utilize the relationship between fractional dose and FLASH effect. Consequently, the non-UFD scheme results in further improvements in normal tissue sparing compared to the UFD scheme, attributed to enhanced fractionation and FLASH effects.

Short-Term Electrical Load Forecasting Based on IDBO-PTCN-GRU Model

Accurate electrical load forecasting is crucial for the stable operation of power systems. However, existing forecasting models face limitations when handling multidimensional features and feature interactions. Additionally, traditional metaheuristic algorithms tend to become trapped in local optima during the optimization process, negatively impacting model performance and prediction accuracy. To address these challenges, this paper proposes a short-term electrical load forecasting method based on a parallel Temporal Convolutional Network–Gated Recurrent Unit (PTCN-GRU) model, optimized by an improved Dung Beetle Optimization algorithm (IDBO). This method employs a parallel TCN structure, using TCNs with different kernel sizes to extract and integrate multi-scale temporal features, thereby overcoming the limitations of traditional TCNs in processing multidimensional input data. Furthermore, this paper enhances the optimization performance and global search capability of the traditional Dung Beetle Optimization algorithm through several key improvements. Firstly, Latin hypercube sampling is introduced to increase the diversity of the initial population. Next, the Golden Sine Algorithm is integrated to refine the search behavior. Finally, a Cauchy–Gaussian mutation strategy is incorporated in the later stages of iteration to further strengthen the global search capability. Extensive experimental results demonstrate that the proposed IDBO-PTCN-GRU model significantly outperforms comparison models across all evaluation metrics. Specifically, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were reduced by 15.01%, 14.44%, and 14.42%, respectively, while the coefficient of determination (R2) increased by 2.13%. This research provides a novel approach to enhancing the accuracy of electrical load forecasting.

Smart transportation solutions for faster emergency medical services response using an enhanced whale optimization algorithm

Smart transportation solutions for faster emergency medical services response using an enhanced whale optimization algorithm

Trajectory tracking control of a mobile robot using fuzzy logic controller with optimal parameters

Abstract This work investigates the use of a fuzzy logic controller (FLC) for two-wheeled differential drive mobile robot trajectory tracking control. Due to the inherent complexity associated with tuning the membership functions of an FLC, this work employs a particle swarm optimization algorithm to optimize the parameters of these functions. In order to automate and reduce the number of rule bases, the genetic algorithm is also employed for this study. The effectiveness of the proposed approach is validated through MATLAB simulations involving diverse path tracking scenarios. The performance of the FLC is compared against established controllers, including minimum norm solution, closed-loop inverse kinematics, and Jacobian transpose-based controllers. The results demonstrate that the FLC offers accurate trajectory tracking with reduced root mean square error and controller effort. An experimental, hardware-based investigation is also performed for further verification of the proposed system. In addition, the simulation is conducted for various paths in the presence of noise in order to assess the proposed controller’s robustness. The proposed method is resilient against noise and disturbances, according to the simulation outcomes.

Research on Multiplication Routine Based on Reconfigurable Four-Valued Logic Processor

Despite the indispensable role of traditional electronic computers in modern society, their limitations in parallel processing capabilities, bit-width constraints, and processor bit-width are becoming increasingly apparent, especially when handling large-scale datasets and complex computational tasks. Although hardware technology and algorithm optimization continue to advance, the arithmetic units of traditional computers—adders—remain constrained by carry delay and bit-width limitations. This bottleneck is particularly pronounced in multiplication operations, mainly when adders are used for partial product accumulation. However, since 2018, the emergence of a new type of Reconfigurable Four-Valued Logic Electronic Processor (RFLEP) has provided a potential solution to these traditional limitations. With its large processor bit-width, flexible bit grouping capabilities, and dynamic hardware function reconfiguration features, this processor has brought revolutionary changes to the field of computing. In this context, this paper proposes and implements a Reconfigurable Four-Valued Logic Multiplication Routine (RFLMR) tailored explicitly for the RFLEP. The RFLMR utilizes the Modified Signed-Digit (MSD) representation method in multi-valued logic combined with the M transformation in four-valued logic to generate partial products. These partial products are then efficiently summed in parallel using the JW-MSD parallel adder, achieving the rapid execution of multiplication operations. Experimental results demonstrate that the multiplication routine based on the RFLEP performs multiplication operations accurately and meets theoretical expectations regarding implementation efficiency and performance. This research not only provides new ideas for developing next-generation high-performance computing systems but also paves the way for exploring more efficient and powerful computing models, heralding a profound transformation in future computing technology.

Particle swarm optimization based on data driven for EV charging station siting

Charging stations are an important support facility for electric vehicles (EVs). Nowadays, the EV industry is developing rapidly, however, the imperfect construction of charging stations or the unreasonable choice of location has seriously reduced the desire of people to buy EVs and led to the problem of difficult charging of EVs in many areas. At present, the research field of EV charging station siting suffers from the inability to quickly and accurately calculate the optimal solution for charging station siting. In this regard, a particle swarm optimization based on deep neural networks modified boundaries (DNNMBPSO) is proposed for solving the problem. DNNMBPSO reduces the convergence value of the objective function by applying deep learning to modify the boundary of the particle swarm optimization. DNNMBPSO is an algorithm that combines heuristic and data driven. In this study, DNNMBPSO is applied for siting study in a system having 50 alternative points, 500 alternative points, and 1000 alternative points and a case of siting of electric vehicle charging stations in Nanning, Guangxi, China. The convergence value of the DNNMBPSO-based objective function is found to be at least 5.5 %, 1.7 %, 8.23 % and 14.7 %, lower compared to genetic algorithms, African vulture optimization algorithm, particle swarm optimization, and grey wolf optimization algorithms, respectively. Traditional heuristic optimization algorithms cannot find optimal solutions in large-scale systems, while DNNMBPSO shows feasibility in large-scale systems.

Flower pollination algorithm with ring topology for multi-solution spaces to solve the disassembly line balancing problem

Abstract The Disassembly Line Balancing Problem (DLBP) is of significant importance in the product recycling process. However, existing DLBP research have primarily proposed improved optimization algorithms for single solution space. To provide decision makers with more efficient disassembly solutions, this paper chooses three classical layouts (straight, U-shaped, and parallel) to broaden the solution space and expands an incomplete DLBP with multi-solution spaces (DLBP-MS). By employing the strategy of incomplete disassembly, only the essential components are retained. In DLBP-MS, the disassembly information from used products is processed and imported into the three types of disassembly spaces to be solved and compared to find suitable disassembly solutions. And a multi-objective mathematical model is developed, this includes factors such as workstation count, free time, disassembly smoothness index, carbon emission, and disassembly revenue. The part constraints are established based on directed graphs, and the encoding and decoding methods for multi-solution spaces disassembly sequences in the random incomplete case are designed respectively. A ring topology based flower pollination algorithm (RTFPA) is introduced to effectively address the DLBP-MS. The solution set obtained during the iterative process is divided into subsets based on the congestion level and the overall diversity is preserved by internal optimization of these subsets. After that, the RTFPA is applied to the DLBP-MS of waste cell phones and computers. The improvement of the algorithm's optimization ability under different solution spaces is verified by comparing the results with those obtained from four other algorithms.

Intelligent design of nerve guidance conduits: An artificial intelligence‐driven fluid structure interaction study on modelling and optimization of nerve growth

AbstractNerve guidance conduits (NGCs) have been shown to be effective in promoting nerve regeneration in a variety of clinical applications, including nerve defects resulting from a trauma or surgery. By providing a conducive environment for nerve growth, NGCs can help to restore function in nerve‐damaged patients. Challenges include limited repair length, difficulty replicating natural nerve, and rapid substance degradation affecting neurotrophic factor delivery. Considering these issues with mass transfer and fluid structure interaction (FSI) emphasizes the need for enhancing nerve regeneration efficiency. To facilitate nerve growth and deliver appropriate amount of growth factors, these conduits need to be designed with specific topological, mechanical, and biological properties. Additionally, considerations must be given to functional mass transfer FSI design. An intelligent NGC design is proposed as an evaluation‐optimization and AI‐based method. It is found that design parameters significantly impact the physical properties being optimized, including hydraulic pressure, porosity, diffusivity, water absorption, and maximum stress. The mathematical surrogate model obtained from data‐based modelling is used for artificial intelligence (AI) optimization algorithms, differential evolution (DE), and non‐dominated sorting genetic algorithm II (NSGA‐II). It is revealed that both DE and NSGA algorithms generate nearly identical solutions, ensuring the robustness of ML optimization. Our results show that NGC with the thickness of 750 μm results in more than 170% augmentation of porosity. Moreover, at a constant ovality, increasing the channel thickness results in more than 39.2% augmentation of the maximum stress. The accurate forecasting of physical characteristics on NGC regarding nerve growth factors enables a hopeful outlook for the future clinical treatment of nerve injuries and advanced tissue engineering.

Computing high-degree polynomial gradients in memory

Specialized function gradient computing hardware could greatly improve the performance of state-of-the-art optimization algorithms. Prior work on such hardware, performed in the context of Ising Machines and related concepts, is limited to quadratic polynomials and not scalable to commonly used higher-order functions. Here, we propose an approach for massively parallel gradient calculations of high-degree polynomials, which is conducive to efficient mixed-signal in-memory computing circuit implementations and whose area scales proportionally with the product of the number of variables and terms in the function and, most importantly, independent of its degree. Two flavors of such an approach are proposed. The first is limited to binary-variable polynomials typical in combinatorial optimization problems, while the second type is broader at the cost of a more complex periphery. To validate the former approach, we experimentally demonstrated solving a small-scale third-order Boolean satisfiability problem based on integrated metal-oxide memristor crossbar circuits, with competitive heuristics algorithm. Simulation results for larger-scale, more practical problems show orders of magnitude improvements in area, speed and energy efficiency compared to the state-of-the-art. We discuss how our work could enable even higher-performance systems after co-designing algorithms to exploit massively parallel gradient computation.