Intelligent Optimization Approach Research Articles

Discovering ABO3-type perovskite with different dielectric constants via intelligent optimization algorithm

Nowadays, dielectric materials are playing an increasingly important role in various fields. A high dielectric constant (D) can store more charge per unit volume, improving performance, reducing device size, lowering D limit cross communication, and enabling better packaging of devices. Differentiating high D and low D has been recognized as a significant concern in electronics. However, calculating the dielectric constant from first principles is notoriously difficult. Therefore, it is essential to find important descriptors for predicting the dielectric constant (D) of different dielectric materials. In this work, a novel intelligence optimization approach was proposed based on data-driven methods to predict the dielectric constant (D) of ABO3-type perovskites. By applying the machine learning (ML) algorithm, key features strongly correlated with D were identified. To reduce feature dimension, Random Forest Regression-Gradient Boosting Regressor (RFR-GBR) feature screening, sure independence screening, and the sparsifying operator approach were employed to compress the feature set for creating valid descriptors. Furthermore, the Shapley additive explanation technique was used to reveal the scaling relation between the dielectric constant and the identified descriptors for predicting the D of ABO3-type perovskites. In addition, a hybrid artificial rabbits optimization algorithm and random forest regression were developed for predicting D, achieving remarkable predictive performance with an R2 score of 0.95, MAE of 0.23, and RMSE of 0.108 using five-fold cross-validation. Ultimately, from a pool of 300 candidate materials, we screened and identified two potential dielectric perovskites with different D values. The proposed framework will facilitate D prediction technology for the discovery of dielectric perovskite materials with favorable performance.

A multi-objective planning and scheduling model for elective and emergency cases in the operating room under uncertainty

Hospitals are paramount hubs for delivering healthcare services, with their Operating Rooms (ORs) as a pivotal and financially substantial component. Efficient surgery ward planning is crucial in healthcare institutions, aiming to improve medical service quality while reducing costs. This research delves into the intricacies of integrated OR planning and scheduling, focusing on elective and emergency patients in an uncertain environment. To address these challenges, a mixed integer programming (MIP) framework is developed to minimize inactivity and patient wait times while optimizing high-priority resource allocation. Both upstream and downstream units of the ward, the Pre-operative Holding Unit (PHU), Post Anesthesia Care Unit (PACU), and Intensive Care Unit (ICU) are included. The inherently uncertain aspects of surgery, including surgical duration, Length of Stay (LOS), and the influx of emergency patients, demand an intelligent optimization approach. Consequently, a robust optimization strategy is harnessed to effectively grapple with this pervasive uncertainty. A deterministic model is introduced and improved using an enhanced epsilon constraint method. The culmination of this analytical journey yields a collection of Pareto-optimal solutions. Empirical results, supported by managerial insights, highlight the superiority of the proposed method over the traditional weighting approach.

Optimal operation and control of hybrid power systems with stochastic renewables and FACTS devices: An intelligent multi-objective optimization approach

This paper delves into the increasingly complex domain of Optimal Power Flow (OPF) within modern power systems, enhanced by the integration of unpredictable renewable energy sources. The research originally integrates stochastic photovoltaic and wind energy sources, along with a suite of Flexible AC Transmission System (FACTS) components – including thyristor-controlled series compensators, static VAR compensators, and thyristor-controlled phase shifters. The primary objective is to solve the OPF problem by reducing generation costs while accommodating the variable nature of renewable energy sources and load demands. This study prioritizes the examination of both constant and fluctuating load requirements. The inherent variability of PV and wind energy, along with load demand, is captured through the modelling of probability density functions. This approach enables a more detailed optimization process, incorporating not just the cost of thermal energy generation but also the scheduling costs of renewable sources and associated penalty costs. Moreover, the study examines the strategic placement and sizing of FACTS components, an aspect essential in minimizing the overall cost of power production. Employing both single- and multi-objective optimization algorithms, the research addresses the OPF problem in a modified IEEE-30 bus system through various case studies. The application of the recently developed flow direction algorithm, including its multi-objective variant with an ε-based constraint-handling mechanism to OPF problem is the primary contributions of this work. The results, benchmarked against several advanced metaheuristic algorithms, reveal the proposed algorithm's superior performance. This comprehensive study not only underscores the potential of integrating renewable energy sources into the grid but also highlights the efficacy of intelligent optimization strategies in managing the complexities of modern power systems.

Advancing capacitive deionization via intelligent optimization approach part 1: Development of tunable low-cost carbon electrodes

This work introduces a novel approach based on utilizing intelligent data-based optimization techniques to fabricate high-quality and low-cost carbon electrodes for capacitive deionization (CDI). The effects of synthesis parameters of peanut shell-derived porous carbon (PSPC) (KOH to Raw ratio, pyrolysis temperature and time) on five factors (production yield, BET surface area, electrode specific capacitance, and ion transfer resistances, including interface and diffusion) were investigated using combined design-of-experiment (DOE), response-surface-methodology (RSM), and Sobol's sensitivity analysis (SA). Optimal scenarios providing maximum production yield, surface area, and specific capacitance, and minimum ion transfer resistances were found using a multi-objective genetic algorithm (GA). Results uncover that increasing the impregnation ratio, pyrolysis temperature and time enhances the PSPC porosity that causes the carbon surface area and electrode specific capacitance to promote, the production yield to reduce and ion transfer resistances to raise. From Sobol's SA, pyrolysis temperature was the most impacting parameter on all decision factors. The optimal PSPC prepared at 750 °C and minimum KOH:Raw = 1 and time = 30 min offered a developed mesoporous structure with BET surface area of 685 m2/g and the resulting electrode had an outstanding specific capacitance (127.13 at 5 mV/s), with reliable durability after 100 cycles and showing the lowest possible ion transfer resistances.

Multi-agent intelligent optimization approach for optimal wireless rapid charging and SOC estimation of hybrid electric vehicle

Multi-agent intelligent optimization approach for optimal wireless rapid charging and SOC estimation of hybrid electric vehicle

A Survey on Intelligent Optimization Approaches to Boiler Combustion Optimization

A Survey on Intelligent Optimization Approaches to Boiler Combustion Optimization

Walrus optimizer: A novel nature-inspired metaheuristic algorithm

Metaheuristic algorithms are intelligent optimization approaches that lead the searching procedure through utilizing exploitation and exploration. The increasing complexity of real-world optimization problem has prompted the development of more metaheuristic algorithms. Hence, this work proposes a novel swarm intelligence algorithm, Walrus optimizer (WO). It is inspired by the behaviors of walruses that choose to migrate, breed, roost, feed, gather and escape by receiving key signals (danger signals and safety signals). To test the capability of the proposed algorithm, 23 standard functions and the benchmark suite from the IEEE (Institute of Electrical and Electronics Engineers) Congress on Evolutionary Computation (CEC) 2021 are used. In addition, to evaluate the practicability of the proposed algorithm to solve various real-world optimization problems, 6 standard classical engineering optimization problems are examined and compared. For statistical purposes, 100 independent optimization runs are conducted to determine the statistical measurements, including the mean, standard deviation, and the computation time of the program, by considering a predefined stopping criterion. Some well-known statistical analyses are also used for comparative purposes, including the Friedman and Wilcoxon analysis. The results demonstrate that the proposed algorithm can provide special stability features and very competitive performance in dealing with high-dimensional benchmarks and real-world problems. The proposal of WO promotes the continuous development and application expansion of artificial intelligence, improves the efficiency of optimization calculation, and provides powerful tools for solving complex problems in the real world. The source code of WO is publicly availabe at https://ww2.mathworks.cn/matlabcentral/fileexchange/154702-walrus-optimizer-wo.

A novel control scheme for automatic voltage regulator using novel modified artificial rabbits optimizer

Automatic voltage regulators (AVRs) are essential components in electrical systems to maintain stable voltage output, ensuring optimal performance and equipment protection. The effectiveness of AVRs rely on key parameters such as voltage regulation, response time, stability, and efficiency. Integrating controllers with AVRs offers centralized monitoring and regulation, enhancing voltage output efficiency. This study therefore first discusses various controller types which showcase distinctive capabilities based on their tuning strategies and then introduces an intelligent optimization approach using fractional-order proportional-integral-derivative with double derivative (FOPIDD2) controller tailored for AVRs. The study adopts the artificial rabbits optimization (ARO) algorithm, fortified with an adaptive local search (ALS) mechanism and experience-based perturbed learning (EPL) strategy. The modified version (m-ARO) enhances solution diversity and navigational efficacy, resulting in improved optimization quality. The study's core objective is to demonstrate the superior performance of the m-ARO-based FOPIDD2 controller in addressing the multifaceted challenges of AVR control, outperforming conventional techniques in stability, speed of response, robustness, and efficiency. To validate the method's efficacy, a comparative analysis is conducted using existing controllers with different tuning algorithms. Results indicate that the proposed m-ARO-based FOPIDD2 controller achieves superior performance metrics, demonstrating its capability. The study also includes a wider perspective by considering nineteen different controllers, reported in the literature, for comprehensive comparison. Notably, the proposed method exhibits the best stability, further affirming its effectiveness.

An intelligent technique for network resource management and analysis of 5G-IoT smart healthcare application

<p>The transformation of traditional hospitals to a smart healthcare system is a patient-centric, reliable, and smarter way to enhance healthcare facilities for individuals. These healthcare devices are based on the Internet of Things (IoT) and conventional network structures such as 3rd generation (3G) or 4th generation (4G). That did not provide the patient health data in the real-time scenario. Therefore, the integrated 5G-IoT system offers distinct benefits such as remote monitoring, surgery, and real-time data analysis of healthcare system that is operated with slice of dedicated network. It generates huge data with the billions of healthcare equipment that can be evaluated for decision making. This enormous data requires low latency, high-security, capacity, and more reliable techniques for evaluation. In this scenario, network slicing is one of the key solutions that can be considered for isolated end-to-end network structures. Network slicing enables logical and independent network virtual networks to be multiplexed for the same physical network. In this article, we propose an intelligent approach for network resource optimization and analysis of a 5G-IoT based smart healthcare network. We examine the performance of the machine learning algorithm by an automatic model, named A Fast Library for automated machine learning (FLAML). In addition, we perform network slicing to obtain resource optimization in 5G-IoT-based healthcare application.<strong></strong></p>

When architecture meets RL+EA: A hybrid intelligent optimization approach for selecting combat system-of-systems architecture

In recent decades, various domains such as military, aerospace and supply chain are becoming increasingly complex, forming system-of-systems (SoSs). The effectiveness of SoSs is mainly determined by their architecture, thus selecting proper architecture for SoSs is crucial to maximize their effectiveness. While most studies focus on specific issues, such as mission planning and SoSs meta-architecture selection, few papers deal with the two issues together. This paper introduces the task oriented SoSs architecture selection problem (TSASP) and proposes an combination of reinforcement learning (RL) and evolutionary algorithm (EA) (CRE) to address this problem. CRE comprises two layers: the inner layer utilizes a DQN-based algorithm to solve the mission planning problem, and the outer layer with improved genetic algorithm optimizes the SoSs architecture selection based on the output of inner one. A synthetic air and missile defense (AMD) example is conducted to test the effectiveness of proposed method, and results show that CRE can improve the mission planning effect by select a feasible architecture.

HYBRID PARTICLE SWARM OPTIMIZATION AND GREY WOLF OPTIMIZER FOR SETTING PID PARAMETERS OF BLDC MOTORS

BLDC (Brushless DC) motors have advantages over asynchronous motors and dc motors in various aspects. Particularly in electric bicycles and flying cars, BLDC motors are utilized widely. Electric bicycles and flying cars are becoming increasingly popular, and as a result, the significance of BLDC motors and their cost-effective and efficient utilization has been growing rapidly. PID (Proportional Integral Derivative) controllers are generally used in motor control because they are cheap and perform well. Many methods have been used to adjust PID parameters. Although methods such as Ziegler-Nichols, Cohen-Coon etc. are widely used, there are also new methods such as optimization algorithms PSO (Particle Swarm Optimization), Whale Optimization Technique, Gray Wolf Optimization technique etc. The hybrid method: HPSOGWO (Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer) is a combination of PSO and GWO (Grey Wolf Optimizer) techniques, and it can be used for tuning PID parameters. As associated with this, the aim of this study is to show the superiority of HPSOGWO algorithm in optimizing the PID parameters. In the content of this study, the essentials regarding the optimization background, and details of the BLDC motor modeling was explained first. After that, the methodology of the hybrid solution was expressed and then the application phase was explained in detail, by including the results generally. In the context of the intelligent optimization approach of this study, the results were obtained in the MATLAB Simulink environment. The application of the used solution method revealed its superiority over the study conducted solely with GWO in various parameters.

Intelligent Optimization Approach to Cell Formation and Product Scheduling for Multifactory Cellular Manufacturing Systems Considering Supply Chain and Operational Error

This study designs a joint decision model to solve cell formation and product scheduling problems together in multifactory cellular manufacturing systems. If a product is processed in a factory with a small logistics cost of its raw material, the logistics cost of the product delivered to the distributor may be large. If managers prefer the pair of equipment unit and worker with a high production rate, operational error rate may be large. Meanwhile, it influences the labor cost, raw material cost, and completion time of each product. Managers can assign the products of a type to several cells for processing. If a cell receives fewer products of a type, it can complete their processing and becomes available to process the products of other types. However, some cells receiving more products of this type may be delayed to handle the products of other types. If a type of product in a cell has a priority to be processed, its backorder cost may be none or reduced, but this schedule may delay other product types with lower priority in the cell, and give rise to their backorder cost. For solving the intertwined optimization problem, an improved bacterial foraging algorithm with a priority rule-based heuristic (IBFAP) is developed to maximize profit. Experiments are conducted to show that IBFAP outperforms the improved bacterial foraging algorithm with no heuristic, the classical bacterial foraging algorithm, genetic algorithm, and simulated annealing.

GA-BiLSTM: an intelligent energy prediction and optimization approach for individual home appliances

GA-BiLSTM: an intelligent energy prediction and optimization approach for individual home appliances

A new approach for flow simulation in complex hydraulic fracture morphology and its application: Fracture connection element method

Efficient flow simulation and optimization methods of hydraulic fracture morphology in unconventional reservoirs are effective ways to enhance oil/gas recovery. Based on the connection element method (CEM) and distribution of stimulated reservoir volume, the complex hydraulic fracture morphology was accurately described using heterogeneous node connection system. Then a new fracture connection element method (FCEM) for fluid flow in stimulated unconventional reservoirs with complex hydraulic fracture morphology was proposed. In the proposed FCEM, the arrangement of dense nodes in the stimulated area and sparse nodes in the unstimulated area ensures the calculation accuracy and efficiency. The key parameter, transmissibility, was also modified according to the strong heterogeneity of stimulated reservoirs. The finite difference and semi-analytical tracking were used to accurately solve the pressure and saturation distribution between nodes. The FCEM is validated by comparing with traditional numerical simulation method, and the results show that the bottom hole pressure simulated by the FCEM is consistent with the results from traditional numerical simulation method, and the matching rate is larger than 95%. The proposed FCEM was also used in the optimization of fracturing parameters by coupling the hydraulic fracture propagation method and intelligent optimization algorithm. The integrated intelligent optimization approach for multi-parameters, such as perforation number, perforation location, and displacement in hydraulic fracturing is proposed. The proposed approach was applied in a shale gas reservoir, and the result shows that the optimized perforation location and morphology distribution are related to the distribution of porosity/permeability. When the perforation location and displacement are optimized with the same fracture number, NPV increases by 70.58%, which greatly improves the economic benefits of unconventional reservoirs. This work provides a new way for flow simulation and optimization of hydraulic fracture morphology of multi-fractured horizontal wells in unconventional reservoirs.

Integrated Intelligent Method Based on Fuzzy Logic for Optimizing Laser Microfabrication Processing of GnPs-Improved Alumina Nanocomposites

Studies on using multifunctional graphene nanostructures to enhance the microfabrication processing of monolithic alumina are still rare and too limited to meet the requirements of green manufacturing criteria. Therefore, this study aims to increase the ablation depth and material removal rate and minimize the roughness of the fabricated microchannel of alumina-based nanocomposites. To achieve this, high-density alumina nanocomposites with different graphene nanoplatelet (GnP) contents (0.5 wt.%, 1 wt.%, 1.5 wt.%, and 2.5 wt.%) were fabricated. Afterward, statistical analysis based on the full factorial design was performed to study the influence of the graphene reinforcement ratio, scanning speed, and frequency on material removal rate (MRR), surface roughness, and ablation depth during low-power laser micromachining. After that, an integrated intelligent multi-objective optimization approach based on the adaptive neuro-fuzzy inference system (ANIFS) and multi-objective particle swarm optimization approach was developed to monitor and find the optimal GnP ratio and microlaser parameters. The results reveal that the GnP reinforcement ratio significantly affects the laser micromachining performance of Al2O3 nanocomposites. This study also revealed that the developed ANFIS models could obtain an accurate estimation model for monitoring the surface roughness, MRR, and ablation depth with fewer errors than 52.07%, 100.15%, and 76% for surface roughness, MRR, and ablation depth, respectively, in comparison with the mathematical models. The integrated intelligent optimization approach indicated that a GnP reinforcement ratio of 2.16, scanning speed of 342 mm/s, and frequency of 20 kHz led to the fabrication of microchannels with high quality and accuracy of Al2O3 nanocomposites. In contrast, the unreinforced alumina could not be machined using the same optimized parameters with low-power laser technology. Henceforth, an integrated intelligence method is a powerful tool for monitoring and optimizing the micromachining processes of ceramic nanocomposites, as demonstrated by the obtained results.

A DRL-Based Intelligent Jamming Approach for Joint Channel and Power Optimization

The traditional jamming methods mainly focus on the optimization of a single domain, such as frequency domain. However, in the practical wireless communication, the jamming methods in single dimension domain are limited and difficult to cope with multidomain scenarios. To overcome these issues, this paper investigates the problem of joint decision-making for jamming channel and power in a dynamic spectrum environment. Firstly, the Markov decision process (MDP) is used to formulate the jamming channel and power joint selection problem. Then, a deep reinforcement learning- (DRL-) based jamming algorithm is proposed with the function of parallel learning and joint decision-making. Specially, to accelerate the learning speed of the algorithm, the prioritized experience reply (PER) technology is introduced. Finally, a practical jamming testbed is built to evaluate the proposed algorithm. The simulation results and the testbed results demonstrate the effectiveness of the proposed algorithm, which can guarantee the jamming effect and maximize the utilization of jamming resources.

Computational intelligent optimization approach based on Particle Swarm Optimization and Extended Finite Element Method for high-cycle fatigue life extension

Computational intelligent optimization approach based on Particle Swarm Optimization and Extended Finite Element Method for high-cycle fatigue life extension

Intelligent Energy-Efficient Train Trajectory Optimization Approach Based on Supervised Reinforcement Learning for Urban Rail Transits

Artificial intelligence of things (AIoT)-enabled intelligent automatic train operation (iATO) is an urgently needed technology to expand the capability of ATO in addressing the real-time responsiveness and dynamic online challenges to energy-efficient train trajectory optimization (TTO) and its associated ride-comfort, punctuality, and safety issues in modern urban rail transit networks. This paper proposes a three-step supervised reinforcement learning-based intelligent energy-efficient train trajectory optimization (SRL-IETTO) approach for iATO by hybrid-integrating deep reinforcement learning (DRL) and supervised learning. First, multiple objectives are formulated based on real-time train operation and systematically integrated into the RL algorithm by a binary function-based goal-directed reward design method. Second, an IETTO model is established to handle uncertain disturbances in real-time train operation and generate optimal energy-efficient train trajectories online by optimizing energy efficiency and receiving supervisory information from trajectories of pre-trained TTO models. Finally, numerical simulations are implemented to validate the effectiveness of the SRL-IETTO using in-service subway line data. The results demonstrate the superiority and improved energy saving of the proposed approach, and confirm its adaptability to online trip time adjustments within the practical running time range under uncertain disturbances with less trip time error compared to other intelligent TTO algorithms.

A combined multi-objective intelligent optimization approach considering techno-economic and reliability factors for hybrid-renewable microgrid systems

This paper proposes a novel optimization strategy for hybrid-renewable energy systems in microgrids. The multi-objective optimization approach is formulated for a PV-wind-diesel-battery hybrid system. Its main objectives are to minimize the levelized cost of energy (LCOE) and loss of power supply probability (LPSP) whilst maximizing the use of renewable energy sources (RES). The proposed optimization strategy combines the Taguchi method with a novel fuzzy decision-maker-based multi-objective optimization algorithm. It implements, a) an energy management strategy to optimize the use of the energy sources, b) a Taguchi method to determine the upper bounds of the model's decision variables, c) a multi-objective moth flame optimization algorithm to optimize the size of the renewable energy sources, and d) a fuzzy decision-making approach to obtain the best Pareto front. The proposed strategy was implemented to optimize the design of a hybrid renewable energy system based on three different scenarios consisting of 10, 15, and 20 residential houses located in Sønderborg, a town in the Region of Southern Denmark. The results of the proposed model in terms of loss of power supply probability and levelized cost of energy for the scenarios I, II, and III are [0.224, 0.754], [0.313, 0.612], and [0.368, 0.547] respectively. In addition, the optimal design of the hybrid renewable-based microgrid system for scenarios I, II, and III are [PV: 32 kW, AD: 4.86, WT: 6], [PV: 36 kW, AD: 4.73, WT: 7], and [PV: 47 kW, AD: 5, WT: 7], respectively. The effectiveness of the proposed multi-objective optimization algorithm (MOMFO) in solving the optimization problem was examined and the results were further compared with those of the Non-Dominated Sorting Algorithm II(NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Social Engineering Optimizer (MOSEO).

Direct Evaluation of Treatment Response in Brain Metastatic Disease with Deep Neuroevolution.

Cancer centers have an urgent and unmet clinical and research need for AI that can guide patient management. A core component of advancing cancer treatment research is assessing response to therapy. Doing so by hand, for example, as per RECIST or RANO criteria, is tedious and time-consuming, and can miss important tumor response information. Most notably, the prevalent response criteria often exclude lesions, the non-target lesions, altogether. We wish to assess change in a holistic fashion that includes all lesions, obtaining simple, informative, and automated assessments of tumor progression or regression. Because genetic sub-types of cancer can be fairly specific and patient enrollment in therapy trials is often limited in number and accrual rate, we wish to make response assessments with small training sets. Deep neuroevolution (DNE) is a novel radiology artificial intelligence (AI) optimization approach that performs well on small training sets. Here, we use a DNE parameter search to optimize a convolutional neural network (CNN) that predicts progression versus regression of metastatic brain disease. We analyzed 50 pairs of MRI contrast-enhanced images as our training set. Half of these pairs, separated in time, qualified as disease progression, while the other 25 image pairs constituted regression. We trained the parameters of a CNN via "mutations" that consisted of random CNN weight adjustments and evaluated mutation "fitness" as summed training set accuracy. We then incorporated the best mutations into the next generation's CNN, repeating this process for approximately 50,000 generations. We applied the CNNs to our training set, as well as a separate testing set with the same class balance of 25 progression and 25 regression cases. DNE achieved monotonic convergence to 100% training set accuracy. DNE also converged monotonically to 100% testing set accuracy. We have thus shown thatDNE can accurately classify brain metastatic disease progression versus regression. Future work will extend the input from 2D image slices to full 3D volumes, and include the category of "no change." We believe that an approach such as ours can ultimately provide a useful and informative complement to RANO/RECIST assessment and volumetric AI analysis.