Optimal Classification of Minerals by Microscopic Image Analysis Based on Seven-State “Deep Learning” Combined with Optimizers

The development of artificial intelligence (AI), particularly deep learning, has made it possible to accelerate and improve the processing of data collected in different fields (commerce, medicine, surveillance or security, agriculture, etc.). Most related works use open source consistent image databases. This is the case for ImageNet reference data such as coco data, IP102, CIFAR-10, STL-10 and many others with variability representatives. The consistency of its images contributes to the spectacular results observed in its fields with deep learning. The application of deep learning which is making its debut in geology does not, to our knowledge, include a database of microscopic images of thin sections of open source rock minerals. In this paper, we evaluate three optimizers under the AlexNet architecture to check whether our acquired mineral images have object features or patterns that are clear and distinct to be extracted by a neural network. These are thin sections of magmatic rocks (biotite and 2-mica granite, granodiorite, simple granite, dolerite, charnokite and gabbros, etc.) which served as support. We use two hyper-parameters: the number of epochs to perform complete rounds on the entire data set and the “learning rate” to indicate how quickly the weights in the network will be modified during optimization. Using Transfer Learning, the three (3) optimizers all based on the gradient descent methods of Stochastic Momentum Gradient Descent (sgdm), Root Mean Square Propagation (RMSprop) algorithm and Adaptive Estimation of moment (Adam) achieved better performance. The recorded results indicate that the Momentum optimizer achieved the best scores respectively of 96.2% with a learning step set to 10−3 for a fixed choice of 350 epochs during this variation and 96, 7% over 300 epochs for the same value of the learning step. This performance is expected to provide excellent insight into image quality for future studies. Then they participate in the development of an intelligent system for the identification and classification of minerals, seven (7) in total (quartz, biotite, amphibole, plagioclase, feldspar, muscovite, pyroxene) and rocks.

BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving Engineering Design Problems

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems.This approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization tasks.In this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)optimizer.The proposed hybrid algorithm will be referred to as BHJO.Through this fusion,the BHJO algorithm aims to leverage the strengths of each optimizer.Before this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and exploitation.This meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization performance.In addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid algorithm.Moreover,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem domains.Similarly,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation metrics.This rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedding light on its effectiveness and reliability in solving optimization problems.Finally,the obtained numerical statistics underwent rigorous analysis using the Friedman post hoc Dunn’s test.The resulting numerical values revealed the BHJO algorithm’s competitiveness in tackling intricate optimization problems,affirming its capability to deliver favorable outcomes in challenging scenarios.

Optimal Flame Detection of Fires in Videos Based on Deep Learning and the Use of Various Optimizers

Deep learning has recently attracted a lot of attention with the aim of developing a fast, automatic and accurate system for image identification and classification. In this work, the focus was on transfer learning and evaluation of state-of-the-art VGG16 and 19 deep convolutional neural networks for fire image classification from fire images. In this study, five different approaches (Adagrad, Adam, AdaMax, Nadam and Rmsprop) based on the gradient descent methods used in parameter updating were studied. By selecting specific learning rates, training image base proportions, number of recursion (epochs), the advantages and disadvantages of each approach are compared with each other in order to achieve the minimum cost function. The results of the comparison are presented in the tables. In our experiment, Adam optimizers with the VGG16 architecture with 300 and 500 epochs tend to steadily improve their accuracy with increasing number of epochs without deteriorating performance. The optimizers were evaluated on the basis of their AUC of the ROC curve. It achieves a test accuracy of 96%, which puts it ahead of other architectures.

Comparison of optimizers for model predictive thermal control of buildings

Considering recent developments in the energy sector,further reduction of electricity cost and flattening of the electric power demand curve are needed.We have focused on an autonomous electric heater control system that can easily be implemented in existing buildings without strict comfort requirements.Examples are winter heating of warehouses and vacation homes,and heat drying of buildings under construction.We have set up a system that typically reduces electricity cost by about 40%on the basis of automatic weather and real time pricing forecasts.The system uses the building as an energy reservoir over periods with high electricity cost.Using a model predictive control system,we compare use of a genetic algorithm,a particle swarm optimization,and a neural network for heater control,all working in a closed loop to reduce the influence of modeling errors.We have simulated the performance of the systems using realistic data and found that all three optimizers give about the same performance,varying only a few percent in efficiency.However,the computational and memory requirements of the neural network are much lower than for the other optimizers,so it is preferable for use with inexpensive microcontrollers.We carried out a full-scale experiment at a residential house and found agreement with simulation results.

A Multi-Objective Particle Swarm Optimization Algorithm Based on Decomposition and Multi-Selection Strategy

The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search efficiency.First,two update strategies based on decomposition are used to update the evolving population and external archive,respectively.Second,a multiselection strategy is designed.The first strategy is for the subspace without a non-dominated solution.Among the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global search.The second strategy is for the subspace with a non-dominated solution.In the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local search.The third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated solutions.In the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to search.Third,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search efficiency.Finally,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test problems.The results show that the proposed algorithm has better performance.

Evolutionary Particle Swarm Optimization Algorithm Based on Collective Prediction for Deployment of Base Stations

The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real life.The development of the Internet of Things(IoT)relies on the support of base stations,which provide a solid foundation for achieving a more intelligent way of living.In a specific area,achieving higher signal coverage with fewer base stations has become an urgent problem.Therefore,this article focuses on the effective coverage area of base station signals and proposes a novel Evolutionary Particle Swarm Optimization(EPSO)algorithm based on collective prediction,referred to herein as ECPPSO.Introducing a new strategy called neighbor-based evolution prediction(NEP)addresses the issue of premature convergence often encountered by PSO.ECPPSO also employs a strengthening evolution(SE)strategy to enhance the algorithm’s global search capability and efficiency,ensuring enhanced robustness and a faster convergence speed when solving complex optimization problems.To better adapt to the actual communication needs of base stations,this article conducts simulation experiments by changing the number of base stations.The experimental results demonstrate thatunder the conditionof 50 ormore base stations,ECPPSOconsistently achieves the best coverage rate exceeding 95%,peaking at 99.4400%when the number of base stations reaches 80.These results validate the optimization capability of the ECPPSO algorithm,proving its feasibility and effectiveness.Further ablative experiments and comparisons with other algorithms highlight the advantages of ECPPSO.

DDoS Attack Autonomous Detection Model Based on Multi-Strategy Integrate Zebra Optimization Algorithm

Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convolutional Neural Networks(CNN)combined with LSTM,and so on are built by simple stacking,which has the problems of feature loss,low efficiency,and low accuracy.Therefore,this paper proposes an autonomous detectionmodel for Distributed Denial of Service attacks,Multi-Scale Convolutional Neural Network-Bidirectional Gated Recurrent Units-Single Headed Attention(MSCNN-BiGRU-SHA),which is based on a Multistrategy Integrated Zebra Optimization Algorithm(MI-ZOA).The model undergoes training and testing with the CICDDoS2019 dataset,and its performance is evaluated on a new GINKS2023 dataset.The hyperparameters for Conv_filter and GRU_unit are optimized using the Multi-strategy Integrated Zebra Optimization Algorithm(MIZOA).The experimental results show that the test accuracy of the MSCNN-BiGRU-SHA model based on the MIZOA proposed in this paper is as high as 0.9971 in the CICDDoS 2019 dataset.The evaluation accuracy of the new dataset GINKS2023 created in this paper is 0.9386.Compared to the MSCNN-BiGRU-SHA model based on the Zebra Optimization Algorithm(ZOA),the detection accuracy on the GINKS2023 dataset has improved by 5.81%,precisionhas increasedby 1.35%,the recallhas improvedby 9%,and theF1scorehas increasedby 5.55%.Compared to the MSCNN-BiGRU-SHA models developed using Grid Search,Random Search,and Bayesian Optimization,the MSCNN-BiGRU-SHA model optimized with the MI-ZOA exhibits better performance in terms of accuracy,precision,recall,and F1 score.

Prediction and optimization of flue pressure in sintering process based on SHAP

Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.

Collaborative Trajectory Planning for Stereoscopic Agricultural Multi-UAVs Driven by the Aquila Optimizer

Stereoscopic agriculture,as an advanced method of agricultural production,poses new challenges for multi-task trajectory planning of unmanned aerial vehicles(UAVs).To address the need for UAVs to perform multi-task trajectory planning in stereoscopic agriculture,a multi-task trajectory planning model and algorithm(IEP-AO)that synthesizes flight safety and flight efficiency is proposed.Based on the requirements of stereoscopic agricultural geomorphological features and operational characteristics,the multi-task trajectory planning model is ensured by constructing targeted constraints at five aspects,including the path,slope,altitude,corner,energy and obstacle threat,to improve the effectiveness of the trajectory planning model.And combined with the path optimization algorithm,an Aquila optimizer(IEP-AO)based on the interference-enhanced combination model is proposed,which can help UAVs to improve the trajectory search capability in complex operation space and large-scale operation tasks,and jump out of the locally optimal trajectory path region timely,to generate the optimal trajectory planning plan that can adapt to the diversity of the tasks and the flight efficiency.Meanwhile,four simulated flights with different operation scales and different scene constraints were conducted under the constructed real 3Dimension scene,and the experimental results can show that the proposedmulti-task trajectory planning method canmeet themulti-task requirements in stereoscopic agriculture and improve the mission execution efficiency and agricultural production effect of UAV.

Application of the improved dung beetle optimizer,muti-head attention and hybrid deep learning algorithms to groundwater depth prediction in the Ningxia area,China

Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.

5DGWO-GAN:A Novel Five-Dimensional Gray Wolf Optimizer for Generative Adversarial Network-Enabled Intrusion Detection in IoT Systems

The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are essential.Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns.This paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT systems.However,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss functions.To address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN hyper-parameters.The 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local minima.The proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model training.The generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded features.We evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and IoT-23.Experiments conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false positives.The 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence trend.The proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and IoT-23 datasets,with values of 0.24,1.10,and 0.09,respectively.Additionally,it attained the highest accuracy,ranging from 94%to 100%.These results suggest a promising direction for future IoT security frameworks,offering a scalable and efficient solution to safeguard against evolving cyber threats.

Optimal Scheduling of an Independent Electro-Hydrogen System with Hybrid Energy Storage Using a Multi-Objective Standardization Fusion Method

In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is crucial.Still,it presents a challenge due to the significant deviations in values ofmultiple optimization objective functions caused by their physical dimensions.These deviations seriously affect the scheduling process.A novel standardization fusion method has been established to address this issue by analyzing the variation process of each objective function’s values.The optimal scheduling results of IEHS with HESS indicate that the economy and overall energy loss can be improved 2–3 times under different optimization methods.The proposed method better balances all optimization objective functions and reduces the impact of their dimensionality.When the cost of BESS decreases by approximately 30%,its participation deepens by about 1 time.Moreover,if the price of the electrolyzer is less than 15￥/kWh or if the cost of the fuel cell drops below 4￥/kWh,their participation will increase substantially.This study aims to provide a more reasonable approach to solving multi-objective optimization problems.

Recent Advancements in the Optimization Capacity Configuration and Coordination Operation Strategy of Wind-Solar Hybrid Storage System

Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longer period.A multi-objective genetic algorithm(MOGA)and state of charge(SOC)region division for the batteries are introduced to solve the objective function and configuration of the system capacity,respectively.MATLAB/Simulink was used for simulation test.The optimization results show that for a 0.5 MW wind power and 0.5 MW photovoltaic system,with a combination of a 300 Ah lithium battery,a 200 Ah lead-acid battery,and a water storage tank,the proposed strategy reduces the system construction cost by approximately 18,000 yuan.Additionally,the cycle count of the electrochemical energy storage systemincreases from4515 to 4660,while the depth of discharge decreases from 55.37%to 53.65%,achieving shallow charging and discharging,thereby extending battery life and reducing grid voltage fluctuations significantly.The proposed strategy is a guide for stabilizing the grid connection of wind and solar power generation,capability allocation,and energy management of energy conservation systems.

Bilevel Optimal Scheduling of Island Integrated Energy System Considering Multifactor Pricing

In this paper,a bilevel optimization model of an integrated energy operator(IEO)–load aggregator(LA)is constructed to address the coordinate optimization challenge of multiple stakeholder island integrated energy system(IIES).The upper level represents the integrated energy operator,and the lower level is the electricity-heatgas load aggregator.Owing to the benefit conflict between the upper and lower levels of the IIES,a dynamic pricing mechanism for coordinating the interests of the upper and lower levels is proposed,combined with factors such as the carbon emissions of the IIES,as well as the lower load interruption power.The price of selling energy can be dynamically adjusted to the lower LA in the mechanism,according to the information on carbon emissions and load interruption power.Mutual benefits and win-win situations are achieved between the upper and lower multistakeholders.Finally,CPLEX is used to iteratively solve the bilevel optimization model.The optimal solution is selected according to the joint optimal discrimination mechanism.Thesimulation results indicate that the sourceload coordinate operation can reduce the upper and lower operation costs.Using the proposed pricingmechanism,the carbon emissions and load interruption power of IEO-LA are reduced by 9.78%and 70.19%,respectively,and the capture power of the carbon capture equipment is improved by 36.24%.The validity of the proposed model and method is verified.

Multi-Stage Voltage Control Optimization Strategy for Distribution Networks Considering Active-Reactive Co-Regulation of Electric Vehicles

The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle(EV)charging resources further aggravate the voltage fluctuation of the distribution network,and the existing research has not deeply explored the EV active-reactive synergistic regulating characteristics,and failed to realize themulti-timescale synergistic control with other regulatingmeans,For this reason,this paper proposes amultilevel linkage coordinated optimization strategy to reduce the voltage deviation of the distribution network.Firstly,a capacitor bank reactive power compensation voltage control model and a distributed photovoltaic(PV)activereactive power regulationmodel are established.Additionally,an external characteristicmodel of EVactive-reactive power regulation is developed considering the four-quadrant operational characteristics of the EVcharger.Amultiobjective optimization model of the distribution network is then constructed considering the time-series coupling constraints of multiple types of voltage regulators.A multi-timescale control strategy is proposed by considering the impact of voltage regulators on active-reactive EV energy consumption and PV energy consumption.Then,a four-stage voltage control optimization strategy is proposed for various types of voltage regulators with multiple time scales.Themulti-objective optimization is solved with the improvedDrosophila algorithmto realize the power fluctuation control of the distribution network and themulti-stage voltage control optimization.Simulation results validate that the proposed voltage control optimization strategy achieves the coordinated control of decentralized voltage control resources in the distribution network.It effectively reduces the voltage deviation of the distribution network while ensuring the energy demand of EV users and enhancing the stability and economic efficiency of the distribution network.

Grid-Connected/Islanded Switching Control Strategy for Photovoltaic Storage Hybrid Inverters Based on Modified Chimpanzee Optimization Algorithm

Uneven power distribution,transient voltage,and frequency deviations are observed in the photovoltaic storage hybrid inverter during the switching between grid-connected and island modes.In response to these issues,this paper proposes a grid-connected/island switching control strategy for photovoltaic storage hybrid inverters based on the modified chimpanzee optimization algorithm.The proposed strategy incorporates coupling compensation and power differentiation elements based on the traditional droop control.Then,it combines the angular frequency and voltage amplitude adjustments provided by the phase-locked loop-free pre-synchronization control strategy.Precise pre-synchronization is achieved by regulating the virtual current to zero and aligning the photovoltaic storage hybrid inverter with the grid voltage.Additionally,two novel operators,learning and emotional behaviors are introduced to enhance the optimization precision of the chimpanzee algorithm.These operators ensure high-precision and high-reliability optimization of the droop control parameters for photovoltaic storage hybrid inverters.A Simulink model was constructed for simulation analysis,which validated the optimized control strategy’s ability to evenly distribute power under load transients.This strategy effectively mitigated transient voltage and current surges during mode transitions.Consequently,seamless and efficient switching between gridconnected and island modes was achieved for the photovoltaic storage hybrid inverter.The enhanced energy utilization efficiency,in turn,offers robust technical support for grid stability.

Optimization Strategies of Na_(3)V_(2)(PO_(4))_(3) Cathode Materials for Sodium‑Ion Batteries

Na_(3)V_(2)(PO_(4))_(3)(NVP)has garnered great attentions as a prospective cathode material for sodium-ion batteries(SIBs)by virtue of its decent theoretical capacity,superior ion conductivity and high structural stability.However,the inherently poor electronic conductivity and sluggish sodium-ion diffusion kinetics of NVP material give rise to inferior rate performance and unsatisfactory energy density,which strictly confine its further application in SIBs.Thus,it is of significance to boost the sodium storage performance of NVP cathode material.Up to now,many methods have been developed to optimize the electrochemical performance of NVP cathode material.In this review,the latest advances in optimization strategies for improving the electrochemical performance of NVP cathode material are well summarized and discussed,including carbon coating or modification,foreign-ion doping or substitution and nanostructure and morphology design.The foreign-ion doping or substitution is highlighted,involving Na,V,and PO_(4)^(3−)sites,which include single-site doping,multiple-site doping,single-ion doping,multiple-ion doping and so on.Furthermore,the challenges and prospects of high-performance NVP cathode material are also put forward.It is believed that this review can provide a useful reference for designing and developing high-performance NVP cathode material toward the large-scale application in SIBs.

Joint Estimation of SOH and RUL for Lithium-Ion Batteries Based on Improved Twin Support Vector Machineh

Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.

An Optimization Algorithm Employing Multiple Metamodels and Optimizers

Modern engineering design optimization often relies on computer simulations to evaluate candidate designs, a setup which results in expensive black-box optimization problems. Such problems introduce unique challenges, which has motivated the application of metamodel-assisted computational intelligence algorithms to solve them. Such algorithms combine a computational intelligence optimizer which employs a population of candidate solutions, with a metamodel which is a computationally cheaper approximation of the expensive computer simulation. However, although a variety of metamodels and optimizers have been proposed, the optimal types to employ are problem dependant. Therefore, a priori prescribing the type of metamodel and optimizer to be used may degrade its effectiveness. Leveraging on this issue, this study proposes a new computational intelligence algorithm which autonomously adapts the type of the metamodel and optimizer during the search by selecting the most suitable types out of a family of candidates at each stage. Performance analysis using a set of test functions demonstrates the effectiveness of the proposed algorithm, and highlights the merit of the proposed adaptation approach.

An Effective Hybridization of Quantum-based Avian Navigation and Bonobo Optimizers to Solve Numerical and Mechanical Engineering Problems

Hybrid metaheuristic algorithms play a prominent role in improving algorithms' searchability by combining each algorithm's advantages and minimizing any substantial shortcomings. The Quantum-based Avian Navigation Optimizer Algorithm (QANA) is a recent metaheuristic algorithm inspired by the navigation behavior of migratory birds. Different experimental results show that QANA is a competitive and applicable algorithm in different optimization fields. However, it suffers from shortcomings such as low solution quality and premature convergence when tackling some complex problems. Therefore, instead of proposing a new algorithm to solve these weaknesses, we use the advantages of the bonobo optimizer to improve global search capability and mitigate premature convergence of the original QANA. The effectiveness of the proposed Hybrid Quantum-based Avian Navigation Optimizer Algorithm (HQANA) is assessed on 29 test functions of the CEC 2018 benchmark test suite with different dimensions, 30, 50, and 100. The results are then statistically investigated by the Friedman test and compared with the results of eight well-known optimization algorithms, including PSO, KH, GWO, WOA, CSA, HOA, BO, and QANA. Ultimately, five constrained engineering optimization problems from the latest test suite, CEC 2020 are used to assess the applicability of HQANA to solve complex real-world engineering optimization problems. The experimental and statistical findings prove that the proposed HQANA algorithm is superior to the comparative algorithms.