Search Optimization Method Research Articles

Design and Optimization of Infinite Impulse Response Full-Band Digital Differentiator Using Evolutionary and Swarm Intelligence Algorithms

In this paper, the design and optimization of infinite impulse response full-band digital differentiator (DD) using evolutionary and swarm intelligence algorithms is investigated. Different objective functions based on the absolute error, the squared absolute error and the min-max optimality criterion are investigated. The optimal DD parameters that result in the best minimum value of the investigated objective functions are obtained using differential evolution, particle swarm optimization, genetic algorithm and cuckoo search optimization methods. These algorithms are used due to their simplicity, efficiency and robustness in solving general multidimensional optimization problems. The investigation outcomes show that minimizing the absolute error gives the most flat magnitude response, and minimizing the squared absolute error gives almost the lowest mean error of the designed DD. In addition, a new objective function is proposed to improve the linearity of the phase response of the designed infinite impulse response full-band DD. It is found that the design of the DD using the differential evolution outperforms or at least is comparable to similar designs reported in the literature using other optimization methods.

Allocation of phasor measurement unit using an admissible searching‐based algorithm A‐star and binary search tree for full interconnected power network observability

AbstractIn this research work, an admissible searching‐based algorithm A‐star and binary search tree is proposed to unravel the optimal phasor measurement unit (PMU) placement problem (OPPP) for achieving full interconnected power network observability considering contingencies. The proposed method is an advanced version of the best‐first search method that finds minuscule paths to the best solution. Moreover, it is an optimal and complete search method. Optimal signifies that the proposed method is guaranteed to unravel the minimum cost from origin to the terminus point and Complete denotes that it is going to determine all the possible paths that are available to us from the origin to the destination. The binary tree is used for this purpose as its structural design is well‐matched with the proposed method. Integrating an A‐star algorithm and binary search tree enhances the optimization and performance toward unraveling the OPPP. Also, for more than one solution of OPPP, redundancy measurement is taken into consideration. The proposed method is applied on standard IEEE‐14, ‐30, ‐118, New England‐39, and northern regional power grid (NRPG) 246‐bus test systems. Furthermore, to check the scalability of the proposed method, the Polish 2383‐bus system is considered. The obtained experimental results were compared to established procedures published in peer‐reviewed journals, and the proposed methodology outperformed the other comparison approaches. Thus, the proposed method successfully accomplishes complete observability of the power networks.

Detection of retinopathy disease using morphological gradient and segmentation approaches in fundus images

Background and objectiveDiabetes-related cases can cause glaucoma, cataracts, optic neuritis, paralysis of the eye muscles, or various retinal damages over time. Diabetic retinopathy is the most common form of blindness that occurs with diabetes. Diabetic retinopathy is a disease that occurs when the blood vessels in the retina of the eye become damaged, leading to loss of vision in advanced stages. This disease can occur in any diabetic patient, and the most important factor in treating the disease is early diagnosis. Nowadays, deep learning models and machine learning methods, which are open to technological developments, are already used in early diagnosis systems. In this study, two publicly available datasets were used. The datasets consist of five types according to the severity of diabetic retinopathy. The objectives of the proposed approach in diabetic retinopathy detection are to positively contribute to the performance of CNN models by processing fundus images through preprocessing steps (morphological gradient and segmentation approaches). The other goal is to detect efficient sets from type-based activation sets obtained from CNN models using Atom Search Optimization method and increase the classification success. MethodsThe proposed approach consists of three steps. In the first step, the Morphological Gradient method is used to prevent parasitism in each image, and the ocular vessels in fundus images are extracted using the segmentation method. In the second step, the datasets are trained with transfer learning models and the activations for each class type in the last fully connected layers of these models are extracted. In the last step, the Atom Search optimization method is used, and the most dominant activation class is selected from the extracted activations on a class basis. ResultsWhen classified by the severity of diabetic retinopathy, an overall accuracy of 99.59% was achieved for dataset #1 and 99.81% for dataset #2. ConclusionsIn this study, it was found that the overall accuracy achieved with the proposed approach increased. To achieve this increase, the application of preprocessing steps and the selection of the dominant activation sets from the deep learning models were implemented using the Atom Search optimization method.

Comparison of machine learning methods for ground settlement prediction with different tunneling datasets

This study integrates different machine learning (ML) methods and 5-fold cross-validation (CV) method to estimate the ground maximal surface settlement (MSS) induced by tunneling. We further investigate the applicability of artificial intelligent (AI) based prediction through a comparative study of two tunnelling datasets with different sizes and features. Four different ML approaches, including support vector machine (SVM), random forest (RF), back-propagation neural network (BPNN), and deep neural network (DNN), are utilized. Two techniques, i.e. particle swarm optimization (PSO) and grid search (GS) methods, are adopted for hyperparameter optimization. To assess the reliability and efficiency of the predictions, three performance evaluation indicators, including the mean absolute error (MAE), root mean square error (RMSE), and Pearson correlation coefficient ( R ), are calculated. Our results indicate that proposed models can accurately and efficiently predict the settlement, while the RF model outperforms the other three methods on both datasets. The difference in model performance on two datasets (Datasets A and B) reveals the importance of data quality and quantity. Sensitivity analysis indicates that Dataset A is more significantly affected by geological conditions, while geometric characteristics play a more dominant role on Dataset B.

Mapping Mineral Prospectivity Using a Hybrid Genetic Algorithm–Support Vector Machine (GA–SVM) Model

Machine learning (ML) as a powerful data-driven method is widely used for mineral prospectivity mapping. This study employs a hybrid of the genetic algorithm (GA) and support vector machine (SVM) model to map prospective areas for Au deposits in Karamay, northwest China. In the proposed method, GA is used as an adaptive optimization search method to optimize the SVM parameters that result in the best fitness. After obtaining evidence layers from geological and geochemical data, GA–SVM models trained using different training datasets were applied to discriminate between prospective and non-prospective areas for Au deposits, and to produce prospectivity maps for mineral exploration. The F1 score and spatial efficiency of classification were calculated to objectively evaluate the performance of each prospectivity model. The best model predicted 95.83% of the known Au deposits within prospective areas, occupying 35.68% of the study area. The results demonstrate the effectiveness of the GA–SVM model as a tool for mapping mineral prospectivity.

Development and validation of a second-order thermal network model for residential buildings

Heating, Ventilation, and Air Conditioning (HVAC) systems can maintain the space air temperature of residential buildings, either directly by heating/cooling the air, or indirectly via heat transfer to and from the building structure that acts as a thermal mass. Hence, HVAC systems can help achieve load shifting, peak load reduction, and/or energy cost saving, thus enabling grid-interactive HVAC operation. A home thermal model that can accurately reflect the dynamics of the space air and interior wall surface temperatures, is therefore valuable. This paper develops such a model using the standard RC (resistance-capacitance) approach. The model contains a virtual envelope node and an internal space node and is thus second-order. A hybrid parameter identification scheme, made up of the least-squares and optimal search methods, is also developed. The proposed model and scheme were validated using data collected from a test home. It was found that a modest amount of training data was sufficient to yield reliable parameter estimates and accurate prediction. It was also found that when making 24-hour-ahead prediction of the space air temperature, both methods had comparable performances when the training data began in a transition season. However, when they began in an HVAC season, the optimal search method performed better. Therefore, the least-squares method is recommended during a transition season due to its lower computational burden, while the optimal search method is recommended during an HVAC season due to its better estimation performance.

The effect of maximum iteration using 3 dimensional limit equilibrium method in open pit mine

Nickel ore mines have a high potential of landslides due to their weak material base, which consists of soil. It is caused by the increase of soil density in rain conditions, leading to decreased soil shear strength (c) and internal friction angle (ϕ). This research aims to determine the optimum value of the maximum iteration number based on the Cuckoo Search and Particle Swarm Optimization search method. In this research, the slope is analyzed using the 3 Dimensional limit equilibrium method “Simplified Bishop,” a slope stability analysis method that uses the principle of static equilibrium. Alongside this method, the Cuckoo Search and Particle Swarm Optimization is adopted. The Cuckoo Search and Particle Swarm Optimization are metaheuristic optimization techniques used as the slipped surface search method. Series of 3-dimensional limit equilibrium computation is performed using different amounts of nests in the cuckoo search method and different particle values and maximum iteration number. Cuckoo Search method to achieve optimal nest 100 and iteration of 80 with the fastest compute time of 3 minutes 49 seconds. While the Particle Swarm Optimization to achieve optimal on particles 60, iteration as much as 480 with a compute time of 6 minutes 46 second, with a factor of safety value of 1,12.

Optimal search methods for selecting distributed species in Gillespie-based kinetic Monte Carlo

Monte Carlo modeling has emerged as a powerful tool to describe system state variations in many engineering systems. If distributed species are involved, the so-called Gillespie-based kinetic Monte Carlo (kMC) simulations are very promising, provided that the search method to identify individual population members is properly chosen. A comparative study is therefore performed on the most promising search methods, using the stochastic simulation algorithm and considering system size variations from 102 to 106 employing 107 randomly selected targets. Attention is paid to already applied search methods as well as novel ones based on recent insights. It is demonstrated that for smaller systems the execution time of the linear search method is the lowest, and for larger systems the quaternary tree-based and tetrasection search methods are most suited. The tests are interpreted based on the search/modification times and an analysis based on iteration numbers is addressed.

An Intelligent Blockchain and Software-Defined Networking-Based Evidence Collection Architecture for Cloud Environment

Cloud forensics is an extension of contemporary forensic science that guards against cybercriminals. However, consolidated data assortment and storage compromise the legitimacy of digital indication. This essay proposes an evolving modern algorithm automated forensic platform based on the blockchain idea. This proposes forensic structure design, evidence gathering, and storage on a blockchain that are peer to peer. Secure Block Verification Mechanism (SBVM) will protect unauthorised users. Secret keys are optimally produced using the cuckoo search optimization method. All data are saved and encrypted at the cloud authentication server for secrecy. Confidentiality-Based Algebraically Homomorphism, a new encryption method, is given to cryptosystem learning. Every data is assigned a block in the SDN controller, and the history is kept as metadata about data. Each block has a Secure Hash Algorithm version 3 of 512-bit hash-based tree. Our approach uses graph theory-based graph neural networks in Smart Contracts to track users’ data (GNNSC). Finally, a blockchain-based evidence graph allows for evidence analysis. The experiments were run in a cloud environment with Python and network simulator-3.30 (for software-defined network). We achieved good results in terms of evidence response time, cloud evidence insertion time, cloud evidence verification time, computational overhead, hash calculation time, key generation times, and entire overall change rate of indication using our newly deliberated forensic construction using blockchain (FAuB).

Geometric Analysis through the Constructal Design of a Sea Wave Energy Converter with Several Coupled Hydropneumatic Chambers Considering the Oscillating Water Column Operating Principle

The work presents a numerical study of a wave energy converter (WEC) device based on the oscillating water column (OWC) operating principle with a variation of one to five coupled chambers. The main objective is to evaluate the influence of the geometry and the number of coupled chambers to maximize the available hydropneumatic power converted in the energy extraction process. The results were analyzed using the data obtained for hydropneumatic power, pressure, mass flow rate, and the calculated performance indicator’s hydropneumatic power. The Constructal Design method associated with the Exhaustive Search optimization method was used to maximize the performance indicator and determine the optimized geometric configurations. The degrees of freedom analyzed were the ratios between the height and length of the hydropneumatic chambers. A wave tank represents the computational domain. The OWC device is positioned inside it, subject to the regular incident waves. Conservation equations of mass and momentum and one equation for the transport of the water volume fraction are solved with the finite volume method (FVM). The multiphase model volume of fluid (VOF) is used to tackle the water–air mixture. The analysis of the results took place by evaluating the performance indicator in each chamber separately and determining the accumulated power, which represents the sum of all the powers calculated in all chambers. The turbine was ignored, i.e., only the duct without it was analyzed. It was found that, among the cases examined, the device with five coupled chambers converts more energy than others and that there is an inflection point in the performance indicator, hydropneumatic power, as the value of the degree of freedom increases, characterizing a decrease in the value of the performance indicator. With the results of the hydropneumatic power, pressure, and mass flow rate, it was possible to determine a range of geometry values that maximizes the energy conversion, taking into account the cases of one to five coupled chambers and the individual influence of each one.

Comparison of the genetic algorithm and pattern search methods for forecasting optimal flow releases in a multi-storage system for flood control

This paper compares the well-known genetic algorithm (GA) and pattern search (PS) optimization methods for forecasting optimal flow releases in a multi-storage system for flood control. The simulation models used by the optimization models include (a) a batch of scripts for data acquisition of forecasted precipitation and their automated post-processing; (b) a hydrological model for rainfall-runoff conversion, and (c) a hydraulic model for simulating river inundation. This paper focuses on (1) demonstrating the application of the framework by applying it to the operation of a hypothetical eight-wetland system in the Cypress Creek watershed in Houston, Texas; and (2) comparing and discussing the performance of the two optimization methods under consideration. The results show that the GA and PS optimal solutions are very similar; however, the computational time required by PS is significantly shorter than that required by GA. The results also show that optimal dynamic water management can significantly mitigate flooding compared to the case without management.

Y-002. Dietary magnesium insufficiency induces salt-sensitive hypertension in mice associated with reduced kidney COMT activity

This paper compares the well-known genetic algorithm (GA) and pattern search (PS) optimization methods for forecasting optimal flow releases in a multi-storage system for flood control. The simulation models used by the optimization models include (a) a batch of scripts for data acquisition of forecasted precipitation and their automated post-processing; (b) a hydrological model for rainfall-runoff conversion, and (c) a hydraulic model for simulating river inundation. This paper focuses on (1) demonstrating the application of the framework by applying it to the operation of a hypothetical eight-wetland system in the Cypress Creek watershed in Houston, Texas; and (2) comparing and discussing the performance of the two optimization methods under consideration. The results show that the GA and PS optimal solutions are very similar; however, the computational time required by PS is significantly shorter than that required by GA. The results also show that optimal dynamic water management can significantly mitigate flooding compared to the case without management.

An automated machine learning tool for breast cancer diagnosis for healthcare professionals

ABSTRACT The paper proposes a hybrid metaheuristic algorithm known as harmony search and simulated annealing (HS-SA) for accurate and precise breast malignancy disclosure by integrating harmony search (HS) and simulated annealing (SA) optimisation methods. An enhanced wavelet-based contourlet transform (WBCT) procedure for mining the highlights of the region of interest (ROI) is explored, that allows execution upgradation over other standard procedures. The anticipated HS-SA algorithm aims to reduce the feature dimensions and assemble at the unparalleled optimal feature subset. The SVM classifier fed with the picke.d feature subsets and assisted by varied kernel functions upheld its classification capacities in contrast with the conformist machine learning classification and optimisation methods. The portrayed computer-aided diagnosis (CAD) model is confronted by evaluating its learning capability on two different breast mammographic datasets i) benchmark BCDR-F03 dataset and ii) local mammographic dataset. Preliminary propagations, experimental outcomes, and quantifiable assessments likewise demonstrate that the proposed model is pragmatic and favourable for the automated breast malignancy findings with optimal performance and fewer overheads. The discoveries show that the proposed CAD system (HS-SA+Kernel SVM) is superior to various characterisation accuracy techniques with an accuracy of 99.89% for the local mammographic dataset and 99.76% for benchmark BCDR-F03 dataset, AUC of 99.41% for the local mammographic dataset and 99.21% for reference BCDR-F03 dataset while keeping the element space restricted to only seven feature subsets and computational prerequisites as low as is judicious.

Coalitional Game-Based Cooperative Computation Offloading in MEC for Reusable Tasks

Mobile-edge computing (MEC) has been a promising solution for Internet-of-Things (IoT) applications to obtain latency reduction and energy savings. In view of the loosely coupled application, multiple devices can use the same task code and different input parameters to obtain diverse results. This motivates us to study the cooperation between devices for eliminating the repeated data transmission. Leveraging coalitional game theory, we formalize the cooperative offloading process of a reusable task into a coalitional game to maximize the cost savings. In particular, we first propose an efficient coalitional game-based cooperative offloading (CGCO) algorithm for the single-task model, and then expand it into a CGCO-M algorithm for the multiple-task model with jointly applying a two-stage flow shop scheduling approach, which helps to obtain an optimal task schedule. It is proved that our CGCO and CGCO-M can achieve the Nash-stable solution with convergence guarantee, and CGCO can obtain an optimal solution. The simulations show that CGCO is equal to the optimal exhaustive search (ES) method and CGCO-M is close to ES in terms of cost ratios. Cost ratios of CGCO and CGCO-M are significantly down by 41.08% and 83.70% compared to local executions, respectively. Meanwhile, CGCO-M obtains 41.46% and 89.74% reductions when reuse factors are 0.1 and 1, which means CGCO-M can save more cost with higher reuse density.

Diagnosis of retinal disorders from Optical Coherence Tomography images using CNN.

An efficient automatic decision support system for detection of retinal disorders is important and is the need of the hour. Optical Coherence Tomography (OCT) is the current imaging modality for the early detection of retinal disorders non-invasively. In this work, a Convolution Neural Network (CNN) model is proposed to classify three types of retinal disorders namely: Choroidal neovascularization (CNV), Drusen macular degeneration (DMD) and Diabetic macular edema (DME). The hyperparameters of the model like batch size, number of epochs, dropout rate, and the type of optimizer are tuned using random search optimization method for better performance to classify different retinal disorders. The proposed architecture provides an accuracy of 97.01%, sensitivity of 93.43%, and 98.07% specificity and it outperformed other existing models, when compared. The proposed model can be used for the large-scale screening of retinal disorders effectively.

Iterated local search with partition crossover for computational protein design.

Structure-based computational protein design (CPD) refers to the problem of finding a sequence of amino acids which folds into a specific desired protein structure, and possibly fulfills some targeted biochemical properties. Recent studies point out the particularly rugged CPD energy landscape, suggesting that local search optimization methods should be designed and tuned to easily escape local minima attraction basins. In this article, we analyze the performance and search dynamics of an iterated local search (ILS) algorithm enhanced with partition crossover. Our algorithm, PILS, quickly finds local minima and escapes their basins of attraction by solution perturbation. Additionally, the partition crossover operator exploits the structure of the residue interaction graph in order to efficiently mix solutions and find new unexplored basins. Our results on a benchmark of 30 proteins of various topology and size show that PILS consistently finds lower energy solutions compared to Rosetta fixbb and a classic ILS, and that the corresponding sequences are mostly closer to the native.

Dynamic Optimal Travel Strategies in Intelligent Stochastic Transit Networks

This paper addresses the search for a run-based dynamic optimal travel strategy, to be supplied through mobile devices (apps) to travelers on a stochastic multiservice transit network, which includes a system forecasting of bus travel times and bus arrival times at stops. The run-based optimal strategy is obtained as a heuristic solution to a Markovian decision problem. The hallmarks of this paper are the proposals to use only traveler state spaces and estimates of dispersion of forecast bus arrival times at stops in order to determine transition probabilities. The first part of the paper analyses some existing line-based and run-based optimal strategy search methods. In the second part, some aspects of dynamic transition probability computation in intelligent transit systems are presented, and a new method for dynamic run-based optimal strategy search is proposed and applied.

Hybrid feature selection method based on particle swarm optimization and adaptive local search method

Machine learning has been expansively examined with data classification as the most popularly researched subject. The accurateness of prediction is impacted by the data provided to the classification algorithm. Meanwhile, utilizing a large amount of data may incur costs especially in data collection and preprocessing. Studies on feature selection were mainly to establish techniques that can decrease the number of utilized features (attributes) in classification, also using data that generate accurate prediction is important. Hence, a particle swarm optimization (PSO) algorithm is suggested in the current article for selecting the ideal set of features. PSO algorithm showed to be superior in different domains in exploring the search space and local search algorithms are good in exploiting the search regions. Thus, we propose the hybridized PSO algorithm with an adaptive local search technique which works based on the current PSO search state and used for accepting the candidate solution. Having this combination balances the local intensification as well as the global diversification of the searching process. Hence, the suggested algorithm surpasses the original PSO algorithm and other comparable approaches, in terms of performance.

Pure Random Orthogonal Search (PROS): A Plain and Elegant Parameterless Algorithm for Global Optimization

A new, fast, elegant, and simple stochastic optimization search method is proposed, which exhibits surprisingly good performance and robustness considering its simplicity. We name the algorithm pure random orthogonal search (PROS). The method does not use any assumptions, does not have any parameters to adjust, and uses basic calculations to evolve a single candidate solution. The idea is that a single decision variable is randomly changed at every iteration and the candidate solution is updated only when an improvement is observed; therefore, moving orthogonally towards the optimal solution. Due to its simplicity, PROS can be easily implemented with basic programming skills and any non-expert in optimization can use it to solve problems and start exploring the fascinating optimization world. In the present work, PROS is explained in detail and is used to optimize 12 multi-dimensional test functions with various levels of complexity. The performance is compared with the pure random search strategy and other three well-established algorithms: genetic algorithms (GA), particle swarm optimization (PSO), and differential evolution (DE). The results indicate that, despite its simplicity, the proposed PROS method exhibits very good performance with fast convergence rates and quick execution time. The method can serve as a simple alternative to established and more complex optimizers. Additionally, it could also be used as a benchmark for other metaheuristic optimization algorithms as one of the simplest, yet powerful, optimizers. The algorithm is provided with its full source code in MATLAB for anybody interested to use, test or explore.

Influence-Based Policy Abstraction for Weakly-Coupled Dec-POMDPs

Decentralized POMDPs are powerful theoretical models for coordinating agents’ decisions in uncertain environments, but the generally-intractable complexity of optimal joint policy construction presents a significant obstacle in applying Dec-POMDPs to problems where many agents face many policy choices. Here, we argue that when most agent choices are independent of other agents’ choices, much of this complexity can be avoided: instead of coordinating full policies, agents need only coordinate policy abstractions that explicitly convey the essential interaction influences. To this end, we develop a novel framework for influence-based policy abstraction for weakly-coupled transition-dependent Dec-POMDP problems that subsumes several existing approaches. In addition to formally characterizing the space of transition-dependent influences, we provide a method for computing optimal and approximately-optimal joint policies. We present an initial empirical analysis, over problems with commonly-studied flavors of transition-dependent influences, that demonstrates the potential computational benefits of influence-based abstraction over state-of-the-art optimal policy search methods.