Random Search Approach Research Articles

Unveiling Novel Direct Bandgap Allotropes of Germanium: A Computational Exploration

Germanium crystallizes in a cubic diamond structure, which restricts its applications in optoelectronics due to its indirect band gap. However, the slight energy difference between the direct and indirect band gaps in germanium presents a promising opportunity to engineer its structure into a direct band gap material, with the hexagonal diamond (lonsdaleite) phase being a notable example. In this study, we conducted an extensive computational search to find germanium allotropes with a direct band gap and low energy using a sensible random structure search approach informed by data-derived interatomic potentials. Among the predicted allotropes, we identified a hexagonal 8H structure with the lowest energy compared to all known germanium allotropes and only 5 meV/atom above the ground state cubic diamond structure. Compared to the cubic diamond phase, which has complete cubicity, the 8H phase consists of 3/4 cubicity and 1/4 hexagonality. This structural motif, coupled with band structure back folding in the hexagonal Brillouin zone, results in a direct band gap of 0.25 eV. Given the prior experimental discovery of 2H and 4H polytypes, the experimental synthesis of the 8H allotrope in germanium is highly feasible. Future research could explore alloying 8H germanium with silicon to optimize the bandgap energy and optical transitions, potentially achieving lifetimes comparable to those of group III-V semiconductors like GaAs.

FrameGraph: A Scalable Performance Evaluation Method for Frame Structure Designs Using Graph Neural Network

Abstract Multilayer perceptron (MLP) and convolutional neural network (CNN) encounter a critical scalability issue when applied to the performance evaluation task for frame structure designs. Specifically, a model of MLP or CNN is limited to structures of a particular topology type and fails immediately when applied to other topology types. In order to tackle this challenge, we propose a scalable performance evaluation method (called FrameGraph) for frame structure designs using graph neural network (GNN), offering applicability to a wide range of topology types simultaneously. FrameGraph consists of two main parts: (1) Components and their connections in a frame structure are denoted as edges and vertices in a graph, respectively. Subsequently, a graph dataset for frame structure designs with different topologies is constructed. (2) A well-defined GNN design space is established with a general GNN layer, and a controlled random search approach is employed to derive the optimal GNN model for this performance evaluation task. In numerical experiments of car door frames and car body frames, FrameGraph achieved the highest prediction precisions (96.28% and 97.87%) across all structural topologies compared to a series of classical GNN algorithms. Furthermore, the comparison with MLP and FEM highlighted FrameGraph's significant efficiency advantage. This verifies the feasibility and optimality of FrameGraph for the performance evaluation task of frame structures with different topologies.

Multi-objective optimization of reactive power dispatch problem using fuzzy tuned mayfly algorithm

The recent multi-objective optimization problems are very complex which require an effective and robust evolutionary method for obtaining a global Pareto-optimal solutions. Most of these multi-objective evolutionary techniques are population-based methods which usually work on random search approaches and often trapped into local minima during their execution. A new fuzzy tuned mayfly algorithm (FTMA) is presented in this paper, to obtain the Pareto-optimal solutions for any complex multi-objective power system problem. It has a self-adapting global exploration capability to get the best Pareto-optimal solutions by varying two crucial parameters i.e., crossover (Pc) and mutation Pm probabilities. Moreover, a model of Pareto-dominance is employed to rank non-dominating solutions to maintain the diversity within the populations. The proposed evolutionary approach is examined on a standard ZDT benchmark test suite and its algorithmic performance is statistically compared with a few of the recent optimization techniques such as NSGA-II, NSGA-III, MMODE, MOAVOA and MMA algorithm. It is further applied to minimize two distinct objective functions (i.e., total transmission loss and voltage stability index) for IEEE-118 bus system and IEEE-24 bus RTS. A comprehensive statistical analysis is presented for the obtained numerical results, which proves that proposed FTMA has better convergence with better solution diversity in comparison to the other competing algorithms. A comprehensive analysis based on statistical results reveal that the proposed FTMA is superior for obtaining better non-dominating pareto-fronts as compared to other competing methods.

Urban traffic flow management on large scale using an improved ACO for a road transportation system

PurposeWith the demographic increase, especially in big cities, heavy traffic, traffic congestion, road accidents and augmented pollution levels hamper transportation networks. Finding the optimal routes in urban scenarios is very challenging since it should consider reducing traffic jams, optimizing travel time, decreasing fuel consumption and reducing pollution levels accordingly. In this regard, the authors propose an enhanced approach based on the Ant Colony algorithm that allows vehicle drivers to search for optimal routes in urban areas from different perspectives, such as shortness and rapidness.Design/methodology/approachAn improved ant colony algorithm (ACO) is used to calculate the optimal routes in an urban road network by adopting an elitism strategy, a random search approach and a flexible pheromone deposit-evaporate mechanism. In addition, the authors make a trade-off between route length, travel time and congestion level.FindingsExperimental tests show that the routes found using the proposed algorithm improved the quality of the results by 30% in comparison with the ACO algorithm. In addition, the authors maintain a level of accuracy between 0.9 and 0.95. Therefore, the overall cost of the found solutions decreased from 67 to 40. In addition, the experimental results demonstrate that the authors’ improved algorithm outperforms not only the original ACO algorithm but also popular meta-heuristic algorithms such as the genetic algorithm (GA) and particle swarm optimization (PSO) in terms of reducing travel costs and improving overall fitness value.Originality/valueThe proposed improvements to the ACO to search for optimal paths for urban roads include incorporating multiple factors, such as travel length, time and congestion level, into the route selection process. Furthermore, random search, elitism strategy and flexible pheromone updating rules are proposed to consider the dynamic changes in road network conditions and make the proposed approach more relevant and effective. These enhancements contribute to the originality of the authors’ work, and they have the potential to advance the field of traffic routing.

0582 Polysomnography Parameters Predict Mean Sleep Latency Test Results Using Machine Learning

Abstract Introduction The mean sleep latency test (MSLT) objectively measures mean sleep latency (MSL) and sleep onset during REM periods (SOREMPs) in the absence of external alerting factors used for diagnoses of central hypersomnolence disorders. Polysomnography (PSG) is routinely performed the night before an MSLT. We explored the use of PSG variables at the individual level to predict MSL and SOREMPs assisted by machine learning methods. Methods 802 PSG/MSLTs from 796 patients (75% female, age 34 years[SD:12.1], BMI 27.9[SD:7.6], ESS 13.9[SD:4.9]) performed at Cleveland Clinic Sleep Disorder Center from 2012 to 2022 were included. We used 5-fold cross-validation experimental settings where five types of machine learning models (logistic regression, support vector machine, multilayer perceptron, random forest, XGBoost) were trained for two separated binary classification tasks: predict MSL< 8 minutes (343 out of 802) and SOREMP>=2 (234 out of 802). Within each fold, we performed a feature selection analysis on the training set in 39 PSG and demographic variables based on Analysis of Variance (ANOVA) with p < 0.05. A random search approach with 1000 combinations was used for machine learning hyperparameter tuning. The area under the ROC (AUC-ROC) score was used to rank the methods based on the test set of each fold (mean+SD). We retrained the best machine learning configuration in a new single train/test (80%/20%) split to evaluate the top 5 features that were more relevant to the best model by computing their Shapley Additive Explanations values (SHAP). Results XGBoost had the best average AUC-ROC for predicting MSL of 0.71 + 0.04. Its most important features out of 14 selected were total recording time, percentage of sleep time, total wake time after sleep onset, non-supine sleep time, and sleep efficiency. Random forest was the best for SOREMP prediction with AUC-ROC of 0.75 + 0.05, and the most important features were REM latency, age, and Epworth Sleepiness Scale score, percentage of time on REM, minimum oxygen saturation, out of 13 selected. Conclusion PSG the night prior to MSLT provides predictive information that can be leveraged to anticipate MSLT results on the individual level. This work provides the foundation to better identify patients with central hypersomnolence disorders. Support (if any)

Learning the Policy for Mixed Electric Platoon Control of Automated and Human-Driven Vehicles at Signalized Intersection: A Random Search Approach

The upgrading and updating of vehicles have accelerated in the past decades. Out of the need for environmental friendliness and intelligence, electric vehicles (EVs) and connected and automated vehicles (CAVs) have become new components of transportation systems. This paper develops a reinforcement learning framework to implement adaptive control for an electric platoon composed of CAVs and human-driven vehicles (HDVs) at a signalized intersection. Firstly, a Markov Decision Process (MDP) model is proposed to describe the decision process of the mixed platoon. Novel state representation and reward function are designed for the model to consider the behavior of the whole platoon. Secondly, in order to deal with the delayed reward, an Augmented Random Search (ARS) algorithm is proposed. The control policy learned by the agent can guide the longitudinal motion of the CAV, which serves as the leader of the platoon. Finally, a series of simulations are carried out in simulation suite SUMO. Compared with several state-of-the-art (SOTA) reinforcement learning approaches, the proposed method can obtain a higher reward. Meanwhile, the simulation results demonstrate the effectiveness of the delay reward, which is designed to outperform distributed reward mechanism} Compared with normal car-following behavior, the sensitivity analysis reveals that the energy can be saved to different extends (39.27%-82.51%) by adjusting the relative importance of the optimization goal. On the premise that travel delay is not sacrificed, the proposed control method can save up to 53.64% electric energy.

SonOpt: understanding the behaviour of bi-objective population-based optimisation algorithms through sound

We present an extension of SonOpt, the first ever openly available tool for the sonification of bi-objective population-based optimisation algorithms. SonOpt has already introduced benefits on the understanding of algorithmic behaviour by proposing the use of sound as a medium for the process monitoring of bi-objective optimisation algorithms. The first edition of SonOpt utilised two different sonification paths to provide information on convergence, population diversity, recurrence of objective values across consecutive generations and the shape of the approximation set. The present extension provides further insight through the introduction of a third sonification path, which involves hypervolume contributions to facilitate the understanding of the relative importance of non-dominated solutions. Using a different sound generation approach than the existing ones, this newly proposed sonification path utilizes pitch deviations to highlight the distribution of hypervolume contributions across the approximation set. To demonstrate the benefits of SonOpt we compare the sonic results obtained from two popular population-based multi-objective optimisation algorithms, Non-Dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), and use a Multi-objective Random Search (MRS) approach as a baseline. The three algorithms are applied to numerous test problems and showcase how sonification can reveal various aspects of the optimisation process that may not be obvious from visualisation alone. SonOpt is available for download at https://github.com/tasos-a/SonOpt-2.0.

Enhancement of Non-Permutation Binomial Power Functions to Construct Cryptographically Strong S-Boxes

A Substitution box (S-box) is an important component used in symmetric key cryptosystems to satisfy Shannon’s property on confusion. As the only nonlinear operation, the S-box must be cryptographically strong to thwart any cryptanalysis tools on cryptosystems. Generally, the S-boxes can be constructed using any of the following approaches: the random search approach, heuristic/evolutionary approach or mathematical approach. However, the current S-box construction has some drawbacks, such as low cryptographic properties for the random search approach and the fact that it is hard to develop mathematical functions that can be used to construct a cryptographically strong S-box. In this paper, we explore the non-permutation function that was generated from the binomial operation of the power function to construct a cryptographically strong S-box. By adopting the method called the Redundancy Removal Algorithm, we propose some enhancement in the algorithm such that the desired result can be obtained. The analytical results of our experiment indicate that all criteria such as bijective, nonlinearity, differential uniformity, algebraic degree and linear approximation are found to hold in the obtained S-boxes. Our proposed S-box also surpassed several bijective S-boxes available in the literature in terms of cryptographic properties.

An Insight into the Prediction of Scale Precipitation in Harsh Conditions Using Different Machine Learning Algorithms

Summary Scale precipitation in petroleum equipment is known as an important problem that causes damages in injection and production wells. Scale precipitation causes equipment corrosion and flow restriction and consequently a reduction in oil production. Due to this fact, the prediction of scale precipitation has vital importance among petroleum engineers. In the current work, different intelligent models, including the decision tree, random forest (RF), artificial neural network (ANN), K-nearest neighbors (KNN), convolutional neural network (CNN), support vector machine (SVM), ensemble learning, logistic regression, Naïve Bayes, and adaptive boosting (AdaBoost), are used to estimate scale formation as a function of pH and ionic compositions. Also, a sensitivity analysis is done to determine the most influential parameters on scale formation. The novelty of this work is to compare the performance of 10 different machine learning algorithms at modeling an extremely non-linear relationship between the inputs and the outputs in scale precipitation prediction. After determining the best models, they can be used to determine scale formation by manipulating the concentration of a variable in accordance with the result of the sensitivity analysis. Different classification metrics, including the accuracy, precision, F1-score, and recall, were used to compare the performance of the mentioned models. Results in the testing phase showed that the KNN and ensemble learning were the most accurate tools based on all performance metrics of solving the classification of scale/no-scale problem. As the output had an extremely non-linear behavior in terms of the inputs, an instance-based learning algorithm such as the KNN best suited the classification task in this study. This argumentation was backed by the classification results. Furthermore, the SVM, Naïve Bayes, and logistic regression performance metrics were not satisfactory in the prediction of scale formation. Note that the hyperparameters of the models were found by grid search and random search approaches. Finally, the sensitivity analysis showed that the variations in the concentration of Ca had the highest impact on scale precipitation.

Development of an Automated and Integrated Algorithm in Determining Optimum Maintenance and Rehabilitation Strategies Considering Multilane Roadways

Pavement management systems include road surveying, data analysis, identification of rehabilitated sections, determination of treatment methods, and budget optimization for maintenance and rehabilitation (M&R). These complex processes challenge many agencies in determining the most appropriate M&R strategies for the current year and choosing rehabilitation alternatives using life cycle cost analysis. In addition, these are time-consuming processes since the analysis and interpretation are done separately. In this study, an automated integrated algorithm is developed to help agencies determine the appropriate rehabilitated homogeneous section and optimize the budget at the network level for single and multilane roads. The algorithm was developed based on the pruned exact linear time and changepoints for a range of penalties methods in determining homogeneous sections from distresses on the surfaces being surveyed on single lanes. An additional algorithm is then proposed to identify homogeneous sections by considering adjacent multilane sections. Finally, the prioritized homogeneous sections for rehabilitation are determined by optimizing the available budget at the network level, considering the distress index and recommended rehabilitation type. During optimization, different budget constraint scenarios were considered to satisfy several requirements from agencies considering a random search approach. The proposed algorithm was applied to real field data where it was found that the recommended M&R strategies for the homogenized sections using the proposed approach are feasible, practical, and reasonable. It is recommended that the performance model used in this study be developed further to improve the proposed integrated algorithm.

A novel genetic algorithm for curriculum sequence optimization

A curriculum sequence represents a match between learners’ preferences, needs, and surroundings from one side, and the learning content characteristics and the pedagogical requirements from the other side. The curriculum sequence adaptation problem (CSA) is considered as an important issue in adaptive and personalized learning field. It concerns the dynamic generation of a personal optimal learning path for a specific learner. This problem has gained an increased research interest in the last decade, and heuristics and meta-heuristics are usually used to solve it. In this direction, this paper summarizes existing works and presents a novel GA-based approach modeled as an objective optimization problem to deal with this problem. The experimental results from simulations showed that the proposed GA could outperform particle swarm optimization (PSO) and a random search approach in many simulated datasets. Moreover, from a pedagogical perspective, positive learners’ feedback and high acceptance towards the proposed approach is indicated.

An enhanced genetic algorithm for solving learning path adaptation problem

Recently, the field of adaptive learning has significantly attracted researchers’ interest. Learning path adaptation problem (LPA) is one of the most challenging problems within this field. It is also a well-known combinatorial optimization problem, its main target is the knowledge resources sequencing offered to a specific learner with a specific context. The learning path candidate solutions can be only approximated as the LPA problem belongs to NP-hard problems and heuristics and meta-heuristics are usually used to solve it. In this direction, this paper summarizes existing works and presents an innovative approach modeled as an objective optimization problem, and an improved Genetic algorithm (GA) is proposed to deal with it. Our contribution does not only reduce the search space size and increase search efficiency, but it is also more explicit in finding the best composition of learning objects for a given learner. Besides the proposed GA, introduces an archive-based bag-of-operators mechanism to tackle two well-known standards GA drawbacks. The simulation results show that the proposed method makes a significant improvement compared to a well-known evolutionary approach, which is the PSO algorithm, and a random search approach. In addition, an empirical experiment is conducted and the results are very encouraging.

Systematic Approach to Find the Global Minimum of Relaxation Dispersion Data for Protein-Induced B-Z Transition of DNA.

Carr–Purcell–Meiboom–Gill (CPMG) relaxation dispersion spectroscopy is commonly used for quantifying conformational changes of protein in μs-to-ms timescale transitions. To elucidate the dynamics and mechanism of protein binding, parameters implementing CPMG relaxation dispersion results must be appropriately determined. Building an analytical model for multi-state transitions is particularly complex. In this study, we developed a new global search algorithm that incorporates a random search approach combined with a field-dependent global parameterization method. The robust inter-dependence of the parameters carrying out the global search for individual residues (GSIR) or the global search for total residues (GSTR) provides information on the global minimum of the conformational transition process of the Zα domain of human ADAR1 (hZαADAR1)–DNA complex. The global search results indicated that a α-helical segment of hZαADAR1 provided the main contribution to the three-state conformational changes of a hZαADAR1—DNA complex with a slow B–Z exchange process. The two global exchange rate constants, kex and kZB, were found to be 844 and 9.8 s−1, respectively, in agreement with two regimes of residue-dependent chemical shift differences—the “dominant oscillatory regime” and “semi-oscillatory regime”. We anticipate that our global search approach will lead to the development of quantification methods for conformational changes not only in Z-DNA binding protein (ZBP) binding interactions but also in various protein binding processes.

Diverse densest binary sphere packings and phase diagram

We revisit the densest binary sphere packings (DBSPs) under periodic boundary conditions and present an updated phase diagram, including newly found 12 putative densest structures over the x-α plane, where x is the relative concentration and α is the radius ratio of the small and large spheres. To efficiently explore the DBSPs, we develop an unbiased random search approach based on both the piling-up method to generate initial structures in an unbiased way and the iterative balance method to optimize the volume of a unit cell while keeping the overlap of hard spheres minimized. With those two methods, we have discovered 12 putative DBSPs and thereby the phase diagram is updated, while our results are consistent with those of a previous study [Hopkins et al., Phys. Rev. E 85, 021130 (2012)]PLEEE81539-375510.1103/PhysRevE.85.021130 with a small correction for the case of 12 or fewer spheres in the unit cell. Five of the discovered 12 DBSPs are identified in the small radius range of 0.42≤α≤0.50, where several structures are competitive to each other with respect to packing fraction. Through the exhaustive search, diverse dense packings are discovered and, accordingly, we find that packing structures achieve high packing fractions by introducing distortion and/or combining a few local dense structural units. Furthermore, we investigate the correspondence of the DBSPs with crystals based on the space group. The result shows that many structural units in real crystals, e.g., LaH_{10} and SrGe_{2-δ} being high-pressure phases, can be understood as DBSPs. The correspondence implies that the densest sphere packings can be used effectively as structural prototypes for searching complex crystal structures, especially for high-pressure phases.

Pressure-Induced Enhancement of Thermoelectric Figure of Merit and Structural Phase Transition in TiNiSn.

Half-Heusler thermoelectric materials are potential candidates for high thermoelectric efficiency. We report high-pressure thermoelectric and structural property measurements, density functional theory calculations on the half-Heusler material TiNiSn, and an increase of 15% in the relative dimensionless figure of merit, ZT, around 3 GPa. Thermal and electrical properties were measured utilizing a specialized sample cell assembly designed for the Paris-Edinburgh large-volume press to a maximum pressure of 3.5 GPa. High-pressure structural measurements performed up to 50 GPa in a diamond-anvil cell indicated the emergence of a new high-pressure phase around 20 GPa. A first-principles structure search performed using an ab initio random structure search approach identified the high-pressure phase as an orthorhombic type, in good agreement with the experimental results.

An Optimization of Multi-Class Document Classification with Computational Search Policy

In the era of internet communication, many electronic documents are spread and flow on the platform of website in every splits of seconds. The research interest for the process of knowledge discovery is changed from the traditional data to online data such as online news document classification. Most percentage of the online data is text document and therefore the optimization of multi-class document classification is becoming a challenge for today society. Traditional search policy for feature selection process is degrading with exhaustive search for complex feature in document classification. Therefore, meta-heuristic based computational search is also becoming good solution to overcome the problem of exhaustive search with exploitation process. The search policy of computational algorithm can provide the global optimal solution with random search approach on both exploitation and exploration process, and the selected search results of feature subsets can support the optimal classification results.

Assessment of ontological structures semantic similarity based on a modified cuckoo search algorithm

Enhancing the effectiveness of knowledge (information) classification and integration based on an assessment of semantic similarity of ontological structures is an urgent scientific problem. Evaluation of equivalent semantic similarity requires considerable computational resources. Consequently, a similarity of predicates of all ontologies’ concepts shall be verified in the exhaustive search. Exact methods do not allow to find a solution in polynomial time, since this problem is trans-computational and requires the use of random search approaches with decentralized control. The paper proposed a modification of the algorithm, inspired by the behavior of cuckoos in the process of nesting parasitism. The cuckoo search optimization algorithm increases the stochasticity of obtained quasi-optimal solutions in comparison with other bioinspired algorithms and increase the search speed. A comparative analysis of the results quality showed that solutions, obtained by the cuckoos’ search, outperform the results of other bioinspired approaches.

Genetic Algorithm – A Sensible Evolutionary Optimization Technique

The study presents a pragmatic outlook of genetic algorithm. Many biological algorithms are inspired for their ability to evolve towards best solutions and of all; genetic algorithm is widely accepted as they well suit evolutionary computing models. Genetic algorithm could generate optimal solutions on random as well as deterministic problems. Genetic algorithm is a mathematical approach to imitate the processes studied in natural evolution. The methodology of genetic algorithm is intensively experimented in order to use the power of evolution to solve optimization problems. Genetic algorithm is an adaptive heuristic search algorithm based on the evolutionary ideas of genetics and natural selection. Genetic algorithm exploits random search approach to solve optimization problems. Genetic algorithm takes benefits of historical information to direct the search into the convergence of better performance within the search space. The basic techniques of evolutionary algorithms are observed to be simulating the processes in natural systems. These techniques are aimed to carry effective population to the next generation and ensure the survival of the fittest. Nature supports the domination of stronger over the weaker ones in any kind. In this study, we proposed the arithmetic views of the behavior and operators of genetic algorithm that support the evolution of feasible solutions to optimized solutions.

Efficient Feature Selection Algorithm Based on Particle Swarm Optimization With Learning Memory

Feature selection is an important pre-processing step in machine learning and data mining tasks, which improves the performance of the learning models by removing redundant and irrelevant features. Many feature selection algorithms have been widely studied, including greedy and random search approaches, to find a subset of the most important features for fulfilling a particular task (i.e., classification and regression). As a powerful swarm-based meta-heuristic method, particle swarm optimization (PSO) is reported to be suitable for optimization problems with continuous search space. However, the traditional PSO has rarely been applied to feature selection as a discrete space search problem. In this paper, a novel feature selection algorithm based on PSO with learning memory (PSO-LM) is proposed. The goal of the learning memory strategy is designed to inherit much more useful knowledge from those individuals who have higher fitness and offer faster progress, and the genetic operation is used to balance the local exploitation and the global exploration of the algorithm. Moreover, the $k$ -nearest neighbor method is used as a classifier to evaluate the classification accuracy of a particle. The proposed method has been evaluated on some international standard data sets, and the results demonstrated its superiority compared with those wrapper-based feature selection methods.

Tuning Pi Controller Bases on Chemical Reaction Optimization Algorithm

This paper we present Chemical Reaction Optimization (CRO) algorithm for determining optimal parameters of PI controller. The model of doubly fed induction generator (DFIG) is used as a plant in this paper. Tuning PI controller using traditional method such as Ziegler-Nichols (ZN) method usually produces large overshoot and Integral time absolute error, integral absolute error and integral square error performance indices. Therefore, recently researchers have applied random search approach such as genetic algorithm (GA) and particle swarm optimization (PSO) and Grey Wolf Optimizer (GWO) to find optimal parameters for PI controller. Among modern heuristics algorithm, CRO was introduced in 2010, it combines features of both GA and Simulated Annealing (SA) to find global minimum in search space. CRO has been applied to solve successfully many optimization problems such as: Minimum transportation cost, resource-constrained project scheduling problem, channel assignment problem in wireless mesh networks, standard continuous benchmark functions, and so on. In this paper we present to apply CRO algorithm to search optimal parameters for PI controller. The comparison between tuning PI controller by CRO and traditional Ziegler-Nichols method is presented. The simulation results show the advantages of PI tuning using CRO compared to traditional method in terms of performance index and setting time.