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Abstract: The study first summarizes the characteristics of various intelligent algorithms such as improved genetic algorithm, differential evolution algorithm and ant colony algorithm adopted in test paper generation, and then proposes the parallel evolution of swarm based on ideas of shared intelligent algorithm and dynamic multi branches tree algorithm, so as to improve searching speed and achieve the effect of short-time optimization. During forming optimal individuals, classified training and repeated recognition by virtue of dynamic multi branches tree can not only avoid premature appearance but also get strong convergence. In addition, when the constraints change, the existing knowledge can be inherited. Facts have shown that this algorithm has certain theoretical significance and reference value to the development of intelligent test paper generation algorithm. [Copyright &y& Elsevier]

Damage detection and localization in civil engineering constructions using dynamic analysis has become an important topic in recent years. This paper presents a methodology based on non-destructive detection, localization and quantification of multiple damages in simple and continuous beams, and a more complex structure, namely two-dimensional frame structure. The proposed methodology makes used of Firefly Algorithm and Genetic Algorithm as optimization tools and the Coordinate Modal Assurance Criterion as an objective function. The results show that the proposed combination of Coordinate Modal Assurance Criterion and Firefly Algorithm or Genetic Algorithm can be easily used to identify multiple local structural damages in complex structures. However, the convergence rate becomes slower for the case of multiple damages compared to the case of single damage. The effect of noise on the algorithm is further investigated. It is found that the proposed technique is able to detect the damage location and its severity with high accuracy in the presence of noise, although the convergence rate became slower than in the case when no noise is present. It is also found that the convergence rate of Firefly Algorithm is much faster than that of Genetic Algorithm. [ABSTRACT FROM AUTHOR]

The ship piping layout is one of the essential tasks in the detailed design stage of a ship. Traditional manual expert design has disadvantages such as low efficiency, reliance on experience, and subjective influence. Therefore, this paper systematically proposes an intelligent arrangement method for ships' single, parallel, and branch pipelines. Firstly, the traditional genetic algorithm is improved and combined with the A* algorithm to solve the intelligent arrangement problem of a single pipeline in ships. Then, the parallel pipeline and branch pipeline are split into multiple single pipelines by combining with the connection point strategy to solve the arrangement problem of parallel pipeline and branch pipeline. Finally, the optimized A*-genetic algorithm proposed in this paper is compared with the A* algorithm, particle swarm algorithm, and the labyrinth-genetic algorithm used in previous research through simulation experiments. The results show that the A*-genetic algorithm of this paper is optimal in six indexes, including length, number of elbows, energy value, fitness value, number of optimal solutions, and average number of convergence generations, in the arrangement of the single pipeline. In solving the parallel pipeline and branch pipeline arrangement problems, the all-around performance of this paper's algorithm is better than that of A*-genetic algorithm and maze–genetic algorithm, respectively. The A*-genetic algorithm of this paper considers the quality of pipeline arrangement and the solution's efficiency. It verifies the adaptability and superiority of the algorithm for the intelligent arrangement of various types of pipelines in ship pipelines. [ABSTRACT FROM AUTHOR]

The increased application of Information and Communications Technology in automation and digitization is the driving concept towards the actualization of the Industrial Revolution 4.0. Many aspects of industrial design and operations including offshore engineering have been automated. However, the selection procedure of mooring design parameters including azimuth angle, pretension, diameter, fairlead slope, and mooring radius, which is a critical aspect of mooring system design has remained heavily based on a manual approach despite the availability of sophisticated hydrodynamic analysis software. Up to date, there is no proper review on optimisation techniques that have the potential of automating these procedures. In the past, some of the few available mooring optimisation procedures consider only the mooring lines for the prediction of optimal platform offset without due consideration to the integrity of the risers. Thus, the significance of adopting an integrated riser-mooring design methodology in the optimisation of mooring design variables is discussed in this paper where the integrity of the riser is adequately represented using a Safe Operation (SAFOP) zone polar diagram. We also review the developmental transition from the use of classical to Evolutionary Algorithms (EA) in the optimisation procedure. EA has been acknowledged as a practical alternative for solving mooring optimisation problems. Hence, some of the EA techniques are presented and their computational capabilities explored. In the last part, a concise review of the application of optimisation techniques for mooring optimisation of the different offshore floating platforms is presented based on their capabilities in minimizing platform offset and efficiency in terms of simulation time. So far, EA techniques like the Differential Evolution (DE), variants of Genetic Algorithms (GA) and Particle Swarm Optimisation (PSO) have been used to optimize mooring line design variables. Based on the discussions, the potentials of DE, RegPSO and other variants of PSO can be further explored for better efficiency. • Previous studies addressing differential evolution (DE), genetic algorithm (GE) and particle swarm optimisation (PSO) techniques used for optimisation of mooring parameters have been critically reviewed. • The integrated riser -mooring design methodology is effective in accounting for integrity of riser and mooring systems. • The application of evolutionary algorithms, particularly the variants and hybrids of DE ,PSO and GA, have proven to be efficient optimisation of mooring systems. • Some variants of PSO like the RegPSO have demonstrated improved capability in terms of escaping premature convergence , thereby resulting in better computational efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Civil Engineering is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, a 30 dB broadband microstrip directional coupler is designed by using a discrete coding type coupling structure with a co‐simulation technique. Combined with the proposed hybrid binary particle swarm genetic optimization algorithm (HBPSGOA) and the carefully designed fitness function, the discrete coding type structure can provide satisfactory flexibility and excellent performance. For a fixed design space, the discrete space can firstly be encoded and mapped to the corresponding coding matrix, and then the optimal coding matrix searched by HBPSGOA will be used to design a broadband coupler with high directivity. The effectiveness of the proposed HBPSGOA, binary particle swarm algorithm (BPSO), and genetic algorithm (GA) to design a discrete coding type coupler is tested. Experimental results prove that the HBPSGOA proposed in this paper has higher optimization efficiency. For the 30 dB broadband microstrip directional coupler designed in this paper, the center frequency is at 2 GHz and the fractional bandwidth is 39.4%. The minimum directivity of the designed coupler is 20 dB in the operating band and the maximum directivity is 39.3 dB at 1.9 GHz. In addition, the reason why the coupler can work in a wideband is investigated. [ABSTRACT FROM AUTHOR]

This paper presents a new hybrid genetic-particle swarm optimization (GPSO) algorithm for solving multi-constrained optimization problems. This algorithm is different from the traditional GPSO algorithm, which adopts genetic algorithm (GA) and particle swarm optimization (PSO) in series, and it combines PSO and GA through parallel architecture, so as to make full use of the high efficiency of PSO and the global optimization ability of GA. The algorithm takes PSO as the main body and runs PSO at the initial stage of optimization, while GA does not participate in operation. When the global best value (gbest) does not change for successive generations, it is assumed that it falls into local optimum. At this time, GA is used to replace PSO for particle selection, crossover and mutation operations to update particles and help particles jump out of local optimum. In addition, the GPSO adopts adaptive inertia weight, adaptive mutation parameters and multi-point crossover operation between particles and personal best value (pbest) to improve the optimization ability of the algorithm. Finally, this paper uses a nonlinear constraint problem (Himmelblau's nonlinear optimization problem) and three structural optimization problems (pressure vessel design problem, the welded beam design problem and the gear train design problem) as test functions and compares the proposed GPSO with the traditional GPSO, dingo optimization algorithm, whale optimization algorithm and grey wolf optimizer. The performance evaluation of the proposed algorithm is carried out by using the evaluation indexes such as best value, mean value, median value, worst value, standard deviation, operation time and convergence speed. The comparison results show that the proposed GPSO has obvious advantages in finding the optimal value, convergence speed and time overhead. [ABSTRACT FROM AUTHOR]

Drones, specifically quadcopters, have increased in importance during the last years due to their wide range of applications, from civil applications to military employment. One of the most important issues in quadcopters is the efficient control system. While many researchers have dealt with building control systems for symmetric quadcopters, this work presents an efficient control system for asymmetric quadcopters using evolutionary computations. The problem is well-defined throughout the paper, and the methodology is explained in detail in the respective sections. A genetic algorithm is used to tune the weighting matrix of the control system after formulating the control system as an optimization problem. The genetic algorithm was fast and active to increase the performance of the proposed system. [ABSTRACT FROM AUTHOR]

Studying the impact of green and high-quality development is of great significance to the healthy growth and sustainable development of enterprises. This paper discusses the influencing factors of the green and high-quality development of enterprises from the perspective of ownership structure and innovation ability, aiming to clarify the impact mechanism of these influencing factors on the green development of enterprises, and combined with emerging machine learning technologies, to propose a novel and effective corporate green high-quality development using a regression prediction model for quality development. Linear regression and one-way ANOVA were used to analyze the influence of each variable on the green and high-quality development of the enterprise, and the weight proportions of each influencing factor under the linear model were obtained. Two machine learning models based on the random forest (RF) algorithm and support vector machine algorithm were established, and the random parameters in the two machine learning algorithms were optimized by a genetic algorithm (GA). The reliability and accuracy of machine learning models and multivariate linear models were compared. The results show that the GA–RF model has superior regression performance compared with other prediction models. This paper provides a convenient machine learning model, which can quickly and effectively predict the green and high-quality development of enterprises, and provide help for enterprise decision-making and government policy formulation. [ABSTRACT FROM AUTHOR]

Distribution process is the core of logistics enterprise system. Efficient distribution process is the key for enterprises to improve logistics service level, gain competitive advantage, and win customers. In order to reduce the design error of the system and ensure the effective operation of the system, this paper establishes the system model and analyzes the properties of the model through the evaluation and analysis of various resources of the distribution system. The future development of intelligent logistics system is not only the key for logistics enterprises to win competition, but also a new measure to promote China's economic development. This is not only a typical NP hard problem, but also a major challenge for the rational and scientific development of the intelligent logistics industry. Based on the previous theoretical research results, this paper intends to explore the intelligent logistics distribution route selection scheme by using mathematical modeling methods such as particle swarm optimization, so as to provide new technologies and methods for logistics distribution operation and management decision-making. The results show that compared with the traditional genetic algorithm, the accuracy of the improved particle swarm optimization algorithm is improved by 10.36%. This method effectively improves the operation cycle and link efficiency and achieves the purpose of optimization. The improved particle swarm optimization algorithm proposed in this paper is not only more suitable and effective for enterprise decision makers to deal with subjective judgments in an imprecise environment. It is also based on the evaluation sequence of each indicator of the alternative obtained from the evaluation and the comprehensive evaluation of all indicators. By weighting and considering the results, the best solution for enterprise logistics and distribution is selected. [ABSTRACT FROM AUTHOR]

This paper delves into an in-depth exploration of speaker recognition methodologies, with a primary focus on three pivotal approaches: feature-level fusion, dimension reduction employing principal component analysis (PCA) and independent component analysis (ICA), and feature optimization through a genetic algorithm (GA) and the marine predator algorithm (MPA). This study conducts comprehensive experiments across diverse speech datasets characterized by varying noise levels and speaker counts. Impressively, the research yields exceptional results across different datasets and classifiers. For instance, on the TIMIT babble noise dataset (120 speakers), feature fusion achieves a remarkable speaker identification accuracy of 92.7%, while various feature optimization techniques combined with K nearest neighbor (KNN) and linear discriminant (LD) classifiers result in a speaker verification equal error rate (SV EER) of 0.7%. Notably, this study achieves a speaker identification accuracy of 93.5% and SV EER of 0.13% on the TIMIT babble noise dataset (630 speakers) using a KNN classifier with feature optimization. On the TIMIT white noise dataset (120 and 630 speakers), speaker identification accuracies of 93.3% and 83.5%, along with SV EER values of 0.58% and 0.13%, respectively, were attained utilizing PCA dimension reduction and feature optimization techniques (PCA-MPA) with KNN classifiers. Furthermore, on the voxceleb1 dataset, PCA-MPA feature optimization with KNN classifiers achieves a speaker identification accuracy of 95.2% and an SV EER of 1.8%. These findings underscore the significant enhancement in computational speed and speaker recognition performance facilitated by feature optimization strategies. [ABSTRACT FROM AUTHOR]

Clustering ensemble can be regarded as a mathematical optimization problem, and the genetic algorithm has been widely used as a powerful tool for solving such optimization problems. However, the existing research on clustering ensemble based on the genetic algorithm model has mainly focused on unsupervised approaches and has been limited by parameters like crossover probability and mutation probability. This paper presents a semi-supervised clustering ensemble based on the genetic algorithm model. This approach utilizes pairwise constraint information to strengthen the crossover process and mutation process, resulting in enhanced overall algorithm performance. To validate the effectiveness of the proposed approach, extensive comparative experiments were conducted on 9 diverse datasets. The results of the experiments demonstrate the superiority of the proposed algorithm in terms of clustering accuracy and robustness. In summary, this paper introduces a novel semi-supervised approach based on the genetic algorithm model. The utilization of pair-wise constraint information enhances the algorithm's performance, making it a promising solution for real-world clustering problems. [ABSTRACT FROM AUTHOR]

Clustering is a technique that segregates a provided dataset into homogenous groups in accordance with the provided features. It aims to determine a structure in a group of unlabelled data. Cluster analysis is an unsupervised learning technology that determines the interesting patterns in data objects without class labels. K mode clustering algorithm seems to be effective in clustering categorical data due to its easy implementation and capability to handle the massive amount of data. But because of its random selectivity of initial centroids, it gives the local optimum solution. The main contribution of the paper is to evaluate the performance of clustering on the various dataset with the proposed system. The proposed method utilizes a genetic-based Metaheuristic encircle algorithm to select enriched features and novel dynamic K modes clustering based on Dimensionality Reduced PSO for clustering process with better computational time. The encircling Prey concept has been incorporated to choose the fitness function and overcome the genetic algorithm limitations in feature selection. This paper integrated the k-modes algorithm with particle swarm optimization algorithm to obtain a global optimum solution and update the initial centroid. Several dataset utilized for the evaluation of the proposed work has been found to achieve low accuracy in the previous work. But the proposed approach's effectiveness has been proved to be better by performing a comparative analysis with the state of art methods in terms of performance metrics such as F1 score, accuracy, NMI. [ABSTRACT FROM AUTHOR]

The efficient design of the aerodynamic surfaces in the aircraft allows the optimal performance of the vehicle and the reduction of the fuel consumption. Among these surfaces, the wing is the main contributor to the force which lifts the aircraft enabling the flight. However, the application of this force out of the center of gravity generates a moment that must be balanced through a force applied in the Horizontal-Tail-Plane (HTP) reaching the longitudinal trim of the aircraft. Traditionally, the design of the wing and HTP have been performed iteratively attaining suboptimal or time ineffective results. In this paper, we solve this problem through the application of a Genetic Algorithm for the combined optimization of the wing and the HTP by adjusting the aspect ratio, the taper ratio, the twist angle and the incidence angle of both surfaces to produce the optimal balance of lift adjusting its distribution to an elliptical configuration and enabling the longitudinal trim. Results show the automatic adjustment of the parameters of the aerodynamic surfaces thus fulfilling the objectives of this paper. [ABSTRACT FROM AUTHOR]

To solve the decision problem of train stopping schemes, this paper introduces the static game into the optimal configuration of stopping time to realize the rational decision of train operation. First, a train energy consumption model is constructed with the lowest energy consumption of train operation as the optimization objective. In addition, a Mustang optimization algorithm based on cubic chaos mapping, the population hierarchy mechanism, the golden sine strategy, and the Levy flight strategy was designed for solving the problem of it being easy for the traditional population intelligence algorithm to fall into a local optimum when solving complex problems. Lastly, simulation experiments were conducted to compare the designed algorithm with PSO, GA, WOA, GWO, and other cutting-edge optimization algorithms in cross-sectional simulations, and the results show that the algorithm had excellent global optimization finding and convergence capabilities. The simulation results show that the research in this paper can provide effective decisions for the dwell time of trains at multiple stations, and promote the intelligent operation of the train system. [ABSTRACT FROM AUTHOR]

Gas gathering pipeline network system is an important process facility for gas field production, which is responsible for collecting, transporting and purifying natural gas produced by wells. In this paper, an optimization model for the layout of star-tree gas gathering pipeline network in discrete space is established to find the most economical design scheme. The decision variables include valve set position, station position and pipeline connection relation. A series of equality and inequality constraints are developed, including node flow balance constraints, pipeline hydraulic constraints and pipeline structure constraints. A global optimization strategy is proposed and an improved genetic algorithm is used to solve the model. To verify the validity of the proposed method, the optimization model is applied to a coalbed methane field gathering pipeline network in China. The results show that the global optimization scheme saves 1489.74×104 RMB (26.36%) in investment cost compared with the original scheme. In addition, the comparison between the global and hierarchical optimization scheme shows that the investment cost of the global optimization scheme is 567.22×104 RMB less than that of the hierarchical optimization scheme, which further proves the superiority of the global optimization method. Finally, the study of this paper can provide theoretical guidance for the design and planning of gas field gathering pipeline network. [ABSTRACT FROM AUTHOR]

Abstract: During a Wet End break, the loss of paper feed through the paper machine causes loss of sensory information and the remaining parts of the process are operated in open-loop. This causes the stock composition in the Headbox to deviate substantially from the nominal specifications, causing paper quality (after start up) and paper machine runability issues. In this work, the Base Sheet Ash measurement of the scanner is estimated using a least absolute value (LAV) model which can then be used for control of the chalk valve during the breaks to keep the Headbox Ash within specified limits. The model is computed using a very fast optimization algorithm which is able to compute the LAV solution using only basic elementary operations. The proposed approach has been developed for a UK paper mill. [Copyright &y& Elsevier]

There is a trend to deploy neural network on edge devices in recent years. While the mainstream of research often concerns with single edge device processing and edge-cloud two-layer neural network collaborative computing, in this paper, we propose partitioning multi-layer edge network for neural network collaborative computing. With the proposed method, sub-models of neural network are deployed on multi-layer edge devices along the communication path from end users to cloud. Firstly, we propose an optimal path selection method to form a neural network collaborative computing path with lowest communication overhead. Secondly, we establish a time-delay optimization mathematical model to evaluate the effects of different partitioning solutions. To find the optimal partition solution, an ordered elitist genetic algorithm (OEGA) is proposed. The experimental results show that, compared with traditional cloud computing, single-device edge computing and edge-cloud collaborative computing, the proposed multi-layer edge network collaborative computing has a smaller runtime delay with limited bandwidth resources, and because of the pipeline computing characteristics, the proposed method has a better response speed when processing large number of requests. Meanwhile, the OEGA algorithm has better performance than conventional methods, and the optimized partitioning method outperforms other methods like random and evenly partition. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, the most common pepper noise in grayscale image noise is investigated in depth in the median filtering algorithm, and the improved median filtering algorithm, adaptive switching median filtering algorithm, and adaptive polar median filtering algorithm are applied to the OTSU algorithm. Two improved OTSU algorithms such as the adaptive switched median filter-based OTSU algorithm and the polar adaptive median filter-based OTSU algorithm are obtained. The experimental results show that the algorithm can better cope with grayscale images contaminated by pretzel noise, and the segmented grayscale images are not only clear but also can better retain the detailed features of grayscale images. A genetic algorithm is a kind of search algorithm with high adaptive, fast operation speed, and good global space finding ability, and it will have a good effect when applied to the threshold finding of the OTSU algorithm. However, the traditional genetic algorithm will fall into the local optimal solution in different degrees when finding the optimal threshold. The advantages of the two interpolation methods proposed in this paper are that one is the edge grayscale image interpolation algorithm using OTSU threshold adaptive segmentation and the other is the edge grayscale image interpolation algorithm using local adaptive threshold segmentation, which can accurately divide the grayscale image region according to the characteristics of different grayscale images and effectively improve the loss of grayscale image edge detail information and jagged blur caused by the classical interpolation algorithm. The visual effect of grayscale images is enhanced by selecting grayscale images from the standard grayscale image test set and interpolating them with bilinear interpolation, bucolic interpolation, NEDI interpolation, and FEOI interpolation for interpolation simulation validation. The subjective evaluation and objective evaluation, as well as the running time, are compared, respectively, showing that the method of this paper can effectively improve the quality of grayscale image interpolation. [ABSTRACT FROM AUTHOR]

In this paper, critical analysis of performance comparison of various optimization techniques for a MRPID (Multi Resolution Proportional Integral Derivative) thermal system controller is presented. This controller controls the uncertainty or mismatch between real and reference temperature. Wavelet coefficients of error between the two temperatures and their corresponding gains are added to produce a control signal for the thermal system. The main purpose of the proposed method is to optimize the tuning parameters of a wavelet-based MRPID controller to regulate the switching pulse of the thermal system. Modeling and simulations are carried out using MATLAB/Simulink@2015. Figures-of-merit such as disturbance rejection, system stability and transient response are also compared using various techniques like Particle swarm optimization (PSO)-MRPID, Genetic Algorithm (GA)-MRPID (existing techniques), Firefly-MRPID controllers and Elephant herding optimization (EHO) based Recurrent neural network (RNN)-MRPID algorithm. Firefly-MRPID and EHO with RNN-based MRPID are proposed for determining unique (best) operating conditions of the thermal system in this paper. [ABSTRACT FROM AUTHOR]

When the pulse current method is used for partial discharge (PD) monitoring of mining cables, the detected PD signals are seriously disturbed by the field noise, which are easily submerged in the noise and cannot be extracted. In order to realize the effective separation of the PD signal and the interference signal of the mining cable and improve the signal-to-noise ratio of the PD signal, a denoising method for the PD signal of the mining cable based on genetic algorithm optimization of variational mode decomposition (VMD) and wavelet threshold is proposed in this paper. Firstly, the genetic algorithm is used to optimize the VMD, and the optimal value of the number of modal components K and the quadratic penalty factor α is determined; secondly, the PD signal is decomposed by the VMD algorithm to obtain K intrinsic mode functions (IMF). Then, wavelet threshold denoising is applied to each IMF, and the denoised IMFs are reconstructed. Finally, the feasibility of the denoising method proposed in this paper is verified by simulation and experiment. [ABSTRACT FROM AUTHOR]

The increasing demand for storing various types of goods has led to a raise in the need for storage capacity in warehousing systems. Autonomous vehicle storage and retrieval systems (AVS/RSs) offer high flexibility by allowing different configurations to meet different storage requirements. The system mainly completes operations through elevators and multiple rail-guided vehicles (RGVs). This paper focuses on the scheduling optimization of compound operations in the AVS/RS to improve system performance. Compound operations involve the coordinated execution of both single-command and double-command operations. A mathematical model with compound operations was proposed and effectively decomposed into a horizontal component for RGVs and a vertical counterpart for the elevator, which can represent the operations of one elevator cooperating with multiple RGVs. The goal of this model was to minimize the makespan for compound operations and to determine the optimal operation sequence and path for RGVs. An improved discrete particle swarm optimization (DPSO) algorithm called AGDPSO was proposed to solve the model. The algorithm combines DPSO and a genetic algorithm in an adaptive manner to prevent the algorithm from falling into local optima and relying solely on the initial solution. Through rigorous optimization, optimal parameters for the algorithm were identified. When assessing the performance of our improved algorithm against various counterparts, considering different task durations and racking configurations, our results showed that AGDPSO outperformed the alternatives, proving its effectiveness in enhancing system efficiency for the model. The findings of this study not only contribute to the optimization of AVS/RS but also offer valuable insights for designing more efficient warehouses. By streamlining scheduling, improving operations, and leveraging advanced optimization techniques, we can create a more robust and effective storage and retrieval system. [ABSTRACT FROM AUTHOR]

Rational scheduling of locomotive paths (the locus of the locomotive point in the train working diagram) is an important step in drawing a locomotive working diagram by a computer. But there are some problems in this process, such as the computer usually drawing a locomotive path that overlaps with another locomotive path (in the circumstances, the actual users of the locomotive working diagrams often misread the locomotive planning). At present, there are many studies about assigning sets of locomotives to each train in a preplanned train schedule; in contrast, the studies of visualizing the locomotive planning are relatively rare. Through investigating the locomotive working diagram users, this paper points out that the layout of locomotive paths should put the distance between lines being as large as possible and should put the number of the intersection between lines being as few as possible as the optimization aim which is based to solve the problem of the lines overlap or the problem of the lines beyond the margins for drawing the locomotive paths. This paper also builds the optimization model of locomotive working diagram layout. Based on determining the position of locomotive paths which can be delineated, a genetic algorithm is used to solve the optimizing model of locomotive working diagram layout in this paper. An example of a train working diagram with 36 trains is given at the end of the paper, which indicates that the optimization model of locomotive working diagram layout can better solve the problem of locomotive planning visualization. [ABSTRACT FROM AUTHOR]

This paper deals with the multiobjective optimization problem of an agro- alimentary production workshop. Three criteria are considered in addition to this initial cost of production: the cost of the out-of-date products, the cost of the distribution discount and the makespan, and a new coding is proposed for this type of workshop. The adopted approach consists in generating optimal solutions diversified in the search space of solutions, and to help the decision maker when it cannot give a particular preference to one of the objective functions to make the good decision with respect to the quoted criteria. [ABSTRACT FROM AUTHOR]

Panax notoginseng is a valuable herb with geographical indication, and the quality and price of P. notoginseng from different origins are very different. Therefore, this paper proposes a rapid and accurate method for identifying the origins of P. notoginseng by collecting the roots of P. notoginseng. This paper improves the whale optimization algorithm in terms of global convergence and convergence speed, introduces the Levy flight strategy and reconstructed whale synergy factor A, and applies it to the parameter optimization of support vector machines, to obtain a high-performance classification model. The improved whale optimization algorithm model identifies the origin of P. notoginseng by discriminating their terahertz spectra. Compared with the commonly used genetic algorithm and the original whale optimization algorithm, improvement in the whale optimization algorithm was able to avoid falling into local optimum solutions more effectively while having a high convergence rate. Accordingly, the improved whale optimization algorithm optimized support vector machine model obtained an overall accuracy of 98.44%, which was significantly higher than the 95.31% overall accuracy of the genetic algorithm optimized support vector machine model and the 96.88% overall accuracy of the whale optimization algorithm optimized support vector machine model. It was concluded that terahertz spectroscopy together with machine learning would be a promising technique for identifying the origins of P. notoginseng. [ABSTRACT FROM AUTHOR]

An integrated decision in supply chain is a significant principle for proper competition in today's market. This paper proposes a novel mathematical model in a two-stage supply chain scheduling to make a coordination between procurement and manufacturing activities. The supply chain scheduling along with the production approach of cellular manufacturing under demand, processing time, and transportation time uncertainties makes business environment sustainably responsive to the changing needs of customers. Uncertainties are formulated by queuing theory. In this paper, a new mixed-integer nonlinear programming formulation is used to determine types of vehicles to carry raw materials, suppliers to procure, priority of each part in order to process, and cell formation to configure work centers. The goal is to minimize total tardiness. A linearization method is used to ease tractability of the model. A genetic algorithm is developed due to the NP-hard nature of the problem. The parameters of the genetic algorithm are set and estimated by Taguchi's experimental design. Numerous test problems are employed to validate the effectiveness of the modeling and the efficiency of solution approaches. Finally, a real case study and a sensitivity analysis are discussed to provide significant managerial insights and assess the applicability of the proposed model. [ABSTRACT FROM AUTHOR]

Due to the characteristics of online learning resource recommendation such as large scale, uneven quality and diversity of preferences, how to accurately obtain various personalized learning resource lists has become an urgent problem to be solved in the field of online learning resource recommendation. This paper proposes an online learning resource recommendation model based on the improved NSGA-Ⅱ algorithm, which integrates the Tabu search algorithm to improve the local search ability of NSGA-Ⅱ algorithm. It takes background fitness, cognitive fitness and diversity as the objective functions for optimization. The dynamic updating of crowding degree is used to avoid the risk that the individuals with low crowding degree in the same area are deleted at the same time. Meanwhile, an adaptive genetic algorithm is applied to assign the optimal crossover rate and the mutation rate according to individual adaptability level, which ensures the convergence of genetic algorithm and the diversity of population. The experimental results show that the proposed model is superior to the traditional recommendation algorithm in terms of accuracy index, mean fitness, recall rate, F1 mean, HV, GD and IGD, etc., thus verifying the feasibility and effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

To enhance the efficiency of system modeling and optimization in the conceptual design stage of satellite parameters, a system modeling and optimization method based on System Modeling Language and Co-evolutionary Algorithm is proposed. At first, the objectives of satellite mission and optimization problems are clarified, and a design matrix of discipline structure is constructed to process the coupling relationship of design variables and constraints of the orbit, payload, power and quality disciplines. In order to solve the problem of increasing nonlinearity and coupling between these disciplines while using a standard collaborative optimization algorithm, an improved genetic algorithm is proposed and applied to system-level and discipline-level models. Finally, the CO model of satellite parameters is solved through the collaborative simulation of Cameo SystemsModeler (CSM) and MATLAB. The result obtained shows that the method proposed in this paper for the conceptual design phase of satellite parameters is efficient and feasible. It can shorten the project cycle effectively and additionally provide a reference for the optimal design of other complex projects. [ABSTRACT FROM AUTHOR]

This paper presents an optimization method for selective harmonic elimination in a cascaded multilevel inverter (MLI) using teaching-learning based optimization (TLBO). The main objective in selective harmonic elimination (SHE) strategy is to eliminate low-order harmonics by solving nonlinear equations and reaching optimal solution, while the fundamental component is satisfied. In this paper, different optimization methods are compared. In this paper, the TLBO as a recently emerged nature-inspired algorithm is presented to provide better results for the SHE in comparison with Genetic algorithm (GA), Particle swarm optimization (PSO), Newton Raphson Method (NR) and Satin Bowerbird algorithm (SBO). For better comparison of those methods and influence of optimal DC source a 7-level inverter is chosen and MATLAB/Simulink software is used for optimization. Simulation results show the superiority of TLBO, higher precision and probability of convergence than other mentioned algorithms. [ABSTRACT FROM AUTHOR]

Power system often experiences the problem of low-frequency electromechanical oscillations which leads the system to unstable condition. The problem can be corrected by implementing power system stabilizers (PSSs) in the excitation control system of alternator. This paper provides a novel and efficient approach to design an Improved Grasshopper Optimization Algorithm (IGOA)-based dual-input controller to damp the inter-area-mode power system oscillations. A three-fold optimization criterion has been formulated to calculate the optimum values of the controllers required for power system stability. The damping performance of the proposed controller is compared with conventional PSS and genetic algorithm-based controllers to validate the better performance of the proposed IGOA-based controller under various system loading conditions and disturbances. [ABSTRACT FROM AUTHOR]

In software testing, the selection of test data is a difficult problem in structural testing. Whether the test data is appropriate or not is directly related to whether the error can be expected to be detected. In the process of software testing, the generation of test data is not only the core problem but also the key and difficulty of software testing. Because of the huge number of test cases and low test efficiency, a powerful optimization algorithm is needed to optimize the initial test cases. As a robust search method, genetic algorithm shows unique advantages and high efficiency in solving high-complexity problems such as large space, multipeak, nonlinear, and global optimization. Based on the application of genetic algorithm, this paper analyzes the optimization path by classifying and calculating the objective function and introducing NSGA-II algorithm, measures the distance between each branch on the processing path sample set, and sorts the path set to obtain the optimal solution. On the basis of the designed model, the experimental results show that the error control rate of the model is 89.4%. Moreover, because of the superiority of NSGA-II algorithm, the probability of comprehensive cross mutation is increased by 56.7%. [ABSTRACT FROM AUTHOR]

This paper proposes a high-satisfaction evaluation method for health-care product design based on the genetic optimization algorithm in order to deeply understand consumers' satisfaction with forest health-care products. This article builds a consumer demand index system for the service content of forest health-care bases from the four levels of environmental conditions, service items, supporting facilities, and service levels. According to the questionnaire survey results of 1000 randomly selected forest health-care consumers, based on the genetic optimization algorithm, the overall design takes the four levels of satisfaction evaluation into account, resulting in a more reasonable evaluation of health-care products. Experiments show that the method proposed in this paper outperforms the traditional method for evaluating health-care products. [ABSTRACT FROM AUTHOR]

Optimization of land surface models has been very challenging due to the increasing complexity of such models. Typical parameter calibration techniques often limit the solution of the spatiotemporal discrepancy in the modeling performance levels especially for regional applications. Thus, in this study, an attempt was made to perform schemebased model optimization by designing a framework for coupling a micro-genetic algorithm (micro-GA) with the Noah land surface model that has multiple physics options (Noah-MP). Micro-GA controls the scheme selections in 10 different land surface parameterization fields in Noah-MP in order to extract the optimal scheme combination for a certain region. This coupling framework was successfully applied to the optimization of the surface water partitioning in the Korean Peninsula, promising not only the effectiveness of the scheme-based optimization but also model diagnosis capability by exploring the scheme sensitivity during the micro-GA evolution process. Then, the method was applied to four different regions in East Asia that have different climatic characteristics. The results indicate that (1) the optimal scheme combinations vary with the regions, (2) schemes related to the surface water partitioning are important for the modeling accuracy, and (3) specialized post-parameter optimization for each region may be required. [ABSTRACT FROM AUTHOR]

Circuit design plays a pivotal role in engineering, ensuring the creation of efficient, reliable, and cost-effective electronic devices. The complexity of modern circuit design problems has led to the exploration of multi-objective optimization techniques for circuit design optimization, as traditional optimization tools fall short in handling such problems. While metaheuristic algorithms, especially genetic algorithms, have demonstrated promise, their susceptibility to premature convergence poses challenges. This paper proposes a pioneering approach, the chaotic multi-objective Runge–Kutta algorithm (CMRUN), for circuit design optimization, building upon the Runge–Kutta optimization algorithm. By infusing chaos into the core RUN structure, a refined balance between exploration and exploitation is obtained, critical for addressing complex optimization landscapes, enabling the algorithm to navigate nonlinear and nonconvex optimization challenges effectively. This approach is extended to accommodate multiple objectives, ultimately generating Pareto Fronts for the multiple circuit design goals. The performance of CMRUN is rigorously evaluated against 11 multiobjective algorithms, encompassing 15 benchmark test functions and practical circuit design scenarios. The findings of this study underscore the efficiency and real-world applicability of CMRUN, offering valuable insights for tailoring optimization algorithms to the real-world circuit design challenges. [ABSTRACT FROM AUTHOR]

In this paper, different methods are utilized to solve the coordination issue involving directional overcurrent relays (DOCRs) and distance relays. The proper coordination of DOCRs and distance relays is a critical issue for system security in electrical networks. Finding the DOCRs setting, pickup current(Ip) and timed dial setting (TDS), and operating time for zone-2 of distance relays is the primary objective of solving the coordination problem. The constant parameters A & B of the directional overcurrent relay that are responsible to control the form of the relay´s characteristics as well as the Ip and TDS have been regarded as variables in this problem. The optimal value for these four DOCR settings has been determined using different optimization techniques. The primary and backup relays must operate sequentially and without any violations, and this must be guaranteed by optimization techniques. In order to determine the operation time for zone-2 and DOCRs setting, optimization methods are examined utilizing the 8-bus and IEEE 30-bus networks. Different optimization algorithms, including recent and traditional techniques, are compared. The obtained results show the superiority of the genetic algorithm (GA) in solving the coordination problem of distance relays and DOCRs. Also, the obtained results prove the ability of the GA method compared to the particle swarm algorithm (PSO), grey wolf optimization (GWO), water cycle technique (WCA), equilibrium optimizer (EO), African vultures optimization algorithm (AVOA), flow direction algorithm (FDA), and gorilla troops optimizer (GTO) techniques. [ABSTRACT FROM AUTHOR]

Radial basis function networks are widely used in a multitude of applications in various scientific areas in both classification and data fitting problems. These networks deal with the above problems by adjusting their parameters through various optimization techniques. However, an important issue to address is the need to locate a satisfactory interval for the parameters of a network before adjusting these parameters. This paper proposes a two-stage method. In the first stage, via the incorporation of grammatical evolution, rules are generated to create the optimal value interval of the network parameters. During the second stage of the technique, the mentioned parameters are fine-tuned with a genetic algorithm. The current work was tested on a number of datasets from the recent literature and found to reduce the classification or data fitting error by over 40% on most datasets. In addition, the proposed method appears in the experiments to be robust, as the fluctuation of the number of network parameters does not significantly affect its performance. [ABSTRACT FROM AUTHOR]

In the realm of bioinformatics, Multiple Sequence Alignment (MSA) is a pivotal technique used to optimize the alignment of multiple biological sequences, guided by specific scoring criteria. Existing approaches addressing the MSA challenge tend to specialize in distinct biological features, leading to variability in alignment outcomes for the same set of sequences. Consequently, this paper proposes an enhanced evolutionary-based approach that simplifies the sequence alignment problem without considering the sequences in the non-dominated solution. Our method employs a multi-objective optimization technique that uniquely excludes non-dominated solution sets, effectively mitigating computational complexities. Utilizing the Sum of Pairs and the Total Conserved Column as primary objective functions, our approach offers a novel perspective. We adopt an integer coding approach to enhance the computational efficiency, representing chromosomes with sets of integers during the alignment process. Using the SABmark and BAliBASE datasets, extensive experimentation is conducted to compare our method with existing ones. The results affirm the superior solution quality achieved by our approach compared to its predecessors. Furthermore, via the Wilcoxon signed-rank test, a statistical analysis underscores the statistical significance of our model's improvement (p < 0.05). This comprehensive approach holds promise for advancing Multiple Sequence Alignment in bioinformatics. [ABSTRACT FROM AUTHOR]

Artificial neural networks are widely established models of computational intelligence that have been tested for their effectiveness in a variety of real-world applications. These models require a set of parameters to be fitted through the use of an optimization technique. However, an issue that researchers often face is finding an efficient range of values for the parameters of the artificial neural network. This paper proposes an innovative technique for generating a promising range of values for the parameters of the artificial neural network. Finding the value field is conducted by a series of rules for partitioning the original set of values or expanding it, the rules of which are generated using grammatical evolution. After finding a promising interval of values, any optimization technique such as a genetic algorithm can be used to train the artificial neural network on that interval of values. The new technique was tested on a wide range of problems from the relevant literature and the results were extremely promising. [ABSTRACT FROM AUTHOR]

The multiple Unmanned Aerial Vehicle (multi-UAV) assisted roadside unit (RSU) data acquisition problem considering the coverage quality is a multiobjective optimization problem, which is a NP-hard problem. Heuristic and hyperheuristic algorithms are effective to solve problems of this type. These algorithms can find the optimal or suboptimal solution in a reasonable time. However, such algorithms still have the problems of low convergence accuracy, slow convergence speed, and being easy to fall into the local optimal solution. In this paper, firstly, according to the specific problem scenarios of roadside unit data collection, minimum cost and maximum coverage models based on task cost and coverage quality are established. Then, to solve the optimization model, combined with the update characteristics of the gray wolf optimization algorithm (GWO) and the whale optimization algorithm (WOA), a hybrid weighted gray wolf and whale optimization algorithm (HWGWOA) is proposed. Finally, to verify the effectiveness of the proposed algorithm, extensive simulation experiments are conducted under four different task acquisition scenarios, and the results are compared with those of genetic algorithm (GA), GWO, and WOA. Simulation results show that the algorithm proposed in this paper not only can get lower task cost and higher coverage quality but also has faster convergence speed and better robustness. Specifically, in terms of task cost, the HWGWOA is about 9.54% lower than the GA, about 7.31% lower than the GWO, and about 5.8% lower than the WOA. In terms of coverage, the HWGWOA is up to about 27.87% higher than the GA, about 15.19% higher than the GWO, and about 9.86% higher than the WOA. Therefore, the algorithm is more suitable for large-scale optimization problems. [ABSTRACT FROM AUTHOR]

In the field of data analysis clustering is considered to be a major tool. Application of clustering in various field of science, has led to advancement in clustering algorithm. Traditional clustering algorithm have lot of defects, while these defects have been addressed but no clustering algorithm can be considered as superior. A new approach based on Kernel Fuzzy C-means clustering using teaching learning-based optimization algorithm (TLBO-KFCM) is proposed in this paper. Kernel function used in this algorithm improves separation and makes clustering more apprehensive. Teaching learning-based optimization algorithm discussed in the paper helps to improve clustering compactness. Simulation using five data sets are performed and the results are compared with two other optimization algorithms (genetic algorithm GA and particle swam optimization PSO). Results show that the proposed clustering algorithm has better performance. Another simulation on same set of data is also performed, and clustering results of TLBO-KFCM are compared with teaching learning-based optimization algorithm with Fuzzy C- Means Clustering (TLBO-FCM). [ABSTRACT FROM AUTHOR]

For any machine learning model, finding the optimal hyperparameter setting has a direct and significant impact on the model's performance. In this paper, we discuss different types of hyperparameter optimization techniques. We compare the performance of some of the hyperparameter optimization techniques on image classification datasets with the help of AutoML models. In particular, the paper studies Bayesian optimization in depth and proposes the use of genetic algorithm, differential evolution and covariance matrix adaptation—evolutionary strategy for acquisition function optimization. Moreover, we compare these variants of Bayesian optimization with conventional Bayesian optimization and observe that the use of covariance matrix adaptation—evolutionary strategy and differential evolution improves the performance of standard Bayesian optimization. We also notice that Bayesian optimization tends to perform poorly when genetic algorithm is used for acquisition function optimization. [ABSTRACT FROM AUTHOR]

In this paper, a system for LTE Cell Outage Compensation (COC) based on hybrid Genetic Algorithms (GA) and Artificial Neural Networks (ANN) has been proposed. COC aims to minimize the impact of cell outage which leads to decrease in operator revenue and/or the customer satisfaction. The proposed system adopts an optimization module to search for an optimal setting of a set of LTE operational parameters to achieve a targeted set of key performance indicators. The optimization process always leads to good enough solutions, but it also requires a huge number of trials. So, in the proposed system, a huge set of outage scenarios is collected along with their optimal argument settings that are acquired by the optimization module and they are used to train an artificial neural network (ANN) module, which acts as an expert that can optimally act on the different situations in real‐time mode. Simulation environment is set to evaluate different LTE measures and Key Performance Indicators (KPIs) on different outage scenarios. Simulation results proved the capability and robustness of the proposed system to minimize the number of users experiencing outage. Simulation results also show that the proposed system achieves optimal parameter settings without violating the overall system performance and with minimal processing time, while introducing significant impact on the performance of LTE. [ABSTRACT FROM AUTHOR]

With the emergence of more and more computing-intensive and latency-sensitive applications, insufficient computing power and energy of user devices has become a common phenomenon. Mobile edge computing (MEC) is an effective solution to this phenomenon. MEC improves task execution efficiency by offloading some tasks to edge servers for execution. In this paper, we consider a device-to-device technology (D2D)-enabled MEC network communication model, and study the subtask offloading strategy and the transmitting power allocation strategy of users. The objective function is to minimize the weighted sum of the average completion delay and average energy consumption of users, which is a mixed integer nonlinear problem. We first propose an enhanced particle swarm optimization algorithm (EPSO) to optimize the transmit power allocation strategy. Then, we utilize the Genetic Algorithm (GA) to optimize the subtask offloading strategy. Finally, we propose an alternate optimization algorithm (EPSO-GA) to jointly optimize the transmit power allocation strategy and the subtask offloading strategy. The simulation results show that the EPSO-GA outperforms other comparative algorithms in terms of the average completion delay, average energy consumption, and average cost. In addition, no matter how the weight coefficients of delay and energy consumption change, the average cost of the EPSO-GA is the least. [ABSTRACT FROM AUTHOR]

This paper represents the execution of a reliable, steady and wideband fractional order digital integrator (FODI) in respect of infinite impulse response (IIR) filter using a recent nature inspired meta-heuristic optimization technique known as mayfly optimization algorithm (MOA). MOA significantly functions stronger against some of the popular algorithms like real-coded genetic algorithm (RGA), particle swarm optimization (PSO), differential evolution (DE), improved particle swarm optimization (IPSO), whale optimization algorithm (WOA) and chaotic oppositional whale optimization algorithm (COWOA) with reference to different magnitude error performances, explanation character accuracy, convergence profile and computational time required to find the optimal solution. In this paper, the authors thoroughly analyze the convergence behavior and magnitude error metrices of the different order FODI's using WOA, COWOA and MOA. MATLAB simulation results also powerfully uphold the magnitude response efficiency and stability of the proposed different order FODI's over some recently published research papers. [ABSTRACT FROM AUTHOR]

In this paper, rainfall droplet impact force is transformed into a measurable voltage signal output via the piezoelectric material direct effect utilized for sensing purposes. The motivating sensor is utilized to measure the peak impact forces of rainfall droplets for further analysis and processing. Constructing a sense for the impact force of rainfall droplets has great implications in many real-life applications that can provide vital information regarding the amplifications of the impact force of rainfall on soil erosion, and the impact on small creatures and plants, etc. The rainfall droplet is set to collide on a very thin aluminum plate with negligible mass that can be presented geometrically as an extended segment of the proposed sensing device. The proposed sensing device is composed of a bimorph simply supported composite-piezoelectric beam that buckles due to the effect of the rain droplets' vertical impact force. The proposed device is designed for optimal performance in terms of the amount of voltage that can be measured. This is accomplished by having the first critical buckling load of the device as less than the impact force of the rainfall droplet. Accordingly, the well-known genetic algorithm (GA) automated optimization technique is utilized in this paper to enhance the measured voltage signal. A proof mass is added to the middle of the beam to amplify the magnitude of the measured voltage signal. The voltage signal is intended to be transferred to the PC via a data acquisition system. The rainfall droplets' peak impact forces are obtained analytically due to the nonlinear behavior of the beam using the Euler–Bernoulli thin beams assumptions. The FE model using COMSOL 6.0 Multiphysics commercial software is used to verify the analytical results. [ABSTRACT FROM AUTHOR]

By triggering the switches in sequence and controlling the brick charging voltage independently, a pulsed power system can generate flexible current waveform in the load, which could allow high-accurate magnetic pressure in experimental materials in the study of EOS data and dynamic response of material characterization. Currently, a 1.5-MA driver with 20 independent bricks has been developed, and a programmable 256-brick parallel discharge driver which is able to generate shaped current greater than 6.5 MA is under construction in the China Academy of Engineering Physics. In this paper, for these two multibrick parallel discharge drivers, the method of optimizing the output current waveform of the driver is studied by using the genetic algorithm and the corresponding optimization results of different target current waveforms are obtained. An experimental platform is set up for studying the gas switch breakdown characteristics. The self-breakdown and triggering breakdown experiments of the switch are conducted, and its breakdown characteristics are researched. Meanwhile, the effect of breakdown delay and jitter on the output current waveform is studied by simulation. This paper is of great significance to the actual operation of the multibrick parallel discharge drivers. [ABSTRACT FROM AUTHOR]