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This paper introduces a novel hybrid optimization algorithm named bat-salp swarm algorithm (BASSA). BASSA integrates the local exploitation capability of bat algorithm (BA) and the global exploration capability of salp swarm algorithm (SSA). Firstly, by introducing the echolocation of BA, the follower updating strategy of SSA is improved. Secondly, the algorithm selects between BA and SSA based on specific conditions. Finally, individuals undergo random differential mutation to increase population diversity, thereby avoiding local optima. To verify the effectiveness of the algorithm, we carry out experiments BASSA on 23 benchmark functions with different dimensions and compare it with 7 optimization algorithms, including BA, SSA, and 7 enhanced versions of SSA. Simulation results indicate that BASSA outperforms standard BA, SSA, and other enhanced algorithms in terms of mean and standard deviation. This suggests a significant improvement in optimization performance, with higher solution accuracy and faster convergence speed. Additionally, through performance evaluation on three real engineering problems, the results indicate that BASSA possesses strong optimization capabilities. [ABSTRACT FROM AUTHOR]

Path planning for unmanned aerial vehicle (UAV) is the process of determining the path that travels through each location of interest within a particular area. There are numerous algorithms proposed and described in the publications to address UAV path planning problems. However, in order to handle the complex and dynamic environment with different obstacles, it is critical to utilize the proper fusion algorithms in planning the UAV path. This paper reviews some hybrid algorithms used in finding the optimal route of UAVs that developed in the last ten years as well as their advantages and disadvantages. The UAV path planning methods were classified into categories of hybrid algorithms based on traditional, heuristic, machine learning approaches. Criteria used to evaluate algorithms include execution time, total cost, energy consumption, robustness, data, computation, obstacle avoidance, and environment. The results of this study provide reference resources for researchers in finding the path for UAVs. [ABSTRACT FROM AUTHOR]

In this paper, we investigated a nonlinear continuous-time switched time-delay (NCTSTD) system for glycerol fed-batch bioconversion to 1, 3-propanediol with unknown time-delay and system parameters. The measured output data was uncertain, while the first moment information about its distribution was available. Our goal was to identify these unknown quantities under the environment of uncertain measurement output data. A distributionally robust parameter estimation problem (i.e., a bi-level parameter estimation (BLPE) problem) subject to the NCTSTD system was presented, where the expectation of the discrepancy between the output of the NCTSTD system and the uncertain measured output data with respect to its probability distributions was included in the cost functional. By applying the duality theory, the BLPE problem was transformed into a single-level parameter estimation (SLPE) problem with non-smooth term approximated by a smoothing technique and its error analysis was given. Then, the gradients of the cost function of the SLPE problem were derived. A hybrid optimization algorithm was proposed for solving the SLPE problem. The paper concluded by presenting the simulation results. [ABSTRACT FROM AUTHOR]

Copyright of Engineering Mechanics / Gongcheng Lixue is the property of Engineering Mechanics Editorial Department 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 order to successfully address the multiprocessor work scheduling issues, a novel hybrid approach is presented in this paper. Executing a group of tasks on a small number of processors is the main goal of a task scheduling method. The job scheduling issue is viewed as a multi-objective optimisation issue in this study. The goals of this optimisation challenge are to determine the system's least energy consumption and minimum makespan (the sum of the schedule lengths for each task's execution). In order to lower the processors' energy usage, the Dynamic Voltage Frequency Scaling (DVFS) level has the task scheduling issue enabled. To determine the goals, a hybrid algorithm called Hybrid Grey Wolf Crow Search Optimisation (HGWCSO) is suggested. The Grey Wolf Optimisation (GWO) and Crow Search Optimisation (CSO) algorithms are combined to create the HGWCSO algorithm. The social structure and hunting strategy of wolves serve as the inspiration for the GWO metaheuristic algorithm. The CSO, on the other hand, is a metaheuristic algorithm that was motivated by crows' clever conduct. The proposed hybrid approach is put into practise using tasks that are produced at random, and the results of this comparison are classic methods. The proposed method's simulation results are contrasted with those from five existing widely used methods. The proposed technique has also been contrasted with some newly created metaheuristic algorithms using various real-world data sets. According to the simulation results, HGWCSO outperforms other algorithms in terms of lowering makespan and energy usage. The proposed algorithm has successfully improved the makespan and energy consumption with a maximum of 31.07% and 58.36%, respectively, compared to other algorithms. [ABSTRACT FROM AUTHOR]

Copyright of Cotton Textile Technology is the property of Cotton Textile Technology Editorial Office 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.)

The selection of the most efficient features for glaucoma identification is the subject of our investigation because this disease is rapidly increasing worldwide. This disease causes lifelong blindness due to damage to the eye's optical nerve. Ophthalmologists have traditionally used tonometry, pachymetry, and other methods to measure intraocular pressure in order to diagnose patients. Yet each of these judgments takes time, requires high professional experience, and can be open to human error (inter-observer variability). Therefore, scholars are currently engaged in the domain of medical imaging, specifically focusing on the analysis of retinal images for the purpose of predicting glaucoma. This research also has the same objective and aims to address the aforementioned challenges. This empirical study proposes an artificial intelligence-based computer-assisted diagnosis (CAD) system which is built to overcome these difficulties by providing the best features for machine learning techniques for categorizing subject retinal pictures as "healthy" or "sick". This study presents a new set of reduced hybrid features that were selected from an initial set of 36 features extracted from fundus images of benchmark datasets that belonged to different classes to categorize patient fundus images into two categories: "healthy" or "infected." The nature inspired computing-based Emperor Penguin Optimization (EPO) algorithm and the Bacterial Foraging Optimization (BFO) algorithm are utilized to implement feature selection (FS) process. Additionally, a novel hybrid algorithm combining these two techniques is also proposed. Seven machine learning (ML) classifiers are engaged to compute eight statistically based performance metrics along with execution time computation, and a comparison of those metrics is also provided in a detailed fashion. The recommended method exhibits a fortunate performance with the highest specificity of 0.9940, sensitivity of 0.9347, and maximum accuracy of 96.55%. Expert medical practitioners who are overworked may receive assistance from the proposed system in making the optimal decisions to preserve human vision. [ABSTRACT FROM AUTHOR]

A hybrid algorithm is introduced to synthesize the nonuniform symmetric linear antenna arrays in this paper. The array element positions and excitation coefficients of the antenna arrays are optimized by using Gray Wolf Algorithm (GWO) and Convex Optimization Algorithm (CO). In order to further reduce the sidelobe level, the linear antenna array is divided into several subarrays under the constraints of element number, aperture size and minimum adjacent element spacing. Array element positions, excitation coefficients of each subarray and the element number of each subarray are optimized simultaneously. Finally, the optimization results are compared with the results obtained by other literatures. It is obviously found that the method proposed in this paper can obtain lower sidelobe level under the same conditions. So, the effectiveness and feasibility of the algorithm proposed in this paper is proved. [ABSTRACT FROM AUTHOR]

Simple Summary: Present algorithms for the detection of pelvic sentinel lymph utilizing only tracers may fail to detect metastatic disease as women with nodal metastases overall have an overall lower mapping rate and that nodes at certain anatomic positions of the pelvic lymphatic pathways map less frequently. The study aimed to assess the increase in the diagnosis of pelvic nodal metastases by adding the removal of non-mapped nodes at defined known high-risk anatomic positions despite mapping at other positions. A 4.3% increase in the detection of pelvic nodal metastases was noted by the presence of isolated non-mapped metastatic nodes in the proximal obturator or external interiliac positions, suggesting that a hybrid algorithm combining tracer (ICG) with the removal of non-mapped nodes at either of the aforementioned positions should be utilized. Aim of the study: to investigate the incidence of non-mapped isolated metastatic pelvic lymph nodes at pre-defined anatomical positions. Patients and Methods: Between June 2019 and January 2024, women with uterine-confined endometrial cancer (EC) deemed suitable for robotic surgery and the detection of pelvic sentinel nodes (SLNs) were included. An anatomically based, published algorithm utilizing indocyanine green (ICG) as a tracer was adhered to. In women where no ICG mapping occurred in either the proximal obturator and/or the interiliac positions, defined as "typical positions", those nodes were removed and designated as "SLN anatomy". Ultrastaging and immunohistochemistry were applied to all SLNs. The proportion of isolated metastatic "SLN anatomy" was evaluated. Results: A non-mapping of either the obturator or interiliac area occurred in 180 of the 620 women (29%). In total, 114 women (18.4%) were node-positive and five of these women (4.3%) had isolated metastases in an "SLN anatomy", suggesting a similar lower sensitivity of the ICG-only algorithm. Conclusion: In an optimized SLN algorithm for endometrial cancer, to avoid undetected nodal metastases in 4.3% of node-positive women, if mapping fails in either the proximal obturator or interiliac area, nodes should be removed from those defined anatomic positions, despite mapping at other positions. [ABSTRACT FROM AUTHOR]

Reservoir operation exhibits traits of nonlinearity, numerous constraints, and nonconvexity. As the number of reservoirs, such as series and parallel reservoirs, increases, the complexity of the reservoirs also increases. This study solves the complex problem using a new hybrid algorithm (MBWOHHO) based on a modified Beluga whale optimization (BWO) with Harris hawks optimization (HHO). First, in the initialization phase, an opposition-based learning strategy (OBL) is incorporated. This strategy reconstructs the initial spatial position of the population using pairwise comparisons to obtain a higher-quality initial population. Then, a differential mechanism is devised during the global search phase. This strategy enhances global exploration capabilities by cross-combining local optimal individuals with ordinary individuals. Finally, BWO and HHO are organically integrated via a population-based mechanism. This strategy effectively maximizes the strengths of both algorithms while maintaining a balance between exploration and exploitation. Several experiments are conducted across various types and complexities of benchmark functions, including 18 classical and 14 CEC2014 functions. The results of the three cascade reservoir optimization experiments show that MBWOHHO has obvious advantages over the comparison algorithms. [ABSTRACT FROM AUTHOR]

The Harris hawk Optimization (HHO) has the advantage of employing various local search strategies to adapt to different situations during the exploitation phase. Hence, HHO can search deeper into discovered prospective regions. The reason of poor robustness is that HHO exhibits high randomness in the exploration phase. In the scene of optimizing high-dimensional or multimodal Tasks, the increased dimensionality and parameter correlation lead to the problem of local optimal regions, which affects the accuracy and convergence of the solution. To address these issues, we propose Dynamic Random Mutation Hybrid Harris hawk Optimization (DRM-HHHO). In the hybrid algorithm, the Kepler Optimization Algorithm (KOA) was enhanced (called EKOA) to replace the exploration phase of HHO to solve the problem of excessive diffusion. At the end of each iteration, the leader's position is perturbed by the Dynamic Random Mutation (DRM) algorithm. The mutation probability is adjusted based on the dimension to suit problems of different scales. We conduct ablation experiment to test the effectiveness of the two improved mechanisms. Testing our method on the CEC2017 and CEC2019 benchmark functions, we find that the significance of mutation probability differences decreases with increasing dimensionality. Compared to other Metaheuristic Algorithms (MAs), such as WOA, TSA, and so on, DRM-HHHO shows superior overall performance, especially in solving high-dimensional problems. Furthermore, we successfully apply DRM-HHHO to optimize the Kernel Extreme Learning Machine (KELM) critical parameters. DRM-HHHO-KELM outperforms four traditional machine learning methods and five MAs-KELM strategies on the Australian Credit and Pima Indian Diabetes datasets. [ABSTRACT FROM AUTHOR]

This paper proposes a metaheuristic algorithm, called advanced differential evolution (ADE), by improving the DE algorithm. The ADE algorithm was developed with the goal of creating an optimization framework that addresses the challenges of exploration and exploitation balance, avoiding local minima, utilizing chaos theory for diverse initialization, and improving solution quality and convergence speed. By incorporating these features, ADE aims to enhance the effectiveness of optimization processes. The proposed algorithm utilizes chaos theory to generate the initial population, which is subsequently divided into two sub-populations with adaptive sizes. The size of each sub-population is determined using a formula based on the number of iterations during the algorithm's execution. The first sub-population has a larger size in the beginning and the second one has a smaller size, but the total size of these two populations is always constant. The main contribution of this paper is the proposal of two novel improved differential evolution algorithms, namely MDE1 and MDE2, which are utilized for exploration within these sub-populations. The proposed ADE is tested on 29 well-known benchmarks and six engineering problems, and the results are compared with seven other algorithms. Various statistical experiments are carried out showing that the proposed algorithm provides significant superiority over other well-known algorithms. [ABSTRACT FROM AUTHOR]

In the realm of power systems, achieving optimal operation while ensuring security remains a paramount challenge. The security constrained optimal power flow (SCOPF) problem deals with optimizing power system operations while taking into account security limitations. Flexible alternating current transmission system (FACTS) is a system consisting of static equipment used for transmitting electrical energy in the form of AC. The static synchronous series compensator (SSSC) is a specific form of series FACTS device. The unified power flow controller (UPFC) is a FACTS device that is connected in parallel and series with a transmission line. In this research, hybrid simulated annealing and tabu search (hybrid SATS) algorithm is designed to solve SCOPF problems that involve use of FACTS devices. The combination of simulated annealing and tabu search is intended to improve algorithm's pace of convergence and the quality of its solutions. Hybrid SATS with FACTS devices are used to investigate line flow limit violations during single line failures and ensure power flows remain within their security limitations. The efficacy of proposed algorithm is demonstrated through case studies utilizing IEEE 30 bus system. These case studies demonstrate algorithm's capabilities to achieve optimal and secure power system functioning to demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press 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.)

AC model is known as the most accurate model for transmission expansion planning (TEP) problem. However, finding an optimal solution is still a difficult task due to the complex, non-linear, and non-convex nature of the ACTEP. In this study, a new hybridization of Differential Evolution (DE) and Zebra Optimization Algorithm (ZOA) algorithms, called DE-ZOA, is proposed to solve the ACTEP problem. The main idea of the hybrid algorithm is to improve the exploitation strategy of the DE algorithm based on the best individual in the population, which is inspired by the ZOA algorithm. The results given by the proposed DE-ZOA algorithm are compared to the original DE, ZOA, and other existing algorithms in both Graver 6 bus and IEEE 24 bus systems. The results show that the total cost obtained by the proposed DE-ZOA algorithm is smaller than 44.33% and 0.66% compared to the ZOA and DE algorithms in IEEE 24 bus systems. The proposed method illustrates the effectiveness of utilizing DE-ZOA algorithm to find optimal solution for the ACTEP problem. [ABSTRACT FROM AUTHOR]

Abstract In the domain of control engineering, effectively tuning the parameters of proportional-integral-derivative (PID) controllers has persistently posed a challenge. This study proposes a hybrid algorithm (HGJGSO) that combines golden jackal optimization (GJO) and golden sine algorithm (Gold-SA) for tuning PID controllers. To accelerate the convergence of GJO, a nonlinear parameter adaptation strategy is incorporated. The improved GJO is combined with Gold-SA, capitalizing on the expedited convergence speed offered by the improved GJO, coupled with the global optimization and precise search capabilities of Gold-SA. HGJGSO maximizes the strengths of two algorithms, facilitating a comprehensive and balanced exploration and exploitation. The effectiveness of HGJGSO is assessed through tuning the PID controllers for three typical systems. The results indicate that HGJGSO surpasses the comparison tuning methods. To evaluate the applicability of HGJGSO, it is used to tune the cascade PID controllers for trajectory tracking in a quadrotor UAV. The results demonstrate the superiority of HGJGSO in addressing practical challenges.

Carrier phase measurement plays a crucial role in achieving rapid and high-precision positioning within a global navigation satellite system (Global navigation satellite system, GNSS). A pivotal aspect of this process is the accurate resolution of the integer ambiguity. Although the particle swarm optimization algorithm (Particle swarm optimization, PSO) demonstrates quick convergence, it tends to become trapped in local optima, showing a relatively weak ability to fix ambiguity. Conversely, the artificial fish school algorithm (Artificial fish swarm algorithm, AFSA) excels the global optimization performance. However, its natural selection mode, which operates without a “leader,” renders the integer ambiguity resolution process more time-consuming. By integrating the strengths of PSA and AFSA, we propose an improved hybrid algorithm, termed the particle swarm and artificial fish swarm (PSOAF) algorithms, to efficiently search for integer ambiguity solutions in GNSS. The process begins by solving the floating-point solution and its corresponding covariance matrix using the carrier phase double-difference equation. Then, to address the correlation issue, the inverse integer Cholesky algorithm is used to effectively decorrelate them. Recognizing the limitations inherent in integer least squares estimation, we further refine the effectiveness of the PSOAF algorithm by optimizing the fitness function. This optimization significantly enhances the convergence speed and search performance of the algorithm, resulting in a precise resolution of the integer ambiguity. In the initial stage of integer ambiguity search, the PSO’s characteristic of rapid convergence facilitates a coarse search, yielding a suboptimal solution. This solution serves as foundational data for the AFSA, guiding the fine search required for integer ambiguity resolution. To verify the PSOAF algorithm’s effectiveness and practicality, we conducted both three-dimensional and twelve-dimensional simulation analyses based on a classical example. The results demonstrate that the PSOAF algorithm not only converges to the optimal solution at an unprecedented rate but also exhibits markedly superior search efficiency compared to single algorithms. Further validation of the PSOAF algorithm’s real-world applicability and effectiveness was sought through experiments utilizing actual Beidou data. The results from these experiments were promising, showing that the baseline resolution error mainly remained within a 10-mm range. This finding confirms the correctness of the double-difference integer ambiguity search conducted using the PSOAF algorithm. The applicability and effectiveness of the PSOAF algorithm in real scenarios are verified. In conclusion, this study underscores the PSOAF algorithm’s significant potential for practical applications, particularly in scenarios involving short baselines.

Traditional collaborative filtering recommendation algorithms suffer from low recommendation efficiency and poor accuracy when calculating similarities between users or items. To address this issue and improve the efficiency of recommendation systems, the paper introduces an algorithm called K-nearest neighbors and non-negative matrix factorization (KNNCNMF) collaborative filtering recommendation algorithm. When calculating the similarity between users or items, the algorithm extracts the latent factors of users and items through matrix decomposition, constructs a low-dimensional dense “user–item factor” matrix, and inputs it into the classifier for rating prediction, which replaces the complex similarity calculation and further improves the efficiency of the user–item similarity calculation. We use performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Precision, and Recall to measure our method. The experimental results show that compared to other algorithms, our method improves the MAE metric by 1.78% on average, the RMSE metric by 4.48% on average, the Precision metric by 4.66% on average, and the Recall metric by 7.95% on average. It proves the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

This research aims to forecast, using various criteria, the flow of soil erosion that will occur at a particular geographical location. As for the training dataset, 80% of the dataset from the sample sites, four hybrid algorithms, namely heap-based optimizer (HBO), political optimizer (PO), teaching-learning based optimization (TLBO), and backtracking search algorithm (BSA) combined with artificial neural network (ANN) was used to create an erosion susceptibility model and establishes a unique and original approach. After it was confirmed to be successful, the algorithms were applied to create a susceptibility map for this area, demonstrating the integrity of the results. The AUC values were computed for every optimisation algorithm used in this study. The optimal estimated accuracy indices for populations of 450 were determined to be 0.9846 using the BSA-MLP training databases. The maximum AUC value for the HBO-MLP training databases with different swarm sizes was 0.9736. A swarm size of 350-300 is considered optimal for forecasting erosion susceptibility mapping in hybrid models. With the same swarm size constraints, the AUC value for training in the TLBO-MLP scenario was 0.996. After 150 swarm size conditions were used to train the PO-MLP model, the AUC values were 0.9845. According to these findings, the TLBO-MLP and PO-MLP algorithms worked best with populations of 50 and 150, respectively. [ABSTRACT FROM AUTHOR]

Throughout the response phase of the disaster, the speedy restoration of transportation by reconnecting the nodes where the connection is broken is absolutely critical for evacuating civilians, providing clear access to hospitals, and distributing aid. Following a disaster, some roads in a disaster area might be closed to transportation. In reality, some roads can be blocked due to debris, and some of roads can be blocked by collapsing. In this model, different types of road unblocking methods are included, and each road can only be opened to access by a vehicle suitable for that method. So, different types of vehicles may be needed to repair the roads depending on the type of damage. In addition, fast-built bridges built both on land and over water are also used if necessary following a disaster. In problems of this nature, it is essential to restore the roads to enable the complete connectivity of the network such that all nodes can be reached by one another. In addition, it is also critical for the speedy reach of critical nodes, such as hospitals, and emergency disaster centers. This study aims to reduce the maximum time for connection and minimize the total time in which to reach critical nodes. For this purpose, we developed a bi-objective mathematical model that considers the multiple vehicle types that can repair different types of damages. Since the problem is NP-hard, two heuristic methods were developed, and the numerical results were presented. It has been observed that the local search algorithm gives better results than the hybrid algorithm. Additionally, different scenario data was produced. Numbers of unconnected components from 3 to 10 are solved with heuristic algorithms for test data containing 80 and 250 nodes, and real-life data containing 223 nodes and 391 edges are solved with heuristic algorithms for the number of unconnected components 6, 9, 12, and 15. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

AbstractIntroduction: Precisely predicting the time and cost of completing projects is vital because the lack of a proper estimation will be accompanied by an irrational upsurge in the exact execution costs compared to the set budget. Using the earned value method (EVM) to predict the time and cost of projects is prevalent. However, using this method alone highlights good accuracy in predicting time and cost of projects. Consequently, models based on EVM were developed.Methods: The present article was developed using the EVM method and hybrid algorithms to predict the time and cost of completing projects. To attain this goal, the data from four dams, A, B, C, and D, were used to build models, and the data of the under-construction dam E were used to validate the models resulting from the modeling stage. To this end, the parameters earned schedule (Month), earned value ($), actual progress (%), and actual cost (%) are used as inputs for predicting time and for predicting cost, as well as these parameters, time is also defined as input of hybrid algorithms.Findings: Comparing the consequences of the hybrid algorithms in the training and test stage designates the high accuracy of the LSSVM-PSO model compared to the LSSVM-GA. The low variance in the error values of these two stages for this model suggests its high generalization ability on unseen data. The use of these hybrid models in forecasting the time for the E dam gave a prior warning for the delay in the completion of the project in the first month. Likewise, in cost predicting, the LSSVM-PSO and LSSVM-GA models issued an early warning in the seventh and ninth months, respectively, for the non-conformity of the project cost with the planned cost. This is while the Kalman filter stated the primary warning to predict the project's completion time in the seventh month, and this model gave no warning regarding the planned cost. Comparing these results with the periodical reports of the E dam construction project designates the excellent performance of hybrid models, particularly the LSSVM-PSO model.

This study introduces the CP-EODE algorithm, a novel hybrid of the Equilibrium Optimizer (EO), and the Differential Evolution (DE) algorithm. It addresses EO's tendency toward premature convergence by enhancing its exploration capabilities. The motivation for this research stems from the need for more efficient and economically viable designs in engineering, particularly in the optimization of shell-and-tube heat exchangers (STHEs) and photovoltaic module parameters. The CP-EODE algorithm leverages DE's exploration strengths to enhance EO's performance adopting a cooperative parallel approach that divides the population into EO and DE sub-populations for a more effective search space exploration. The validation of CP-EODE's effectiveness commenced with its application to 29 benchmark functions, where a Friedman test yielded an F r value of 22.19, significantly surpassing the critical value of 7.81, and a Wilcoxon signed rank test confirmed statistical improvements with a z-score beyond − 1.96. Four quality metrics were discussed to provide a detailed assessment of CP-EODE's performance highlighting its robust exploration and exploitation capabilities. In practical applications, CP-EODE demonstrated significant advancements in STHE design achieving up to 19.706% reduction in heat exchanger surface area compared to traditional and other algorithmic designs, showcasing its potential to enhance engineering design efficiency and cost-effectiveness. A critical examination of the algorithm's convergence rate revealed its swift optimization capability, with rapid attainment of optimal solutions in various test cases, further establishing CP-EODE's advantage in handling complex optimization challenges. Through rigorous statistical analysis and performance metrics, CP-EODE emerges as a superior solution for complex optimization challenges, underscoring the value of hybrid algorithmic strategies in engineering optimizations. [ABSTRACT FROM AUTHOR]

Accurate heat flux prediction is a key problem in the numerical simulation of hypersonic flows. When the singularity axis appears in the grid, it further increases the difficulty of accurate flow simulation and heat flux calculation. Aiming at the problems of instability of flow structure and large errors of heat flux prediction in the finite difference calculation of singularity axis problems, a hybrid algorithm for solving the singularity axis problem with high precision difference schemes is proposed by combining the flux reconstruction and conservation reconstruction. The algorithm is based on conservative variables reconstruction near the singularity axis, which can reflect the influence of the flow field distribution near the singularity axis. The algorithm can effectively solve the singularity axis problem in numerical simulation. Applying the hybrid algorithm with WENO3-JS and WENO3-PRM scheme, the numerical simulations of hypersonic flow around sphere, HB-2, sphere cone and Apollo are carried out. The results show that the new method can effectively solve the problems of flow instability with singularity axis, and can achieve accurate heat flux prediction. [ABSTRACT FROM AUTHOR]

Grayscale image fusion is an important part of the digital image processing field, which is important for the integration of image information. This paper proposes a hybrid algorithm that addresses the problem of unclear edges caused by traditional image fusion algorithms. The proposed algorithm combines the discrete fractional wavelet transform with multi-scale morphology and the pulse coupled neural network. The hybrid algorithm employs discrete fractional wavelet transform to decompose the source images and obtain subbands that include both high- and low-frequency components. An image enhancement method, enhanced by multi-scale morphological operations, is developed to process the low-frequency subband. Additionally, a simplified pulse coupled neural network method is employed to adapt the high-frequency components and generate the high-frequency decision map. Fused images show that proposed algorithm effectively suppresses the Gibbs effect. Simulation experiments confirm that the fusion effect of the hybrid algorithm in this paper is better than the existing five classical algorithms, indicating that the hybrid algorithm is an efficient grayscale image fusion method. [ABSTRACT FROM AUTHOR]

This research deals with optimizing the welded I-girder of the single-beam bridge as a variant solution for this type of crane. The mass optimization is achieved by minimization of the welded I-girder cross-section area. The paper takes the following criteria as the constraint functions: strength, local stresses from the trolley wheel pressure, local buckling of the girder plates, global girder stability, deflection, relaxation time and the corresponding design-technological limitations. The hybridization of two new-generation metaheuristic algorithms, the Sine Cosine Algorithm and the Whale Optimization Algorithm, and original algorithms were used for the optimization procedures. The justification for applying these methods in solving practical engineering problems was confirmed by comparing the results with real solutions. As a result, significant material savings were achieved in this research, within the range of 39.52–51.92%. Also, comparing the results indicates the justification and advantages of the proposed hybridization procedure to the mentioned algorithms. [ABSTRACT FROM AUTHOR]

During the cantilever casting process of reinforced concrete arch bridges with cantilever cast‐in‐situ method, it is difficult to select representative training samples for reliability analysis due to its complex structural system. Many random variables and large computational sample size, this paper proposes to solve the reliability indexes based on the combination of the improved sparrow search algorithm (LGSSA) and the support vector regression (SVR) method. Firstly, random variables are selected according to the actual situation of the bridge structure. Then representative training samples are designed to be substituted into the finite element model through the homogeneous method. The resultant data samples are used to fit the functional function by the support vector regression. Then combined with the penalized function method to transform the nonlinear optimization into the problem of solving the extreme value of the function. Based on the improved SSA to solve the extreme value of the final function. Finally the reliability index of the structure is obtained. With the background of reinforced concrete arch bridge of 200 m, the method is used to analyze the reliability of its buckling cable stress, arch stress, buckling tower deviation and structural system reliability during the cantilever casting process. The results show that the overall structural reliability of the arch ring during cantilever casting is 3.502–3.608. The indexes of buckling cable stress reliability are 3.806–6.784. The indexes of arch ring stress reliability are 4.379–7.562, and the indexes of buckling tower deflection reliability are 3.608–8.123. [ABSTRACT FROM AUTHOR]

Nowadays, stabilizing raw soil with straw fibers represents a sustainable eco-building practice. Moreover, the rebirth of the earth's construction is primarily driven by the material's thermal properties. In the present work, a 3D study of the earthen-straw composite material's thermal behavior has been carried out to promote its use in construction and ensure satisfactory living conditions for the building. To consider the most possible influencing factors, the effect of fibers concentration on thermal conductivity was investigated for the straw contents of 5%, 10% and 15% by volume of earthen. Thus, a 3D model with a random distribution of straw fibers considered as cylinders without overlapping was designed under Catia V5. The distribution uses datasets from a hybrid algorithm developed for this purpose, which is then imported to Ansys Software to carry out the simulation allowing the computation of the thermal conductivity of the homogeneous material. It can be concluded that the proposed hybrid algorithm was efficient since the 3D numerical results based on the geometry generated by the algorithm's output were satisfactory in comparison with those deducted from the 2D study and the existing theoretical models. On the other hand, adding straw fibers in the raw earth matrix with thermal conductivity equal to 0.75 W/(m.K) has enhanced its thermal insulation. Indeed, for 15% of straw fiber's contents, we have found 0.633 W/(m.K) for the thermal conductivity of the earthen-straw composite material, which is equivalent to a decrease of 15.6% in this intrinsic parameter. [ABSTRACT FROM AUTHOR]

Feature selection techniques play a vital role in the processes that deal with enormous amounts of data. These techniques have become extremely crucial and necessary for data mining and machine learning problems. Researchers have always been in a race to develop and provide libraries and frameworks to standardise this procedure. In this work, we propose a hybrid meta‐heuristic algorithm to facilitate the problem of feature selection for classification problems in machine learning. It is a python based, lucid and efficient algorithm geared towards optimising and striking a balance between the number of features selected and accuracy. The proposed work is a binary hybrid of existing meta‐heuristic algorithms, the particle swarm optimisation (PSO) algorithm, and the firefly algorithm (FA) such that it blends the best of each algorithm to provide an optimised and efficient way of solving the said problem. The suggested approach is assessed against six datasets from different domains that are publicly available at the UCI repository to demonstrate its validity. The datasets are Breast cancer, Iris, WBC, Mushroom, Glass ID, and Abalone. This approach has also been evaluated against similar, such evolutionary‐based approaches to prove its superiority. Various metrics such as accuracy, precision, recall, f1 score, number of selected features, and run time have been analysed, measured, and compared. The hybrid firefly particle swarm optimisation algorithm is found to be suitable for feature selection problems. [ABSTRACT FROM AUTHOR]

Linear engineering projects require careful consideration of multiple construction scenarios. These scenarios include linear, bar and block activities, with the direction and velocity of linear activities varying, as well as the number of construction teams involved. Resource constraints further complicate scheduling. To address these challenges, a two-stage dynamic scheduling model was developed in this study. The first stage involved selecting a scheduling strategy based on available resources to meet the project deadline, while the second involved developing two cost-effective models to determine the best plan. To optimize the model, a hybrid algorithm incorporating an elite strategy, an adaptive operator and a catastrophe operator was established. The capabilities of the proposed model were demonstrated through several application examples, highlighting the benefits of improved consideration of construction scenarios in linear engineering. Overall, the model's ability to identify a reasonable range of resource allocations highlights its effectiveness in optimizing linear engineering project schedules. [ABSTRACT FROM AUTHOR]

Aiming at overcoming drawbacks of imbalance between the global and local search ability of a single heuristic algorithm, this paper proposed a slime mould and flower pollination hybrid algorithm based on dynamic dual population, named HASMFP. Firstly, HASMFP adopted a grouping mechanism that took the distance between individual inside population and the current optimal individual into consideration to dynamically divide whole population into slime mold subpopulation and pollen subpopulation to balance the exploration and development capabilities of the algorithm more effectively. Secondly, HASMFP used a ranking mechanism based on similarity and fitness to improve diversity of slime mold population, and further increased the probability to jump out of local optimal. Finally, HASMFP also adopted a dynamic weights and constant shrinkage coefficients with an elite guidance terms to further enhance the local and global search ability of standard flower pollination algorithm at the same time. It used 12 test functions from CEC2017 test suit as the testbed to evaluate the performance of HASMFP with other 5 algorithms: ISMA, DTSMA, HLFPA, SCFPA, and tMFPA. It conducted ablation experiments to evaluate the effectiveness of all improvement strategies applied in HASMFP. Experimental result shows that HASMFP can rank first under the combination of all improvement strategies. The result of Friedman test based on experimental data illustrates that HASMFP can achieve the supreme performance among all evaluated algorithms. [ABSTRACT FROM AUTHOR]

Although there is currently no unified standard theoretical formula for calculating the contact stress of cylindrical gears with a circular arc tooth trace (referred to as CATT gear), a mathematical model for determining the contact stress of CATT gear is essential for studying how parameters affect its contact stress and building the contact stress limit state equation for contact stress reliability analysis. In this study, a mathematical relationship between design parameters and contact stress is formulated using the KrigingMetamodel. To enhance the model's accuracy, we propose a new hybrid algorithm that merges the genetic algorithm with the Quantum Particle Swarm optimization algorithm, leveraging the strengths of each. Additionally, the "parental inheritance+self-learning" optimization model is used to fine-tune the KrigingMetamodel's parameters. Following this, amathematicalmodel for calculating the contact stress of Variable Hyperbolic Circular-Arc-Tooth-Trace (VH-CATT) gears using the optimized Kriging model was developed. We then examined how different gear parameters affect the VH-CATT gears' contact stress. Our simulation results show: (1) Improvements in R², RMSE, and RMAE. R² rose from0.9852 to 0.9974 (a 1.22% increase), nearing 1, suggesting the optimized Kriging Metamodel's global error is minimized. Meanwhile, RMSE dropped from3.9210 to 1.6492, a decline of 57.94%. The global error of the GA-IQPSO-Kriging algorithm was also reduced, with RMAE decreasing by 58.69% from 0.1823 to 0.0753, showing the algorithm's enhanced precision. In a comparison of ten experimental groups selected randomly, the GA-IQPSO-Kriging and FEM-based contact analysis methods were used to measure contact stress. Results revealed a maximum error of 12.11667 MPA, which represents 2.85% of the real value. (2) Several factors, including the pressure angle, tooth width, modulus, and tooth line radius, are inversely related to contact stress. The descending order of their impact on the contact stress is: tooth line radius>modulus>pressure angle>tooth width. (3) Complex interactions are noted among various parameters. Specifically, when the tooth line radius interacts with parameters such as pressure angle, tooth width, and modulus, the resulting stress contour is nonlinear, showcasing amultifaceted contour plane. However, when tooth width, modulus, and pressure angle interact, the stress contour is nearly linear, and the contour plane is simpler, indicating a weaker coupling among these factors. [ABSTRACT FROM AUTHOR]

Equilibrium optimizer (EO) is a novel metaheuristic algorithm that exhibits superior performance in solving global optimization problems, but it may encounter drawbacks such as imbalance between exploration and exploitation capabilities, and tendency to fall into local optimization in tricky multimodal problems. In order to address these problems, this study proposes a novel ensemble algorithm called hybrid moth equilibrium optimizer (HMEO), leveraging both the moth flame optimization (MFO) and EO. The proposed approach first integrates the exploitation potential of EO and then introduces the exploration capability of MFO to help enhance global search, local fine-tuning, and an appropriate balance during the search process. To verify the performance of the proposed hybrid algorithm, the suggested HMEO is applied on 29 test functions of the CEC 2017 benchmark test suite. The test results of the developed method are compared with several well-known metaheuristics, including the basic EO, the basic MFO, and some popular EO and MFO variants. Friedman rank test is employed to measure the performance of the newly proposed algorithm statistically. Moreover, the introduced method has been applied to address the mobile robot path planning (MRPP) problem to investigate its problem-solving ability of real-world problems. The experimental results show that the reported HMEO algorithm is superior to the comparative approaches. [ABSTRACT FROM AUTHOR]

This paper addresses the unconstrained binary quadratic problem (UQP). This problem consists in minimizing a quadratic function a binary variables (0−1 variables). Accordingly, in this work, a hybrid algorithm called (HA), based on simulated annealing algorithm with some combination procedures, have been proposed and a branch and bound procedure, based on this algorithm (HA) and semidefinite programming problem (SDP), has been applied. The purpose of this approach is to facilitate the resolution of the initial problem and reduce its dimension by using some fixing criteria in a repeat loop. Numerical results are presented to consolidate the demonstrated theoretical results and prove effectiveness and performance in speed and quality of our new approach. [ABSTRACT FROM AUTHOR]

Due to the extremely irregular nonlinear degradation of images obtained in real underwater environments, it is difficult for existing underwater image enhancement methods to stably restore degraded underwater images, thus making it challenging to improve the efficiency of marine work. We propose a hybrid algorithm model for underwater image color restoration and texture enhancement, termed HA-Net. First, we introduce a dynamic color correction algorithm based on depth estimation to restore degraded images and mitigate color attenuation in underwater images by calculating the depth of targets and backgrounds. Then, we propose a multi-scale U-Net to enhance the network's feature extraction capability and introduce a parallel attention module to capture image spatial information, thereby improving the model's accuracy in recognizing deep semantics such as fine texture. Finally, we propose a global information compensation algorithm to enhance the output image's integrity and boost the network's learning ability. Experimental results on synthetic standard data sets and real data demonstrate that our method produces images with clear texture and bright colors, outperforming other algorithms in both subjective and objective evaluations, making it more suitable for real marine environments. [ABSTRACT FROM AUTHOR]

This article focuses principally on the comparison baseline and the optimized flow efficiency of the final stage of an axial turbine operating on a gas–steam mixture by applying a hybrid Nelder–Mead and the particle swarm optimization method. Optimization algorithms are combined with CFD calculations to determine the flowpaths and thermodynamic parameters. The working fluid in this study is a mixture of steam and gas produced in a wet combustion chamber, therefore the new turbine type is currently undergoing theoretical research. The purpose of this work is to redesign and examine the last stage of the gas–steam turbine's flow characteristics. Among the optimized variables, there are parameters characterizing the shape of the endwall contours within the rotor domain. The values of the maximized objective function, which is the isentropic efficiency of the turbine stage, are found from the 3D RANS computation of the flowpath geometry changing during the improvement scheme. The optimization process allows the stage efficiency to be increased by almost 4 percentage points. To achieve high-quality results, a mesh of over 20 million elements is used, where the percentage error in efficiency between the previous and current mesh sizes drops below 0.05%. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid approach organized in four phases is proposed to solve the multi-objective trajectory planning problem for industrial robots. In the first phase, a transcription of the original problem into a standard multi-objective parametric optimization problem is achieved by adopting an adequate parametrization scheme for the continuous robot configuration variables. Then, in the second phase, a global search is performed using a population-based search metaheuristic in order to build a first approximation of the Pareto front (PF). In the third phase, a local search is applied in the neighborhood of each solution of the PF approximation using a deterministic algorithm in order to generate new solutions. Finally, in the fourth phase, results of the global and local searches are gathered and postprocessed using a multi-objective direct search method to enhance the quality of compromise solutions and to converge toward the true optimal PF. By combining different optimization techniques, we intend not only to improve the overall search mechanism of the optimization strategy but also the resulting hybrid algorithm should keep the robustness of the population-based algorithm while enjoying the theoretical properties of convergence of the deterministic component. Also, the proposed approach is modular and flexible, and it can be implemented in different ways according to the applied techniques in the different phases. In this paper, we illustrate the efficiency of the hybrid framework by considering different techniques available in various numerical optimization libraries which are combined judiciously and tested on various case studies. [ABSTRACT FROM AUTHOR]

The sine cosine algorithm (SCA) and multi-verse optimizer (MVO) are the recognized optimization strategies frequently employed in numerous scientific areas. However, both SCA and MVO grapple with optimizing the transition between the exploitation and exploration mechanisms. Furthermore, MVO exhibits constraints in its exploitation capabilities. To tackle these limitations, this paper introduces a hybrid model termed SMVO, combining the advantages of both SCA and MVO. This hybrid approach seeks to harmonize exploitation and exploration stages by leveraging the unique advantages of each parent algorithm. The efficacy of SMVO was assessed using 23 benchmark test functions, revealing its competitive performance against not only SCA and MVO but also the ant lion optimization (ALO) and the dragonfly algorithm (DA). Additionally, SMVO's applicability was further validated by successfully addressing three distinct engineering optimization challenges, underscoring its stability and promise as a global optimization tool. [ABSTRACT FROM AUTHOR]

Emotion recognition using physiological signals has gained significant attention in recent years due to its potential applications in various domains, such as healthcare and entertainment. EEG signals have been particularly useful in emotion recognition due to their non-invasive nature and high temporal resolution. However, the development of accurate and efficient algorithms for emotion classification using EEG signals remains a challenging task. This paper proposes a novel hybrid algorithm for emotion classification based on EEG signals, which combines multiple adaptive network models and probabilistic neural networks. The research aims to improve the recognition accuracy of three and four emotions, which has been a challenge for existing approaches. The proposed model consists of N adaptively neuro-fuzzy inference system (ANFIS) classifiers designed in parallel, in which N is the number of emotion classes. The selected features with the most appropriate distribution for classification are given as input vectors to the ANFIS structures, and the system is trained. The outputs of these trained ANFIS models are combined to create a feature vector, which provides the inputs for adaptive networks, and the system is trained to acquire the emotional recognition output. The performance of the proposed model has been evaluated for classification on well-known emotion benchmark datasets, including DEAP and Feeling Emotions. The study results indicate that the model achieves an accuracy rate of 73.49% on the DEAP datasets and 95.97% on the Feeling Emotions datasets. These results demonstrate that the proposed model efficiently recognizes emotions and exhibits a promising classification performance. [ABSTRACT FROM AUTHOR]

The current paper seeks to present a hybrid harmony search algorithm, using CS and HS. The proposed algorithm takes advantage of using a method to initialize the harmony memory (HM) with the help of the cuckoo search optimization algorithm. The HS algorithm is inspired by the method used by musicians to create and enhance harmony in music. It follows 3 main principles of pitch adjustment (PA), HM consideration (HMC), and random selection (RS). The performance of HS can be affected by its poor accuracy in optimization and speed of convergence, 2 main issues with it. However, to improve the HS algorithm, Cuckoo search can be used as it can do a local search using one parameter only, other than the size of the population, making it work more efficiently. This current method has been tested for its validity and efficiency in performance by being implemented on many international standard optimization problems. The results confirm the fact that the performance of the currently proposed algorithm is better and more efficient in finding solutions compared to HS and other algorithms. [ABSTRACT FROM AUTHOR]

Selecting facility locations requires significant investment to anticipate and prepare for disruptive events like earthquakes, floods, or labor strikes. In practice, location choices account for facility capacities, which often cannot change during disruptions. When a facility fails, demand transfers to others only if spare capacity exists. Thus, capacitated reliable facility location problems (CRFLP) under uncertainty are more complex than uncapacitated versions. To manage uncertainty and decide effectively, stochastic programming (SP) methods are often employed. Two commonly used SP methods are approximation methods, i.e., Sample Average Approximation (SAA), and decomposition methods, i.e., Progressive Hedging Algorithm (PHA). SAA needs large sample sizes for performance guarantee and turn into computationally intractable. On the other hand, PHA, as an exact method for convex problems, suffers from the need to iteratively solve numerous sub-problems which are computationally costly. In this paper, we developed two novel algorithms integrating SAA and PHA for solving the CRFLP under uncertainty. The developed methods are innovative in that they blend the complementary aspects of PHA and SAA in terms of exactness and computational efficiency, respectively. Further, the developed methods are practical in that they allow the specialist to adjust the tradeoff between the exactness and speed of attaining a solution. We present the effectiveness of the developed integrated approaches, Sampling Based Progressive Hedging Algorithm (SBPHA) and Discarding SBPHA (d-SBPHA), over the pure strategies (i.e. SAA). The validation of the methods is demonstrated through two-stage stochastic CRFLP. Promising results are attained for CRFLP, and the method has great potential to be generalized for SP problems. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Shanghai Jiaotong University (Science) is the property of Springer Nature 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.)

There have been numerous studies on the control of quadcopters. These studies mainly aim to control the flight behavior of quadcopters. To achieve this, researchers have been developing new tools and testing new methods. One of the developed tools is the 3-DOF Hover system, which enables researchers to analyze the flight behaviors of quadcopters, such as roll, pitch, and yaw, even in a physically limited area or only in a computer environment. The control method applied in the control of the 3-DOF Hover system has been determined by the manufacturer as Linear-Quadratic Regulator (LQR). LQR has control parameters that are complex to calculate. This complex calculation process creates an optimization problem. Beyond controlling the 3-DOF Hover system using LQR, this study focuses on calculating the complex control parameters of LQR using optimization algorithms when controlling a dynamic system with LQR. This study includes well-known algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA), as well as an innovative approach known Gray Wolf Optimization (GWO). These algorithms were selected due to their proven effectiveness in various studies. Based on the results obtained from these algorithms, a hybrid algorithm incorporating SA and GWO is proposed. The aim of this hybrid algorithm is to combine the advantages of different methods and achieve a more effective and efficient optimization process. The mentioned hybrid algorithm, obtained by combining SA and GWO, is named hSA-GWO. This hSA-GWO is compared with traditional algorithms, and the comparison results show that the proposed hybrid algorithm can be used as an alternative and competitive method for controlling the flight behaviors of quadcopters. [ABSTRACT FROM AUTHOR]