Using an Improved Ant Lion Optimizer Algorithm and Wireless Network to Explore Nondestructive Testing Technology for Water Conservancy Engineering

The Antlion Optimization (ALO) algorithm is a meta-heuristic optimization algorithm based on the hunting of ants by antlions. The basic inadequacy of this algorithm is that it has long run time because of the random walk model used for the ant's movement. We improved some mechanisms in ALO algorithm, such as ants' random walking, reproduction, sliding ants towards antlion, elitism, and selection procedure. This proposed algorithm is called Improved Antlion Optimization (IALO) algorithm. To show the performance of the proposed IALO algorithm, we used different measurement metrics, such as mean best, standard deviation, optimality, accuracy, CPU time, and number of function evaluations (NFE). The proposed IALO algorithm was tested for different benchmark test functions taken from the literature. There are no studies regarding time analysis of ALO algorithm found in the literature. This study firstly aims to demonstrate the success of the proposed IALO algorithm especially in runtime analysis. Secondly, the IALO algorithm was also applied to the Quadratic Assignment Problem (QAP) known as a difficult combinatorial optimization problem. In QAP tests, the performance of the IALO algorithm was compared with the performances of the classic ALO algorithm and 14 well-known and recent meta-heuristic algorithms. The results of the benchmark test functions show that IALO algorithm is able to provide very competitive results in terms of mean best/standard deviation, optimality, accuracy, CPU time and NFE metrics. The CPU time results prove that IALO algorithm is faster than the classic ALO algorithm. As a result of the QAP tests, the proposed IALO algorithm has the best performance according to the mean cost, worst cost and standard deviation. The source codes of QAP with the proposed IALO algorithm are publicly available at https://github.com/uguryuzgec/QAP-with-IALO.

In order to solve the limitations of ant lion optimization algorithm such as local optimum slow convergence speed and so on this paper applies optimization strategy Logistic chaotic map and reverse learning to improve the selection of random ant lion update ant position and elite ant lion respectively. The improved ant lion optimization algorithm is compared and analyzed by selecting standard test functions, and the optimization accuracy and convergence speed of the algorithm are obviously improved. The improved ant lion optimization algorithm combined with a series of pipe routing engineering rules is applied to the pipe routing of aeroengine. The simulation of a pipe routing example is carried out by Siemens NX11.0 Grip and MATLAB, and the effectiveness of the proposed method is verified again.

From the measurement point of view, it is observed that antlion optimization algorithm (ALO) runs slower than other heuristic algorithms and it needs to be improved in terms of optimality and accuracy. For this reason, improved antlion optimization algorithm via tournament selection (IALOT) is presented in this study. IALOT, ALO, particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms have been evaluated using benchmark test functions such as time, optimality, accuracy, CPU time, number of function evaluations (NFE), mean best solution and standard deviation. In summary, elite antlion selection, random walks, and other parts of the antlion optimization algorithm have been developed. As a result, the IALOT algorithm has shown better results than ALO algorithm.

Swarm intelligence based image enhancement method is an important method to improve the image visual effect. In order to improve the image enhancement algorithm based on the antlion optimization, a quadratic interpolation technique is introduced to improve the image enhancement algorithm of the antlion optimization method which has the disadvantages of slow convergence speed and low computational accuracy. The new algorithm uses quadratic interpolation to accelerate the convergence speed of ant population and antlion population, to balance the global and local searching ability of the antlion algorithm, so as to improve the estimation precision of the optimal parameters in the incomplete Beta function and improve the image quality. The two evaluation indexes verify that the new algorithm is better than the contrast algorithm in statistical meaning, and extend the research of the antlion optimization algorithm.

Improved antlion optimization algorithm via tournament selection and its application to parallel machine scheduling

Switched Reluctance Motor (SRM) is an electrical motor which operates by a reluctance torque that has stator and rotor salient poles. It also has a simple configuration and reasonable power electronics necessity for both AC and DC machines within adjustable-speed drives. The problem working on it is, SRM with doubly salient poles introduce a torque ripple in its output which leads to deteriorated performance on speed control. In this proposed work, torque ripple reduction and speed control of SRM is implemented, utilizing an asymmetrical converter with PI-PWM controller with the aid of Bio-inspired algorithms. By utilizing the best value of flux, torque ripple and integral square error of speed and settling times, controller design is performed. The proposed research work compares three optimization algorithms namely Crow Search Algorithm, an Improved Ant Lion Optimization Algorithm and Modified Chaotic Starling Particle Swarm technique to control the speed and torque reduction of SRM in aerospace applications. To obtain the optimal parameter values, optimization techniques are introduced and the optimized controller results are compared with other conventional controller results. In normal operation, the engine operates with two phases simultaneously, but it is modelled to meet the load specification, which is single and double phase faulty. Detection of fault gives a great outcome in control of an electrical drive system. Reduction of faulty operation time leads to better motor lifetime that is obtained by early detection of a fault. This paper presents how the five-phase switched reluctance motor is designed to meet the needs of flap actuators in mid-sized aircraft. Electrical systems are modelled to provide the right functionality at the right time in advanced aircraft. However, requirement of power for the landing gear and secondary flight control only a short duration. The system is powered on and off as required, thus preserving power. Control performance and fault analysis were verified by the results of the MATLAB simulation. Experiments have been carried out more effectively to compare the results obtained with those of simulations.

Since reform and opening, especially since entering the new century, economy in China has maintained a rapid and healthy development and more and more key hydraulic projects and hydropower stations have present the characteristics of rapid speed, short period and good benefits, but at the same time, they also have bought new challenges for soil and water conservation and ecological environment protection. In the late 1990s, China has instituted a set of laws and regulations on soil and water conservation and ecological environment protection to reinforce environment management in development and construction projects. We were implementing ecological environment construction engineering of soil and water conservation at the same time when the Three Gorges Hydraulic Project on the Yangtze River, Xiaolangdi and Wanjiazhai Hydraulic Projects on the Yellow River, Dachaoshan Hydraulic Project on Lancang River and other key hydraulic projects and hydropower stations were being constructed, and have achieved notable effects. The common characteristics of the soil and water conservation and ecological environment construction of the main key hydraulic projects are: First, making water and soil conservation plans and its special designs at the beginning of major project design. Second, implementing prevention-control measures of water and soil loss when the project was under construction. Third, accepting major project consisted of special acceptance of water and soil conservation engineering, performing the national requirements of the three-simultaneity system between the construction and development projects and the water and soil conservation very well.

Application of multimedia technology in water conservancy and hydropower engineering

The 2020 4th International Conference on Water Conservancy, Hydropower and Building Engineering (WCHBE 2020) was planned to be held in Lanzhou, China during July 3-5, 2020. WCHBE 2020 was hosted by AEIC Academic Exchange Information Center. Due to the impact of COVID-19, many communities from all over the world were under strict health measures and strict travel restrictions, and participants of 2020 4th International Conference on Water Conservancy, Hydropower and Building Engineering (WCHBE 2020) also meet with the difficulty of travel restrictions. For communities, if our conference was not held in a virtual form, it would add huge work on traffic system, Exit-Entry system, medical system, etc., which is quite a serious issue, to actively respond to the call of the government, to strengthen the protection work, to effectively reduce people gathering and prevent COVID-19, considering the situation that most of the authors would like to publish their articles and make academic communications as scheduled, WCHBE 2020 was held on-line instead of postponing the conference. It was a challenge, not only to the organizers, but also to the participants who were delivering their speeches using videoconference tools, under different time zones.The conference is an international conference for the presentation of technological advances and research results in the fields of water conservancy, hydropower and building engineering. The conference will bring together about 97 leading researchers, engineers and scientists in the domain of interest from China, Italy, Malaysia, Mexico and Thailand. The conference model was divided into two parts, including keynote presentations and online discussion. In the first part, each keynote speakers were allocated 30 minutes to present their talks via Zoom. After the keynote talks, all participants joined in a WeChat communication group to discuss more about the talks and presentations. We are honored to have invited three distinguished experts as our keynote speakers. Prof. Hengjia Zhang, Gansu Agricultural University, China, was the first one to perform a thought-provoking speech on Optimization of Limited Irrigation Schedules for Spring Maize (Zea mays) in a Desert Oasis Region. He also was the Conference Chair who mainly devoted to the teaching and research work of the efficient use of agricultural water and soil resources. Secondly, Prof. Shaohua Marko Hsu from Feng Chia University held his speech on Using Porous Material for Riverbank and Riverbed Protection. Thirdly, Prof. Ahmad Safuan Bin A. Rashid from Universiti Teknologi Malaysia, Malaysia talked about Application of UAV-TLS Photogrammetry in Determination of Rock Slope Stability. Their insightful speeches had triggered heated discussion in the second session of the conference. The WeChat discussion lasted for about 30 minutes. Every participant praised this conference for disseminating useful and insightful knowledge.We are glad to share with you that we still received lots of submissions from the conference during this special period. Hence, we selected a bunch of high-quality papers and compiled them into the proceedings after rigorously reviewed them. These papers feature following two categories but are not limited to: Civil, Architectural Engineering, Water conservancy and hydropower, and other related fields. All the papers have been through rigorous review and process to meet the requirements of International publication standard.We would like to express our gratitude to everyone involved. Without this dedicated cooperation, it would be impossible to hold the conference at such a high level and prepare the WCHBE 2020 proceedings.Committee of WCHBE 2020The Committee member is available in this pdf.

With the increasing complexity of financial markets, it is gradually becoming a research hotspot in the field of accurate stock market prediction. The ant lion optimization algorithm, due to its excellent global search ability and the advantages of machine learning models in data mining, aims to further improve the accuracy of stock market prediction. Therefore, this paper will explore how to combine the ant lion optimization algorithm with machine learning models to achieve effective prediction of stock market trends. First, a stock relationship graph is constructed using graph neural networks, with stocks as nodes and relationships between stocks as edges. The node embedding technique of graph neural networks is used to extract feature representations of stocks. Then, the ant lion algorithm is used to optimize the parameters of the graph neural network, enabling it to better fit the historical data of the stock market. Finally, the trained model is used to predict the future trend of the stock market. Through experimental verification, it has been found that the proposed method has high fitness during the training process and short training time; the loss value of ant lion’s optimized machine learning algorithm is relatively small, and the model prediction accuracy is high. Applying this method to Apple, Facebook, and Tesla results in higher strategic trading returns. This method improves the accuracy of stock market prediction by optimizing machine learning algorithms with ant lions, providing investors with more reliable decision support.

Nowadays, excellent software tools have been offered to project management along with fast development of computer technique, among which PROJECT is the excellent application software for project management in MICROSOFT Company, which can fully adapt to and meet requirements of the whole process for project management. In this thesis, problems and disadvantages existing in project management of current water conservancy and hydropower engineering are analyzed; concrete practices are conducted by applying PROJECT software in danger-removing and reinforcing projects of Qianjin Reservoir in Huadian City, Jilin Province and positive publicity value of PROJECT software in project management is demonstrated based on the accumulation of managerial experience of water conservancy and hydropower engineering

This paper presents the fractional-order digital integrator design employing digital lattice wave structure. The ideal frequency response is approximated through the optimal lattice wave coefficients which are obtained using the ant lion optimization algorithm. In terms of the root mean square error and the quantity of multipliers, the suggested lattice wave fractional-order digital integrator performs better than the current designs.

Suspended sediment load (SSL) estimation is a required exercise in water resource management. This article proposes the use of hybrid artificial neural network (ANN) models, for the prediction of SSL, based on previous SSL values. Different input scenarios of daily SSL were used to evaluate the capacity of the ANN-ant lion optimization (ALO), ANN-bat algorithm (BA) and ANN-particle swarm optimization (PSO). The Goorganrood basin in Iran was selected for this study. First, the lagged SSL data were used as the inputs to the models. Next, the rainfall and temperature data were used. Optimization algorithms were used to fine-tune the parameters of the ANN model. Three statistical indexes were used to evaluate the accuracy of the models: the root-mean-square error (RMSE), mean absolute error (MAE) and Nash-Sutcliffe efficiency (NSE). An uncertainty analysis of the predicting models was performed to evaluate the capability of the hybrid ANN models. A comparison of models indicated that the ANN-ALO improved the RMSE accuracy of the ANN-BA and ANN-PSO models by 18% and 26%, respectively. Based on the uncertainty analysis, it can be surmised that the ANN-ALO has an acceptable degree of uncertainty in predicting daily SSL. Generally, the results indicate that the ANN-ALO is applicable for a variety of water resource management operations.

Power losses and voltage deviations in distribution power networks (DPNs) are high since they carry more power demand than transmission power networks. Also, voltage deviation beyond the allowable range causes voltage stability problems in the DPN. The power loss (PL) in the DPN should be kept at the minimum level for the economic operation of the electric grid. Integrating distributed generation (DG) in appropriate sites of the power networks can minimize the power losses and voltage drops. An integrated optimization approach is proposed in this paper, by combining an analytical and metaheuristic algorithm to optimize the placement and sizing of multiple DGs. The active power loss sensitivity (APLS) index is an analytical mathematical computation approach used to identify the optimal bus locations for DG placement. The modified ant lion optimization (MALO) algorithm is applied to optimize the ratings of the DG systems. The MALO algorithm is proposed by adopting the Lévy flights (LF) pattern in the random walk process (RWP). LF representation of RWPs enhances the exploration phase of the ALO algorithm and helps to obtain the near-optimal solution. The proposed integrated approach optimizes multiple units of photovoltaic (PV) and wind turbine (WT) units to minimize the multi-objective function, including AP loss and voltage deviation (VD) minimizations. The effectiveness of the proposed integrated approach is validated on the IEEE 69-bus, 85-bus, and 118-bus radial DPNs. Besides, the simulation study is extended for ant lion optimization (ALO), BAT, and artificial bee colony (ABC) algorithms-based techniques. The integrated approach has reduced the total AP loss of the IEEE 69-bus and 85-bus radial DPN from 225 kW to 70.51 kW and 316.12 kW to 162.80 kW, respectively, for the optimized three PV DG units allocation. Likewise, the total AP loss of the 118-bus radial DPN is cut down from 1296.3 kW to 432.3 kW after the optimized five PV DG units allocation. Meanwhile, the total AP LOSS of the 69-bus, 85-bus, and 118-bus radial DPNs is reduced to 4.78 kW, 53.87 kW, and 112.2 kW, respectively, after the optimized WT DG allocation. Additionally, the optimized inclusion of multiple DG units significantly minimized the VD of the DPNs. The minimum VD of the 69-bus, 85-bus, and 118-bus test systems is reduced from 0.0908 p.u., 0.1297 p.u., and 0.1312 p.u. to 0.0174 p.u., 0.0384 p.u., and 0.0201 p.u., respectively, for the multiple PV unit allocations. Similarly, the minimum VDs of the 69-bus, 85-bus, and 118-bus radial DPNs are minimized to 0.0048 p.u., 0.0190 p.u., and 0.0093 p.u., respectively, following the multiple WT DG unit allocations. The simulation findings of the APLS-MALO integrated approach are related to the various optimization techniques. The comparative study reveals that the proposed integrated approach gives a more effective and efficient solution than ALO, BAT, ABC, and other optimization techniques. Finally, the simulation findings of the APLS-MALO integrated technique are verified via the calculation of conventional statistical metrics and the conduction of a non-parametric Wilcoxon test.

In this chapter, a novel algorithm named as modified ant lion optimizer (MALO) algorithm has been suggested for the improvement of convergence behavior of the existing ALO algorithm. Moreover, a new form of turbo code (TC) termed as 4D-TC has also been projected for the enhancement of the minimum Hamming distance of the existing TCs. Thereafter, the MALO algorithm has been judiciously used for the design of power efficient 4D-TC. From the simulated outcomes, it is obvious that the high-grade act of MALO algorithm is pretty remarkable than the existing ALO as well as other optimization procedures like particle swarm optimization, Cuckoo search and Harmony search in the matter of exactness and confluence standpoint. Additionally, it has been witnessed that the projected modification on ALO algorithm provides the best optimum value close to theoretical limit by adopting fast exploration and exploitation process over the other algorithms. At the same time, it has also been established that the projected 4D-TC offers substantial improvement in BER act over the others like parallel concatenated convolution turbo code (PCCTC), serially concatenated convolution turbo code (SCCTC), and 3D-TC. The supremacy of MALO has been observed for eight states as well as 16 states 4D-TCs over the other variance like PCCTC, SCCTC, 3D-TC and proposed 4D-TC by means of bit error rate (BER) performance by allocating the optimum power to the systematic and parity bits. Analogous enhancements in BER act have been detected with puncturing as well.