Tetra Optimization Based Hybrid Parameters for OFDM Modulated Wireless Sensor Network

Ant lion optimization for short-term wind integrated hydrothermal power generation scheduling

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.

Development of an Enhanced Ant Lion Optimization Algorithm and its Application in Antenna Array Synthesis

Regular maintenance of equipments improves their lifespan as well as performance. In order to enhance the overall performance of the power systems, preventive maintenance scheduling of generating units are essential. The stochastic model which includes the forced outage rates of units and this reliability model reflects the risk associated with the maintenance schedules. Thus, the probabilistic approach for reliability evaluation is more consistent than deterministic ones. The main purpose of this paper is to propose a promising tool to address the preventive generator maintenance scheduling problems considering reliability issues. Inspiring foraging behavior of ant lions in nature, Ant Lion Optimizer (ALO) is developed and is chosen as the prime optimization tool for solving the stochastic model based generator maintenance scheduling problem. The intended ALO algorithm is implemented on the standard test systems including IEEE-30 bus, RTS-9 unit, 21-unit and IEEE-32 unit. Numerical results comparisons validate that the ALO is a promising alternative for solving stochastic preventive GMS problems.

Predicting heat transfer rate of a ribbed triple-tube heat exchanger working with nanofluid using neural network enhanced by advanced optimization algorithms

In today's world, the demand for the electric power is increasing rapidly; to beat this, several power generation resources are constructing in all over the world. The essential for smart electrical systems having minimum technical loss and environmental effect is providing motivation to go for Distributed Generations (DGs) which may offer different benefits such as reduced transmission and distribution system resources, improved reliability, better power quality. The connection of DG in the distribution system might cause difficulties in the operation of protective apparatus and protection coordination. This paper presents techniques to determine maximum permissible size and location of a DG take into account the certain constraints to insure protection system coordination besides system power loss reduction, fault current reduction and enhancement the voltage profile. The main purpose of this paper is to study the recloser-fuse miscoordination problem that arises due to the dispersion of DG in the distribution networks. Multi Optimization techniques such as Whale Optimization Algorithm (WOA), Ant Lion Optimization Algorithm (ALO), and Crow Search Optimization Algorithm (CSA) are used to find optimal size and allocation of DG for atypical radial distribution network considering protection coordination constraints. A comparison between the results of the suggested methods will be displayed. The performance of the proposed methodology is examined through its application for a modified Roy Billinton Test System (RBTS) BUS2.

The aluminum reduction is a typical industry with high-energy consumption, and one of the ways to effectively solve the problem of high energy consumption is to reduce the cell voltage in a proper way. It is hard to express the relationship between the cell voltage and the corresponding technical conditions. In present, data mining technology highly automated analysis of enterprise data is usually used to find the optimal cell voltage and its corresponding optimal technical conditions. In order to improve the accuracy of the model, the method of density-based spatial clustering of applications with noise (DBSCAN) is adopted to remove the abnormal operating points from the raw data. Then, in order to realize the real-time measurement of cell voltage, the prediction model of cell voltage is established by least squares support vector machine (LSSVM), and optimized by Ant Lion Optimizer(ALO). Additionally, the cell voltage optimization control model is developed by analysis of the technical conditions. The cell voltage optimization control model is solved by using the ALO algorithm. The simulation results reveal that the adoption of ALO-LSSVM model significantly improves the robustness and accuracy of the cell voltage prediction, and it provides a powerful technical preparation for optimizing cell voltage. The experimental effect shows that the ALO model is stable, and the average value of cell voltage optimization is 3.8647v. It is used to guide process operation, and the theoretical DC power consumption is 14.9% lower than before optimization, achieving energy saving.

The product design process has a very important effect on product costs. It aims to develop products that are able to compete by achieving an optimum design in the product design process. This research presents the first application in the literature of the grey wolf, whale, water cycle, ant lion and sine-cosine optimization algorithms for the optimum design of vehicle components. In this study, the optimal structural model of a vehicle connecting rod was determined. In the optimization process, various design alternatives were created by using the latin hypercube method. Stress analysis was performed for each of these designs. According to the generated responses, equations for objective and constraint functions were obtained. The optimization problem was solved using the above mentioned algorithms which have been newly developed in the literature, resulting in optimum connection rod design. The results demonstrate that the grey wolf, whale, water cycle, ant lion and sine-cosine algorithms are very important options in optimizing design and manufacturing optimization problems.

In this paper, the problem of population districting in the health system of South Khorasan province has been investigated in the form of an optimization problem. Now that the districting problem is considered as a strategic matter, it is vital to obtain efficient solutions in order to implement in the system. Therefore in this study two meta-heuristic algorithms, Ant Lion Optimizer (ALO) and Grey Wolf Optimizer (GWO), have been applied to solve the problem in the dimensions of the real world. The objective function of the problem is to maximize the population balance in each district. Problem constraints include unique assignment as well as non-existent allocation of abnormalities. Abnormal allocation means compactness, lack of contiguous, and absence of holes in the districts. According to the obtained results, GWO has a higher level of performance than the ALO. The results of this problem can be applied as a useful scientific tool for districting in other organizations and fields of application.

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 paper, Ant Lion Optimizer (ALO) was presented to train Multi-Layer Perceptron (MLP). ALO was used to find the weights and biases of the MLP to achieve a minimum error and a high classification rate. Four standard classification datasets were used to benchmark the performance of the proposed method. In addition, the performance of the proposed method were compared with three well-known optimization algorithms, namely, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO). The experimental results showed that the ALO algorithm with the MLP was very competitive as it solved the local optima problem and achieved a high accuracy rate.

Ant lion optimiser (ALO) is a new nature‐inspired swarm intelligence optimisation algorithm that mimics the hunting mechanism of antlions in nature. ALO has been proved to have the merits of high exploitation and convergence speed benefiting from adaptive boundary shrinking mechanism and elitism. In this work, visual tracking is expressed as searching for object in whole search space by interaction between antlions and ants. A novel ALO‐based visual tracking framework is proposed and the adaptation and sensitivity of the parameters in ALO are discussed to improve tracking performance. In addition, considering that ALO tracker needs a lot of iteration consumption, kernel correlation filter with deep feature is integrated into the ALO tracking framework (ALOKCF) to improve track efficiency. Extensive experimental results prove that the ALO tracker is very competitive compared to other trackers, especially for abrupt motion tracking. At the same time, two visual tracking benchmarks are used to verify ALOKCF tracker achieves state‐of‐the‐art performance.

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.

Ant lion optimizer (ALO) is a newly developed population-based search algorithm inspired by hunting mechanism of antlions and based on five steps of hunting the ants, i.e., the random walk of ants, building traps, entrapment of ants in traps, catching preys and re-building traps. This paper presents the application of ALO algorithm for the solution of non-convex and dynamic economic load dispatch problem of electric power system. The performance of ALO algorithm is tested for economic load dispatch problem of four IEEE benchmarks of small-scale power systems, and the results are verified by a comparative study with lambda iteration method, particle swarm optimization algorithm, genetic algorithm, artificial bee colony, evolutionary programming and Grey Wolf optimizer (GWO). Comparative results show that the performance of ant lion optimizer algorithm is better than recently developed GWO algorithm and other well-known heuristics and meta-heuristics search algorithms.

Functional Link Neural Network (FLNN) has becoming as an important tool used in machine learning due to its modest architecture. FLNN requires less tunable weights for training as compared to the standard multilayer feed forward network such as Multilayer Perceptron (MLP). Since FLNN uses Backpropagation algorithm as the standard learning algorithm, the method however prone to get trapped in local minima which affect its performance. This paper proposed the implementation of Ant Lion Algorithm as learning algorithm to train the FLNN for classification tasks. The Ant Lion Optimizer (ALO) is the metaheuristic optimization algorithm that mimics the hunting mechanism of antlions in nature. The result of the classification made by FLNN-ALO is compared with the standard FLNN model to examine whether the ALO learning algorithm is capable of training the FLNN network and improve its performance. From the result achieved, it can be seen that the implementation of the proposed learning algorithm for FLNN performs the classification task quite well and yields better accuracy on the unseen data.