Journal articles: 'Ameliorated ant lion optimization algorithm'

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Mallala, Balasubbareddy, Dwivedi Divyanshi, Venkata Krishna Murthy Garikamukkala, and Sowjan Kumar Kotte. "Optimal power flow solution with current injection model of generalized interline power flow controller using ameliorated ant lion optimization." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 1 (2023): 1060–77. https://doi.org/10.11591/ijece.v13i1.pp1060-1077.

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Optimal power flow (OPF) solutions with generalized interline power flow controller (GIPFC) devices play an imperative role in enhancing the power system’s performance. This paper used a novel ant lion optimization (ALO) algorithm which is amalgamated with Lévy flight operator, and an effectual algorithm is proposed named as, ameliorated ant lion optimization (AALO) algorithm. It is being implemented to solve single objective OPF problem with the latest flexible alternating current transmission system (FACTS) controller named as GIPFC. GIPFC can control a couple of transmission lines concurrently and it also helps to control the sending end voltage. In this paper, current injection modeling of GIPFC is being incorporated in conventional Newton-Raphson (NR) load flow to improve voltage of the buses and focuses on minimizing the considered objectives such as generation fuel cost, emissions, and total power losses by fulfilling equality, in-equality. For optimal allocation of GIPFC, a novel Lehmann-SymanzikZimmermann (LSZ) approach is considered. The proposed algorithm is validated on single benchmark test functions such as Sphere, Rastrigin function then the proposed algorithm with GIPFC has been testified on standard IEEE-30 bus system.

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Balasubbareddy, Mallala, Divyanshi Dwivedi, Garikamukkala Venkata Krishna Murthy, and Kotte Sowjan Kumar. "Optimal power flow solution with current injection model of generalized interline power flow controller using ameliorated ant lion optimization." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 1 (2023): 1060. http://dx.doi.org/10.11591/ijece.v13i1.pp1060-1077.

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<span lang="EN-US">Optimal power flow (OPF) solutions with generalized interline power flow controller (GIPFC) devices play an imperative role in enhancing the power system’s performance. This paper used a novel ant lion optimization (ALO) algorithm which is amalgamated with Lévy flight operator, and an effectual algorithm is proposed named as, ameliorated ant lion optimization (AALO) algorithm. It is being implemented to solve single objective OPF problem with the latest flexible alternating current transmission system (FACTS) controller named as GIPFC. GIPFC can control a couple of transmission lines concurrently and it also helps to control the sending end voltage. In this paper, current injection modeling of GIPFC is being incorporated in conventional Newton-Raphson (NR) load flow to improve voltage of the buses and focuses on minimizing the considered objectives such as generation fuel cost, emissions, and total power losses by fulfilling equality, in-equality. For optimal allocation of GIPFC, a novel Lehmann-Symanzik-Zimmermann (LSZ) approach is considered. The proposed algorithm is validated on single benchmark test functions such as Sphere, Rastrigin function then the proposed algorithm with GIPFC has been testified on standard IEEE-30 bus system.</span>

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Hassanien, Aboul Ella, and Ramadan Babers. "Metaheuristic Algorithms for Detect Communities in Social Networks: A Comparative Analysis Study." International Journal of Rough Sets and Data Analysis 5, no. 2 (2018): 25–45. http://dx.doi.org/10.4018/ijrsda.2018040102.

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This article presents a comparative analysis between Cuckoo Search Optimization Algorithm, Lion Optimization Algorithm and Ant-Lion Optimization Algorithm. Zachary karate Club, The Bottlenose Dolphin Network, American College Football Network, and Facebook used as benchmark datasets for comparison, the results proved those algorithms can define the structure and detect communities of complex networks with high accuracy and quality based on different method that it used. The Cuckoo Search Optimization Algorithm is the best algorithm compared to Ant-Lion Optimization Algorithm and Lion Optimization Algorithm as it got greatest number of communities, detect communities in used benchmark datasets with average accuracy %69, average modularity %62 and average fitness %60.

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Hamouda, Eslam, Sara El-Metwally, and Mayada Tarek. "Ant Lion Optimization algorithm for kidney exchanges." PLOS ONE 13, no. 5 (2018): e0196707. http://dx.doi.org/10.1371/journal.pone.0196707.

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Guo, Hao. "Research on Ant Lion Optimization Algorithm for BP Neural Network in Transformer Fault Diagnosis." Journal of Big Data and Computing 2, no. 3 (2024): 1–5. https://doi.org/10.62517/jbdc.202401301.

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Aiming at the problem of low accuracy in transformer fault diagnosis, an Ant Lion Optimization (ALO) algorithm is proposed to optimize the BP neural network for transformer fault diagnosis. By using the ant lion optimization algorithm to optimize the weights and thresholds of the BP neural network, the problem of premature convergence of the BP neural network can be avoided, and the accuracy of the transformer fault diagnosis model can be improved. The BP neural network model optimized by the ant lion optimization algorithm was used for transformer fault diagnosis. To verify the effectiveness of the proposed method, it was compared with the genetic algorithm optimized BP neural network (GA-BP) and the artificial bee colony (ABC-BP) algorithm optimized BP neural network methods. The experimental results showed that the proposed method has higher fault diagnosis accuracy.

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Liu, Songzi, Mou Lv, and Hongwei Li. "Intelligent Leakage Location of Urban Small Water Supply Network." Journal of Physics: Conference Series 2185, no. 1 (2022): 012041. http://dx.doi.org/10.1088/1742-6596/2185/1/012041.

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Abstract In this paper, the leakage location model of pipe network is established based on EPANET software. Two intelligent swarm optimization algorithms, ant lion optimization algorithm and particle swarm optimization algorithm, are used to solve the model. Taking the industrial water supply network of a coastal city in North China as an example, the operation of the two algorithms is analyzed and compared. The results show that the ant lion optimization algorithm has stronger global optimization ability and higher search efficiency in the problem of leakage location; it also has high application value in practical engineering.

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Wang, Wenjing, and Renjun Zhou. "Application of improved ant-lion algorithm for power systems." PLOS ONE 19, no. 12 (2024): e0311563. https://doi.org/10.1371/journal.pone.0311563.

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An improved ant-lion algorithm is proposed to solve the load allocation problem to improve the efficiency of load allocation in the power system. The global search capability and optimization performance of the algorithm have been significantly improved by introducing elite weights and chaotic search mechanisms. The innovation of the research lies in not only optimizing economic goals, but also considering environmental goals, achieving dual optimization of economy and environment. The average running time of the proposed algorithm in Sphere function and Griebank function was 2.67s and 1.64s, respectively. The required number of iterations was significantly better than other algorithms. In the verification of solving economic load dispatch, the improved ant-lion optimizer achieved a total fuel cost reduction of 0.10% -2.39% and 6% in both 3-unit and 6-unit simulations, respectively, compared to the other three algorithms. In the verification of solving environmental and economic load dispatch, considering the valve point effect, this proposed optimization scheme had a total fuel cost of 622.46 $/hr and a total emission of 0.20 tons/h. The total objective function was 1542.54 $/hr, which was an average reduction of 53.55 $/hr compared to the other five algorithms. Therefore, improving the ant-lion optimizer can enhance its optimization performance. The improved ant-lion optimizer has positive application significance in power system load dispatch and can achieve superior load dispatch results.

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Hardiansyah, Hardiansyah. "Dynamic economic emission dispatch using ant lion optimization." Bulletin of Electrical Engineering and Informatics 9, no. 1 (2020): 12–20. http://dx.doi.org/10.11591/eei.v9i1.1664.

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This paper aims to propose a new meta-heuristic search algorithm, called Ant Lion Optimization (ALO). The ALO is a newly developed population-based search algorithm inspired hunting mechanism of ant lions. The proposed algorithm is presented to solve the dynamic economic emission dispatch (DEED) problem with considering the generator constraints such as ramp rate limits, valve-point effetcs, prohibited operating zones and transmission loss. The 5-unit generation system for a 24 h time interval has been taken to validate the efficiency of the proposed algorithm. Simulation results clearly show that the proposed method outperforms in terms of solution quality when compared with the other optimization algorithms reported in the literature.

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Hardiansyah, Hardiansyah. "Dynamic economic emission dispatch using ant lion optimization." Bulletin of Electrical Engineering and Informatics 9, no. 1 (2020): 12–20. https://doi.org/10.11591/eei.v9i1.1664.

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This paper aims to propose a new meta-heuristic search algorithm, called Ant Lion Optimization (ALO). The ALO is a newly developed population-based search algorithm inspired hunting mechanism of ant lions. The proposed algorithm is presented to solve the dynamic economic emission dispatch (DEED) problem with considering the generator constraints such as ramp rate limits, valve-point effects, prohibited operating zones and transmission loss. The 5-unit generation system for a 24 h time interval has been taken to validate the efficiency of the proposed algorithm. Simulation results clearly show that the proposed method outperforms in terms of solution quality when compared with the other optimization algorithms reported in the literature.

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Ali, E. S., S. M. Abd Elazim, and A. Y. Abdelaziz. "Ant Lion Optimization Algorithm for Renewable Distributed Generations." Energy 116 (December 2016): 445–58. http://dx.doi.org/10.1016/j.energy.2016.09.104.

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Jin, Qibing, and Yuming Zhang. "Parameter Optimization of Active Disturbance Rejection Controller Using Adaptive Differential Ant-Lion Optimizer." Algorithms 15, no. 1 (2022): 19. http://dx.doi.org/10.3390/a15010019.

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Parameter optimization in the field of control engineering has always been a research topic. This paper studies the parameter optimization of an active disturbance rejection controller. The parameter optimization problem in controller design can be summarized as a nonlinear optimization problem with constraints. It is often difficult and complicated to solve the problem directly, and meta-heuristic algorithms are suitable for this problem. As a relatively new method, the ant-lion optimization algorithm has attracted much attention and study. The contribution of this work is proposing an adaptive ant-lion algorithm, namely differential step-scaling ant-lion algorithm, to optimize parameters of the active disturbance rejection controller. Firstly, a differential evolution strategy is introduced to increase the diversity of the population and improve the global search ability of the algorithm. Then the step scaling method is adopted to ensure that the algorithm can obtain higher accuracy in a local search. Comparison with existing optimizers is conducted for different test functions with different qualities, the results show that the proposed algorithm has advantages in both accuracy and convergence speed. Simulations with different algorithms and different indexes are also carried out, the results show that the improved algorithm can search better parameters for the controllers.

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Horng, Shih-Cheng, and Chin-Tan Lee. "Integration of Ordinal Optimization with Ant Lion Optimization for Solving the Computationally Expensive Simulation Optimization Problems." Applied Sciences 11, no. 1 (2020): 136. http://dx.doi.org/10.3390/app11010136.

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The optimization of several practical large-scale engineering systems is computationally expensive. The computationally expensive simulation optimization problems (CESOP) are concerned about the limited budget being effectively allocated to meet a stochastic objective function which required running computationally expensive simulation. Although computing devices continue to increase in power, the complexity of evaluating a solution continues to keep pace. Ordinal optimization (OO) is developed as an efficient framework for solving CESOP. In this work, a heuristic algorithm integrating ordinal optimization with ant lion optimization (OALO) is proposed to solve the CESOP within a short period of time. The OALO algorithm comprises three parts: approximation model, global exploration, and local exploitation. Firstly, the multivariate adaptive regression splines (MARS) is adopted as a fitness estimation of a design. Next, a reformed ant lion optimization (RALO) is proposed to find N exceptional designs from the solution space. Finally, a ranking and selection procedure is used to decide a quasi-optimal design from the N exceptional designs. The OALO algorithm is applied to optimal queuing design in a communication system, which is formulated as a CESOP. The OALO algorithm is compared with three competing approaches. Test results reveal that the OALO algorithm identifies solutions with better solution quality and better computing efficiency than three competing algorithms.

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Liu, Yingjie, and Dawei Cui. "Estimation algorithm for vehicle state estimation using ant lion optimization algorithm." Advances in Mechanical Engineering 14, no. 3 (2022): 168781322210858. http://dx.doi.org/10.1177/16878132221085839.

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In order to solve the problem of vehicle state estimation, an unscented Kalman filter state parameter estimator based on the Ant lion algorithm is proposed. Aiming at the uncertainty of the noise covariance matrix in the unscented Kalman filter (UKF) process, the Ant lion optimization algorithm (ALO) is used to optimize it. Based on the purpose, a 3-DOF nonlinear vehicle estimation model with Magic formula tire model was established firstly. Then the slalom road operating condition was simulated. The simulation results show that the estimated values of the key state variables are in better agreement with the virtual test values indicating the proposed algorithm having a good estimation performance. And also, compared with the estimation results of the UKF algorithm, the maximum error and the root mean square error of the estimation algorithm proposed in this paper are both smaller than the estimated value of the UKF algorithm. The results of a real-vehicle experiment demonstrate that the proposed method can be used effectively and accurately for solving the vehicle-state estimation problem. The study can provide precise status information for vehicle stability control under extreme conditions.

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Sunil Srinivas, B., and A. Govardhan. "Feature selection using ant lion optimization algorithm in text categorization." International Journal of Engineering & Technology 8, no. 4 (2019): 582. http://dx.doi.org/10.14419/ijet.v8i4.10898.

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This is Big Data decade with extensive increase in the textual information where the text classification is the significant approach for processing and organizing textual information. Text categorization refers to the process of spontaneously allotting documents to the relevant classes. The key features of these text classification issue is tremendous increase in higher dimensionality of text information. Meta-Heuristics Approaches are effortlessly employed to obtain optimal solutions for high dimensional datasets in text categorization. However, some of these approaches like genetic algorithm and particle swarm optimization gives a sub-optimal solutions, the convergence time is more compared to other approaches and cannot guarantee the global maxima to the text categorization. Thus, in this paper, a nature-inspired optimization approach depending on catching mechanism of antlions in the environment known as Ant Lion Optimizer (ALO) Approach, is applied to resolve higher dimensionality issues prior to text classification. The precision and recall values for the proposed is comparatively effective when compared with the existing text categorization dimensionality reduction techniques.

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Babers, Ramadan, and Aboul Ella Hassanien. "A Nature-Inspired Metaheuristic Cuckoo Search Algorithm for Community Detection in Social Networks." International Journal of Service Science, Management, Engineering, and Technology 8, no. 1 (2017): 50–62. http://dx.doi.org/10.4018/ijssmet.2017010104.

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In last few years many approaches have been proposed to detect communities in social networks using diverse ways. Community detection is one of the important researches in social networks and graph analysis. This paper presents a cuckoo search optimization algorithm with Lévy flight for community detection in social networks. Experimental on well-known benchmark data sets demonstrates that the proposed algorithm can define the structure and detect communities of complex networks with high accuracy and quality. In addition, the proposed algorithm is compared with some swarms algorithms including discrete bat algorithm, artificial fish swarm, discrete Krill Herd, ant lion algorithm and lion optimization algorithm and the results show that the proposed algorithm is competitive with these algorithms.

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Li, Zirui, and Jiali Ouyang. "Optimal Design Method for Sub-array Beamforming Based on an Improved Ant Lion Algorithm." Journal of Physics: Conference Series 2437, no. 1 (2023): 012106. http://dx.doi.org/10.1088/1742-6596/2437/1/012106.

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Abstract The sub-array division plays an important role in reducing the system complexity, computing complexity and hardware cost of large aperture phased array radar. The traditional sub-array partition method does not consider the suppression of side lobe and interference as well as the weight optimization of sub-array. In this paper, a sub-array-level pattern optimization design method based on improved ant lion algorithm is proposed. Firstly, the anti-collision factor and elimination mechanism are introduced into ant lion algorithm. Then, by constraining the total number of elements and sub-array, the appropriate number of elements and sub-array weight of each subarray are solved, and then the height of peak sidelobe and the depth of null are optimized. The simulation results show that this method has better effects in sub-array optimization, sidelobe reduction and zero depth reduction in comparision with other algrithems.

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Petrović, Milica, Jelena Petronijević, Marko Mitić, Najdan Vuković, Zoran Miljković, and Bojan Babić. "The Ant Lion Optimization Algorithm for Integrated Process Planning and Scheduling." Applied Mechanics and Materials 834 (April 2016): 187–92. http://dx.doi.org/10.4028/www.scientific.net/amm.834.187.

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Process planning and scheduling are two of the most important manufacturing functions which are usually performed sequentially in traditional approaches. Considering the fact that these functions are usually complementary, it is necessary to integrate them so as to improve performance of a manufacturing system. This paper presents implementation of novel nature-inspired Ant Lion Optimization (ALO) algorithm for solving this combinatorial optimization problem effectively. As the ALO algorithm mimics the intelligent behavior of antlions in hunting ants, the main steps of hunting prey, its mathematical modeling, and optimization procedure for integration of process planning and scheduling is proposed. The algorithm is implemented in Matlab environment and run on the 3.10 GHz processor with 2 GBs of RAM memory. Experimental results show applicability of the proposed approach in solving integrated process planning and scheduling problem.

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Pamarthi, Nagaraju, and N. Nagamalleswara Rao. "Exponential Ant-Lion Rider Optimization for Privacy Preservation in Cloud Computing." Web Intelligence 19, no. 4 (2022): 275–93. http://dx.doi.org/10.3233/web-210473.

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The innovative trend of cloud computing is outsourcing data to the cloud servers by individuals or enterprises. Recently, various techniques are devised for facilitating privacy protection on untrusted cloud platforms. However, the classical privacy-preserving techniques failed to prevent leakage and cause huge information loss. This paper devises a novel methodology, namely the Exponential-Ant-lion Rider optimization algorithm based bilinear map coefficient Generation (Exponential-AROA based BMCG) method for privacy preservation in cloud infrastructure. The proposed Exponential-AROA is devised by integrating Exponential weighted moving average (EWMA), Ant Lion optimizer (ALO), and Rider optimization algorithm (ROA). The input data is fed to the privacy preservation process wherein the data matrix, and bilinear map coefficient Generation (BMCG) coefficient are multiplied through Hilbert space-based tensor product. Here, the bilinear map coefficient is obtained by multiplying the original data matrix and with modified elliptical curve cryptography (MECC) encryption to maintain data security. The bilinear map coefficient is used to handle both the utility and the sensitive information. Hence, an optimization-driven algorithm is utilized to evaluate the optimal bilinear map coefficient. Here, the fitness function is newly devised considering privacy and utility. The proposed Exponential-AROA based BMCG provided superior performance with maximal accuracy of 94.024%, maximal fitness of 1, and minimal Information loss of 5.977%.

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Wu, Ke, Houfei Fang, and Bingen Yang. "Modeling, Analyses, and Optimization of Planar Active Frame Structures Composed of Piezoelectric Beams." International Journal of Structural Stability and Dynamics 19, no. 12 (2019): 1950146. http://dx.doi.org/10.1142/s0219455419501463.

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Frame structures are widely used in engineering applications, especially in space structures. For special use such as shape and vibration control of such structures, piezoelectric patches are usually placed on the beam surfaces to form active frame structures. To perform shape control or vibration control tasks, modeling methods for the formed active frame structures need to be studied. This paper develops a new distributed model of an active frame structure composed of multilayer piezoelectric beam components. First, the governing equations of a beam, bonded with piezoelectric patches, are developed via the generalized Hamilton principle, by considering the transverse shear strain. Then, the analytical solutions of the governing equations and the generalized element stiffness matrix are derived through the distributed transfer function formulation. Finally, the analytical solution of the entire system is obtained by the technique for assembling element stiffness matrix. In numerical simulations, buckling and vibration of an active frame structure are both studied. In addition, a novel Improved Ant Lion algorithm is proposed for optimal design of the frame structures. The optimization examples confirm that the proposed algorithm is more efficient than other existing popular algorithms such as Genetic Algorithm (GA) and Ant Lion Optimization (ALO) algorithm.

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Utama, Dana Marsetiya, Dian Setiya Widodo, Muhammad Faisal Ibrahim, and Shanty Kusuma Dewi. "An effective hybrid ant lion algorithm to minimize mean tardiness on permutation flow shop scheduling problem." International Journal of Advances in Intelligent Informatics 6, no. 1 (2020): 23. http://dx.doi.org/10.26555/ijain.v6i1.385.

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This article aimed to develop an improved Ant Lion algorithm. The objective function was to minimize the mean tardiness on the flow shop scheduling problem with a focus on the permutation flow shop problem (PFSP). The Hybrid Ant Lion Optimization Algorithm (HALO) with local strategy was proposed, and from the total search of the agent, the NEH-EDD algorithm was applied. Moreover, the diversity of the nominee schedule was improved through the use of swap mutation, flip, and slide to determine the best solution in each iteration. Finally, the HALO was compared with some algorithms, while some numerical experiments were used to show the performances of the proposed algorithms. It is important to note that comparative analysis has been previously conducted using the nine variations of the PFSSP problem, and the HALO obtained was compared to other algorithms based on numerical experiments.

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Geng, Kaifeng, Chunming Ye, Zhen hua Dai, and Li Liu. "Bi-Objective Re-Entrant Hybrid Flow Shop Scheduling considering Energy Consumption Cost under Time-of-Use Electricity Tariffs." Complexity 2020 (February 22, 2020): 1–17. http://dx.doi.org/10.1155/2020/8565921.

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Re-entrant hybrid flow shop scheduling problem (RHFSP) is widely used in industries. However, little attention is paid to energy consumption cost with the raise of green manufacturing concept. This paper proposes an improved multiobjective ant lion optimization (IMOALO) algorithm to solve the RHFSP with the objectives of minimizing the makespan and energy consumption cost under Time-of-Use (TOU) electricity tariffs. A right-shift operation is then used to adjust the starting time of operations by avoiding the period of high electricity price to reduce the energy consumption cost as far as possible. The experimental results show that IMOALO algorithm is superior to multiobjective ant lion optimization (MOALO) algorithm, NSGA-II, and MOPSO in terms of the convergence, dominance, and diversity of nondominated solutions. The proposed model can make enterprises avoid high price period reasonably, transfer power load, and reduce the energy consumption cost effectively. Meanwhile, parameter analysis indicates that the period of TOU electricity tariffs and energy efficiency of machines have great impact on the scheduling results.

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Gürgen, Ayşenur. "Optimization and characterization of wood decay mushroom Ganoderma adspersum extract: A comparison between response surface methodology and artificial neural network-ant lion algorithm." Maderas. Ciencia y Tecnología 27 (April 14, 2025): e2525. https://doi.org/10.22320/s0718221x/2025.25.

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In this study, the bioactive properties of Ganoderma adspersum, a wood-decaying mushroom, were investigated. The study was designed in three steps: an experimental study, optimization of extraction conditions, and determination of bioactive properties of the optimum extracts. The main research problem was to determine the most effective extraction conditions to maximize the bioactive potential of G. adspersum using advanced optimization techniques. The extraction conditions were designed according to the I-optimal design and optimized using both the response surface method and the integration of artificial neural networks–ant lion algorithm. In the third step of the study, the bioactive properties of the two estimated extraction conditions and the extraction condition providing the highest total antioxidant status value obtained from the experimental studies were evaluated. Antioxidant activity, total phenolic and flavonoid content, antimicrobial properties, anticholinesterase activity, and phenolic content of three different optimum extracts were determined. As a result, the optimum extraction conditions suggested by artificial neural networks–ant lion algorithm optimization showed the best overall bioactive activity, highlighting the effectiveness of hybrid artificial intelligence-based models in bioactive compound extraction processes.

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Guo, Wuzheng, Yuanfa Ji, Xiyan Sun, and Xizi Jia. "Solving Integer Ambiguity Based on an Improved Ant Lion Algorithm." Sensors 25, no. 4 (2025): 1212. https://doi.org/10.3390/s25041212.

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In GNSS, a double-difference carrier phase observation model is typically employed, and high-accuracy position coordinates can be obtained by resolving the integer ambiguity within the model through algorithmic processing. To address the challenge of a double-difference integer ambiguity resolution, an enhanced Simulated Annealing Ant Lion Optimizer (SAALO) is proposed. This algorithm is designed to efficiently resolve integer ambiguities. First, the performance of the SAALO algorithm was evaluated by comparing its solving speed and success rate with those of the Ant Lion Optimization Algorithm (ALO), the LAMBDA algorithm and the MLAMBDA algorithm. The results demonstrate that the SAALO algorithm achieved a solution success rate that was 0.0496 s and 0.01 s faster than the LAMBDA and M-LAMBDA algorithms, respectively. Second, to further validate the high-dimensional ambiguity resolution capability of the SAALO algorithm, integer ambiguity resolution tests were conducted in both 6-dimensional and 12-dimensional scenarios. The results indicate that the SAALO algorithm achieves a success rate exceeding 98%, confirming its robust performance in high-dimensional problem-solving. Finally, the practical application of the SAALO algorithm was tested in short- and medium-baseline scenarios using a single-frequency GPS system. With a baseline length of 42.7 km, the SAALO algorithm exhibited a slightly faster average solution time compared to the LAMBDA algorithm, while its solution success rate was 5.2% higher. These findings underscore the effectiveness and reliability of the SAALO algorithm in real-world GNSS applications.

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Recio-Colmenares, Roxana, Kelly Joel Gurubel-Tun, and Virgilio Zúñiga-Grajeda. "Optimal Neural Tracking Control with Metaheuristic Parameter Identification for Uncertain Nonlinear Systems with Disturbances." Applied Sciences 10, no. 20 (2020): 7073. http://dx.doi.org/10.3390/app10207073.

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In this paper, we propose an inverse optimal neural control strategy for uncertain nonlinear systems subject to external disturbances. This control strategy is developed based on a neural observer for the estimation of unmeasured states and inverse optimal control theory for trajectory tracking. The stabilization of states along the desired trajectory is ensured via a control Lyapunov function. The optimal parameters of the control law are identified by different nature-inspired metaheuristic algorithms, namely: Ant Lion Optimizer, Grey Wolf Optimizer, Harris Hawks Optimization, and Whale Optimization Algorithm. Finally, a highly nonlinear biological system subject to parameter uncertainties and external disturbances (Activated Sludge Model) is proposed to validate the control strategy. Simulation results demonstrate that the control law with Ant Lion Optimizer outperforms the other optimization methods in terms of trajectory tracking in the presence of disturbances. The control law with Harris Hawks Optimization shows a better convergence of the neural states in presence of parameter uncertainty.

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Elango, Umamaheswari, Ganesan Sivarajan, Abirami Manoharan, and Subramanian Srikrishna. "Preventive maintenance scheduling using analysis of variance-based ant lion optimizer." World Journal of Engineering 15, no. 2 (2018): 254–72. http://dx.doi.org/10.1108/wje-06-2017-0145.

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Purpose Generator maintenance scheduling (GMS) is an essential task for electric power utilities as the periodical maintenance activity enhances the lifetime and also ensures the reliable and continuous operation of generating units. Though numerous meta-heuristic algorithms have been reported for the GMS solution, enhancing the existing techniques or developing new optimization procedure is still an interesting research task. The meta-heuristic algorithms are population based and the selection of their algorithmic parameters influences the quality of the solution. This paper aims to propose statistical tests guided meta-heuristic algorithm for solving the GMS problems. Design/methodology/approach The intricacy characteristics of the GMS problem in power systems necessitate an efficient and robust optimization tool. Though several meta-heuristic algorithms have been applied to solve the chosen power system operational problem, tuning of their control parameters is a protracting process. To prevail over the previously mentioned drawback, the modern meta-heuristic algorithm, namely, ant lion optimizer (ALO), is chosen as the optimization tool for solving the GMS problem. Findings The meta-heuristic algorithms are population based and require proper selection of algorithmic parameters. In this work, the ANOVA (analysis of variance) tool is proposed for selecting the most feasible decisive parameters in algorithm domain, and the statistical tests-based validation of solution quality is described. The parametric and non-parametric statistical tests are also performed to validate the selection of ALO against the various competing algorithms. The numerical and statistical results confirm that ALO is a promising tool for solving the GMS problems. Originality/value As a first attempt, ALO is applied to solve the GMS problem. Moreover, the ANOVA-based parameter selection is proposed and the statistical tests such as Wilcoxon signed rank and one-way ANOVA are conducted to validate the applicability of the intended optimization tool. The contribution of the paper can be summarized in two folds: the ANOVA-based ALO for GMS applications and statistical tests-based performance evaluation of intended algorithm.

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Gao, Zheng-Ming, and Juan Zhao. "An Improved Grey Wolf Optimization Algorithm with Variable Weights." Computational Intelligence and Neuroscience 2019 (June 2, 2019): 1–13. http://dx.doi.org/10.1155/2019/2981282.

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With a hypothesis that the social hierarchy of the grey wolves would be also followed in their searching positions, an improved grey wolf optimization (GWO) algorithm with variable weights (VW-GWO) is proposed. And to reduce the probability of being trapped in local optima, a new governing equation of the controlling parameter is also proposed. Simulation experiments are carried out, and comparisons are made. Results show that the proposed VW-GWO algorithm works better than the standard GWO, the ant lion optimization (ALO), the particle swarm optimization (PSO) algorithm, and the bat algorithm (BA). The novel VW-GWO algorithm is also verified in high-dimensional problems.

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Hans, Rahul, and Harjot Kaur. "Hybrid binary Sine Cosine Algorithm and Ant Lion Optimization (SCALO) approaches for feature selection problem." International Journal of Computational Materials Science and Engineering 09, no. 01 (2020): 1950021. http://dx.doi.org/10.1142/s2047684119500210.

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These days, a massive quantity of data is produced online and is incorporated into a variety of datasets in the form of features, however there are lot of features in these datasets that may not be relevant to the problem. In this perspective, feature selection aids to improve the classification accuracy with lesser number of features, which can be well thought-out as an optimization problem. In this paper, Sine Cosine Algorithm (SCA) hybridized with Ant Lion Optimizer (ALO) to form a hybrid Sine Cosine Ant Lion Optimizer (SCALO) is proposed. The proposed algorithm is mapped to its binary versions by using the concept of transfer functions, with the objective to eliminate the inappropriate features and to enhance the accuracy of the classification algorithm (or in any case remains the same). For the purpose of experimentation, this research considers 18 diverse datasets and moreover, the performance of the binary versions of SCALO is compared with some of the latest metaheuristic algorithms, on the basis of various criterions. It can be observed that the binary versions of SCALO perform better than the other algorithms on various evaluation criterions for solving feature selection problem.

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Sudhakara Reddy, A. V., and M. Damodar Reddy. "Optimal Capacitor Allocation for the Reconfigured Network using Ant Lion Optimization Algorithm." International Journal of Applied Engineering Research 12, no. 12 (2017): 3084. http://dx.doi.org/10.37622/ijaer/12.12.2017.3084-3089.

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Jack Russel Fernandis. "ALOA: Ant Lion Optimization Algorithm-based Deep Learning for Breast Cancer Classification." Multimedia Research 4, no. 1 (2021): 32–43. http://dx.doi.org/10.46253/j.mr.v4i1.a5.

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Chen, Wei, Panlong Yang, Wei Zhao, and Linna Wei. "Improved Ant Lion Optimizer for Coverage Optimization in Wireless Sensor Networks." Wireless Communications and Mobile Computing 2022 (August 16, 2022): 1–15. http://dx.doi.org/10.1155/2022/8808575.

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Coverage optimization is an important research topic in wireless sensor networks (WSNs). By studying the coverage optimization problem, the coverage rate of the network can be improved, and the number of redundant sensors can be reduced. In order to improve the coverage performance of wireless sensor networks, we propose an improved ant lion optimizer (IALO) to solve the coverage optimization problem in a WSN. Firstly, we give a network coverage optimization model with the objective of maximizing network coverage rate. Secondly, we alternately execute Cuckoo Search (CS) and Cauchy mutation to update the positions of the ants to enhance population diversity and accelerate convergence speed. Then, we introduce differential evolution (DE) to mutate the population of antlions to improve the convergence accuracy of our algorithm. We compare IALO with the original ant lion optimizer (ALO) and other algorithms on 9 benchmark functions to verify its effectiveness. Finally, IALO is applied to the coverage optimization in wireless sensor networks. Simulation results show that, compared with previous works, IALO provides higher coverage rate, makes the sensor distribution more uniform, and effectively reduces the deployment cost.

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Ning, Yawei, Minglei Ren, Shuai Guo, et al. "An Advanced Multi-Objective Ant Lion Algorithm for Reservoir Flood Control Optimal Operation." Water 16, no. 6 (2024): 852. http://dx.doi.org/10.3390/w16060852.

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Multi-objective reservoir operation of reservoir flood control involves numerous factors and complex model solving, and exploring effective methods for solving the operation models has always been a hot topic in reservoir optimization operation research. The Multi-Objective Ant Lion Algorithm (MOALO) is an emerging heuristic intelligent optimization algorithm, but it has not yet been applied in reservoir optimization operation. Testing the effectiveness of this method on multi-objective reservoir scheduling and further improving the optimization performance of this method is of great significance for enhancing the overall benefits of reservoir operation. In this study, MOALO is applied to the optimal scheduling of reservoir flood control. To increase the search efficiency of MOLAO, the advanced MOALO method (AMOLAO) is proposed by reconstructing the search distribution in MOALO using a power function. Taking the Songshu Reservoir and Dongfeng Reservoir in the Fuzhou River Basin in Dalian City as an example, MOALO, AMOLAO, and other two traditional methods are applied for solving the multi-objective reservoir operation problem. Results show that the AMOALO method has high search efficiency, strong optimization ability, and good stability. AMOALO performs better than MOALO and the two traditional methods. The study provides an efficient method for solving the problems in multi-objective reservoir operation.

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He, Wang, Min Liu, Chaowen Zuo, and Kai Wang. "Massive Multi-Source Joint Outbound and Benefit Distribution Model Based on Cooperative Game." Energies 16, no. 18 (2023): 6590. http://dx.doi.org/10.3390/en16186590.

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In light of the challenges posed by the widespread distribution of new energy sources in China and their distance from load centers, the power system must effectively integrate both new energy and thermal power transmission. To address this issue, we propose a dynamic coordinated scheduling model that combines wind, photovoltaic, and thermal power to optimize the profit of the energy complementary delivery system. Additionally, we present an improved ant lion optimization algorithm to investigate the coordinated scheduling and benefit distribution of these three power sources. This paper introduces a cooperative mode for benefit distribution and utilizes an enhanced Shapley value method to allocate the benefits of joint operation among the three parties. The distribution of benefits is based on the contribution of each party to the joint proceeds, considering the profit levels of joint outbound and independent outbound modes. Through our analysis, we demonstrate that the upgraded ant lion optimization algorithm facilitates finding the global optimal solution more effectively within the feasible zone. Furthermore, our suggested three-party combined scheduling model and profit-sharing approach are shown to be superior and feasible.

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Çınar, Hasan, and Ilyas Kandemir. "Active Energy Management Based on Meta-Heuristic Algorithms of Fuel Cell/Battery/Supercapacitor Energy Storage System for Aircraft." Aerospace 8, no. 3 (2021): 85. http://dx.doi.org/10.3390/aerospace8030085.

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This paper presents the application of an active energy management strategy to a hybrid system consisting of a proton exchange membrane fuel cell (PEMFC), battery, and supercapacitor. The purpose of energy management is to control the battery and supercapacitor states of charge (SOCs) as well as minimizing hydrogen consumption. Energy management should be applied to hybrid systems created in this way to increase efficiency and control working conditions. In this study, optimization of an existing model in the literature with different meta-heuristic methods was further examined and results similar to those in the literature were obtained. Ant lion optimizer (ALO), moth-flame optimization (MFO), dragonfly algorithm (DA), sine cosine algorithm (SCA), multi-verse optimizer (MVO), particle swarm optimization (PSO), and whale optimization algorithm (WOA) meta-heuristic algorithms were applied to control the flow of power between sources. The optimization methods were compared in terms of hydrogen consumption and calculation time. Simulation studies were conducted in Matlab/Simulink R2020b (academic license). The contribution of the study is that the optimization methods of ant lion algorithm, moth-flame algorithm, and sine cosine algorithm were applied to this system for the first time. It was concluded that the most effective method in terms of hydrogen consumption and computational burden was the sine cosine algorithm. In addition, the sine cosine algorithm provided better results than similar meta-heuristic algorithms in the literature in terms of hydrogen consumption. At the same time, meta-heuristic optimization algorithms and equivalent consumption minimization strategy (ECMS) and classical proportional integral (PI) control strategy were compared as a benchmark study as done in the literature, and it was concluded that meta-heuristic algorithms were more effective in terms of hydrogen consumption and computational time.

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Yıldız, Mustafa, Natee Panagant, Nantiwat Pholdee, Sujin Bureerat, Sadiq M. Sait, and Ali Rıza Yıldız. "Hybrid Taguchi-Lévy flight distribution optimization algorithm for solving real-world design optimization problems." Materials Testing 63, no. 6 (2021): 547–51. http://dx.doi.org/10.1515/mt-2020-0091.

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Abstract The Lévy flight distribution optimization algorithm is a recently developed meta-heuristic. In this study, the Lévy flight distribution optimization algorithm and the Taguchi method are hybridized to solve the shape optimization problem, which is the final step in developing optimum structural components. The new method is termed the hybrid Lévy flight distribution and Taguchi (HLFD-T) algorithm. Geometric dimensions are used as design variables in the optimization, and the problem is aimed at mass minimization. The constraint in the problem is the maximum stress value. The well-known Kriging meta-modeling approach and a specifically developed hybrid approach have been coupled in this paper to find the component’s optimal geometry. The results show that the proposed hybrid algorithm (HLFD-T) has more robust features than the ant lion algorithm, the whale algorithm, and the Lévy flight distribution optimization algorithm for obtaining an optimal component geometry.

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Liu, Rui. "Energy Management Strategy for Wind Solar Storage Microgrid Based on Improved Ant Lion Optimizer." Journal of Energy Research and Reviews 16, no. 7 (2024): 15–29. http://dx.doi.org/10.9734/jenrr/2024/v16i7359.

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The energy management of microgrids involves optimizing the capacity configuration, which significantly impacts the economic and stable operation of microgrids. This paper presents a control strategy for microgrid operation that effectively manages distributed power sources and energy storage to optimize capacity configuration. A mathematical optimization model for microgrid energy management is established considering minimum annual cost and optimal scale constraints. The traditional Ant Lion Optimizer (ALO) is improved by using dynamic weight coefficients and chaotic mapping to enhance the diversity of the population and improve the convergence of the algorithm. This can effectively avoid local optimal solutions and premature problems, and improve the convergence speed and search ability of the ALO algorithm. Based on this control strategy and improved ALO algorithm, simulations and tests were conducted using actual data from an independent microgrid, resulting in the optimal solution for the microgrid capacity allocation model. The case study results validate the practicality of the proposed microgrid operation control strategy as well as the superiority of the improved ant colony optimization algorithm.

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Tian, Fengqing, Donghua Zhang, Ying Yuan, Guangchun Fu, Xiaomin Li, and Guanghua Chen. "Fog Computing Task Scheduling of Smart Community Based on Hybrid Ant Lion Optimizer." Symmetry 15, no. 12 (2023): 2206. http://dx.doi.org/10.3390/sym15122206.

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Due to the problem of large latency and energy consumption of fog computing in smart community applications, the fog computing task-scheduling method based on Hybrid Ant Lion Optimizer (HALO) is proposed in this paper. This method is based on the Ant Lion Optimizer (ALO. Firstly, chaotic mapping is adopted to initialize the population, and the quality of the initial population is improved; secondly, the Adaptive Random Wandering (ARW) method is designed to improve the solution efficiency; finally, the improved Dynamic Opposite Learning Crossover (DOLC) strategy is embedded in the generation-hopping stage of the ALO to enrich the diversity of the population and improve the optimization-seeking ability of ALO. HALO is used to optimize the scheduling scheme of fog computing tasks. The simulation experiments are conducted under different data task volumes, compared with several other task scheduling algorithms such as the original algorithm of ALO, Genetic Algorithm (GA), Whale Optimizer Algorithm (WOA) and Salp Swarm Algorithm (SSA). HALO has good initial population quality, fast convergence speed, and high optimization-seeking accuracy. The scheduling scheme obtained by the proposed method in this paper can effectively reduce the latency of the system and reduce the energy consumption of the system.

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Navnath Dattatraya, Kale, K. Raghava Rao, and D. Satish Kumar. "Architectural analysis for lifetime maximization and energy efficiency in hybridized WSN model." International Journal of Engineering & Technology 7, no. 2.7 (2018): 494. http://dx.doi.org/10.14419/ijet.v7i2.7.10870.

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It is well known that WSN is one of the leading techniques in granting pervasive computing for various applications regarding health sector and communication sector. However, the raising of issues in WSN is still a burden cause because of certain renowned terms like energy consumption and network lifetime extension. Clustering is a major contribution in any network and moreover Cluster Head selection is also a vital role since it is additively responsible in sending data to the base station, which means that Cluster Head directly makes its communication with base station. Day by day, the researches in cluster head selection get increased, but the requirements are not yet fulfilled. This paper proposes a energy efficient cluster head selection algorithm for maximizing the WSN lifetime. This paper develops a hybrid optimization process termed Group Search Ant Lion with Levy Flight (GAL-LF) for selecting the Cluster head in WSN. The proposed model is compared to the conventional models such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Group Search Optimization (GSO), Ant Lion Optimization (ALO) and Cuckoo Search (CS). The outcome of the simulation result shows the superiority of the proposed model by prolonging the lifetime of the network.

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Ali Abttan, Rana, Adnan Hasan Tawafan, and Samar Jaafar Ismael. "Economic dispatch by optimization techniques." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 3 (2022): 2228. http://dx.doi.org/10.11591/ijece.v12i3.pp2228-2241.

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The current paper offers the solution strategy for the economic dispatch problem in electric power system implementing ant lion optimization algorithm (ALOA) and bat algorithm (BA) techniques. In the power network, the economic dispatch (ED) is a short-term calculation of the optimum performance of several electricity generations or a plan of outputs of all usable power generation units from the energy produced to fulfill the necessary demand, although equivalent and unequal specifications need to be achieved at minimal fuel and carbon pollution costs. In this paper, two recent meta-heuristic approaches are introduced, the BA and ALOA. A rigorous stochastically developmental computing strategy focused on the action and intellect of ant lions is an ALOA. The ALOA imitates ant lions' hunting process. The introduction of a numerical description of its biological actions for the solution of ED in the power framework. These algorithms are applied to two systems: a small scale three generator system and a large scale six generator. Results show were compared on the metrics of convergence rate, cost, and average run time that the ALOA and BA are suitable for economic dispatch studies which is clear in the comparison set with other algorithms. Both of these algorithms are tested on IEEE-30 bus reliability test system.

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Rana, Ali Abttan, Hasan Tawafan Adnan, and Jaafar Ismael Samar. "Economic dispatch by optimization techniques." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 3 (2022): 2228–41. https://doi.org/10.11591/ijece.v12i3.pp2228-2241.

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The current paper offers the solution strategy for the economic dispatch problem in electric power system implementing ant lion optimization algorithm (ALOA) and bat algorithm (BA) techniques. In the power network, the economic dispatch (ED) is a short-term calculation of the optimum performance of several electricity generations or a plan of outputs of all usable power generation units from the energy produced to fulfill the necessary demand, although equivalent and unequal specifications need to be achieved at minimal fuel and carbon pollution costs. In this paper, two recent meta-heuristic approaches are introduced, the BA and ALOA. A rigorous stochastically developmental computing strategy focused on the action and intellect of ant lions is an ALOA. The ALOA imitates ant lions' hunting process. The introduction of a numerical description of its biological actions for the solution of ED in the power framework. These algorithms are applied to two systems: a small scale three generator system and a large scale six generator. Results show were compared on the metrics of convergence rate, cost, and average run time that the ALOA and BA are suitable for economic dispatch studies which is clear in the comparison set with other algorithms. Both of these algorithms are tested on IEEE-30 bus reliability test system.

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Nawal, Taleb, Bentouati Bachir, Chettih Saliha, Harrouz Abdelkader, and Ragab El-Sehiemy. "Renewable Energy Sources Scheduling Approach for Windfarm Layout Optimization by Using Ant Lion Optimization Algorithm." Applied Mechanics and Materials 905 (February 15, 2022): 79–92. http://dx.doi.org/10.4028/p-1bvgm9.

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The increasing penetration of RES and the intermittent nature of various distributed power generation (DG) resources have created uncertainty in variable power production and power systems. The overall energy output of a wind farm may be optimized by strategically positioning wind turbines. This paper proposes a three-step strategy to dealing with the difficult-to-control problem of wind farm layout optimization. To construct the non-wake and wake impacts at various levels, three case scenarios are studied. The proposed strategy is used to a particular Ant-Lion Optimization Algorithm (ALOA) as a novel approach to producing highly efficient optimal output power, as well as case studies using actual wind data assessing potential turbine site. Finally, the simulation results demonstrate that the suggested approach is robust in ALOA design because it further reduces the objective function on the best new outcomes to implement all network restrictions acquired via the analysis.

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Kotapuri, Mercy Rosalina, and Rajesh Kumar Samala. "Fuzzy Logic Controlled Based Ant-Lion Optimization Hybridization for Economic Power Dispatch." Journal Européen des Systèmes Automatisés 53, no. 5 (2020): 725–31. http://dx.doi.org/10.18280/jesa.530515.

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The demand on the power system rising more rapidly is causing to increase the power system size and capacity. There is a need of interconnection of various generating stations to meet the increased load demand. Economical unit commitment is necessary for plant operation with the advancement in power system integration. The Economical Power Dispatch (EPD) is to find the most favourable combination of generating systems output powers which reduce the fuel cost by satisfying all system constraints. This research involves the fuzzy logic controller (FLC) has been hybridized with Ant-Lion Optimization (ALO) algorithm for EPD. By using this new hybrid technique, minimization of total operating cost by economically dispatch the power to meet the required load and also minimization of system total losses by optimum allocation of DG units were done. Fuel cost function and demand on system are modeled by fuzzy membership functions. The ALO is used to obtain the schedule the committed generating unit’s outputs so as to meet the required load demand. This proposed FLC based ALO technique executed with MATLAB software and applied on IEEE-30 system. Effectiveness of this projected algorithm is determined and evaluated with standalone techniques like conventional ALO, ALO-PSO algorithms.

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Ali, E. S., S. M. Abd Elazim, and A. Y. Abdelaziz. "Ant Lion Optimization Algorithm for optimal location and sizing of renewable distributed generations." Renewable Energy 101 (February 2017): 1311–24. http://dx.doi.org/10.1016/j.renene.2016.09.023.

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Banadkooki, Fatemeh Barzegari, Mohammad Ehteram, Ali Najah Ahmed, et al. "Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm." Environmental Science and Pollution Research 27, no. 30 (2020): 38094–116. http://dx.doi.org/10.1007/s11356-020-09876-w.

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Ali, E. S., S. M. Abd Elazim, and A. Y. Abdelaziz. "Optimal allocation and sizing of renewable distributed generation using ant lion optimization algorithm." Electrical Engineering 100, no. 1 (2016): 99–109. http://dx.doi.org/10.1007/s00202-016-0477-z.

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M., Mohankumar, Balamurugan K., Singaravel G., and Menaka S.R. "A Dynamic Workflow Scheduling Method based on MCDM Optimization that Manages Priority Tasks for Fault Tolerance." International Academic Journal of Science and Engineering 11, no. 1 (2024): 09–14. http://dx.doi.org/10.9756/iajse/v11i1/iajse1102.

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Because it offers efficient on-demand service delivery over the internet, cloud computing has become more and more popular. An architectural model for cloud computing energy management is provided by the suggested Ant Lion algorithm. Virtual Machines (VMs) in cloud systems are assigned to hosts based not so much on their overall and long-term use, but rather on their immediate resource consumption, including RAM availability. The placement and scheduling processes are frequently computationally demanding and have the potential to affect the performance of deployed virtual machines. In this research work, we offer a strategy that considers the historical resource use of virtual machines (VMs) over time while scheduling them in the cloud. Our goal is to use the Ant lion approach to schedule virtual machines (VMs) in a way that maximizes performance by evaluating the utilization levels of prior VMs. The goal is to reduce the degradation of performance brought about by Cloud administration tasks like as virtual machine deployment, which might impact systems that have already been installed. Furthermore, congested virtual machines (VMs) sometimes take up resources from nearby VMs, increasing the VMs' actual CPU use. Our results show that by learning and adjusting to system behavior over time, our strategy outperforms conventional instant-based physical machine selection. We offer the idea of scheduling virtual machines (VMs) using resource monitoring data from past VM resource use. By using the Ant lion classifier, four fewer physical machines are needed.

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Guerra, Juan F., Ramon Garcia-Hernandez, Miguel A. Llama, and Victor Santibañez. "A Comparative Study of Swarm Intelligence Metaheuristics in UKF-Based Neural Training Applied to the Identification and Control of Robotic Manipulator." Algorithms 16, no. 8 (2023): 393. http://dx.doi.org/10.3390/a16080393.

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This work presents a comprehensive comparative analysis of four prominent swarm intelligence (SI) optimization algorithms: Ant Lion Optimizer (ALO), Bat Algorithm (BA), Grey Wolf Optimizer (GWO), and Moth Flame Optimization (MFO). When compared under the same conditions with other SI algorithms, the Particle Swarm Optimization (PSO) stands out. First, the Unscented Kalman Filter (UKF) parameters to be optimized are selected, and then each SI optimization algorithm is executed within an off-line simulation. Once the UKF initialization parameters P0, Q0, and R0 are obtained, they are applied in real-time in the decentralized neural block control (DNBC) scheme for the trajectory tracking task of a 2-DOF robot manipulator. Finally, the results are compared according to the criteria performance evaluation using each algorithm, along with CPU cost.

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Ashish Kumar and Amar Partap Singh Pharwaha. "Design and Optimization of Micro-Machined Sierpinski Carpet Fractal Antenna Using Ant Lion Optimization." International Journal of Engineering and Technology Innovation 10, no. 4 (2020): 306–18. http://dx.doi.org/10.46604/ijeti.2020.5596.

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This study investigates the optimized Sierpinski carpet fractal patch antenna and also explores the possibility of the integration of the proposed design with monolithic microwave integrated circuits. The optimization process has been performed using an ant lion optimization algorithm to achieve the required operating frequency and impedance matching. Further, due to surface waves excitation in the high index substrates used for the antenna design, the performance of the antenna degrades. Therefore, a process of micro-machining has been adopted to overcome this limitation. The micro-machining process creates an air cavity underneath the patch which further creates the low index environment in the patch antenna causing drastic improvement in the performance parameters along with the compatibility with monolithic microwave integrated circuits. The design shows multiple resonance frequencies in X-band and Ku-band. The proposed micro-machined design shows the resonance at 7.9 GHz, 9.6 GHz, 13.6 GHz, and 19 GHz with a maximum gain of 6 dBi.

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Li, Zhi, Shu-Chuan Chu, Jeng-Shyang Pan, Pei Hu, and Xingsi Xue. "A Mahalanobis Surrogate-Assisted Ant Lion Optimization and Its Application in 3D Coverage of Wireless Sensor Networks." Entropy 24, no. 5 (2022): 586. http://dx.doi.org/10.3390/e24050586.

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Metaheuristic algorithms are widely employed in modern engineering applications because they do not need to have the ability to study the objective function’s features. However, these algorithms may spend minutes to hours or even days to acquire one solution. This paper presents a novel efficient Mahalanobis sampling surrogate model assisting Ant Lion optimization algorithm to address this problem. For expensive calculation problems, the optimization effect goes even further by using MSAALO. This model includes three surrogate models: the global model, Mahalanobis sampling surrogate model, and local surrogate model. Mahalanobis distance can also exclude the interference correlations of variables. In the Mahalanobis distance sampling model, the distance between each ant and the others could be calculated. Additionally, the algorithm sorts the average length of all ants. Then, the algorithm selects some samples to train the model from these Mahalanobis distance samples. Seven benchmark functions with various characteristics are chosen to testify to the effectiveness of this algorithm. The validation results of seven benchmark functions demonstrate that the algorithm is more competitive than other algorithms. The simulation results based on different radii and nodes show that MSAALO improves the average coverage by 2.122% and 1.718%, respectively.

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Azeez Asmael, Ahmed Abdul, and Basman Al-Nedawe. "Energy efficient WSN using hybrid modification PEGASIS with ant lion optimization." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 1 (2021): 273. http://dx.doi.org/10.11591/ijeecs.v23.i1.pp273-284.

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<p>Wireless sensor nodes consist of tiny electronic devices that can sense, transmit, and measure data from physical environments such as the field of minter surveillance. These sensor nodes significantly depend on batteries to gain energy which is used to operations associated with communication and computation. Generally, designing communication protocols is feasible to achieve effective usage of these energy resources of the sensor node. Both reported medium access control and routing can achieve energy-saving that supporting real time functionality. This paper emphasizes the use of hybrid modified PEGASIS-Ant lion optimization. Several steps are entailed in this research. First is random distribution of node followed by clustering the map as a circular region. Then, the nodes are connected to the closest node in that region. In consequence, PEGASIS-Ant lion optimization is applied to enhance the connection of the nodes and accomplish the maximum life batter of the sensor. At last, the experiments performed in this work demonstrate that the proposed optimization technique operates well in terms of network latency, power duration and energy’s consumption. Furthermore, the life span of the nodes has improved greatly by 87% over the original algorithm that accomplished a rate of life nodes of 60%.</p>

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Asmael, Ahmed Abdul Azeez, and Basman Al-Nedawe. "Energy efficient WSN using hybrid modification PEGASIS with ant lion optimization." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 1 (2021): 273–84. https://doi.org/10.11591/ijeecs.v23.i1.pp273-284.

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Wireless sensor nodes consist of tiny electronic devices that can sense, transmit, and measure data from physical environments such as the field of minter surveillance. These sensor nodes significantly depend on batteries to gain energy which is used to operations associated with communication and computation. Generally, designing communication protocols is feasible to achieve effective usage of these energy resources of the sensor node. Both reported medium access control and routing can achieve energy-saving that supporting real time functionality. This paper emphasizes the use of hybrid modified PEGASIS-Ant lion optimization. Several steps are entailed in this research. First is random distribution of node followed by clustering the map as a circular region. Then, the nodes are connected to the closest node in that region. In consequence, PEGASIS-Ant lion optimization is applied to enhance the connection of the nodes and accomplish the maximum life batter of the sensor. At last, the experiments performed in this work demonstrate that the proposed optimization technique operates well in terms of network latency, power duration and energy’s consumption. Furthermore, the life span of the nodes has improved greatly by 87% over the original algorithm that accomplished a rate of life nodes of 60%.

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Journal articles on the topic 'Ameliorated ant lion optimization algorithm'

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