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Mehta, Pranav, Betul Sultan Yildiz, Sadiq M. Sait, and Ali Riza Yildiz. "Hunger games search algorithm for global optimization of engineering design problems." Materials Testing 64, no. 4 (April 1, 2022): 524–32. http://dx.doi.org/10.1515/mt-2022-0013.
Повний текст джерелаАнотація:
Abstract The modernization in automobile industries has been booming in recent times, which has led to the development of lightweight and fuel-efficient design of different automobile components. Furthermore, metaheuristic algorithms play a significant role in obtaining superior optimized designs for different vehicle components. Hence, a hunger game search (HGS) algorithm is applied to optimize the automobile suspension arm (SA) by reduction of mass vis-à-vis volume. The performance of the HGS algorithm was accomplished by comparing the achieved results with the well-established metaheuristics (MHs), such as salp swarm optimizer, equilibrium optimizer, Harris Hawks optimizer (HHO), chaotic HHO, slime mould optimizer, marine predator optimizer, artificial bee colony optimizer, ant lion optimizer, and it was found that the HGS algorithm is able to pursue the best optimized solution subjecting to critical constraints. Moreover, the HGS algorithm can realize the least weight of the SA subjected to maximum stress values. Hence, the adopted algorithm can be found robust in terms of obtaining the best global optimum solution.
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Abdel-Basset, Mohamed, Reda Mohamed, Karam M. Sallam, and Ripon K. Chakrabortty. "Light Spectrum Optimizer: A Novel Physics-Inspired Metaheuristic Optimization Algorithm." Mathematics 10, no. 19 (September 23, 2022): 3466. http://dx.doi.org/10.3390/math10193466.
Повний текст джерелаАнотація:
This paper introduces a novel physical-inspired metaheuristic algorithm called “Light Spectrum Optimizer (LSO)” for continuous optimization problems. The inspiration for the proposed algorithm is the light dispersions with different angles while passing through rain droplets, causing the meteorological phenomenon of the colorful rainbow spectrum. In order to validate the proposed algorithm, three different experiments are conducted. First, LSO is tested on solving CEC 2005, and the obtained results are compared with a wide range of well-regarded metaheuristics. In the second experiment, LSO is used for solving four CEC competitions in single objective optimization benchmarks (CEC2014, CEC2017, CEC2020, and CEC2022), and its results are compared with eleven well-established and recently-published optimizers, named grey wolf optimizer (GWO), whale optimization algorithm (WOA), and salp swarm algorithm (SSA), evolutionary algorithms like differential evolution (DE), and recently-published optimizers including gradient-based optimizer (GBO), artificial gorilla troops optimizer (GTO), Runge–Kutta method (RUN) beyond the metaphor, African vultures optimization algorithm (AVOA), equilibrium optimizer (EO), grey wolf optimizer (GWO), Reptile Search Algorithm (RSA), and slime mold algorithm (SMA). In addition, several engineering design problems are solved, and the results are compared with many algorithms from the literature. The experimental results with the statistical analysis demonstrate the merits and highly superior performance of the proposed LSO algorithm.
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Khan, Muhammad Fahad, Muqaddas Bibi, Farhan Aadil, and Jong-Weon Lee. "Adaptive Node Clustering for Underwater Sensor Networks." Sensors 21, no. 13 (June 30, 2021): 4514. http://dx.doi.org/10.3390/s21134514.
Повний текст джерелаАнотація:
Monitoring of an underwater environment and communication is essential for many applications, such as sea habitat monitoring, offshore investigation and mineral exploration, but due to underwater current, low bandwidth, high water pressure, propagation delay and error probability, underwater communication is challenging. In this paper, we proposed a sensor node clustering technique for UWSNs named as adaptive node clustering technique (ANC-UWSNs). It uses a dragonfly optimization (DFO) algorithm for selecting ideal measure of clusters needed for routing. The DFO algorithm is inspired by the swarming behavior of dragons. The proposed methodology correlates with other algorithms, for example the ant colony optimizer (ACO), comprehensive learning particle swarm optimizer (CLPSO), gray wolf optimizer (GWO) and moth flame optimizer (MFO). Grid size, transmission range and nodes density are used in a performance matrix, which varies during simulation. Results show that DFO outperform the other algorithms. It produces a higher optimized number of clusters as compared to other algorithms and hence optimizes overall routing and increases the life span of a network.
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Sameh, Mariam A., Mostafa I. Marei, M. A. Badr, and Mahmoud A. Attia. "An Optimized PV Control System Based on the Emperor Penguin Optimizer." Energies 14, no. 3 (February 1, 2021): 751. http://dx.doi.org/10.3390/en14030751.
Повний текст джерелаАнотація:
During the day, photovoltaic (PV) systems are exposed to different sunlight conditions in addition to partial shading (PS). Accordingly, maximum power point tracking (MPPT) techniques have become essential for PV systems to secure harvesting the maximum possible power from the PV modules. In this paper, optimized control is performed through the application of relatively newly developed optimization algorithms to PV systems under Partial Shading (PS) conditions. The initial value of the duty cycle of the boost converter is optimized for maximizing the amount of power extracted from the PV arrays. The emperor penguin optimizer (EPO) is proposed not only to optimize the initial setting of duty cycle but to tune the gains of controllers used for the boost converter and the grid-connected inverter of the PV system. In addition, the performance of the proposed system based on the EPO algorithm is compared with another newly developed optimization technique based on the cuttlefish algorithm (CFA). Moreover, particle swarm optimization (PSO) algorithm is used as a reference algorithm to compare results with both EPO and CFA. PSO is chosen since it is an old, well-tested, and effective algorithm. For the evaluation of performance of the proposed PV system using the proposed algorithms under different PS conditions, results are recorded and introduced.
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Mehta, Pranav, Betül Sultan Yıldız, Nantiwat Pholdee, Sumit Kumar, Ali Riza Yildiz, Sadiq M. Sait, and Sujin Bureerat. "A novel generalized normal distribution optimizer with elite oppositional based learning for optimization of mechanical engineering problems." Materials Testing 65, no. 2 (February 1, 2023): 210–23. http://dx.doi.org/10.1515/mt-2022-0259.
Повний текст джерелаАнотація:
Abstract Optimization of engineering discipline problems are quite a challenging task as they carry design parameters and various constraints. Metaheuristic algorithms can able to handle those complex problems and realize the global optimum solution for engineering problems. In this article, a novel generalized normal distribution algorithm that is integrated with elite oppositional-based learning (HGNDO-EOBL) is studied and employed to optimize the design of the eight benchmark engineering functions. Moreover, the statistical results obtained from the HGNDO-EOBL are collated with the data obtained from the well-established algorithms such as whale optimizer, salp swarm optimizer, LFD optimizer, manta ray foraging optimization algorithm, hunger games search algorithm, reptile search algorithm, and INFO algorithm. For each of the cases, a comparison of the statistical results suggests that HGNDO-EOBL is superior in terms of realizing the prominent values of the fitness function compared to established algorithms. Accordingly, the HGNDO-EOBL can be adopted for a wide range of engineering optimization problems.
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Ewees, Ahmed A., Zakariya Yahya Algamal, Laith Abualigah, Mohammed A. A. Al-qaness, Dalia Yousri, Rania M. Ghoniem, and Mohamed Abd Elaziz. "A Cox Proportional-Hazards Model Based on an Improved Aquila Optimizer with Whale Optimization Algorithm Operators." Mathematics 10, no. 8 (April 12, 2022): 1273. http://dx.doi.org/10.3390/math10081273.
Повний текст джерелаАнотація:
Recently, a new optimizer, called the Aquila Optimizer (AO), was developed to solve different optimization problems. Although the AO has a significant performance in various problems, like other optimization algorithms, the AO suffers from certain limitations in its search mechanism, such as local optima stagnation and convergence speed. This is a general problem that faces almost all optimization problems, which can be solved by enhancing the search process of an optimizer using an assistant search tool, such as using hybridizing with another optimizer or applying other search techniques to boost the search capability of an optimizer. Following this concept to address this critical problem, in this paper, we present an alternative version of the AO to alleviate the shortcomings of the traditional one. The main idea of the improved AO (IAO) is to use the search strategy of the Whale Optimization Algorithm (WOA) to boost the search process of the AO. Thus, the IAO benefits from the advantages of the AO and WOA, and it avoids the limitations of the local search as well as losing solutions diversity through the search process. Moreover, we apply the developed IAO optimization algorithm as a feature selection technique using different benchmark functions. More so, it is tested with extensive experimental comparisons to the traditional AO and WOA algorithms, as well as several well-known optimizers used as feature selection techniques, like the particle swarm optimization (PSO), differential evaluation (DE), mouth flame optimizer (MFO), firefly algorithm, and genetic algorithm (GA). The outcomes confirmed that the using of the WOA operators has a significant impact on the AO performance. Thus the combined IAO obtained better results compared to other optimizers.
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Yıldız, Betül Sultan, Vivek Patel, Nantiwat Pholdee, Sadiq M. Sait, Sujin Bureerat, and Ali Rıza Yıldız. "Conceptual comparison of the ecogeography-based algorithm, equilibrium algorithm, marine predators algorithm and slime mold algorithm for optimal product design." Materials Testing 63, no. 4 (April 1, 2021): 336–40. http://dx.doi.org/10.1515/mt-2020-0049.
Повний текст джерелаАнотація:
Abstract Vehicle component design is crucial for developing a vehicle prototype, as optimum parts can lead to cost reduction and performance enhancement of the vehicle system. The use of metaheuristics for vehicle component optimization has been commonplace due to several advantages: robustness and simplicity. This paper aims to demonstrate the shape design of a vehicle bracket by using a newly invented metaheuristic. The new optimizer is termed the ecogeography-based optimization algorithm (EBO). This is arguably the first vehicle design application of the new optimizer. The optimization problem is posed while EBO is implemented to solve the problem. It is found that the design results obtained from EBO are better when compared to other optimizers such as the equilibrium optimization algorithm, marine predators algorithm, slime mold algorithm.
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Zhang, Runze, and Yujie Zhu. "Predicting the Mechanical Properties of Heat-Treated Woods Using Optimization-Algorithm-Based BPNN." Forests 14, no. 5 (May 2, 2023): 935. http://dx.doi.org/10.3390/f14050935.
Повний текст джерелаАнотація:
This paper aims to enhance the accuracy of predicting the mechanical behavior of wood subjected to thermal modification using an improved dung beetle optimization (IDBO) model. The IDBO algorithm improves the original DBO algorithm via three main steps: (1) using piece-wise linear chaotic mapping (PWLCM) to generate the initial dung beetle species and increase its heterogeneity; (2) adopting an adaptive nonlinear decreasing producer ratio model to control the number of producers and boost the algorithm’s convergence rate; and (3) applying a dimensional learning-enhanced foraging (DLF) search strategy that optimizes the algorithm’s ability to explore and exploit the search space. The IDBO algorithm is evaluated on 14 benchmark functions and outperforms other algorithms. The IDBO algorithm is then applied to optimize a back-propagation (BP) neural network for predicting five mechanical property parameters of heat-treated larch-sawn timber. The results indicate that the IDBO-BP model significantly reduces the error compared with the BP, tent-sparrow search algorithm (TSSA)-BP, grey wolf optimizer (GWO)-BP, nonlinear adaptive grouping grey wolf optimizer (IGWO)-BP and DBO-BP models, demonstrating its superiority in predicting the physical characteristics of lumber after heat treatment.
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AlRassas, Ayman Mutahar, Mohammed A. A. Al-qaness, Ahmed A. Ewees, Shaoran Ren, Mohamed Abd Elaziz, Robertas Damaševičius, and Tomas Krilavičius. "Optimized ANFIS Model Using Aquila Optimizer for Oil Production Forecasting." Processes 9, no. 7 (July 9, 2021): 1194. http://dx.doi.org/10.3390/pr9071194.
Повний текст джерелаАнотація:
Oil production forecasting is one of the essential processes for organizations and governments to make necessary economic plans. This paper proposes a novel hybrid intelligence time series model to forecast oil production from two different oil fields in China and Yemen. This model is a modified ANFIS (Adaptive Neuro-Fuzzy Inference System), which is developed by applying a new optimization algorithm called the Aquila Optimizer (AO). The AO is a recently proposed optimization algorithm that was inspired by the behavior of Aquila in nature. The developed model, called AO-ANFIS, was evaluated using real-world datasets provided by local partners. In addition, extensive comparisons to the traditional ANFIS model and several modified ANFIS models using different optimization algorithms. Numeric results and statistics have confirmed the superiority of the AO-ANFIS over traditional ANFIS and several modified models. Additionally, the results reveal that AO is significantly improved ANFIS prediction accuracy. Thus, AO-ANFIS can be considered as an efficient time series tool.
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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 (August 21, 2023): 393. http://dx.doi.org/10.3390/a16080393.
Повний текст джерелаАнотація:
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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Yang, Fan, Kewen Xia, Shurui Fan, and Zhiwei Zhang. "Equalization Optimizer-Based LSTM Application in Reservoir Identification." Computational Intelligence and Neuroscience 2022 (September 9, 2022): 1–20. http://dx.doi.org/10.1155/2022/7372984.
Повний текст джерелаАнотація:
In recent years, the use of long short-term memory (LSTM) has made significant contributions to various fields and the use of intelligent optimization algorithms combined with LSTM is also one of the best ways to improve model shortcomings and increase classification accuracy. Reservoir identification is a key and difficult point in the process of logging, so using LSTM to identify the reservoir is very important. To improve the logging reservoir identification accuracy of LSTM, an improved equalization optimizer algorithm (TAFEO) is proposed in this paper to optimize the number of neurons and various parameters of LSTM. The TAFEO algorithm mainly employs tent chaotic mapping to enhance the population diversity of the algorithm, convergence factor is introduced to better balance the local and global search, and then, a premature disturbance strategy is employed to overcome the shortcomings of local minima. The optimization performance of the TAFEO algorithm is tested with 16 benchmark test functions and Wilcoxon rank-sum test for optimization results. The improved algorithm is superior to many intelligent optimization algorithms in accuracy and convergence speed and has good robustness. The receiver operating characteristic (ROC) curve is used to evaluate the performance of the optimized LSTM model. Through the simulation and comparison of UCI datasets, the results show that the performance of the LSTM model based on TAFEO has been significantly improved, and the maximum area under the ROC curve value can get 99.43%. In practical logging applications, LSTM based on an equalization optimizer is effective in well-logging reservoir identification, the highest recognition accuracy can get 95.01%, and the accuracy of reservoir identification is better than other existing identification methods.
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Alzaghoul, Esra F., and Sandi N. Fakhouri. "Collaborative Strategy for Grey Wolf Optimization Algorithm." Modern Applied Science 12, no. 7 (June 21, 2018): 73. http://dx.doi.org/10.5539/mas.v12n7p73.
Повний текст джерелаАнотація:
Grey wolf Optimizer (GWO) is one of the well known meta-heuristic algorithm for determining the minimum value among a set of values. In this paper, we proposed a novel optimization algorithm called collaborative strategy for grey wolf optimizer (CSGWO). This algorithm enhances the behaviour of GWO that enhances the search feature to search for more points in the search space, whereas more groups will search for the global minimal points. The algorithm has been tested on 23 well-known benchmark functions and the results are verified by comparing them with state of the art algorithms: Polar particle swarm optimizer, sine cosine Algorithm (SCA), multi-verse optimizer (MVO), supernova optimizer as well as particle swarm optimizer (PSO). The results show that the proposed algorithm enhanced GWO behaviour for reaching the best solution and showed competitive results that outperformed the compared meta-heuristics over the tested benchmarked functions.
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Hudaib, Amjad A., and Hussam N. Fakhouri. "Supernova Optimizer: A Novel Natural Inspired Meta-Heuristic." Modern Applied Science 12, no. 1 (December 22, 2017): 32. http://dx.doi.org/10.5539/mas.v12n1p32.
Повний текст джерелаАнотація:
Bio and natural phenomena inspired algorithms and meta-heuristics provide solutions to solve optimization and preliminary convergence problems. It significantly has wide effect that is integrated in many scientific fields. Thereby justifying the relevance development of many applications that relay on optimization algorithms, which allow finding the best solution in the shortest possible time. Therefore it is necessary to further consider and develop new swarm intelligence optimization algorithms. This paper proposes a novel optimization algorithm called supernova optimizer (SO) inspired by the supernova phenomena in nature. SO mimics this natural phenomena aiming to improve the three main features of optimization; exploration, exploitation, and local minima avoidance. The proposed meta-heuristic optimizer has been tested over 20 will known benchmarks functions, the results have been verified by a comparative study with the state of art optimization algorithms Grey Wolf Optimizer (GWO), A Sine Cosine Algorithm for solving optimization problems (SCA), Multi-Verse Optimizer (MVO), Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm (MFO), The Whale Optimization Algorithm (WOA), Polar Particle Swarm Optimizer (PLOARPSO) and with Particle Swarm Optimizer (PSO). The results showed that SO provided very competitive and effective results. It outperforms the best state-of-art algorithms that are compared to on the most of the tested benchmark functions.
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Dehghani, Mohammad, Zeinab Montazeri, Ali Dehghani, Ricardo Ramirez-Mendoza, Haidar Samet, Josep Guerrero, and Gaurav Dhiman. "MLO: Multi Leader Optimizer." International Journal of Intelligent Engineering and Systems 13, no. 6 (December 31, 2020): 364–73. http://dx.doi.org/10.22266/ijies2020.1231.32.
Повний текст джерелаАнотація:
Optimization is a topic that has always been discussed in all different fields of science. One of the most effective techniques for solving such problems is optimization algorithms. In this paper, a new optimizer called Multi-Leader optimizer (MLO) is developed in which multiple leaders guide members of the population towards the optimal answer. MLO is mathematically modelled based on the process of advancing members of the population and following the leaders. MLO performance in optimization is examined on twenty-three standard objective functions. The results of this optimization are compared with the results of the other eight existing optimization algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Teaching-Learning-Based Optimization (TLBO), Gray Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), Emperor Penguin Optimizer (EPO), Shell Game Optimization (SGO), and Hide Objects Game Optimization (HOGO). Based on the analysis of the simulation results on unimodal test functions to evaluate exploitation ability and multimodal test functions in order to evaluate exploration ability, it has been determined that MLO has a higher ability to solve optimization problems than existing optimization algorithms.
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Ali, Mona A. S., Fathimathul Rajeena P. P., and Diaa Salama Abd Elminaam. "A Feature Selection Based on Improved Artificial Hummingbird Algorithm Using Random Opposition-Based Learning for Solving Waste Classification Problem." Mathematics 10, no. 15 (July 29, 2022): 2675. http://dx.doi.org/10.3390/math10152675.
Повний текст джерелаАнотація:
Recycling tasks are the most effective method for reducing waste generation, protecting the environment, and boosting the overall national economy. The productivity and effectiveness of the recycling process are strongly dependent on the cleanliness and precision of processed primary sources. However, recycling operations are often labor intensive, and computer vision and deep learning (DL) techniques aid in automatically detecting and classifying trash types during recycling chores. Due to the dimensional challenge posed by pre-trained CNN networks, the scientific community has developed numerous techniques inspired by biology, swarm intelligence theory, physics, and mathematical rules. This research applies a new meta-heuristic algorithm called the artificial hummingbird algorithm (AHA) to solving the waste classification problem based on feature selection. However, the performance of the AHA is barely satisfactory; it may be stuck in optimal local regions or have a slow convergence. To overcome these limitations, this paper develops two improved versions of the AHA called the AHA-ROBL and the AHA-OBL. These two versions enhance the exploitation stage by using random opposition-based learning (ROBL) and opposition-based learning (OBL) to prevent local optima and accelerate the convergence. The main purpose of this paper is to apply the AHA-ROBL and AHA-OBL to select the relevant deep features provided by two pre-trained models of CNN (VGG19 & ResNet20) to recognize a waste classification. The TrashNet dataset is used to verify the performance of the two proposed approaches (the AHA-ROBL and AHA-OBL). The effectiveness of the suggested methods (the AHA-ROBL and AHA-OBL) is compared with that of 12 modern and competitive optimizers, namely the artificial hummingbird algorithm (AHA), Harris hawks optimizer (HHO), Salp swarm algorithm (SSA), aquila optimizer (AO), Henry gas solubility optimizer (HGSO), particle swarm optimizer (PSO), grey wolf optimizer (GWO), Archimedes optimization algorithm (AOA), manta ray foraging optimizer (MRFO), sine cosine algorithm (SCA), marine predators algorithm (MPA), and rescue optimization algorithm (SAR). A fair evaluation of the proposed algorithms’ performance is achieved using the same dataset. The performance analysis of the two proposed algorithms is applied in terms of different measures. The experimental results confirm the two proposed algorithms’ superiority over other comparative algorithms. The AHA-ROBL and AHA-OBL produce the optimal number of selected features with the highest degree of precision.
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Wang, Dan, Congcong Xiong, and Wei Huang. "Group Search Optimizer for the Mobile Location Management Problem." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/430705.
Повний текст джерелаАнотація:
We propose a diversity-guided group search optimizer-based approach for solving the location management problem in mobile computing. The location management problem, which is to find the optimal network configurations of management under the mobile computing environment, is considered here as an optimization problem. The proposed diversity-guided group search optimizer algorithm is realized with the aid of diversity operator, which helps alleviate the premature convergence problem of group search optimizer algorithm, a successful optimization algorithm inspired by the animal behavior. To address the location management problem, diversity-guided group search optimizer algorithm is exploited to optimize network configurations of management by minimizing the sum of location update cost and location paging cost. Experimental results illustrate the effectiveness of the proposed approach.
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Yue, Zhihang, Sen Zhang, and Wendong Xiao. "A Novel Hybrid Algorithm Based on Grey Wolf Optimizer and Fireworks Algorithm." Sensors 20, no. 7 (April 10, 2020): 2147. http://dx.doi.org/10.3390/s20072147.
Повний текст джерелаАнотація:
Grey wolf optimizer (GWO) is a meta-heuristic algorithm inspired by the hierarchy of grey wolves (Canis lupus). Fireworks algorithm (FWA) is a nature-inspired optimization method mimicking the explosion process of fireworks for optimization problems. Both of them have a strong optimal search capability. However, in some cases, GWO converges to the local optimum and FWA converges slowly. In this paper, a new hybrid algorithm (named as FWGWO) is proposed, which fuses the advantages of these two algorithms to achieve global optima effectively. The proposed algorithm combines the exploration ability of the fireworks algorithm with the exploitation ability of the grey wolf optimizer (GWO) by setting a balance coefficient. In order to test the competence of the proposed hybrid FWGWO, 16 well-known benchmark functions having a wide range of dimensions and varied complexities are used in this paper. The results of the proposed FWGWO are compared to nine other algorithms, including the standard FWA, the native GWO, enhanced grey wolf optimizer (EGWO), and augmented grey wolf optimizer (AGWO). The experimental results show that the FWGWO effectively improves the global optimal search capability and convergence speed of the GWO and FWA.
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Muhammed, Danial Abdulkareem, Soran A. M. Saeed, and Tarik Ahmed Rashid. "Improved Fitness-Dependent Optimizer Algorithm." IEEE Access 8 (2020): 19074–88. http://dx.doi.org/10.1109/access.2020.2968064.
Повний текст джерела
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Lan, Zhouxin, Qing He, Hongzan Jiao, and Liu Yang. "An Improved Equilibrium Optimizer for Solving Optimal Power Flow Problem." Sustainability 14, no. 9 (April 21, 2022): 4992. http://dx.doi.org/10.3390/su14094992.
Повний текст джерелаАнотація:
With the rapid development of the economy, the quality of power systems has assumed an increasingly prominent influence on people’s daily lives. In this paper, an improved equilibrium optimizer (IEO) is proposed to solve the optimal power flow (OPF) problem. The algorithm uses the chaotic equilibrium pool to enhance the information interaction between individuals. In addition, a nonlinear dynamic generation mechanism is introduced to balance the global search and local development capabilities. At the same time, the improved algorithm uses the golden sine strategy to update the individual position and enhance the ability of the algorithm to jump out of local optimums. Sixteen benchmark test functions, Wilcoxon rank sum test and 30 CEC2014 complex test function optimization results show that the improved algorithm has better global searching ability than the basic equilibrium optimizer, as well as faster convergence and a more accurate solution than other improved equilibrium optimizers and metaheuristic algorithms. Finally, the improved algorithm is applied to the standard IEEE 30-bus test systems for different objectives. The obtained results demonstrate that the improved algorithm has better solutions than other algorithms in the literature for solving the optimal power flow problem.
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Alkasassbeh, Abdelmajeed, Hatem H. Almasaeid, and Bilal Yasin. "Critical Failure Mode Determination of Steel Moment Frames by Plastic Analysis Optimization Principles." Buildings 13, no. 8 (August 7, 2023): 2008. http://dx.doi.org/10.3390/buildings13082008.
Повний текст джерелаАнотація:
Determining the failure or failure mode of structures has long been a challenge for civil engineers. Traditional methods for analyzing structures are costly and complex. Plastic analysis, which involves combining pre-defined mechanisms, offers a less complex approach. However, as the number of potential mechanism combinations, or the search space, increases with the growing complexity of structural members, the effectiveness of this method diminishes. To address this issue, optimizers have been applied in the field of structural engineering to efficiently solve problems with large search spaces. Population-based meta-heuristic algorithms are widely used for their reduced dependency on input parameters. This research focuses on implementing the plastic theory of steel frames using MATLAB software, employing virtual work concepts and pre-defined mechanism combinations. A novel binary dolphin echolocation algorithm is proposed based on the principles of the primary algorithm. This algorithm is then utilized to optimize the plastic analysis method and determine the failure load factor and critical failure mode for sample frames. Additionally, the grey wolf optimizer and whale optimization algorithm are applied to optimize the problem, and the performance of all three algorithms is compared. The results demonstrate that the proposed algorithm yields accurate results with a minor margin of error compared to the other two algorithms.
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Yang, Jie, and Hongyuan Gao. "Cultural Emperor Penguin Optimizer and Its Application for Face Recognition." Mathematical Problems in Engineering 2020 (November 5, 2020): 1–16. http://dx.doi.org/10.1155/2020/9579538.
Повний текст джерелаАнотація:
Face recognition is an important technology with practical application prospect. One of the most popular classifiers for face recognition is support vector machine (SVM). However, selection of penalty parameter and kernel parameter determines the performance of SVM, which is the major challenge for SVM to solve classification problems. In this paper, with a view to obtaining the optimal SVM model for face recognition, a new hybrid intelligent algorithm is proposed for multiparameter optimization problem of SVM, which is a fusion of cultural algorithm (CA) and emperor penguin optimizer (EPO), namely, cultural emperor penguin optimizer (CEPO). The key aim of CEPO is to enhance the exploitation capability of EPO with the help of cultural algorithm basic framework. The performance of CEPO is evaluated by six well-known benchmark test functions compared with eight state-of-the-art algorithms. To verify the performance of CEPO-SVM, particle swarm optimization-based SVM (PSO-SVM), genetic algorithm-based SVM (GA-SVM), CA-SVM, and EPO-SVM, moth-flame optimization-based SVM (MFO-SVM), grey wolf optimizer-based SVM (GWO-SVM), cultural firework algorithm-based SVM (CFA-SVM), and emperor penguin and social engineering optimizer-based SVM (EPSEO-SVM) are used for the comparison experiments. The experimental results confirm that the parameters optimized by CEPO are more instructive to make the classification performance of SVM better in terms of accuracy, convergence rate, stability, robustness, and run time.
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Ahmed, Nessma M., Mohamed Ebeed, Gaber Magdy, Khairy Sayed, Samia Chehbi Gamoura, Ahmed Sayed M. Metwally, and Alaa A. Mahmoud. "A New Optimized FOPIDA-FOIDN Controller for the Frequency Regulation of Hybrid Multi-Area Interconnected Microgrids." Fractal and Fractional 7, no. 9 (September 4, 2023): 666. http://dx.doi.org/10.3390/fractalfract7090666.
Повний текст джерелаАнотація:
This paper proposes a combined feedback and feed-forward control system to support the frequency regulation of multi-area interconnected hybrid microgrids considering renewable energy sources (RESs). The proposed control system is based on a fractional-order proportional-integral-derivative-accelerated (FOPIDA) controller in the feed-forward direction and a fractional-order integral-derivative with a low-pass filter compensator (FOIDN) controller in the feedback direction, referred to as a FOPIDA-FOIDN controller. Moreover, the parameters of the proposed FOPIDA-FOIDN controller (i.e., twelve parameters in each area) are optimally tuned using a proposed hybrid of two metaheuristic optimization algorithms, i.e., hybrid artificial gorilla troops optimizer (AGTO) and equilibrium optimizer (EO), and this hybrid is referred to as HGTOEO. The robustness and reliability of the proposed control system are validated by evaluating its performance in comparison to that of other counterparts’ controllers utilized in the literature, such as PID, FOPID, and tilt integral derivative (TID) controller, under the different operating conditions of the studied system. Furthermore, the proficiency of the proposed HGTOEO algorithm is checked against other powerful optimizers, such as the genetic algorithm, Jaya algorithm, improved Jaya algorithm, multi-verse optimizer, and cost-effective multi-verse optimizer, to optimally design the PID controller for the load frequency control of the studied two-area interconnected microgrid. The MATLAB simulation results demonstrate the viability and dependability of the proposed FOPIDA-FOIDN controller based on the HGTOEO algorithm under a variety of load perturbations and random production of RESs.
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Kitonyi, Peter Mule, and Davies Rene Segera. "Hybrid Gradient Descent Grey Wolf Optimizer for Optimal Feature Selection." BioMed Research International 2021 (August 28, 2021): 1–33. http://dx.doi.org/10.1155/2021/2555622.
Повний текст джерелаАнотація:
Feature selection is the process of decreasing the number of features in a dataset by removing redundant, irrelevant, and randomly class-corrected data features. By applying feature selection on large and highly dimensional datasets, the redundant features are removed, reducing the complexity of the data and reducing training time. The objective of this paper was to design an optimizer that combines the well-known metaheuristic population-based optimizer, the grey wolf algorithm, and the gradient descent algorithm and test it for applications in feature selection problems. The proposed algorithm was first compared against the original grey wolf algorithm in 23 continuous test functions. The proposed optimizer was altered for feature selection, and 3 binary implementations were developed with final implementation compared against the two implementations of the binary grey wolf optimizer and binary grey wolf particle swarm optimizer on 6 medical datasets from the UCI machine learning repository, on metrics such as accuracy, size of feature subsets, F -measure, accuracy, precision, and sensitivity. The proposed optimizer outperformed the three other optimizers in 3 of the 6 datasets in average metrics. The proposed optimizer showed promise in its capability to balance the two objectives in feature selection and could be further enhanced.
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Nasir Ghafil, Hazim, Shaymaa Alsamia, and Károly Jármai. "Fertilization optimization algorithm on CEC2015 and large scale problems." Pollack Periodica 17, no. 1 (March 25, 2022): 24–29. http://dx.doi.org/10.1556/606.2021.00343.
Повний текст джерелаАнотація:
Abstract This work, presents a novel optimizer called fertilization optimization algorithm, which is based on levy flight and random search within a search space. It is a biologically inspired algorithm by the fertilization of the egg in reproduction of mammals. The performance of the algorithm was compared with other optimization algorithms on CEC2015 time expensive benchmarks and large scale optimization problems. Remarkably, the fertilization optimization algorithm has overcome other optimizers in many cases and the examination and comparison results are encouraging to use the fertilization optimization algorithm in other possible applications.
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Euldji, Rafik, Noureddine Batel, Redha Rebhi, Noureddine Kaid, Chutarat Tearnbucha, Weerawat Sudsutad, Giulio Lorenzini, Hijaz Ahmad, Houari Ameur, and Younes Menni. "Optimal Backstepping-FOPID Controller Design for Wheeled Mobile Robot." Journal Européen des Systèmes Automatisés 55, no. 1 (February 28, 2022): 97–107. http://dx.doi.org/10.18280/jesa.550110.
Повний текст джерелаАнотація:
A design of an optimal backstepping fractional order proportional integral derivative (FOPID) controller for handling the trajectory tracking problem of wheeled mobile robots (WMR) is examined in this study. Tuning parameters is a challenging task, to overcome this issue a hybrid meta-heuristic optimization algorithm has been utilized. This evolutionary technique is known as the hybrid whale grey wolf optimizer (HWGO), which benefits from the performances of the two traditional algorithms, the whale optimizer algorithm (WOA) and the grey wolf optimizer (GWO), to obtain the most suitable solution. The efficiency of the HWGO algorithm is compared against those of the original algorithms WOA, GWO, the particle swarm optimizer (PSO), and the hybrid particle swarm grey wolf optimizer (HPSOGWO). The simulation results in MATLAB–Simulink environment revealed the highest efficiency of the suggested HWGO technique compared to the other methods in terms of settling and rise time, overshoot, as well as steady-state error. Finally, a star trajectory is made to illustrate the capability of the mentioned controller.
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He, Xiaoxian, Hanning Chen, Ben Niu, and Jie Wang. "Root Growth Optimizer with Self-Similar Propagation." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/498626.
Повний текст джерелаАнотація:
Most nature-inspired algorithms simulate intelligent behaviors of animals and insects that can move spontaneously and independently. The survival wisdom of plants, as another species of biology, has been neglected to some extent even though they have evolved for a longer period of time. This paper presents a new plant-inspired algorithm which is called root growth optimizer (RGO). RGO simulates the iterative growth behaviors of plant roots to optimize continuous space search. In growing process, main roots and lateral roots, classified by fitness values, implement different strategies. Main roots carry out exploitation tasks by self-similar propagation in relatively nutrient-rich areas, while lateral roots explore other places to seek for better chance. Inhibition mechanism of plant hormones is applied to main roots in case of explosive propagation in some local optimal areas. Once resources in a location are exhausted, roots would shrink away from infertile conditions to preserve their activity. In order to validate optimization effect of the algorithm, twelve benchmark functions, including eight classic functions and four CEC2005 test functions, are tested in the experiments. We compared RGO with other existing evolutionary algorithms including artificial bee colony, particle swarm optimizer, and differential evolution algorithm. The experimental results show that RGO outperforms other algorithms on most benchmark functions.
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Wang, Jingqin, Bingpeng Zhang, and Liang Shu. "Research on Non-Intrusive Load Recognition Method Based on Improved Equilibrium Optimizer and SVM Model." Electronics 12, no. 14 (July 19, 2023): 3138. http://dx.doi.org/10.3390/electronics12143138.
Повний текст джерелаАнотація:
Non-intrusive load monitoring is the main trend of green energy-saving electricity consumption at present, and load identification is a core part of non-invasive load monitoring. A support vector machine (SVM) is commonly used in load recognition, but there are still some problems in the parameter selection, resulting in a low recognition accuracy. Therefore, an improved equilibrium optimizer (IEO) is proposed to optimize the parameters of the SVM. Firstly, household appliance data are collected, and load features are extracted to build a self-test dataset; and secondly, Bernoulli chaotic mapping, adaptive factors and the Levy flight were introduced to improve the traditional equilibrium optimizer algorithm. The performance of the IEO algorithm is validated on test functions, and the SVM is optimized using the IEO algorithm to establish the IEO-SVM load identification model. Finally, the recognition effect of the IEO-SVM model is verified based on the self-test dataset and the public dataset. The results show that the IEO algorithm has good optimization accuracy and convergence speed on the test function. The IEO-SVM load recognition model achieves an accuracy of 99.428% on the self-test dataset and 100% accuracy on the public dataset, and the classification performance is significantly better than other classification algorithms, which can complete the load recognition task well.
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P, Kalaiarasi, and P. Esther Rani. "A Novel Approach for Optimization of Convolution Neural Network with Particle Swarm Optimization and Genetic Algorithm for Face Recognition." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 4s (May 5, 2023): 215–23. http://dx.doi.org/10.17762/ijritcc.v11i4s.6531.
Повний текст джерелаАнотація:
Convolutional neural networks are contemporary deep learning models that are employed for many various applications. In general, the filter size, number of filters, number of convolutional layers, number of fully connected layers, activation function and learning rate are some of the hyperparameters that significantly determine how well a CNN performs.. Generally, these hyperparameters are selected manually and varied for each CNN model depending on the application and dataset. During optimization, CNN could get stuck in local minima. To overcome this, metaheuristic algorithms are used for optimization. In this work, the CNN structure is first constructed with randomly chosen hyperparameters and these parameters are optimized using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. A CNN with optimized hyperparameters is used for face recognition. CNNs optimized with these algorithms use RMSprop optimizer instead of stochastic gradient descent. This RMSprop optimizer helps the CNN reach global minimum quickly. It has been observed that optimizing with GA and PSO improves the performance of CNNs. It also reduces the time it takes for the CNN to reach the global minimum.
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Abasi, Ammar Kamal, Sharif Naser Makhadmeh, Mohammed Azmi Al-Betar, Osama Ahmad Alomari, Mohammed A. Awadallah, Zaid Abdi Alkareem Alyasseri, Iyad Abu Doush, Ashraf Elnagar, Eman H. Alkhammash, and Myriam Hadjouni. "Lemurs Optimizer: A New Metaheuristic Algorithm for Global Optimization." Applied Sciences 12, no. 19 (October 6, 2022): 10057. http://dx.doi.org/10.3390/app121910057.
Повний текст джерелаАнотація:
The Lemur Optimizer (LO) is a novel nature-inspired algorithm we propose in this paper. This algorithm’s primary inspirations are based on two pillars of lemur behavior: leap up and dance hub. These two principles are mathematically modeled in the optimization context to handle local search, exploitation, and exploration search concepts. The LO is first benchmarked on twenty-three standard optimization functions. Additionally, the LO is used to solve three real-world problems to evaluate its performance and effectiveness. In this direction, LO is compared to six well-known algorithms: Salp Swarm Algorithm (SSA), Artificial Bee Colony(ABC), Sine Cosine Algorithm (SCA), Bat Algorithm (BA), Flower Pollination Algorithm (FPA), and JAYA algorithm. The findings show that the proposed algorithm outperforms these algorithms in fourteen standard optimization functions and proves the LO’s robust performance in managing its exploration and exploitation capabilities, which significantly leads LO towards the global optimum. The real-world experimental findings demonstrate how LO may tackle such challenges competitively.
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Deshpande, Ajinkya, and Alexey Melnikov. "Capturing Symmetries of Quantum Optimization Algorithms Using Graph Neural Networks." Symmetry 14, no. 12 (December 7, 2022): 2593. http://dx.doi.org/10.3390/sym14122593.
Повний текст джерелаАнотація:
Quantum optimization algorithms are some of the most promising algorithms expected to show a quantum advantage. When solving quadratic unconstrained binary optimization problems, quantum optimization algorithms usually provide an approximate solution. The solution quality, however, is not guaranteed to be good enough to warrant selecting it over the classical optimizer solution, as it depends on the problem instance. Here, we present an algorithm based on a graph neural network that can choose between a quantum optimizer and classical optimizer using performance prediction. In addition, we present an approach that predicts the optimal parameters of a variational quantum optimizer. We tested our approach with a specific quantum optimizer, the quantum approximate optimization algorithm, applied to the Max-Cut problem, which is an example of a quadratic unconstrained binary optimization problem. We observed qualitatively and quantitatively that graph neural networks are suited for a performance prediction of up to nine-vertex Max-Cut instances with a quantum approximate optimization algorithm with a depth of up to three. For the performance prediction task, the average difference between the actual quantum algorithm performance and the predicted performance is below 19.7% and, for the parameter prediction task, the solution using the predicted parameters is within 2.7% of the optimal parameter solution. Our method therefore has the capacity to find problems that are best suited for quantum solvers. The proposed method and the corresponding algorithm can be used for hybrid quantum algorithm selection.
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Jing-Sen Liu, Jing-Sen Liu, Qing-Qing Liu Jing-Sen Liu, and Fang Zuo Qing-Qing Liu. "A Guided Mutation Grey Wolf Optimizer Algorithm Integrating Flower Pollination Mechanism." 電腦學刊 33, no. 2 (April 2022): 051–67. http://dx.doi.org/10.53106/199115992022043302005.
Повний текст джерелаАнотація:
<p>The basic Grey Wolf Optimizer (GWO) has some shortcomings, for example, the convergence speed is slow, it is easy to fall into local extremum, and high-dimensional optimization ability is poor and so on. In response to these shortcomings, an improved grey wolf algorithm which combines flower pollination mechanism, teaching mechanism and polynomial variation is proposed in this study. The flower pollination mechanism is integrated with GWO algorithm, Levy distribution is introduced into the global search of grey wolf population. And the double random mechanism is added in the local search, for these improvements, this algorithm’s overall optimization performance is improved. The teaching mechanism is added to wolf to improve the algorithm’s convergence speed. Polynomial mutation is applied to the individuals with poor optimization effect to improve the algorithm’s accuracy and its ability to jump out of local extremum. Theoretical analysis shows that the time complexity of the improved algorithm is the same as that of the basic algorithm. The test results of five representative comparison algorithms on multiple different characteristics and different dimensions of CEC2017 benchmark functions and two classical engineering problems show that FMGWO algorithm has high optimization accuracy, convergence speed and solution stability. Therefore, it has obvious advantages in global optimization.</p> <p> </p>
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Jing-Sen Liu, Jing-Sen Liu, Qing-Qing Liu Jing-Sen Liu, and Fang Zuo Qing-Qing Liu. "A Guided Mutation Grey Wolf Optimizer Algorithm Integrating Flower Pollination Mechanism." 電腦學刊 33, no. 2 (April 2022): 051–67. http://dx.doi.org/10.53106/199115992022043302005.
Повний текст джерелаАнотація:
<p>The basic Grey Wolf Optimizer (GWO) has some shortcomings, for example, the convergence speed is slow, it is easy to fall into local extremum, and high-dimensional optimization ability is poor and so on. In response to these shortcomings, an improved grey wolf algorithm which combines flower pollination mechanism, teaching mechanism and polynomial variation is proposed in this study. The flower pollination mechanism is integrated with GWO algorithm, Levy distribution is introduced into the global search of grey wolf population. And the double random mechanism is added in the local search, for these improvements, this algorithm’s overall optimization performance is improved. The teaching mechanism is added to wolf to improve the algorithm’s convergence speed. Polynomial mutation is applied to the individuals with poor optimization effect to improve the algorithm’s accuracy and its ability to jump out of local extremum. Theoretical analysis shows that the time complexity of the improved algorithm is the same as that of the basic algorithm. The test results of five representative comparison algorithms on multiple different characteristics and different dimensions of CEC2017 benchmark functions and two classical engineering problems show that FMGWO algorithm has high optimization accuracy, convergence speed and solution stability. Therefore, it has obvious advantages in global optimization.</p> <p> </p>
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Madhiarasan, Manoharan, Daniel T. Cotfas, and Petru A. Cotfas. "Barnacles Mating Optimizer Algorithm to Extract the Parameters of the Photovoltaic Cells and Panels." Sensors 22, no. 18 (September 15, 2022): 6989. http://dx.doi.org/10.3390/s22186989.
Повний текст джерелаАнотація:
The goal of this research is to accurately extract the parameters of the photovoltaic cells and panels and to reduce the extracting time. To this purpose, the barnacles mating optimizer algorithm is proposed for the first time to extract the parameters. To prove that the algorithm succeeds in terms of accuracy and quickness, it is applied to the following photovoltaic cells: monocrystalline silicon, amorphous silicon, RTC France, and the PWP201, Sharp ND-R250A5, and Kyocera KC200GT photovoltaic panels. The mathematical models used are single and double diodes. Datasets for these photovoltaic cells and panels were used, and the results obtained for the parameters were compared with the ones obtained using other published methods and algorithms. Six statistical tests were used to analyze the performance of the barnacles mating optimizer algorithm: the root mean square error mean, absolute percentage error, mean square error, mean absolute error, mean bias error, and mean relative error. The results of the statistical tests show that the barnacles mating optimizer algorithm outperforms several algorithms. The tests about the computational time were made using two computer configurations. Using the barnacles mating optimizer algorithm, the computational time decreases more than 30 times in comparison with one of the best algorithms, hybrid successive discretization algorithm.
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Mohammed, Reham H., Ahmed M. Ismaiel, Basem E. Elnaghi, and Mohamed E. Dessouki. "African vulture optimizer algorithm based vector control induction motor drive system." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 3 (June 1, 2023): 2396. http://dx.doi.org/10.11591/ijece.v13i3.pp2396-2408.
Повний текст джерелаАнотація:
<span lang="EN-US">This study describes a new optimization approach for three-phase induction motor speed drive to minimize the integral square error for speed controller and improve the dynamic speed performance. The new proposed algorithm, African vulture optimizer algorithm (AVOA) optimizes internal controller parameters of a fuzzy like proportional differential (PD) speed controller. The AVOA is notable for its ease of implementation, minimal number of design parameters, high convergence speed, and low computing burden. This study compares fuzzy-like PD speed controllers optimized with AVOA to adaptive fuzzy logic speed regulators, fuzzy-like PD optimized with genetic algorithm (GA), and proportional integral (PI) speed regulators optimized with AVOA to provide speed control for an induction motor drive system. The drive system is simulated using MATLAB/Simulink and laboratory prototype is implemented using DSP-DS1104 board. The results demonstrate that the suggested fuzzy-like PD speed controller optimized with AVOA, with a speed steady state error performance of 0.5% compared to the adaptive fuzzy logic speed regulator’s 0.7%, is the optimum alternative for speed controller. The results clarify the effectiveness of the controllers based on fuzzy like PD speed controller optimized with AVOA for each performance index as it provides lower overshoot, lowers rising time, and high dynamic response.</span>
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Ali, Mohammed Hamouda, Ahmed M. A. Soliman, and Salah K. Elsayed. "Optimal power flow using archimedes optimizer algorithm." International Journal of Power Electronics and Drive Systems (IJPEDS) 13, no. 3 (September 1, 2022): 1390. http://dx.doi.org/10.11591/ijpeds.v13.i3.pp1390-1405.
Повний текст джерелаАнотація:
<span>This article proposes a new metaheuristic algorithm called Archimedes optimization algorithm (AOA) for solving optimization problems of optimal power flow (OPF) utilizing the renewable energy sources (RES) for minimizing different single-objective and multi-objective functions based on minimization of fuel cost, power losses of transmission lines, emission and voltage profile improvement. Also, mathematical formulation of (OPF) is introduced by converting the function with multiple objectives based on price and weighting parameters into a single objective function. Also, the effect of optimal RES is merged into the OPF problem. Notably, optimal RES placement yields even more effective solution. AOA was inspired by an intriguing physical law known as Archimedes' Principle. To prove the effectiveness of the AOA proposed algorithm, it compared with different recent algorithms for solving the optimal power flow problems and testing them to one standard system of the IEEE30-bus test system. The superiority of the proposed AOA algorithm is proven also by applying them on the IEEE30-bus modified system with optimal allocation of renewable energy source (RES). The results demonstrate that the proposed algorithm is more successful and efficient than the other optimization methods in the title of resolving OPF problems.</span>
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Long, Nguyen Ngoc, Nguyen Huu Quyet, Nguyen Xuan Tung, Bui Tien Thanh, and Tran Ngoc Hoa. "Damage Identification of Suspension Footbridge Structures using New Hunting-based Algorithms." Engineering, Technology & Applied Science Research 13, no. 4 (August 9, 2023): 11085–90. http://dx.doi.org/10.48084/etasr.5983.
Повний текст джерелаАнотація:
Metaheuristic algorithms have been applied to tackle challenging optimization problems in various domains, such as health, education, manufacturing, and biology. In particular, the field of Structural Health Monitoring (SHM) has received significant interest, particularly in the area of damage identification in structures. Popular optimization algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Cuckoo Search (CS), Teaching Learning Based Optimization (TLBO), Artificial Hummingbird Algorithm (AHA), Moth Flame Optimizer (MFO), among others, have been employed to address this problem. However, notwithstanding the wide recognition of the current algorithms, their constraints are commonly acknowledged. Hence, this article advocates for the adoption of innovative hunting-inspired algorithms, namely the Ant Lion Optimizer (ALO), African Vulture Optimization Algorithm (AVOA), Grey Wolf Optimizer (GWO), Marine Predator Algorithm (MPA), and Whale Optimization Algorithm (WOA), which emulate the behaviors of wildlife species, to discern the areas and magnitudes of deterioration in a suspension footbridge. Moreover, in order to reduce computational time, only natural frequencies are applied as objective functions. The obtained results indicate that all the utilized algorithms can accurately detect the damages in the considered structure.
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Abd-El Wahab, Ahmed M., Salah Kamel, Mohamed H. Hassan, Mohamed I. Mosaad, and Tarek A. AbdulFattah. "Optimal Reactive Power Dispatch Using a Chaotic Turbulent Flow of Water-Based Optimization Algorithm." Mathematics 10, no. 3 (January 24, 2022): 346. http://dx.doi.org/10.3390/math10030346.
Повний текст джерелаАнотація:
In this study, an optimization algorithm called chaotic turbulent flow of water-based optimization (CTFWO) algorithm is proposed to find the optimal solution for the optimal reactive power dispatch (ORPD) problem. The ORPD is formulated as a complicated, mixed-integer nonlinear optimization problem, comprising control variables which are discrete and continuous. The CTFWO algorithm is used to minimize voltage deviation (VD) and real power loss (P_loss) for IEEE 30-bus and IEEE 57-bus power systems. These goals can be achieved by obtaining the optimized voltage values of the generator, the transformer tap changing positions, and the reactive compensation. In order to evaluate the ability of the proposed algorithm to obtain ORPD problem solutions, the results of the proposed CTFWO algorithm are compared with different algorithms, including artificial ecosystem-based optimization (AEO), the equilibrium optimizer (EO), the gradient-based optimizer (GBO), and the original turbulent flow of water-based optimization (TFWO) algorithm. These are also compared with the results of the evaluated performance of various methods that are used in many recent papers. The experimental results show that the proposed CTFWO algorithm has superior performance, and is competitive with many state-of-the-art algorithms outlined in some of the recent studies in terms of solution accuracy, convergence rate, and stability.
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Dehghani, Mohammad, Zeinab Montazeri, Gaurav Dhiman, O. P. Malik, Ruben Morales-Menendez, Ricardo A. Ramirez-Mendoza, Ali Dehghani, Josep M. Guerrero, and Lizeth Parra-Arroyo. "A Spring Search Algorithm Applied to Engineering Optimization Problems." Applied Sciences 10, no. 18 (September 4, 2020): 6173. http://dx.doi.org/10.3390/app10186173.
Повний текст джерелаАнотація:
At present, optimization algorithms are used extensively. One particular type of such algorithms includes random-based heuristic population optimization algorithms, which may be created by modeling scientific phenomena, like, for example, physical processes. The present article proposes a novel optimization algorithm based on Hooke’s law, called the spring search algorithm (SSA), which aims to solve single-objective constrained optimization problems. In the SSA, search agents are weights joined through springs, which, as Hooke’s law states, possess a force that corresponds to its length. The mathematics behind the algorithm are presented in the text. In order to test its functionality, it is executed on 38 established benchmark test functions and weighed against eight other optimization algorithms: a genetic algorithm (GA), a gravitational search algorithm (GSA), a grasshopper optimization algorithm (GOA), particle swarm optimization (PSO), teaching–learning-based optimization (TLBO), a grey wolf optimizer (GWO), a spotted hyena optimizer (SHO), as well as an emperor penguin optimizer (EPO). To test the SSA’s usability, it is employed on five engineering optimization problems. The SSA delivered better fitting results than the other algorithms in unimodal objective function, multimodal objective functions, CEC 2015, in addition to the optimization problems in engineering.
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Mittal, Nitin, Urvinder Singh, and Balwinder Singh Sohi. "Modified Grey Wolf Optimizer for Global Engineering Optimization." Applied Computational Intelligence and Soft Computing 2016 (2016): 1–16. http://dx.doi.org/10.1155/2016/7950348.
Повний текст джерелаАнотація:
Nature-inspired algorithms are becoming popular among researchers due to their simplicity and flexibility. The nature-inspired metaheuristic algorithms are analysed in terms of their key features like their diversity and adaptation, exploration and exploitation, and attractions and diffusion mechanisms. The success and challenges concerning these algorithms are based on their parameter tuning and parameter control. A comparatively new algorithm motivated by the social hierarchy and hunting behavior of grey wolves is Grey Wolf Optimizer (GWO), which is a very successful algorithm for solving real mechanical and optical engineering problems. In the original GWO, half of the iterations are devoted to exploration and the other half are dedicated to exploitation, overlooking the impact of right balance between these two to guarantee an accurate approximation of global optimum. To overcome this shortcoming, a modified GWO (mGWO) is proposed, which focuses on proper balance between exploration and exploitation that leads to an optimal performance of the algorithm. Simulations based on benchmark problems and WSN clustering problem demonstrate the effectiveness, efficiency, and stability of mGWO compared with the basic GWO and some well-known algorithms.
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Kumar, Shailender, Kamran Sayeed, Anubhav Chhikara, and Durin Dai. "Forecasting Energy Demand of India Using Integrated Grey Wolf Optimizer." Journal of Computational and Theoretical Nanoscience 17, no. 8 (August 1, 2020): 3605–12. http://dx.doi.org/10.1166/jctn.2020.9239.
Повний текст джерелаАнотація:
In this paper we provide a new methodology for estimating the future primary energy demands of India. Firstly, we propose a new algorithm known as Integrated Grey wolf Optimizer. This new algorithm is an improvement over Grey wolf optimizer to deal with multimodal functions. Economic factors such as GDP (Gross Domestic Product), Population, Coal production and Petroleum production are used as mathematical parameters for our objective function. The coefficients of this two-form model (i.e., Linear and Quadratic) are optimized using the new Integrated Grey wolf optimizer. The highlight of this extract is the new Integrated version of grey wolf optimizer, which improves the exploration capability of the algorithm to deal with local minima stagnation. The results of this modified version are better than traditional Grey wolf optimizer and provides better accuracy and less errors. The last 14 years of historical information of India are used as datasets for the respective parameters. Coefficients obtained after the optimization are used for forecasting in three different cases which are Rapid (7.5% rise in GDP), Moderate (6.5%) and (5.5%) Slow growth of country.
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Ali, Mona A. S., Fathimathul Rajeena P. P., and Diaa Salama Abd Elminaam. "An Efficient Heap Based Optimizer Algorithm for Feature Selection." Mathematics 10, no. 14 (July 8, 2022): 2396. http://dx.doi.org/10.3390/math10142396.
Повний текст джерелаАнотація:
The heap-based optimizer (HBO) is an innovative meta-heuristic inspired by human social behavior. In this research, binary adaptations of the heap-based optimizer B_HBO are presented and used to determine the optimal features for classifications in wrapping form. In addition, HBO balances exploration and exploitation by employing self-adaptive parameters that can adaptively search the solution domain for the optimal solution. In the feature selection domain, the presented algorithms for the binary Heap-based optimizer B_HBO are used to find feature subsets that maximize classification performance while lowering the number of selected features. The textitk-nearest neighbor (textitk-NN) classifier ensures that the selected features are significant. The new binary methods are compared to eight common optimization methods recently employed in this field, including Ant Lion Optimization (ALO), Archimedes Optimization Algorithm (AOA), Backtracking Search Algorithm (BSA), Crow Search Algorithm (CSA), Levy flight distribution (LFD), Particle Swarm Optimization (PSO), Slime Mold Algorithm (SMA), and Tree Seed Algorithm (TSA) in terms of fitness, accuracy, precision, sensitivity, F-score, the number of selected features, and statistical tests. Twenty datasets from the UCI repository are evaluated and compared using a set of evaluation indicators. The non-parametric Wilcoxon rank-sum test was used to determine whether the proposed algorithms’ results varied statistically significantly from those of the other compared methods. The comparison analysis demonstrates that B_HBO is superior or equivalent to the other algorithms used in the literature.
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Tariq Ibrahim, Hadeel, Wamidh Jalil Mazher, and Enas Mahmood Jassim. "Feature Selection: Binary Harris Hawk Optimizer Based Biomedical Datasets." Inteligencia Artificial 25, no. 70 (November 9, 2022): 33–49. http://dx.doi.org/10.4114/intartif.vol25iss70pp33-49.
Повний текст джерелаАнотація:
Feature selection (FS) is an essential preprocessing step in utmost solutions for the high-dimensional problem to reduce the number of features by deleting irrelevant and redundant data that preserve a suitable grade of classification accuracy. Feature selection can be treated as an optimization problem. Heuristic optimization algorithms are hopeful approaches to solve feature selection problems because of their difficulty, especially in high-dimensional data. Binary Harris hawk optimization (BHHO) is one of the lately suggested metaheuristic algorithms that has been demonstrated to be used more efficiently in facing some optimization problems. Support vector machines (SVMs) are a vital technique that are employed competently to resolve classification issues. We modified the BHHO algorithm with SVM classifier to solve the feature selection issue. This study suggests BHHO-FS to fix the feature selection problem in biomedical datasets. We ran the proposed approach BHHO-FS on real biomedical datasets with 17 types of cancer for Iraqi patients in 2010-2012. The experimental results demonstrate the supremacy of the proposed BHHO-FS in terms of three performance metrics: feature selection accuracy, runtime and number of selected features compared to four other state-of-art algorithms: Fire Fly (FF) algorithm, Genetic Algorithm (GA), Grasshopper Optimization Algorithm (GOA) and Particle Swarm Algorithm (PSO). Comparative experiments designate the importance of the proposed approach in comparison with the other four mentioned algorithms. The implementation of the proposed BHHO-FS approach on 17 datasets for different types of cancers reveals 99.967% average accuracy.
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Qi, Xiangbo, Zhonghu Yuan, and Yan Song. "A Hybrid Pathfinder Optimizer for Unconstrained and Constrained Optimization Problems." Computational Intelligence and Neuroscience 2020 (May 29, 2020): 1–25. http://dx.doi.org/10.1155/2020/5787642.
Повний текст джерелаАнотація:
Hybridization of metaheuristic algorithms with local search has been investigated in many studies. This paper proposes a hybrid pathfinder algorithm (HPFA), which incorporates the mutation operator in differential evolution (DE) into the pathfinder algorithm (PFA). The proposed algorithm combines the searching ability of both PFA and DE. With a test on a set of twenty-four unconstrained benchmark functions including both unimodal continuous functions, multimodal continuous functions, and composition functions, HPFA is proved to have significant improvement over the pathfinder algorithm and the other comparison algorithms. Then HPFA is used for data clustering, constrained problems, and engineering design problems. The experimental results show that the proposed HPFA got better results than the other comparison algorithms and is a competitive approach for solving partitioning clustering, constrained problems, and engineering design problems.
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Mustaffa, Zuriani, Mohd Herwan Sulaiman, and Yuhanis Yusof. "An Application of Grey Wolf Optimizer for Commodity Price Forecasting." Applied Mechanics and Materials 785 (August 2015): 473–78. http://dx.doi.org/10.4028/www.scientific.net/amm.785.473.
Повний текст джерелаАнотація:
Over the recent decades, there are many nature inspired optimization algorithms have been introduced. In this study, a newly algorithm namely Grey Wolf Optimizer (GWO) is employed for gasoline price forecasting. The performance of GWO is compared against the results produced by Artificial Bee Colony (ABC) algorithm and Differential Evolution (DE) algorithm. Measured based on Mean Absolute Percentage Error (MAPE) and prediction accuracy, the GWO is proven to produce significantly better results as compared to the identified algorithms.
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Tawhid, Mohamed A., and Ahmed F. Ali. "Multidirectional Grey Wolf Optimizer Algorithm for Solving Global Optimization Problems." International Journal of Computational Intelligence and Applications 17, no. 04 (December 2018): 1850022. http://dx.doi.org/10.1142/s1469026818500220.
Повний текст джерелаАнотація:
In this paper, we propose a new hybrid population-based meta-heuristics algorithm inspired by grey wolves in order to solve integer programming and minimax problems. The proposed algorithm is called Multidirectional Grey Wolf Optimizer (MDGWO) algorithm. In the proposed algorithm, we try to accelerate the standard grey wolf optimizer algorithm (GWO) by invoking the multidirectional search method with it in order to accelerate the search instead of letting the standard GWO run for more iterations without significant improvement in the results. MDGWO starts the search by applying the standard GWO search for a number of iterations, and then the best-obtained solution is passed to the multidirectional search method as an intensification process in order to accelerate the search and overcome the slow convergence of the standard GWO algorithm. We test MDGWO algorithm on seven integer programming problems and 10 minimax problems. Moreover, we compare against 11 algorithms for solving integer programming problems and 10 algorithms for solving minimax problems. Furthermore, we show the efficiency of the proposed algorithm and its ability to solve integer and minimax optimization problems in reasonable time by giving several results of the experiments.
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Lu, Shi Lei, and Shun Zheng Yu. "An Improved Swarm Optimizer for RFID Network Scheduling." Applied Mechanics and Materials 427-429 (September 2013): 600–605. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.600.
Повний текст джерелаАнотація:
Optimization of network scheduling is a significant way to improve the performance of the radio frequency identification (RFID) networks. This paper proposes an improved particle swarm optimization algorithm (PSO). It uses an animal foraging strategy to maintain a high diversity of swarms, which can protect them from premature convergence. The proposed algorithm is used to optimize the network performance by determining the optimal work status of readers. It has been tested in two different RFID network topologies to evaluate the effectivenesss. The simulation results reveal that the proposed algorithm outperforms the other algorithms in terms of optimization precision.
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(Rahkar Farshi), Taymaz Akan, Saeid Agahian, and Rahim Dehkharghani. "BinBRO: Binary Battle Royale Optimizer algorithm." Expert Systems with Applications 195 (June 2022): 116599. http://dx.doi.org/10.1016/j.eswa.2022.116599.
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Faramarzi, Afshin, Mohammad Heidarinejad, Brent Stephens, and Seyedali Mirjalili. "Equilibrium optimizer: A novel optimization algorithm." Knowledge-Based Systems 191 (March 2020): 105190. http://dx.doi.org/10.1016/j.knosys.2019.105190.
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ZHANG, Baohang, Haichuan YANG, Tao ZHENG, Rong-Long WANG, and Shangce GAO. "A Non-Revisiting Equilibrium Optimizer Algorithm." IEICE Transactions on Information and Systems E106.D, no. 3 (March 1, 2023): 365–73. http://dx.doi.org/10.1587/transinf.2022edp7119.
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Jiang, Hao, Jiuxiang Song, Baowei Zhang, and Yonghua Wang. "Yarn unevenness prediction using generalized regression neural network under various optimization algorithms." Journal of Engineered Fibers and Fabrics 17 (January 2022): 155892502210930. http://dx.doi.org/10.1177/15589250221093019.
Повний текст джерелаАнотація:
Unevenness is one of the important parameters for evaluating yarn quality, but the current prediction accuracy of yarn unevenness is low. One of the important reasons is that there are few sample dataset for yarn unevenness prediction. For this problem, this paper applies generalized regression neural network to predict the unevenness of the yarn. Then, the generalized regression neural network is optimized by using particle swarm optimization, fruit fly optimization algorithm, and gray wolf optimizer, respectively. Finally, the optimized models were experimentally validated for their effectiveness. The experimental results show that the generalized regression neural network optimized by gray wolf optimizer has the best effect and the fastest optimization speed; the generalized regression neural network optimized by particle swarm optimization algorithm has the middle optimization speed; the generalized regression neural network optimized by fruit fly optimization algorithm has the worst effect and the slowest optimization speed.