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1
Masrom, Suraya, Abdullah Sani Abd Rahman, Nasiroh Omar, and Suriani Rapa’ee. "PSO-GAScript: A Domain-specific Scripting Language for Meta-heuristics Algorithm." International Journal of Emerging Technology and Advanced Engineering 12, no. 7 (July 4, 2022): 86–93. http://dx.doi.org/10.46338/ijetae0722_09.
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PSO-GAScript is a domain-specific scripting language designed to support easy and rapid implementation of meta-heuristics algorithms focused on Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The programming language has been developed to allow the hybridization of the two meta-heuristics algorithms. Hybridizations between PSO and GA are proven to be a comprehensive tool for solving different kinds of optimization problems. Moreover, the two algorithms have achieved a remarkable improvement from the adaptation of dynamic parameterization. Nevertheless, implementing the suitable hybrid algorithms is a considerably difficult, which in most cases is time consuming. To the best of our knowledge, the existing tools are not adequately designed to enable users to easily develop the meta-heuristics hybridization of PSO-GA with dynamic parameterizations. This paper presents the fundamental research methodology of domain-specific scripting language from the language design, constructs and evaluations focused on the case of PSO-GA hybridization and dynamic parameterizations. The PSO-GAScript are shown to easily use with minimal number of codes lines and concisely describe the meta-heuristics algorithms in a directly publishable form.
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Gautam, Divya. "SECURING MOBILE ADHOC NETWORKS AND CLOUD ENVIRONMENT." International Journal of Engineering Technologies and Management Research 5, no. 2 (April 27, 2020): 84–89. http://dx.doi.org/10.29121/ijetmr.v5.i2.2018.617.
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Securing mobile adhoc networks and cloud environment in opposition to denial of service attack by examine and predict the network traffic. DDoS attacks are most important threats next to the accessibility of cloud services. Prevention mechanisms to protect next to DDoS attacks are not forever efficient on their own. Unite dissimilar method (load balancing, throttling and Honey pots) to build hybrid defense method, in meticulous with dissimilar cloud computing layers, is extremely recommended. In this paper, a variety of DDoS attacks have been presented. We as well highlighted the defense methods to counter attack dissimilar types DDoS attacks in the cloud environment. This paper proposes SVM-based algorithm to anomaly intrusion detection. A multiclass SVM algorithm with parameter optimized by PSO (MSVM-PSO) is accessible to find out a classifier to detect multiclass attacks. This paper will extend the proposed techniques to new computing environments Mobile Ad-Hoc Networks to detect anomalous physical or virtual nodes.
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Wang, Yuheng, Kashif Habib, Abdul Wadood, and Shahbaz Khan. "The Hybridization of PSO for the Optimal Coordination of Directional Overcurrent Protection Relays of the IEEE Bus System." Energies 16, no. 9 (April 26, 2023): 3726. http://dx.doi.org/10.3390/en16093726.
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The hybridization of PSO for the Optimal Coordination of Directional Overcurrent Protection Relays (DOPR) of the IEEE bus system proposes a new method for coordinating directional overcurrent protection relays in power systems. The method combines the hybrid particle swarm optimization (HPSO) algorithm and a heuristic PSO algorithm to find the minimum total operating time of the directional overcurrent protection relays with speed and accuracy. The proposed method is tested on the IEEE 4-bus, 6-bus, and 8-bus systems, and the results are compared with those obtained using traditional coordination methods. The collected findings suggest that the proposed method may produce better coordination and faster operation of DOPRs than the previous methods, with an increase of up to 74.9% above the traditional technique. The hybridization of the PSO algorithm and heuristic PSO algorithm offers a promising approach to optimize power system protection.
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Jain, Meetu, Vibha Saihjpal, Narinder Singh, and Satya Bir Singh. "An Overview of Variants and Advancements of PSO Algorithm." Applied Sciences 12, no. 17 (August 23, 2022): 8392. http://dx.doi.org/10.3390/app12178392.
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Particle swarm optimization (PSO) is one of the most famous swarm-based optimization techniques inspired by nature. Due to its properties of flexibility and easy implementation, there is an enormous increase in the popularity of this nature-inspired technique. Particle swarm optimization (PSO) has gained prompt attention from every field of researchers. Since its origin in 1995 till now, researchers have improved the original Particle swarm optimization (PSO) in varying ways. They have derived new versions of it, such as the published theoretical studies on various parameters of PSO, proposed many variants of the algorithm and numerous other advances. In the present paper, an overview of the PSO algorithm is presented. On the one hand, the basic concepts and parameters of PSO are explained, on the other hand, various advances in relation to PSO, including its modifications, extensions, hybridization, theoretical analysis, are included.
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Michaloglou, Alkmini, and Nikolaos L. Tsitsas. "A Brain Storm and Chaotic Accelerated Particle Swarm Optimization Hybridization." Algorithms 16, no. 4 (April 13, 2023): 208. http://dx.doi.org/10.3390/a16040208.
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Brain storm optimization (BSO) and particle swarm optimization (PSO) are two popular nature-inspired optimization algorithms, with BSO being the more recently developed one. It has been observed that BSO has an advantage over PSO regarding exploration with a random initialization, while PSO is more capable at local exploitation if given a predetermined initialization. The two algorithms have also been examined as a hybrid. In this work, the BSO algorithm was hybridized with the chaotic accelerated particle swarm optimization (CAPSO) algorithm in order to investigate how such an approach could serve as an improvement to the stand-alone algorithms. CAPSO is an advantageous variant of APSO, an accelerated, exploitative and minimalistic PSO algorithm. We initialized CAPSO with BSO in order to study the potential benefits from BSO’s initial exploration as well as CAPSO’s exploitation and speed. Seven benchmarking functions were used to compare the algorithms’ behavior. The chosen functions included both unimodal and multimodal benchmarking functions of various complexities and sizes of search areas. The functions were tested for different numbers of dimensions. The results showed that a properly tuned BSO–CAPSO hybrid could be significantly more beneficial over stand-alone BSO, especially with respect to computational time, while it heavily outperformed stand-alone CAPSO in the vast majority of cases.
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Adhikari, Ratnadip, and R. K. Agrawal. "Hybridization of Artificial Neural Network and Particle Swarm Optimization Methods for Time Series Forecasting." International Journal of Applied Evolutionary Computation 4, no. 3 (July 2013): 75–90. http://dx.doi.org/10.4018/jaec.2013070107.
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Recently, Particle Swarm Optimization (PSO) has evolved as a promising alternative to the standard backpropagation (BP) algorithm for training Artificial Neural Networks (ANNs). PSO is advantageous due to its high search power, fast convergence rate and capability of providing global optimal solution. In this paper, the authors explore the improvements in forecasting accuracies of feedforward as well as recurrent neural networks through training with PSO. Three widely popular versions of the basic PSO algorithm, viz. Trelea-I, Trelea-II and Clerc-Type1 are used to train feedforward ANN (FANN) and Elman ANN (EANN) models. A novel nonlinear hybrid architecture is proposed to incorporate the training strengths of all these three PSO algorithms. Experiments are conducted on four real-world time series with the three forecasting models, viz. Box-Jenkins, FANN and EANN. Obtained results clearly demonstrate the superior forecasting performances of all three PSO algorithms over their BP counterpart for both FANN as well as EANN models. Both PSO and BP based neural networks also achieved notably better accuracies than the statistical Box-Jenkins methods. The forecasting performances of the neural network models are further improved through the proposed hybrid PSO framework.
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Lenin, K. "HYBRIDIZATION OF ANT COLONY ALGORITHM AND PARTICLE SWARM OPTIMIZATION ALGORITHM FOR REDUCTION OF REAL POWER LOSS." International Journal of Research -GRANTHAALAYAH 6, no. 12 (December 31, 2018): 121–27. http://dx.doi.org/10.29121/granthaalayah.v6.i12.2018.1092.
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In this work Ant colony optimization algorithm (ACO) & particle swarm optimization (PSO) algorithm has been hybridized (called as APA) to solve the optimal reactive power problem. In this algorithm, initial optimization is achieved by particle swarm optimization algorithm and then the optimization process is carry out by ACO around the best solution found by PSO to finely explore the design space. In order to evaluate the proposed APA, it has been tested on IEEE 300 bus system and compared to other standard algorithms. Simulations results show that proposed APA algorithm performs well in reducing the real power loss.
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Bi, Ya, Anthony Lam, Huiqun Quan, Hui Liu, and Cunfa Wang. "A comprehensively improved particle swarm optimization algotithm to guarantee particle activity." Izvestiya vysshikh uchebnykh zavedenii. Fizika, no. 5 (2021): 94–101. http://dx.doi.org/10.17223/00213411/64/5/94.
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The particle swarm optimization algorithm has the disadvantages, for instance, the convergence viscosity of the algorithm is reduced at the post evolution phase, the optimization search efficiency is reduced, the algorithm is easy to be inserted with local extremum during the calculation of complex problem of high-dimensional multiple extremum, and the convergence thereof is low. As to the disadvantage of the PSO, we proposed a particle swarm optimization of comprehensive improvement strategy, which is a simple particle swarm optimization with dynamic adaptive hybridization of extremum disturbance and cross (ecds-PSO algorithm). This new comprehensive improved particle swarm algorithm discards the particle velocity and reduces the PSO from the second order to the first order difference equation. The evolutionary process is only controlled by the variables of the particles position. The hybridization operation of increasing the extremum disturbance and introducing genetic algorithm can accelerate the particles to overstep the local extremum. The mathematical derivation and a plurality of comparative experiment provide us the following information: the improved particle swarm optimization is a simple and effective optimization algorithm which can improve the algorithm accuracy, convergence viscosity and ability of avoiding the local extremum, and effectively reduce the calculation complexity.
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Zhang, Yudong, Shuihua Wang, and Genlin Ji. "A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications." Mathematical Problems in Engineering 2015 (2015): 1–38. http://dx.doi.org/10.1155/2015/931256.
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Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.
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Xiao, Heng, Yokoya, and Toshiharu Hatanaka. "Multifactorial Particle Swarm Optimization Enhanced by Hybridization With Firefly Algorithm." International Journal of Swarm Intelligence Research 12, no. 3 (July 2021): 172–87. http://dx.doi.org/10.4018/ijsir.2021070108.
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In recent years, evolutionary multitasking has received attention in the evolutionary computation community. As an evolutionary multifactorial optimization method, multifactorial evolutionary algorithm (MFEA) is proposed to realize evolutionary multitasking. One concept called the skill factor is introduced to assign a preferred task for each individual in MFEA. Then, based on the skill factor, there are some multifactorial optimization solvers including swarm intelligence that have been developed. In this paper, a PSO-FA hybrid model with a model selection mechanism triggered by updating the personal best memory is applied to multifactorial optimization. The skill factor reassignment is introduced in this model to enhance the search capability of the hybrid swarm model. Then numerical experiments are carried out by using nine benchmark problems based on typical multitask situations and by comparing with a simple multifactorial PSO to show the effectiveness of the proposed method.
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Cherki, Imene, Abdelkader Chaker, Zohra Djidar, Naima Khalfallah, and Fadela Benzergua. "A Sequential Hybridization of Genetic Algorithm and Particle Swarm Optimization for the Optimal Reactive Power Flow." Sustainability 11, no. 14 (July 16, 2019): 3862. http://dx.doi.org/10.3390/su11143862.
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In this paper, the problem of the Optimal Reactive Power Flow (ORPF) in the Algerian Western Network with 102 nodes is solved by the sequential hybridization of metaheuristics methods, which consists of the combination of both the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO). The aim of this optimization appears in the minimization of the power losses while keeping the voltage, the generated power, and the transformation ratio of the transformers within their real limits. The results obtained from this method are compared to those obtained from the two methods on populations used separately. It seems that the hybridization method gives good minimizations of the power losses in comparison to those obtained from GA and PSO, individually, considered. However, the hybrid method seems to be faster than the PSO but slower than GA.
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Sahli, Z., A. Hamouda, S. Sayah, D. Trentesaux, and A. Bekrar. "Efficient Hybrid Algorithm Solution for Optimal Reactive Power Flow Using the Sensitive Bus Approach." Engineering, Technology & Applied Science Research 12, no. 1 (February 12, 2022): 8210–16. http://dx.doi.org/10.48084/etasr.4680.
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This paper presents the design and application of an efficient hybrid algorithm for solving the Optimal Reactive Power Flow (ORPF) problem. The ORPF is formulated as a nonlinear constrained optimization problem where the active power losses must be minimized. The proposed approach is based on the hybridization of Particle Swarm Optimization (PSO) and Tabu-Search (TS) technique. The proposed PSO-TS approach is used to find the settings of the control variables (i.e. generation bus voltages, transformer taps, and shunt capacitor sizes) which minimize transmission active power losses. The bus locations of the shunt capacitors are identified according to sensitive buses. To show the effectiveness of the proposed method, it is applied to the IEEE 30 bus benchmark test system and is compared with PSO and TS without hybridization, along with some other published approaches. The obtained results reveal the effectiveness of the proposed method in dealing with the highly nonlinear constrained nature of the ORPF problem.
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Gayatri, R., and N. Baskar. "Evaluating Process Parameters of Multi-Pass Turning Process Using Hybrid Genetic Simulated Swarm Algorithm." Journal of Advanced Manufacturing Systems 14, no. 04 (September 29, 2015): 215–33. http://dx.doi.org/10.1142/s0219686715500146.
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Evolutionary computation is one of the important problems solving method frequently used by the researchers. The choice of an algorithm to optimize the problem is determined by some sort of reliability of the researcher with that technique. To overcome the limitations in individual algorithms and to achieve synergic effects, fusion or hybridization of two or more algorithms is carried out. Hybrid algorithms have gained popularity because there is no evidence that a universal optimal strategy exists for solving optimization problems. In this work, a hybrid algorithm called hybrid genetic simulated swarm (HGSS) algorithm is proposed to optimize the parameters of multi-pass turning operation. The HGSS algorithm is a fusion of genetic algorithm (GA), simulated annealing (SA) and particle swarm optimization (PSO) algorithms. The objectives of this work are (i) to explore and exploit the problem search space through hybridization, (ii) to justify that proficient hybridization of evolutionary algorithms (EAs) will yield an efficient means to solve the optimization problems. In this work, the EAs such as GA, SA and PSO are also applied to optimize parameters and results are compared with HGSS. The results of the proposed work HGSS are very effective than other algorithms.
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Junian, Wahyu Eko, and Hendra Grandis. "HYBRID PARTICLE SWARM OPTIMIZATION AND GREY WOLF OPTIMIZER ALGORITHM FOR CONTROLLED SOURCE AUDIO-FREQUENCY MAGNETOTELLURICS (CSAMT) ONE-DIMENSIONAL INVERSION MODELLING." Rudarsko-geološko-naftni zbornik 38, no. 3 (2023): 65–80. http://dx.doi.org/10.17794/rgn.2023.3.6.
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The Controlled Source Audio-frequency Magnetotellurics (CSAMT) is a geophysical method utilizing artificial electromagnetic signal source to estimate subsurface resistivity structures. One-dimensional (1D) inversion modelling of CSAMT data is non-linear and the solution can be estimated by using global optimization algorithms. Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) are well-known population-based algorithms having relatively simple mathematical formulation and implementation. Hybridization of PSO and GWO algorithms (called hybrid PSO-GWO) can improve the convergence capability to the global solution. This study applied the hybrid PSO-GWO algorithm for 1D CSAMT inversion modelling. Tests were conducted with synthetic CSAMT data associated with 3-layer, 4-layer and 5-layer earth models to determine the performance of the algorithm. The results show that the hybrid PSO-GWO algorithm has a good performance in obtaining the minimum misfit compared to the original PSO and GWO algorithms. The hybrid PSO-GWO algorithm was also applied to invert CSAMT field data for gold mineralization exploration in the Cibaliung area, Banten Province, Indonesia. The algorithm was able to reconstruct the resistivity model very well which is confirmed by the results from inversion of the data using standard 2D MT inversion software. The model also agrees well with the geological information of the study area.
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Xiao, Heng, and Toshiharu Hatanaka. "Model Selecting PSO-FA Hybrid for Complex Function Optimization." International Journal of Swarm Intelligence Research 12, no. 3 (July 2021): 215–32. http://dx.doi.org/10.4018/ijsir.2021070110.
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Swarm intelligence is inspired by natural group behavior. It is one of the promising metaheuristics for black-box function optimization. Then plenty of swarm intelligence algorithms such as particle swarm optimization (PSO) and firefly algorithm (FA) have been developed. Since these swarm intelligence models have some common properties and inherent characteristics, model hybridization is expected to adjust a swarm intelligence model for the target problem instead of parameter tuning that needs some trial and error approach. This paper proposes a PSO-FA hybrid algorithm with a model selection strategy. An event-driven trigger based on the personal best update makes each individual do the model selection that focuses on the personal study process. By testing the proposed hybrid algorithm on some benchmark problems and comparing it with a simple PSO, the standard PSO 2011, FA, HFPSO to show how the proposed hybrid swarm averagely performs well in black-box optimization problems.
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Zheng, Yukun, Ruyue Sun, Yixiang Liu, Yanhong Wang, Rui Song, and Yibin Li. "A Hybridization Grey Wolf Optimizer to Identify Parameters of Helical Hydraulic Rotary Actuator." Actuators 12, no. 6 (May 25, 2023): 220. http://dx.doi.org/10.3390/act12060220.
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Based on the grey wolf optimizer (GWO) and differential evolution (DE), a hybridization algorithm (H-GWO) is proposed to avoid the local optimum, improve the diversity of the population, and compromise the exploration and exploitation appropriately. The mutation and crossover principles of the DE algorithm are introduced into the GWO algorithm, and the opposition-based optimization learning technology is combined to update the GWO population to increase the population diversity. The algorithm is then benchmarked against nine typical test functions and compared with other state-of-the-art meta-heuristic algorithms such as particle swarm optimization (PSO), GWO, and DE. The results show that the proposed H-GWO algorithm can provide very competitive results. On this basis, the forgetting factor recursive least squares (FFRLS) method and the proposed H-GWO algorithm are combined to establish a parameter identification algorithm to identify parameters of the helical hydraulic rotary actuator (HHRA) with nonlinearity and uncertainty questions. In addition, the proposed method is verified by practical identification experiments. After comparison with the least squares (LS), recursive least squares (RLS), FFRLS, PSO, and GWO results, it can be concluded that the proposed method (H-GWO) has higher identification accuracy.
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Lenin, K. "BANKS CAPACITOR COMPENSATION FOR CRITICAL NODAL DETECTION BY AUGMENTED RED WOLF OPTIMIZATION ALGORITHM." International Journal of Research -GRANTHAALAYAH 6, no. 10 (October 31, 2018): 169–75. http://dx.doi.org/10.29121/granthaalayah.v6.i10.2018.1175.
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In this paper Banks Capacitor Compensation for Critical Nodal Detections by Augmented Red Wolf Optimization Algorithm has been worked out. Projected ERWO algorithm hybridizes the wolf optimization (WO) algorithm with swarm based algorithm called as particle swarm optimization (PSO) algorithm. In the approach each Red wolf has a flag vector, and length is equivalent to the whole sum of numbers which features in the dataset of the wolf optimization (WO). Exploration capability of the projected Red wolf optimization algorithm has been enriched by hybridization of both WO with PSO. Efficiency of the projected Enriched Red wolf optimization (ERWO) is tested in standard IEEE 57 bus test system. Simulation study indicates Enriched Red wolf optimization (ERWO) algorithm performs well in tumbling the actual power losses.
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Cavalcanti-Júnior, George M., Fernando B. Lima-Neto, and Carmelo J. A. Bastos-Filho. "On the Analysis of HPSO Improvement by Use of the Volitive Operator of Fish School Search." International Journal of Swarm Intelligence Research 4, no. 1 (January 2013): 62–77. http://dx.doi.org/10.4018/jsir.2013010103.
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Swarm Intelligence algorithms have been extensively applied to solve optimization problems. However, in some domains even well-established techniques such as Particle Swarm Optimization (PSO) may not present the necessary ability to generate diversity during the process of the swarm convergence. Indeed, this is the major difficulty to use PSO to tackle dynamic problems. Many efforts to overcome this weakness have been made. One of them is through the hybridization of the PSO with other algorithms. For example, the Volitive PSO is a hybrid algorithm that presents as good performance on dynamic problems by applying a very interesting feature, the collective volitive operator, which was extracted from the Fish School Search algorithm and embedded into PSO. In this paper, the authors investigated further hybridizations in line with the Volitive PSO approach. This time they used the Heterogeneous PSO instead of the PSO, and named this novel approach Volitive HPSO. In the paper, the authors investigate the influence of the collective volitive operator (of FSS) in the HPSO. The results show that this operator significantly improves HPSO performance when compared to the non-hybrid approaches of PSO and its variations in dynamic environments.
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Chen, Q., J. Jiang, M. Du, L. Zhou, C. Jing, and C. Lu. "A HYBRIDIZATION OF AN IMPROVED PARTICLE SWARM OPTIMIZATION AND FUZZY K-MEANS ALGORITHM FOR HYPERSPECTRAL IMAGE CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1833–39. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1833-2019.
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<p><strong>Abstract.</strong> A particle swarm optimization (PSO) algorithm has been widely used in the field of remote sensing image classification. We proposed the IPSO-FKM algorithm, which use the improved PSO (IPSO) algorithm to optimize the initial parameters of the Fuzzy K-Means (FKM) clustering algorithm. We combine the crossover operator of genetic algorithms with PSO, and introduce the fuzzy membership degree of fuzzy mathematics into K-means clustering algorithm. Then we use the IPSO-FKM algorithm to optimize the classification results of the Hyperion remotely sensed images, and use FKM, IPSO, and IPSO-FKM to extract the land cover information on the wetlands in Dongting Lakes, China. The experimental results have been validated by the classification results of MLC and the field investigation data. The validation results have been evaluated from three perspectives: the overall classification accuracy and the Kappa coefficient from the pixel perspective, the intra-cluster distance and the inter-cluster distance from the feature perspective, and the partition coefficient and partition entropy from the information perspective. According to the comparison of IPSO and FKM algorithms，the IPSO-FKM algorithm has a better performance than the others in all three respects. Additionally, in terms of the fitness convergence, the IPSO-FKM algorithm has a better searching velocity and better convergence to lower the quantization errors compared with the other two algorithms.</p>
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Lenin, K. "ACTIVE POWER LOSS DIMINUTION & VOLTAGE STABILITY ENHANCEMENT BY RED WOLF OPTIMIZATION ALGORITHM." International Journal of Research -GRANTHAALAYAH 6, no. 11 (November 30, 2018): 355–65. http://dx.doi.org/10.29121/granthaalayah.v6.i11.2018.1139.
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In this paper optimal reactive power dispatch problem (ORPD), has been solved by Enriched Red Wolf Optimization (ERWO) algorithm. Projected ERWO algorithm hybridizes the wolf optimization (WO) algorithm with swarm based algorithm called as particle swarm optimization (PSO) algorithm. In the approach each Red wolf has a flag vector, and length is equivalent to the whole sum of numbers which features in the dataset of the wolf optimization (WO). Exploration capability of the projected Red wolf optimization algorithm has been enriched by hybridization of both WO with PSO. Efficiency of the projected Enriched Red wolf optimization (ERWO) evaluated in standard IEEE 30 bus test system. Simulation study indicates Enriched Red wolf optimization (ERWO) algorithm performs well in tumbling the actual power losses& particularly voltage stability has been enriched.
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Wu, Tie Bin, Tao Yun Zhou, Wen Li, Gao Feng Zhu, and Yun Lian Liu. "Particle Swarm Algorithm Based on Boundary Buffering-Natural Evolution and its Application in Constrained Optimization." Applied Mechanics and Materials 670-671 (October 2014): 1517–21. http://dx.doi.org/10.4028/www.scientific.net/amm.670-671.1517.
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A particle swarm algorithm (PSO) based on boundary buffering-natural evolution was proposed for solving constrained optimization problems. By buffering the particles that cross boundaries, the diversity of populations was intensified; to accelerate the convergence speed and avoid local optimum of PSO, natural evolution was introduced. In other words, particle hybridization and mutation strategies were applied; and by combining the modified feasible rules, the constrained optimization problems were solved. The simulation results proved that the method was effective in solving this kind of problems.
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Koyuncu, Hasan, and Rahime Ceylan. "A PSO based approach: Scout particle swarm algorithm for continuous global optimization problems." Journal of Computational Design and Engineering 6, no. 2 (August 27, 2018): 129–42. http://dx.doi.org/10.1016/j.jcde.2018.08.003.
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Abstract In the literature, most studies focus on designing new methods inspired by biological processes, however hybridization of methods and hybridization way should be examined carefully to generate more suitable optimization methods. In this study, we handle Particle Swarm Optimization (PSO) and an efficient operator of Artificial Bee Colony Optimization (ABC) to design an efficient technique for continuous function optimization. In PSO, velocity and position concepts guide particles to achieve convergence. At this point, variable and stable parameters are ineffective for regenerating awkward particles that cannot improve their personal best position (Pbest). Thus, the need for external intervention is inevitable once a useful particle becomes an awkward one. In ABC, the scout bee phase acts as external intervention by sustaining the resurgence of incapable individuals. With the addition of a scout bee phase to standard PSO, Scout Particle Swarm Optimization (ScPSO) is formed which eliminates the most important handicap of PSO. Consequently, a robust optimization algorithm is obtained. ScPSO is tested on constrained optimization problems and optimum parameter values are obtained for the general use of ScPSO. To evaluate the performance, ScPSO is compared with Genetic Algorithm (GA), with variants of the PSO and ABC methods, and with hybrid approaches based on PSO and ABC algorithms on numerical function optimization. As seen in the results, ScPSO results in better optimal solutions than other approaches. In addition, its convergence is superior to a basic optimization method, to the variants of PSO and ABC algorithms, and to the hybrid approaches on different numerical benchmark functions. According to the results, the Total Statistical Success (TSS) value of ScPSO ranks first (5) in comparison with PSO variants; the second best TSS (2) belongs to CLPSO and SP-PSO techniques. In a comparison with ABC variants, the best TSS value (6) is obtained by ScPSO, while TSS of BitABC is 2. In comparison with hybrid techniques, ScPSO obtains the best Total Average Rank (TAR) as 1.375, and TSS of ScPSO ranks first (6) again. The fitness values obtained by ScPSO are generally more satisfactory than the values obtained by other methods. Consequently, ScPSO achieve promising gains over other optimization methods; in parallel with this result, its usage can be extended to different working disciplines. Highlights PSO parameters are ineffective to regenerate the awkward particle that cannot improve its pbest. An external intervention is inevitable once a particle becomes an awkward one. ScPSO is obtained with the addition of scout bee phase into the PSO. So an evolutionary method eliminating the most important handicap of PSO is gained. ScPSO is compared with the variants and with hybrid versions of PSO and ABC methods. According to the experiments, ScPSO results in better optimal solutions. The fitness values of ScPSO are generally more satisfactory than the others. Consequently, ScPSO achieve promising gains over other optimization methods. In parallel with this, its usage can be extended to different working disciplines.
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Jiang, Shanhe, Chaolong Zhang, and Shijun Chen. "Sequential Hybrid Particle Swarm Optimization and Gravitational Search Algorithm with Dependent Random Coefficients." Mathematical Problems in Engineering 2020 (April 21, 2020): 1–17. http://dx.doi.org/10.1155/2020/1957812.
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Particle swarm optimization (PSO) has been proven to show good performance for solving various optimization problems. However, it tends to suffer from premature stagnation and loses exploration ability in the later evolution period when solving complex problems. This paper presents a sequential hybrid particle swarm optimization and gravitational search algorithm with dependent random coefficients called HPSO-GSA, which first incorporates the gravitational search algorithm (GSA) with the PSO by means of a sequential operating mode and then adopts three learning strategies in the hybridization process to overcome the aforementioned problem. Specifically, the particles in the HPSO-GSA enter into the PSO stage and update their velocities by adopting the dependent random coefficients strategy to enhance the exploration ability. Then, the GSA is incorporated into the PSO by using fixed iteration interval cycle or adaptive evolution stagnation cycle strategies when the swarm drops into local optimum and fails to improve their fitness. To evaluate the effectiveness and feasibility of the proposed HPSO-GSA, the simulations were conducted on benchmark test functions. The results reveal that the HPSO-GSA exhibits superior performance in terms of accuracy, reliability, and efficiency compared to PSO, GSA, and other recently developed hybrid variants.
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Bao, Zongfan, Yongquan Zhou, Liangliang Li, and Mingzhi Ma. "A Hybrid Global Optimization Algorithm Based on Wind Driven Optimization and Differential Evolution." Mathematical Problems in Engineering 2015 (2015): 1–20. http://dx.doi.org/10.1155/2015/389630.
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This paper presents a new hybrid global optimization algorithm, which is based on the wind driven optimization (WDO) and differential evolution (DE), named WDO-DE algorithm. The WDO-DE algorithm is based on a double population evolution strategy, the individuals in a population evolved by wind driven optimization algorithm, and a population of individuals evolved from difference operation. The populations of individuals both in WDO and DE employ an information sharing mechanism to implement coevolution. This paper chose fifteen benchmark functions to have a test. The experimental results show that the proposed algorithm can be feasible in both low-dimensional and high-dimensional cases. Compared to GA-PSO, WDO, DE, PSO, and BA algorithm, the convergence speed and precision of WDO-DE are higher. This hybridization showed a better optimization performance and robustness and significantly improves the original WDO algorithm.
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Rosić, Maja, Miloš Sedak, Mirjana Simić, and Predrag Pejović. "Chaos-Enhanced Adaptive Hybrid Butterfly Particle Swarm Optimization Algorithm for Passive Target Localization." Sensors 22, no. 15 (July 31, 2022): 5739. http://dx.doi.org/10.3390/s22155739.
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This paper considers the problem of finding the position of a passive target using noisy time difference of arrival (TDOA) measurements, obtained from multiple transmitters and a single receiver. The maximum likelihood (ML) estimator’s objective function is extremely nonlinear and non-convex, making it impossible to use traditional optimization techniques. In this regard, this paper proposes the chaos-enhanced adaptive hybrid butterfly particle swarm optimization algorithm, named CAHBPSO, as the hybridization of butterfly optimization (BOA) and particle swarm optimization (PSO) algorithms, to estimate passive target position. In the proposed algorithm, an adaptive strategy is employed to update the sensory fragrance of BOA algorithm, and chaos theory is incorporated into the inertia weight of PSO algorithm. Furthermore, an adaptive switch probability is employed to combine global and local search phases of BOA with the PSO algorithm. Additionally, the semidefinite programming is employed to convert the considered problem into a convex one. The statistical comparison on CEC2014 benchmark problems shows that the proposed algorithm provides a better performance compared to well-known algorithms. The CAHBPSO method surpasses the BOA, PSO and semidefinite programming (SDP) algorithms for a broad spectrum of noise, according to simulation findings, and achieves the Cramer–Rao lower bound (CRLB).
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Ong, Bun Theang, and Masao Fukushima. "Automatically Terminated Particle Swarm Optimization with Principal Component Analysis." International Journal of Information Technology & Decision Making 14, no. 01 (January 2015): 171–94. http://dx.doi.org/10.1142/s0219622014500837.
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A hybrid Particle Swarm Optimization (PSO) that features an automatic termination and better search efficiency than classical PSO is presented. The proposed method is combined with the so-called "Gene Matrix" to provide the search with a self-check in order to determine a proper termination instant. Its convergence speed and reliability are also increased by the implementation of the Principal Component Analysis (PCA) technique and the hybridization with a local search method. The proposed algorithm is denominated as "Automatically Terminated Particle Swarm Optimization with Principal Component Analysis" (AT-PSO-PCA). The computational experiments demonstrate the effectiveness of the automatic termination criteria and show that AT-PSO-PCA enhances the convergence speed, accuracy and reliability of the PSO paradigm. Furthermore, comparisons with state-of-the-art evolutionary algorithms (EA) yield competitive results even under the automatically detected termination instant.
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Chutani, Sonia, and Jagbir Singh. "Optimal Design of RC Frames using a Modified Hybrid PSOGSA Algorithm." Archives of Civil Engineering 63, no. 4 (December 1, 2017): 123–34. http://dx.doi.org/10.1515/ace-2017-0044.
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AbstractThe present study has been taken up to emphasize the role of the hybridization process for optimizing a given reinforced concrete (RC) frame. Although various primary techniques have been hybrid in the past with varying degree of success, the effect of hybridization of enhanced versions of standard optimization techniques has found little attention. The focus of the current study is to see if it is possible to maintain and carry the positive effects of enhanced versions of two different techniques while using their hybrid algorithms. For this purpose, enhanced versions of standard particle swarm optimization (PSO) and a standard gravitational search algorithm (GSA), were considered for optimizing an RC frame. The enhanced version of PSO involves its democratization by considering all good and bad experiences of the particles, whereas the enhanced version of the GSA is made self-adaptive by considering a specific range for certain parameters, like the gravitational constant and a set of agents with the best fitness values. The optimization process, being iterative in nature, has been coded in C++. The analysis and design procedure is based on the specifications of Indian codes. Two distinct advantages of enhanced versions of standard PSO and GSA, namely, better capability to escape from local optima and a faster convergence rate, have been tested for the hybrid algorithm. The entire formulation for optimal cost design of a frame includes the cost of beams and columns. The variables of each element of structural frame have been considered as continuous and rounded off appropriately to consider practical limitations. An example has also been considered to emphasize the validity of this optimum design procedure.
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Ting, T. O., H. C. Ting, and T. S. Lee. "Taguchi-Particle Swarm Optimization for Numerical Optimization." International Journal of Swarm Intelligence Research 1, no. 2 (April 2010): 18–33. http://dx.doi.org/10.4018/jsir.2010040102.
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In this work, a hybrid Taguchi-Particle Swarm Optimization (TPSO) is proposed to solve global numerical optimization problems with continuous and discrete variables. This hybrid algorithm combines the well-known Particle Swarm Optimization Algorithm with the established Taguchi method, which has been an important tool for robust design. This paper presents the improvements obtained despite the simplicity of the hybridization process. The Taguchi method is run only once in every PSO iteration and therefore does not give significant impact in terms of computational cost. The method creates a more diversified population, which also contributes to the success of avoiding premature convergence. The proposed method is effectively applied to solve 13 benchmark problems. This study’s results show drastic improvements in comparison with the standard PSO algorithm involving continuous and discrete variables on high dimensional benchmark functions.
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Lenin, K. "REAL POWER LOSS REDUCTION BY ENHANCED ACCLIMATIZED BACTERIAL EXPLORATION ALGORITHM." International Journal of Research -GRANTHAALAYAH 6, no. 3 (March 31, 2018): 182–90. http://dx.doi.org/10.29121/granthaalayah.v6.i3.2018.1513.
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This paper presents Enhanced Acclimatized Bacterial Exploration (EBE) algorithm to solve reactive power problem. Bacterial Search Optimization Algorithm has recently emerged as a very powerful technique based on the behaviour of E-coli bacteria. In order to speed up the convergence of Bacterial search Optimization Algorithm, this paper proposed a new hybridization between Bacterial Search Optimization Algorithm (BSO) and Particle Swarm Optimization (PSO). In order to evaluate the proposed Enhanced Acclimatized Bacterial Exploration (EBE) algorithm, it has been tested in standard IEEE 118 & practical 191 bus test systems and compared to other standard algorithms.
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Lenin, K. "REAL POWER LOSS REDUCTION & VOLTAGE STABILITY AMPLIFICATION BY HYBRIDIZATION OF RESTARTED SIMULATED ANNEALING WITH PARTICLE SWARM OPTIMIZATION ALGORITHM." International Journal of Research -GRANTHAALAYAH 6, no. 9 (September 30, 2018): 246–58. http://dx.doi.org/10.29121/granthaalayah.v6.i9.2018.1229.
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This paper presents an algorithm for solving the multi-objective reactive power dispatch problem in a power system. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. Evolutionary algorithm and Swarm Intelligence algorithm (EA, SI), a part of Bio inspired optimization algorithm, have been widely used to solve numerous optimization problem in various science and engineering domains. Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm that shares many similarities with evolutionary computation techniques. However, the PSO is driven by the simulation of a social psychological metaphor motivated by collective behaviors of bird and other social organisms instead of the survival of the fittest individual. The Simulated Annealing (SA) algorithm is a probabilistic hill-climbing technique that is based on the annealing/cooling process of metals. In total, most moves may be accepted at initial stages, but at the final stage only improving ones are likely to be allowed. This can help the procedure jump out of a local minimum. However, sometimes it is better to move back to a former solution that was significantly better rather than always moving from the current state. This process is called “restarting” of SA & called as Restarted Simulated Annealing (RSA). In this paper we proposed a hybridized restarted simulated annealing particle swarm optimization (RSAPSO) technique to find global minima more efficiently and robustly. The proposed RSAPSO combines the global search ability of PSO and the local search ability of RSA, and offsets the weaknesses of each other. In order to evaluate the proposed algorithm, it has been tested on IEEE 30 bus system and compared to other reported algorithms.
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Mammeri, E., A. Ahriche, A. Necaibia, and A. Bouraiou. "New MPPT Hybrid Controller based on Genetic Algorithms and Particle Swarm Optimization for Photovoltaic Systems." International Journal of Circuits, Systems and Signal Processing 17 (March 6, 2023): 83–91. http://dx.doi.org/10.46300/9106.2023.17.10.
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Traditional Maximum Power Point Tracking (MPPT) techniques are unable to reach high performance in photovoltaic (PV) system under partial shading conditions because of the multi-peaks present in the Power-Voltage curve. For that, particle Swarm Optimization (PSO) and genetic algorithms (GA) have been combined in recent years. However, these algorithms demonstrate some drawbacks in tracking accuracy and convergence rates, which impair control performance. In this paper, a new controller based on hybridization of PSO and GA is introduced to track the global maximum power point (GMPP). The proposed algorithm (HPGA) increases the balance rate between exploration and exploitation due to the cascade design of GA and PSO. Thus, the GMPP tracking of both algorithms will be improved. Simulations are carried out based on ISOFOTON-75W PV modules to prove the high performance of the proposed algorithm. From the obtained results, we conclude that HPGA shows fast convergence and very good tracking accuracy of GMPP in PV system even under different shading patterns.
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Venkatesan, Chandrasekaran, Raju Kannadasan, Dhanasekar Ravikumar, Vijayaraja Loganathan, Mohammed H. Alsharif, Daeyong Choi, Junhee Hong, and Zong Woo Geem. "Re-Allocation of Distributed Generations Using Available Renewable Potential Based Multi-Criterion-Multi-Objective Hybrid Technique." Sustainability 13, no. 24 (December 12, 2021): 13709. http://dx.doi.org/10.3390/su132413709.
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Integration of Distributed generations (DGs) and capacitor banks (CBs) in distribution systems (DS) have the potential to enhance the system’s overall capabilities. This work demonstrates the application of a hybrid optimization technique the applies an available renewable energy potential (AREP)-based, hybrid-enhanced grey wolf optimizer–particle swarm optimization (AREP-EGWO-PSO) algorithm for the optimum location and sizing of DGs and CBs. EGWO is a metaheuristic optimization technique stimulated by grey wolves, and PSO is a swarm-based metaheuristic optimization algorithm. Hybridization of both algorithms finds the optimal solution to a problem through the movement of the particles. Using this hybrid method, multi-criterion solutions are obtained, such as technical, economic, and environmental, and these are enriched using multi-objective functions (MOF), namely minimizing active power losses, voltage deviation, the total cost of electrical energy, total emissions from generation sources and enhancing the voltage stability index (VSI). Five different operational cases were adapted to validate the efficacy of the proposed scheme and were performed on two standard distribution systems, namely, IEEE 33- and 69-bus radial distribution systems (RDSs). Notably, the proposed AREP-EGWO-PSO algorithm compared the AREP at the candidate locations and re-allocated the DGs with optimal re-sizing when the EGWO-PSO algorithm failed to meet the AREP constraints. Further, the simulated results were compared with existing optimization algorithms considered in recent studies. The obtained results and analysis show that the proposed AREP-EGWO-PSO re-allocates the DGs effectively and optimally, and that these objective functions offer better results, almost similar to EGWO-PSO results, but more significant than other existing optimization techniques.
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Lenin, K. "ACTIVE POWER LOSS REDUCTION BY ASSORTED ALGORITHMS." International Journal of Research -GRANTHAALAYAH 6, no. 5 (May 31, 2018): 263–75. http://dx.doi.org/10.29121/granthaalayah.v6.i5.2018.1448.
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This paper presents assorted algorithms for solving optimal reactive power problem. Symbiosis modeling (SM), which extends the dynamics of the canonical PSO algorithm by adding a significant ingredient that takes into account the symbiotic co evolution between species, Hybridization of Evolutionary algorithm with Conventional Algorithm (HCA) that uses the abilities of evolutionary and conventional algorithm and Genetical Swarm Optimization (GS), which combines Genetic Algorithms (GA) and Particle Swarm Optimization (PSO).All the above said SM, HCA,GS algorithms are used to augment the convergence rate with good Exploration & Exploitation. All the three SM, HCA, GS is applied to Reactive Power optimization problem and has been evaluated in standard IEEE 30 System. The results shows that all the three algorithms perform well in solving the reactive power problem with rapid convergence rate .Of all the three algorithms SM has the slight edge in reducing the real power loss over HCA&GS.
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Hayat, Iqbal, Adnan Tariq, Waseem Shahzad, Manzar Masud, Shahzad Ahmed, Muhammad Umair Ali, and Amad Zafar. "Hybridization of Particle Swarm Optimization with Variable Neighborhood Search and Simulated Annealing for Improved Handling of the Permutation Flow-Shop Scheduling Problem." Systems 11, no. 5 (April 26, 2023): 221. http://dx.doi.org/10.3390/systems11050221.
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Permutation flow-shop scheduling is the strategy that ensures the processing of jobs on each subsequent machine in the exact same order while optimizing an objective, which generally is the minimization of makespan. Because of its NP-Complete nature, a substantial portion of the literature has mainly focused on computational efficiency and the development of different AI-based hybrid techniques. Particle Swarm Optimization (PSO) has also been frequently used for this purpose in the recent past. Following the trend and to further explore the optimizing capabilities of PSO, first, a standard PSO was developed during this research, then the same PSO was hybridized with Variable Neighborhood Search (PSO-VNS) and later on with Simulated Annealing (PSO-VNS-SA) to handle Permutation Flow-Shop Scheduling Problems (PFSP). The effect of hybridization was validated through an internal comparison based on the results of 120 different instances devised by Taillard with variable problem sizes. Moreover, further comparison with other reported hybrid metaheuristics has proved that the hybrid PSO (HPSO) developed during this research performed exceedingly well. A smaller value of 0.48 of ARPD (Average Relative Performance Difference) for the algorithm is evidence of its robust nature and significantly improved performance in optimizing the makespan as compared to other algorithms.
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Aljohani, Tawfiq M., Ahmed F. Ebrahim, and Osama Mohammed. "Single and Multiobjective Optimal Reactive Power Dispatch Based on Hybrid Artificial Physics–Particle Swarm Optimization." Energies 12, no. 12 (June 18, 2019): 2333. http://dx.doi.org/10.3390/en12122333.
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The optimal reactive power dispatch (ORPD) problem represents a noncontinuous, nonlinear, highly constrained optimization problem that has recently attracted wide research investigation. This paper presents a new hybridization technique for solving the ORPD problem based on the integration of particle swarm optimization (PSO) with artificial physics optimization (APO). This hybridized algorithm is tested and verified on the IEEE 30, IEEE 57, and IEEE 118 bus test systems to solve both single and multiobjective ORPD problems, considering three main aspects. These aspects include active power loss minimization, voltage deviation minimization, and voltage stability improvement. The results prove that the algorithm is effective and displays great consistency and robustness in solving both the single and multiobjective functions while improving the convergence performance of the PSO. It also shows superiority when compared with results obtained from previously reported literature for solving the ORPD problem.
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Dif, Nassima, and Zakaria Elberrichi. "A Novel Dynamic Hybridization Method for Best Feature Selection." International Journal of Applied Metaheuristic Computing 12, no. 2 (April 2021): 85–99. http://dx.doi.org/10.4018/ijamc.2021040106.
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Hybrid metaheuristics has received a lot of attention lately to solve combinatorial optimization problems. The purpose of hybridization is to create a cooperation between metaheuristics for better solutions. Most proposed works were interested in static hybridization. The objective of this work is to propose a novel dynamic hybridization method (GPBD) that generates the most suitable sequential hybridization between GA, PSO, BAT, and DE metaheuristics, according to each problem. The authors choose to test this approach for solving the best feature selection problem in a wrapper tactic, performed on face image recognition datasets, with the k-nearest neighbor (KNN) learning algorithm. The comparative study of the metaheuristics and their hybridization GPBD shows that the proposed approach achieved the best results. It was definitely competitive with other filter approaches proposed in the literature. It achieved a perfect accuracy score of 100% for Orl10P, Pix10P, and PIE10P datasets.
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Ramírez-Ochoa, Dynhora-Danheyda, Luis Asunción Pérez-Domínguez, Erwin-Adán Martínez-Gómez, and David Luviano-Cruz. "PSO, a Swarm Intelligence-Based Evolutionary Algorithm as a Decision-Making Strategy: A Review." Symmetry 14, no. 3 (February 24, 2022): 455. http://dx.doi.org/10.3390/sym14030455.
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Companies are constantly changing in their organization and the way they treat information. In this sense, relevant data analysis processes arise for decision makers. Similarly, to perform decision-making analyses, multi-criteria and metaheuristic methods represent a key tool for such analyses. These analysis methods solve symmetric and asymmetric problems with multiple criteria. In such a way, the symmetry transforms the decision space and reduces the search time. Therefore, the objective of this research is to provide a classification of the applications of multi-criteria and metaheuristic methods. Furthermore, due to the large number of existing methods, the article focuses on the particle swarm algorithm (PSO) and its different extensions. This work is novel since the review of the literature incorporates scientific articles, patents, and copyright registrations with applications of the PSO method. To mention some examples of the most relevant applications of the PSO method; route planning for autonomous vehicles, the optimal application of insulin for a type 1 diabetic patient, robotic harvesting of agricultural products, hybridization with multi-criteria methods, among others. Finally, the contribution of this article is to propose that the PSO method involves the following steps: (a) initialization, (b) update of the local optimal position, and (c) obtaining the best global optimal position. Therefore, this work contributes to researchers not only becoming familiar with the steps, but also being able to implement it quickly. These improvements open new horizons for future lines of research.
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Sengupta, Saptarshi, Sanchita Basak, and Richard Peters. "Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives." Machine Learning and Knowledge Extraction 1, no. 1 (October 10, 2018): 157–91. http://dx.doi.org/10.3390/make1010010.
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Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems that cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social co-operation of birds and fish schools and draws heavily from the evolutionary behavior of these organisms. This paper serves to provide a thorough survey of the PSO algorithm with special emphasis on the development, deployment, and improvements of its most basic as well as some of the very recent state-of-the-art implementations. Concepts and directions on choosing the inertia weight, constriction factor, cognition and social weights and perspectives on convergence, parallelization, elitism, niching and discrete optimization as well as neighborhood topologies are outlined. Hybridization attempts with other evolutionary and swarm paradigms in selected applications are covered and an up-to-date review is put forward for the interested reader.
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Lenin, Kanagasabai. "Hybridization of Genetic Particle Swarm Optimization Algorithm with Symbiotic Organisms Search Algorithm for Solving Optimal Reactive Power Dispatch Problem." Journal of Applied Science, Engineering, Technology, and Education 3, no. 1 (June 20, 2020): 12–21. http://dx.doi.org/10.35877/454ri.asci31106.
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In this work Hybridization of Genetic Particle Swarm Optimization Algorithm with Symbiotic Organisms Search Algorithm (HGPSOS) has been done for solving the power dispatch problem. Genetic particle swarm optimization problem has been hybridized with Symbiotic organisms search (SOS) algorithm to solve the problem. Genetic particle swarm optimization algorithm is formed by combining the Particle swarm optimization algorithm (PSO) with genetic algorithm (GA). Symbiotic organisms search algorithm is based on the actions between two different organisms in the ecosystem- mutualism, commensalism and parasitism. Exploration process has been instigated capriciously and every organism specifies a solution with fitness value. Projected HGPSOS algorithm improves the quality of the search. Proposed HGPSOS algorithm is tested in IEEE 30, bus test system- power loss minimization, voltage deviation minimization and voltage stability enhancement has been attained.
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Utamima, Amalia, and Angelia Melani Andrian. "Penyelesaian Masalah Penempatan Fasilitas dengan Algoritma Estimasi Distribusi dan Particle Swarm Optimization." Journal of Information Systems Engineering and Business Intelligence 2, no. 1 (April 29, 2016): 11. http://dx.doi.org/10.20473/jisebi.2.1.11-16.
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Abstrak—Masalah penempatan fasilitas pada garis lurus dikenal sebagai problem Penempatan Fasilitas pada Satu Baris (PFSB). Tujuan PFSB, yang dikategorikan sebagai masalah NP-Complete, adalah untuk mengatur tata letak sehingga jumlah jarak antara pasangan semua fasilitas bisa diminimalisir. Algoritma Estimasi Distribusi (EDA) meningkatkan kualitas solusi secara efisien dalam beberapa pengoperasian pertama, namun keragaman dalam solusi hilang secara pesat ketika semakin banyak iterasi dijalankan. Untuk menjaga keragaman, hibridisasi dengan algoritma meta-heuristik diperlukan. Penelitian ini mengusulkan EDAPSO, algoritma yang terdiri dari hibridisasi EDA dan Particle Swarm Optimization (PSO). Tujuan dari penelitian ini yaitu untuk menguji performa algoritma EDAPSO dalam menyelesaikan PFSB.Kinerja EDAPSO yang diuji dalam 10 masalah benchmark PFSB dan EDAPSO berhasil mencapai solusi optimal.Kata kunci—penempatan fasilitas, algoritma estimasi distribusi, particle swarm optimizationAbstract—The layout positioning problem of facilities on a straight line is known as Single Row Facility Layout Problem (PFSB). Categorized as NP-Complete problem, PFSB aim to arrange the layout so that the sum of distances between all facilities’ pairs can be minimized. Estimation of Distribution Algorithm (EDA) improves the solution quality efficiently in first few runs, but the diversity lost grows rapidly as more iterations are run. To maintain the diversity, hybridization with meta-heuristic algorithms is needed. This research proposes EDAPSO, an algorithm which consists of hybridization of EDA and Particle Swarm Optimization (PSO). The objective of this research is to test the performance of EDAPSO algorithm for solving PFSB. EDAPSO’s performance is tested in 10 benchmark problems of PFSB and it successfully achieves optimum solution.Keywords— facility layout, estimation distribution algorithm, particle swarm optimization
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Chopra, Namarta, Y. S. Brar, and J. S. Dhillon. "Hybridized Particle Swarm Optimization on Constrained Economic Dispatch Problem." Journal of Computational and Theoretical Nanoscience 17, no. 1 (January 1, 2020): 322–28. http://dx.doi.org/10.1166/jctn.2020.8669.
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The hybridization of particle swarm optimization (PSO) with simplex search method (SSM) is presented on the problem of economic dispatch in the thermal plants so as to minimizes the overall operating fuel cost while subjected to various constraints. This hybridization of stochastic with deterministic optimization method helps the global optimum solution to further refine by the local search. It also overcome some of the drawbacks of conventional PSO like premature convergence and stagnation in the solution if the number of iterations are increased. This proposed optimization method is used to get the overall minimum cost of fuel by including transmission line losses and valve point loading effect (VPLE) in the classical problem of economic dispatch, so as to have the more practical impact in the case considered. The validness of the suggested algorithm is tested using small scale and large scale system and the analogy of results obtained are done with existing algorithms cited in the literature, showing improvement of 29.3% in small scale system and 6.4% in large scale system, which proves the robustness of the suggested approach.
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Santos, Pitther N., Victor Dmitriev, and Karlo Q. da Costa. "Optimization of Modified Yagi-Uda Nanoantenna Arrays Using Adaptive Fuzzy GAPSO." International Journal of Antennas and Propagation 2021 (February 17, 2021): 1–11. http://dx.doi.org/10.1155/2021/8874385.
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This paper presents an optimization of the radiation and absorption characteristics of modified Yagi-Uda (YU) nanoantenna arrays. Four geometries of antennas are considered: conventional YU fed by voltage source and transmission line, and YU with a loop element fed by voltage source and transmission line. The numerical electromagnetic simulations of these nanoantennas were made by the method of moments (MoM). The optimization method used is the adaptive fuzzy GAPSO, which consists of hybridization between genetic algorithm (GA) and particle swarm optimization (PSO), with a fuzzy system employed to adapt the inertial weight ω and the acceleration coefficients C1 and C2 of PSO. The optimized results show that the modified YU nanoantennas present better characteristics of gain, directivity, and radiation efficiency than the conventional YU antenna.
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Pluhacek, Michal, Adam Viktorin, Roman Senkerik, Tomas Kadavy, and Ivan Zelinka. "Extended experimental study on PSO with partial population restart based on complex network analysis." Logic Journal of the IGPL 28, no. 2 (October 1, 2018): 211–25. http://dx.doi.org/10.1093/jigpal/jzy046.
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Abstract This extended study presents a hybridization of particle swarm optimization (PSO) with complex network construction and analysis. A partial population restart is performed in certain moments of the run of the algorithm based on the information obtained from a complex network analysis. The complex network structure represents the communication in the population. We present experimental results of the method alongside with statistical evaluation and discuss future possibilities of this approach. The main goal of the work is not to propose a new highly competitive PSO variant but to present the possibility of using the unconventional tool as an alternative to conventional diversity measures. The main benefit of the network analysis is that it has same-time requirements regardless of the dimension of the problem.
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Riaz, Muhammad, Aamir Hanif, Shaik Javeed Hussain, Muhammad Irfan Memon, Muhammad Umair Ali, and Amad Zafar. "An Optimization-Based Strategy for Solving Optimal Power Flow Problems in a Power System Integrated with Stochastic Solar and Wind Power Energy." Applied Sciences 11, no. 15 (July 27, 2021): 6883. http://dx.doi.org/10.3390/app11156883.
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In an effort to reduce greenhouse gas emissions, experts are looking to substitute fossil fuel energy with renewable energy for environmentally sustainable and emission free societies. This paper presents the hybridization of particle swarm optimization (PSO) with grey wolf optimization (GWO), namely a hybrid PSO-GWO algorithm for the solution of optimal power flow (OPF) problems integrated with stochastic solar photovoltaics (SPV) and wind turbines (WT) to enhance global search capabilities towards an optimal solution. A solution approach is used in which SPV and WT output powers are estimated using lognormal and Weibull probability distribution functions respectively, after simulation of 8000 Monte Carlo scenarios. The control variables include the forecast real power generation of SPV and WT, real power of thermal generators except slack-bus, and voltages of all voltage generation buses. The total generation cost of the system is considered the main objective function to be optimized, including the penalty and reserve cost for underestimation and overestimation of SPV and WT, respectively. The proposed solution approach for OPF problems is verified on the modified IEEE 30 bus test system. The performance and robustness of the proposed hybrid PSO-GWO algorithm in solving the OPF problem is assessed by comparing the results with five other metaheuristic optimization algorithms for the same test system, under the same control variables and system constraints. Simulation results confirm that the hybrid PSO-GWO algorithm performs well compared to other algorithms and shows that it can be an efficient choice for the solution of OPF problems.
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Mangalampalli, Sudheer, Vamsi Krishna Mangalampalli, and Sangram Keshari Swain. "Multi Objective Task Scheduling Algorithm in Cloud Computing Using the Hybridization of Particle Swarm Optimization and Cuckoo Search." Journal of Computational and Theoretical Nanoscience 17, no. 12 (December 1, 2020): 5346–57. http://dx.doi.org/10.1166/jctn.2020.9427.
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Rapid growth has been occurred in the IT industry with the emergence of Cloud computing in terms of the resources provisioned to the users in a seamless and flexible way. Task Scheduling is a prodigious challenge in the Cloud Computing. It is difficult to schedule the continuously varying requests to schedule on continuously varying resources. The existing approaches haven’t considered all the metrics while considering only the metrics like makespan and waiting time. In this paper, our focus is to formulate a Multi objective approach which is used to optimally map and load balance the tasks in the cloud by calculating the task priority and VM priority based on the electricity price per unit cost while minimizing the makespan, migration time and the power cost in the datacenters. The proposed algorithm is modeled using the hybridized approach by combining PSO and Cuckoo search algorithms. It is simulated on cloudsim simulator and it is compared against the basic ACO, GA, PSO and CS algorithms and our algorithm is outperformed against these basic algorithms with concerned parameters such as makespan, Migration time and the Total Power cost in the datacenters.
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ABDERRAHIM, ALLANI, EL-GHAZALI TALBI, and MELLOULI KHALED. "HYBRIDIZATION OF GENETIC AND QUANTUM ALGORITHM FOR GENE SELECTION AND CLASSIFICATION OF MICROARRAY DATA." International Journal of Foundations of Computer Science 23, no. 02 (February 2012): 431–44. http://dx.doi.org/10.1142/s0129054112400217.
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In this work, we hybridize the Genetic Quantum Algorithm with the Support Vector Machines classifier for gene selection and classification of high dimensional Microarray Data. We named our algorithm GQA SVM. Its purpose is to identify a small subset of genes that could be used to separate two classes of samples with high accuracy. A comparison of the approach with different methods of literature, in particular GA SVM and PSO SVM [2], was realized on six different datasets issued of microarray experiments dealing with cancer (leukemia, breast, colon, ovarian, prostate, and lung) and available on Web. The experiments clearified the very good performances of the method. The first contribution shows that the algorithm GQA SVM is able to find genes of interest and improve the classification on a meaningful way. The second important contribution consists in the actual discovery of new and challenging results on datasets used.
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Hachimi, H., S. Assif, Y. Aoues, Abdelkhalak El Hami, Rachid Ellaia, and M. Agouzoul. "Optimization of the Solder Joints of an Electronic Card Using Heuristic Algorithm." International Journal of Engineering Research in Africa 30 (May 2017): 39–48. http://dx.doi.org/10.4028/www.scientific.net/jera.30.39.
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In this paper, a new hybrid method of optimization by the heuristics algorithms to evaluate the reliability of the electronic card by simulating its thermo-mechanical behavior is presented. A model of simulation by finite element is developed to consider the maximal deformations due to temperature; a mechanico- computing coupling is used to find the optimal structure. Embedded electronic systems are playing a very important role in several areas, such as in automotive, aerospace, telecommunications and medical sectors. To properly perform their functions, electronic systems must be reliable [18].This powerful and robust algorithm which is based on hybridization of Differential Evolutionary algorithm with Particle Swarm Optimization (PSO) gives performance results [7].
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Hameed, Mohammed Majeed, Mustafa Abbas Abed, Nadhir Al-Ansari, and Mohamed Khalid Alomar. "Predicting Compressive Strength of Concrete Containing Industrial Waste Materials: Novel and Hybrid Machine Learning Model." Advances in Civil Engineering 2022 (March 23, 2022): 1–19. http://dx.doi.org/10.1155/2022/5586737.
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In the construction and cement manufacturing sectors, the development of artificial intelligence models has received remarkable progress and attention. This paper investigates the capacity of hybrid models conducted for predicting the compressive strength (CS) of concrete where the cement was partially replaced with ground granulated blast-furnace slag ( FS ) and fly ash ( FA ) materials. Accurate estimation of CS can reduce the cost and laboratory tests. Since the traditional method of calculation CS is complicated and requires lots of effort, this article presents new predictive models called SVR − PSO and SVR − GA , that are a hybridization of support vector regression ( SVR ) with improved particle swarm algorithm ( PSO ) and genetic algorithm ( GA ). Furthermore, the hybrid models (i.e., SVR − PSO and SVR − GA ) were used for the first time to predict CS of concrete where the cement component is partially replaced. The improved PSO and GA are given essential roles in tuning the hyperparameters of the SVR model, which have a significant influence on model accuracy. The suggested models are evaluated against extreme learning machine (ELM) via quantitative and visual evaluations. The models are evaluated using eight statistical parameters, and then the SVR-PSO has provided the highest accuracy than comparative models. For instance, the SVR − PSO during the testing phase provided fewer root mean square error RMSE with 1.386 MPa, a higher Nash–Sutcliffe model efficiency coefficient ( NE ) of 0.972, and lower uncertainty at 95% ( U 95 ) with 28.776%. On the other hand, the SVR − GA and ELM models provide lower accuracy with RMSE of 2.826 MPa and 2.180, NE with 0.883 and 0.930, and U 95 with 518.686 183.182, respectively. Sensitivity analysis is carried out to select the influential parameters that significantly affect CS . Overall, the proposed model showed a good prediction of CS of concrete where cement is partially replaced and outperformed 14 models developed in the previous studies.
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Sabeti, Malihe, Laleh Karimi, Naemeh Honarvar, Mahsa Taghavi, and Reza Boostani. "QUANTUMIZED GENETIC ALGORITHM FOR SEGMENTATION AND OPTIMIZATION TASKS." Biomedical Engineering: Applications, Basis and Communications 32, no. 03 (June 2020): 2050022. http://dx.doi.org/10.4015/s1016237220500222.
Full textAbstract:
Specialists mostly assess the skeletal maturity of short-height children by observing their left hand X-Ray image (radiograph), whereas precise separation of areas capturing the bones and growing plates is always not possible by visual inspection. Although a few attempts are made to estimate a suitable threshold for segmenting digitized radiograph images, their results are not still promising. To finely estimate segmentation thresholds, this paper presents the quantumized genetic algorithm (QGA) that is the integration of quantum representation scheme in the basic genetic algorithm (GA). This hybridization between quantum inspired computing and GA has led to an efficient hybrid framework that achieves better balance between the exploration and the exploitation capabilities. To assess the performance of the proposed quantitative bone maturity assessment framework, we have collected an exclusive dataset including 65 left-hand digitized images, aged from 3 to 13 years. Thresholds are estimated by the proposed method and the results are compared to harmony search algorithm (HSA), particle swarm optimization (PSO), quantumized PSO and standard GA. In addition, for more comparison of the proposed method and the other mentioned evolutionary algorithms, ten known benchmarks of complex functions are considered for optimization task. Our results in both segmentation and optimization tasks show that QGA and GA provide the best optimization results in comparison with the other mentioned algorithms. Moreover, the empirical results demonstrate that QGA is able to provide better diversity than that of GA.
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Lakhbab, Halima. "A Novel Hybrid Approach for Optimizing the Localization of Wireless Sensor Networks." MATEC Web of Conferences 200 (2018): 00005. http://dx.doi.org/10.1051/matecconf/201820000005.
Full textAbstract:
Wireless sensor networks are used for monitoring the environment and controlling the physical environment. Information gathered by the sensors is only useful if the positions of the sensors are known. One of the solutions for this problem is Global Positioning System (GPS). However, this approach is prohibitively costly; both in terms of hardware and power requirements. Localization is defined as finding the physical coordinates of a group of nodes. Localization is classified as an unconstrained optimization problem. In this work, we propose a new algorithm to tackle the problem of localization; the algorithm is based on a hybridization of Particle Swarm Optimization (PSO) and Simulated Annealing (SA). Simulation results are given to illustrate the robustness and efficiency of the presented algorithm.