Journal articles: 'Modified moth swarm algorithm'

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Phu, Trieu Ha, Minh Hoang Hanh, Thanh Nguyen Thuan, and Trung Nguyen Thang. "Modified moth swarm algorithm for optimal economic load dispatch problem." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 4 (2020): 2140–47. https://doi.org/10.12928/TELKOMNIKA.v18i4.15032.

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In this study, optimal economic load dispatch problem (OELD) is resolved by a novel improved algorithm. The proposed modified moth swarm algorithm (MMSA), is developed by proposing two modifications on the classical moth swarm algorithm (MSA). The first modification applies an effective formula to replace an ineffective formula of the mutation technique. The second modification is to cancel the crossover technique. For proving the efficient improvements of the proposed method, different systems with discontinuous objective functions as well as complicated constraints are used. Experiment results on the investigated cases show that the proposed method can get less cost and achieve stable search ability than MSA. As compared to other previous methods, MMSA can archive equal or better results. From this view, it can give a conclusion that MMSA method can be valued as a useful method for OELD problem.

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Ha, Phu Trieu, Hanh Minh Hoang, Thuan Thanh Nguyen, and Thang Trung Nguyen. "Modified moth swarm algorithm for optimal economic load dispatch problem." TELKOMNIKA (Telecommunication Computing Electronics and Control) 18, no. 4 (2020): 2140. http://dx.doi.org/10.12928/telkomnika.v18i4.15032.

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Zhang, Chen, Kourosh Sedghisigarchi, Rachel Sheinberg, Shashank Narayana Gowda, and Rajit Gadh. "Optimizing Voltage Stability in Distribution Networks via Metaheuristic Algorithm-Driven Reactive Power Compensation from MDHD EVs." World Electric Vehicle Journal 14, no. 11 (2023): 310. http://dx.doi.org/10.3390/wevj14110310.

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The deployment of medium-duty and heavy-duty (MDHD) electric vehicles (EVs), characterized by their substantial battery capacity and high charging power demand, poses a potential threat to voltage stability within distribution networks. One possible solution to voltage instability is reactive power compensation from charging MDHD EVs. However, this process must be carefully facilitated in order to be effective. This paper introduces an innovative distribution network voltage stability solution by first identifying the network’s weakest buses and then utilizing a metaheuristic algorithm to schedule reactive power compensation from MDHD EVs. In the paper, multiple metaheuristic algorithms, including genetic algorithms, particle swarm optimization, moth flame optimization, salp swarm algorithms, whale optimization, and grey wolf optimization, are subjected to rigorous evaluation concerning their efficacy in terms of voltage stability improvement, power loss reduction, and computational efficiency. The proposed methodology optimizes power flow with the salp swarm algorithm, which was determined to be the most effective tool, to mitigate voltage fluctuations and enhance overall stability. The simulation results, conducted on a modified IEEE 33 bus distribution system, convincingly demonstrate the algorithm’s efficacy in augmenting voltage stability and curtailing power losses, supporting the reliable and efficient integration of MDHD EVs into distribution networks.

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Thanh, Long Duong, Thanh Nguyen Thuan, Phan Van-Duc, and Trung Nguyen Thang. "Determining optimal location and size of capacitors in radial distribution networks using moth swarm algorithm." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (2020): 4514–21. https://doi.org/10.11591/ijece.v10i5.pp4514-4521.

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In this study, the problem of optimal capacitor location and size determination (OCLSD) in radial distribution networks for reducing losses is unraveled by moth swarm algorithm (MSA). MSA is one of the most powerful meta-heuristic algorithm that is taken from the inspiration of the food source finding behavior of moths. Four study cases of installing different numbers of capacitors in the 15-bus radial distribution test system including two, three, four and five capacitors areemployed to run the applied MSA for an investigation of behavior and assessment of performances. Power loss and the improvement of voltage profile obtained by MSA are compared with those fromother methods. As a result, it can be concluded that MSA can give a good truthful and effective solution method for OCLSD problem.

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Varshali, Jaiswal, Sharma Varsha, and Varma Sunita. "MMFO: modified moth flame optimization algorithm for region based RGB color image segmentation." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 1 (2020): 196–201. https://doi.org/10.11591/ijece.v10i1.pp196-201.

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Region-based color image segmentation is elementary steps in image processing and computer vision. The region-based color image segmentation has faced the problem of multidimensionality. The color image is considered in five-dimensional problems, in which three dimensions in color (RGB) and two dimensions in geometry (luminosity layer and chromaticity layer). In this paper, L*a*b color space conversion has been used to reduce the one dimension and geometrically it converts in the array hence the further one dimension has been reduced. This paper introduced, an improved algorithm modified moth flame optimization (MMFO) algorithm for RGB color image segmentation which is based on bio-inspired techniques. The simulation results of MMFO for region based color image segmentation are performed better as compared to PSO and GA, in terms of computation times for all the images. The experiment results of this method gives clear segments based on the different color and the different number of clusters is used during the segmentation process.

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Zaky, Alaa A., Ahmed Fathy, Hegazy Rezk, Konstantina Gkini, Polycarpos Falaras, and Amlak Abaza. "A Modified Triple-Diode Model Parameters Identification for Perovskite Solar Cells via Nature-Inspired Search Optimization Algorithms." Sustainability 13, no. 23 (2021): 12969. http://dx.doi.org/10.3390/su132312969.

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Recently, perovskite solar cells (PSCs) have been widely investigated as an efficient alternative for silicon solar cells. In this work, a proposed modified triple-diode model (MTDM) for PSCs modeling and simulation was used. The Bald Eagle Search (BES) algorithm, which is a novel nature-inspired search optimizer, was suggested for solving the model and estimating the PSCs device parameters because of the complex nature of determining the model parameters. Two PSC architectures, namely control and modified devices, were experimentally fabricated, characterized and tested in the lab. The I–V datasets of the fabricated devices were recorded at standard conditions. The decision variables in the proposed optimization process are the nine and ten unknown parameters of triple-diode model (TDM) and MTDM, respectively. The direct comparison with a number of modern optimization techniques including grey wolf (GWO), particle swarm (PSO) and moth flame (MFO) optimizers, as well as sine cosine (SCA) and slap swarm (SSA) algorithms, confirmed the superiority of the proposed BES approach, where the Root Mean Square Error (RMSE) objective function between the experimental data and estimated characteristics achieves the least value.

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Elattar, Ehab E. "Optimal Power Flow of a Power System Incorporating Stochastic Wind Power Based on Modified Moth Swarm Algorithm." IEEE Access 7 (2019): 89581–93. http://dx.doi.org/10.1109/access.2019.2927193.

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Sharma, Ritu, Raginee Sharma, and Dr Achala Jain. "A Comparative Analysis of a Hybrid System with Hybrid Methodologies." International Journal of Innovative Technology and Exploring Engineering 11, no. 7 (2022): 17–20. http://dx.doi.org/10.35940/ijitee.g9969.0611722.

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Economic Load Dispatch (ELD) is an important optimization problem in the energy system. Economic Dispatch (ED) is a short-term determination of the optimal performance of a set of power generation assets to meet the system load at the lowest possible cost, taking into account transmission and operational constraints. Economic dispatch problems are solved by dedicated computer software that needs to take into account the operational and system limitations of available resources and corresponding transmission functions. Economic load balancing provides optimal cost savings for power plant operations where methodologies can be applied in a variety of ways, from traditional to advanced. To achieve this, traditional methods have been used from the last few years to the 90's, but in the last few decades AI methods have met their needs and validated satisfactory results. Some advanced hybrid techniques used are the Modified Salp Swarm Optimization Algorithm (MSSA) with Artificial Intelligent (AI) technique aided with Particle Swarm Optimization (PSO) technique, Improved Moth-Fly Optimization Algorithm (IMFOA) with the Recurrent Neural Network (RNN), the Improved Fruit Fly Optimization Algorithm (IFOA) with Artificial Neural Network (ANN) system and Lightning Search Algorithm (LSA) with Genetic Algorithm (GA) which will encourage the researches for providing better solution for economic load dispatch problem is presented in this paper.

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Duman, Serhat. "A Modified Moth Swarm Algorithm Based on an Arithmetic Crossover for Constrained Optimization and Optimal Power Flow Problems." IEEE Access 6 (2018): 45394–416. http://dx.doi.org/10.1109/access.2018.2849599.

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Ritu, Sharma, Sharma Raginee, and Achala Jain Dr. "A Comparative Analysis of a Hybrid System with Hybrid Methodologies." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 11, no. 7 (2022): 17–20. https://doi.org/10.35940/ijitee.G9969.0611722.

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<strong>Abstract</strong>: Economic Load Dispatch (ELD) is an important optimization problem in the energy system. Economic Dispatch (ED) is a short-term determination of the optimal performance of a set of power generation assets to meet the system load at the lowest possible cost, taking into account transmission and operational constraints. Economic dispatch problems are solved by dedicated computer software that needs to take into account the operational and system limitations of available resources and corresponding transmission functions. Economic load balancing provides optimal cost savings for power plant operations where methodologies can be applied in a variety of ways, from traditional to advanced. To achieve this, traditional methods have been used from the last few years to the 90's, but in the last few decades AI methods have met their needs and validated satisfactory results. Some advanced hybrid techniques used are the Modified Salp Swarm Optimization Algorithm (MSSA) with Artificial Intelligent (AI) technique aided with Particle Swarm Optimization (PSO) technique, Improved Moth-Fly Optimization Algorithm (IMFOA) with the Recurrent Neural Network (RNN), the Improved Fruit Fly Optimization Algorithm (IFOA) with Artificial Neural Network (ANN) system and Lightning Search Algorithm (LSA) with Genetic Algorithm (GA) which will encourage the researches for providing better solution for economic load dispatch problem is presented in this paper.

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Alzaidi, Khalid Mohammed Saffer, Oguz Bayat, and Osman N. Uçan. "A Heuristic Approach for Optimal Planning and Operation of Distribution Systems." Journal of Optimization 2018 (June 3, 2018): 1–19. http://dx.doi.org/10.1155/2018/6258350.

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The efficient planning and operation of power distribution systems are becoming increasingly significant with the integration of renewable energy options into power distribution networks. Keeping voltage magnitudes within permissible ranges is vital; hence, control devices, such as tap changers, voltage regulators, and capacitors, are used in power distribution systems. This study presents an optimization model that is based on three heuristic approaches, namely, particle swarm optimization, imperialist competitive algorithm, and moth flame optimization, for solving the voltage deviation problem. Two different load profiles are used to test the three modified algorithms on IEEE 123- and IEEE 13-bus test systems. The proposed optimization model uses three different cases: Case 1, changing the tap positions of the regulators; Case 2, changing the capacitor sizes; and Case 3, integrating Cases 1 and 2 and changing the locations of the capacitors. The numerical results of the optimization model using the three heuristic algorithms are given for the two specified load profiles.

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Khamari, Dillip, Rabindra Kumar Sahu, and Sidhartha Panda. "A Modified Moth Swarm Algorithm-Based Hybrid Fuzzy PD–PI Controller for Frequency Regulation of Distributed Power Generation System with Electric Vehicle." Journal of Control, Automation and Electrical Systems 31, no. 3 (2020): 675–92. http://dx.doi.org/10.1007/s40313-020-00565-0.

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Amiri, Farhad, Mohsen Eskandari, and Mohammad Hassan Moradi. "Improved Load Frequency Control in Power Systems Hosting Wind Turbines by an Augmented Fractional Order PID Controller Optimized by the Powerful Owl Search Algorithm." Algorithms 16, no. 12 (2023): 539. http://dx.doi.org/10.3390/a16120539.

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The penetration of intermittent wind turbines in power systems imposes challenges to frequency stability. In this light, a new control method is presented in this paper by proposing a modified fractional order proportional integral derivative (FOPID) controller. This method focuses on the coordinated control of the load-frequency control (LFC) and superconducting magnetic energy storage (SMES) using a cascaded FOPD–FOPID controller. To improve the performance of the FOPD–FOPID controller, the developed owl search algorithm (DOSA) is used to optimize its parameters. The proposed control method is compared with several other methods, including LFC and SMES based on the robust controller, LFC and SMES based on the Moth swarm algorithm (MSA)–PID controller, LFC based on the MSA–PID controller with SMES, and LFC based on the MSA–PID controller without SMES in four scenarios. The results demonstrate the superior performance of the proposed method compared to the other mentioned methods. The proposed method is robust against load disturbances, disturbances caused by wind turbines, and system parameter uncertainties. The method suggested is characterized by its resilience in addressing the challenges posed by load disturbances, disruptions arising from wind turbines, and uncertainties surrounding system parameters.

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Mohamed, Emad A., and Yasunori Mitani. "Load frequency control enhancement of islanded micro-grid considering high wind power penetration using superconducting magnetic energy storage and optimal controller." Wind Engineering 43, no. 6 (2019): 609–24. http://dx.doi.org/10.1177/0309524x18824533.

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This article proposes a robust load frequency control using a new optimal proportional–integral–derivative controller–based genetic moth swarm algorithm for islanded microgrids considering high wind power penetration. In such microgrids, the replacement of conventional generator units with a large number of renewable energy sources reduces the system inertia, which in turn causes undesirable influence on microgrid frequency stability, leading to weakening of the microgrid. Furthermore, sudden load shedding, load restoring, and short circuits caused large frequency fluctuations which threaten the system security and could lead to complete blackouts as well as damages to the system equipment. In order to solve this challenge, this study proposes a new coordinated optimal load frequency control plus modified control signal to superconducting magnetic energy storage for compensating the microgrid frequency deviation (∆ f). To prove the effectiveness of the proposed coordinated control strategy, an islanded microgrid was tested for the MATLAB/Simulink simulation. The physical constraints of the turbines such as generation rate constraints and speed governor dead band are considered in this study. The results confirmed the effectiveness and robustness of the proposed coordination performance against all scenarios of different load profiles, wind power fluctuation, and system uncertainties in microgrid integrated with high penetration of wind farms. Moreover, the results have been compared with both: the optimal load frequency control with/without the effect of conventional superconducting magnetic energy storage.

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Panteleev, A. V., and I. S. Nadorov. "Application of the modified method simulating the behavior of a flock of Moths to solve the optimal open loop control problem of a mobile robot movement." Modelling and Data Analysis 15, no. 1 (2025): 81–109. https://doi.org/10.17759/mda.2025150105.

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<p>The article proposes a modification of the metaheuristic optimization method simulating the behavior of a swarm of moths, which belongs to the group of bio-inspired methods. A step-by-step algorithm is formed and the efficiency of the modified method is studied on a generally accepted set of test functions of many variables with a complex structure of level surfaces, on the problem of optimal open loop control with a known exact solution, as well as on the problem of determining the parameters of the tension/compression spring with constraints such as inequalities. The advantage of the modified method over the original version is shown. It is demonstrated that the method allows finding solutions of sufficiently good quality in a time acceptable from a practical point of view. A solution to an applied problem of finding the optimal open loop control of a mobile robot on a plane in the presence of obstacles is given. The goal of the control is to achieve a given end point while minimizing the elapsed time and fulfilling the condition of enveloping forbidden areas. To find the control law as a function of time, a piecewise constant approximation was used, which allows reducing the problem to finding a finite number of unknown parameters. The solutions of the terminal problem of speed of response with different structure and parameters of the composite quality functional are considered using the sequential application of the developed modified method simulating the behavior of a swarm of moths, the random search method with sequential reduction of the study area and the path-relinking method. The results of comparison with known solutions are presented.</p>

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Aguilar-Mejía, Omar, Hertwin Minor-Popocatl, and Ruben Tapia-Olvera. "Comparison and Ranking of Metaheuristic Techniques for Optimization of PI Controllers in a Machine Drive System." Applied Sciences 10, no. 18 (2020): 6592. http://dx.doi.org/10.3390/app10186592.

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Proportional integral (PI) control is still the most widely deployed controller in the industrial drives due to its simplicity and the fact that it is easy to understand and implement. Nevertheless, they are successes applied to systems with a complex behavior with a nonlinear representation, but a disadvantage is the procedure to find the optimal PI controller gains. The optimal values of PI parameters must be computed during the tuning process. However, traditional tuning techniques are based on model and do not provide optimal adjustment parameters for the PI controllers because the transient response could produce oscillations and a large overshoot. In this paper, six swarm intelligence-based algorithms (whale, moth-flame, flower pollination, dragonfly, cuckoo search, and modified flower pollination), are correctly conditioned and delimited to tune the PI controllers, the results are probed in a typical industry actuator. Also, a rigorous study is developed to evaluate the quality and reliability of these algorithms by a statistical analysis based on non-parametric test and post-hoc test. Finally, with the obtained results, some time simulations are carried out to corroborate that the nonlinear system performance is improved for high precision industrial applications subjected to endogenous and exogenous uncertainties in a wide range of operating conditions.

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Oliva, Diego, Sara Esquivel-Torres, Salvador Hinojosa, et al. "Opposition-based moth swarm algorithm." Expert Systems with Applications 184 (December 2021): 115481. http://dx.doi.org/10.1016/j.eswa.2021.115481.

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Luque-Chang, Alberto, Erik Cuevas, Marco Pérez-Cisneros, Fernando Fausto, Arturo Valdivia-González, and Ram Sarkar. "Moth Swarm Algorithm for Image Contrast Enhancement." Knowledge-Based Systems 212 (January 2021): 106607. http://dx.doi.org/10.1016/j.knosys.2020.106607.

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Mohamed, Al-Attar Ali, Yahia S. Mohamed, Ahmed A. M. El-Gaafary, and Ashraf M. Hemeida. "Optimal power flow using moth swarm algorithm." Electric Power Systems Research 142 (January 2017): 190–206. http://dx.doi.org/10.1016/j.epsr.2016.09.025.

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Ishtiaq, Atif, Sheeraz Ahmed, Muhammad Fahad Khan, Farhan Aadil, Muazzam Maqsood, and Salabat Khan. "Intelligent clustering using moth flame optimizer for vehicular ad hoc networks." International Journal of Distributed Sensor Networks 15, no. 1 (2019): 155014771882446. http://dx.doi.org/10.1177/1550147718824460.

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Vehicular ad hoc networks consist of access points for communication, transmission, and collecting information of nodes and environment for managing traffic loads. Clustering can be performed in the vehicular ad hoc networks for achieving the desired goals. Due to the random range of vehicular ad hoc networks, stability is the major issue on which major research is still in progress. In this article, a moth flame optimization–driven clustering algorithm is presented for vehicular ad hoc networks, replicating the social behavior of moth flames in creating efficient clusters. The proposed framework is extracted from the living routine of moth flames. The proposed framework allows efficient communication by creating the augmented number of clusters due to which it is termed as intelligent algorithm. Besides this, the use of unsupervised clustering technique emphasizes to call it as an intelligent clustering algorithm. The recommended intelligent clustering using moth flame optimization framework is executed for resolving and optimizing the clustering problem in vehicular ad hoc networks, the primary focus of the proposed scheme is to improve the stability in vehicular ad hoc networks. This proposed method can also be used for the transmission of data in vehicular networks. Intelligent clustering using moth flame optimization is then proved by relative study with two variants of particle swarm optimization: multiple-objective particle swarm optimization and comprehensive learning particle swarm optimization and a variant of ant colony optimization: ant colony optimization–based clustering algorithm for vehicular ad hoc network. The simulation demonstrates that the intelligent clustering using moth flame optimization is provisioning optimal outcomes in contrast to widely known metaheuristics. Furthermore, it provides a robust routing mechanism based on the clustering. It is suitable for large highways for the productivity of quality communication, reliable delivery for each vehicle and can be widely applicant.

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Sami N. Hussein and Nazar K. Hussein. "Improving Moth-Flame Optimization Algorithm by using Slime-Mould Algorithm." Tikrit Journal of Pure Science 27, no. 1 (2022): 99–109. http://dx.doi.org/10.25130/tjps.v27i1.86.

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The MFO algorithm is one of the modern optimization algorithms based on swarm intelligence, and the SMA algorithm is also one of the latest algorithms in the same field and has the advantages of fast convergence, high convergence accuracy, robust and robust. In this research paper, we introduce an optimized algorithm for MFO based on the SMA algorithm to get better performance using the features in the two algorithms, and two different algorithms are proposed in this field. The two predicted new algorithms were tested with standard test functions and the results were encouraging compared to the standard algorithms.

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Adamu, Zainab Muhammad, Emmanuel Gbenga Dada, and Stephen Bassi Joseph. "Moth Flame Optimization Algorithm for Optimal FIR Filter Design." International Journal of Intelligent Systems and Applications 13, no. 5 (2021): 24–34. http://dx.doi.org/10.5815/ijisa.2021.05.03.

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This paper presents the application of Moth Flame optimization (MFO) algorithm to determine the best impulse response coefficients of FIR low pass, high pass, band pass and band stop filters. MFO was inspired by observing the navigation strategy of moths in nature called transverse orientation composed of three mathematical sub-models. The performance of the proposed technique was compared to those of other well-known high performing optimization techniques like techniques like Particle Swarm Optimization (PSO), Novel Particle Swarm Optimization (NPSO), Improved Novel Particle Swarm Optimization (INPSO), Genetic Algorithm (GA), Parks and McClellan (PM) Algorithm. The performances of the MFO based designed optimized FIR filters have proved to be superior as compared to those obtained by PSO, NPSO, INPSO, GA, and PM Algorithm. Simulation results indicated that the maximum stop band ripples 0.057326, transition width 0.079 and fitness value 1.3682 obtained by MFO is better than that of PSO, NPSO, INPSO, GA, and PM Algorithms. The value of stop band ripples indicated the ripples or fluctuations obtained at the range which signals are attenuated is very low. The reduced value of transition width is the rate at which a signal changes from either stop band to pass band of a filter or vice versa is very good. Also, small fitness value in an indication that the values of the control variable of MFO are very near to its optimum solutions. The proposed design technique in this work generates excellent solution with high computational efficiency. This shows that MFO algorithm is an outstanding technique for FIR filter design.

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Mu, Ai-Qin, De-Xin Cao, and Xiao-Hua Wang. "A Modified Particle Swarm Optimization Algorithm." Natural Science 01, no. 02 (2009): 151–55. http://dx.doi.org/10.4236/ns.2009.12019.

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Zhang, Zhe, Limin Jia, and Yong Qin. "Modified constriction particle swarm optimization algorithm." Journal of Systems Engineering and Electronics 26, no. 5 (2015): 1107–13. http://dx.doi.org/10.1109/jsee.2015.00120.

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Miloud, Mihoubi, Rahmoun Abdellatif, and Pascal Lorenz. "Moth Flame Optimization Algorithm Range-Based for Node Localization Challenge in Decentralized Wireless Sensor Network." International Journal of Distributed Systems and Technologies 10, no. 1 (2019): 82–109. http://dx.doi.org/10.4018/ijdst.2019010106.

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Recently developments in wireless sensor networks (WSNs) have raised numerous challenges, node localization is one of these issues. The main goal in of node localization is to find accurate position of sensors with low cost. Moreover, very few works in the literature addressed this issue. Recent approaches for localization issues rely on swarm intelligence techniques for optimization in a multi-dimensional space. In this article, we propose an algorithm for node localization, namely Moth Flame Optimization Algorithm (MFOA). Nodes are located using Euclidean distance, thus set as a fitness function in the optimization algorithm. Deploying this algorithm on a large WSN with hundreds of sensors shows pretty good performance in terms of node localization. Computer simulations show that MFOA converge rapidly to an optimal node position. Moreover, compared to other swarm intelligence techniques such as Bat algorithm (BAT), particle swarm optimization (PSO), Differential Evolution (DE) and Flower Pollination Algorithm (FPA), MFOA is shown to perform much better in node localization task.

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Zhou, Yongquan, Xiao Yang, Ying Ling, and Jinzhong Zhang. "Meta-heuristic moth swarm algorithm for multilevel thresholding image segmentation." Multimedia Tools and Applications 77, no. 18 (2018): 23699–727. http://dx.doi.org/10.1007/s11042-018-5637-x.

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Mat Yahya, Nafrizuan, Nur Atikah Nor’Azlan, and M. Osman Tokhi. "Parametric Study of Dual-particle Swarm Optimisation-modified Adaptive Bats Sonar Algorithm on Multi-objective Benchmark Test Functions." Mekatronika 1, no. 2 (2019): 72–80. http://dx.doi.org/10.15282/mekatronika.v1i2.4988.

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    An integrated algorithm for solving multi-objective optimisation problems using a dual- level searching approach is presented. The proposed algorithm named as dual-particle swarm optimisation-modified adaptive bats sonar algorithm (D-PSO-MABSA) where the concept of echolocation of a colony of bats to find prey in the modified adaptive bats sonar algorithm is combined with the established particle swarm optimisation algorithm. The proposed algorithm combines the advantages of both particle swarm optimisation and modified adaptive bats sonar algorithm approach to handling the complexity of multi-objective optimisation problems. These include swarm flight attitude and swarm searching strategy. The performance of the algorithm is verified through several multi- objective optimisation benchmark test functions. The acquired results show that the proposed algorithm performs well to produce a reliable Pareto front. The proposed algorithm can thus be an effective method for solving multi-objective optimisation problems.   

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Et. al., Vijaya Bhaskar K,. "Modern Swarm Intelligence based Algorithms for Solving Optimal Power Flow Problem in a Regulated Power System Framework." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 1786–93. http://dx.doi.org/10.17762/turcomat.v12i2.1515.

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This paper presents artificial swarm intelligent based algorithms viz., Firefly Algorithm (FFA), Dragonfly Algorithm (DA) and Moth Swarm Algorithm (MSA) to take care of the issues related to optimal power flow (OPF) problem in a power system network. The optimal values of various decision variables obtained by swarm intelligent based algorithms can optimize various objective function of OPF problem. This article is focused with four objectives such as minimization of total fuel cost (TFC) and total active power loss (TAPL); improvisation of total voltage profile (TVD) and voltage stability index (VSI). The effectiveness of various swam intelligent algorithms are investigated on a standard IEEE-30 bus. The performance of distinct algorithms is compared with statistical measures and convergence characteristics.

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Xu, Xiaomei, and Ping Lin. "Parameter identification of sound absorption model of porous materials based on modified particle swarm optimization algorithm." PLOS ONE 16, no. 5 (2021): e0250950. http://dx.doi.org/10.1371/journal.pone.0250950.

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Porous materials have been widely used in the field of noise control. The non-acoustical parameters involved in the sound absorption model have an important effect on the sound absorption performance of porous materials. How to identify these non-acoustical parameters efficiently and accurately is an active research area and many researchers have devoted contributions on it. In this study, a modified particle swarm optimization algorithm is adopted to identify the non-acoustical parameters of the jute fiber felt. Firstly, the sound absorption model used to predict the sound absorption coefficient of the porous materials is introduced. Secondly, the model of non-acoustical parameter identification of porous materials is established. Then the modified particle swarm optimization algorithm is introduced and the feasibility of the algorithm applied to the parameter identification of porous materials is investigated. Finally, based on the sound absorption coefficient measured by the impedance tube the modified particle swarm optimization algorithm is adopted to identify the non-acoustical parameters involved in the sound absorption model of the jute fiber felt, and the identification performance and the computational performance of the algorithm are discussed. Research results show that compared with other identification methods the modified particle swarm optimization algorithm has higher identification accuracy and is more suitable for the identification of non-acoustical parameters of the porous materials. The sound absorption coefficient curve predicted by the modified particle swarm optimization algorithm has good consistency with the experimental curve. In the aspect of computer running time, compared with the standard particle swarm optimization algorithm, the modified particle swarm optimization algorithm takes shorter running time. When the population size is larger, modified particle swarm optimization algorithm has more advantages in the running speed. In addition, this study demonstrates that the jute fiber felt is a good acoustical green fibrous material which has excellent sound absorbing performance in a wide frequency range and the peak value of its sound absorption coefficient can reach 0.8.

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Ajeil, Fatin Hassan, Ibraheem Kasim Ibraheem, Ahmad Taher Azar, and Amjad J. Humaidi. "Autonomous navigation and obstacle avoidance of an omnidirectional mobile robot using swarm optimization and sensors deployment." International Journal of Advanced Robotic Systems 17, no. 3 (2020): 172988142092949. http://dx.doi.org/10.1177/1729881420929498.

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The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. Two modifications are suggested to improve the searching process of the standard bat algorithm with the result of two novel algorithms. The first algorithm is a Modified Frequency Bat algorithm, and the second is a hybridization between the Particle Swarm Optimization with the Modified Frequency Bat algorithm, namely, the Hybrid Particle Swarm Optimization-Modified Frequency Bat algorithm. Both Modified Frequency Bat and Hybrid Particle Swarm Optimization-Modified Frequency Bat algorithms have been integrated with a proposed technique for obstacle detection and avoidance and are applied to different static and dynamic environments using free-space modeling. Moreover, a new procedure is proposed to convert the infeasible solutions suggested via path the proposed swarm-inspired optimization-based path planning algorithm into feasible ones. The simulations are run in MATLAB environment to test the validation of the suggested algorithms. They have shown that the proposed path planning algorithms result in superior performance by finding the shortest and smoothest collision-free path under various static and dynamic scenarios.

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Danin, Zekharya, Abhishek Sharma, Moshe Averbukh, and Arabinda Meher. "Improved Moth Flame Optimization Approach for Parameter Estimation of Induction Motor." Energies 15, no. 23 (2022): 8834. http://dx.doi.org/10.3390/en15238834.

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The effective deployment of electrical energy has received attention because of its environmental implications. On the other hand, induction motors are the primary equipment used in many industries. Industrial facilities demand the maximum percentage of energy. This energy demand is determined by the operating circumstances imposed by the internal characteristics of the induction motor. Because internal parameters of an induction motor are not immediately measurable, they must be obtained through an identification process. This paper proposed an improved version of moth flame optimization (IMFO) for the efficient parameter estimation of induction motors. A steady-state equivalent circuit of the induction motor is employed for the simulation. The proposed technique handles the parameter estimation problem better than moth flame optimization (MFO), particle swarm optimization (PSO), the flower pollination algorithm (FPA), the tunicate swarm algorithm (TSA), and the sine cosine algorithm (SCA). The anticipated IMFO reduces the cost function by 49.38% as compared with the basic version of MFO.

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Alade, Oyekale Abel, Roselina Sallehuddin, and Nor Haizan Mohamed Radzi. "Enhancing extreme learning machines classification with moth-flame optimization technique." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 2 (2022): 1027. http://dx.doi.org/10.11591/ijeecs.v26.i2.pp1027-1035.

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Extreme Learning Machine (ELM) algorithm assigns the input weights and biases in a “one-time stamp” fashion, this method makes the algorithm to be ill-conditioned and reduces its classification accuracy. The contribution of this work is the enhancement of the performance of ELM with the Moth-Flame Optimization (MFO) algorithm to improve classification accuracy. A hybrid of the Moth-Flame Optimization and Extreme Learning Machine (MFO-ELM) algorithm is implemented in MATLAB. MFO ensures a concurrent simulation of exploration and exploitation of the search space to select an optimum candidate solution. The candidate solution is reshaped into input weights and biases for ELM classification. The hybrid algorithm is validated on five life-selected datasets. The performance improvement of MFO-ELM is compared with ELM-optimized Particle Swarm Optimization (PSO-ELM) and Competitive Swarm Optimization (CSO-ELM) algorithms. The improvement rates are qualitatively and quantitatively evaluated to show the improvement of MFO-ELM on ELM and the other meta-heuristic algorithms. MFO-ELM improved the accuracies of the basic ELM in all 100% of the simulations and performed better than the other meta-heuristic algorithms in 80% of the simulations. The performance of MFO-ELM is more competitive, and it is recommended for solving classification problems.

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Ramaporselvi, R., and G. Geetha. "Congestion management in deregulated power system using adaptive moth swarm optimization." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 41, no. 1 (2021): 334–56. http://dx.doi.org/10.1108/compel-06-2021-0198.

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Purpose The purpose of this paper is to enhance the line congestion and to minimize power loss. Transmission line congestion is considered the most acute trouble during the operation of the power system. Therefore, congestion management acts as an effective tool in using the available power without breaking the system hindrances or limitations. Design/methodology/approach Over the past few years, determining the optimal location and size of the devices have pinched a great deal of consideration. Numerous approaches have been established to mitigate the congestion rate, and this paper aims to enhance the line congestion and minimize power loss by determining the compensation rate and optimal location of a thyristor-switched capacitor (TCSC) using adaptive moth swarm optimization (AMSO) algorithm. Findings An AMSO algorithm uses the performances of moth flame and the chaotic local search-based shrinking scheme of the bacterial foraging optimization algorithm. The proposed AMSO approach is executed and discussed for the IEEE-30 bus system for determining the optimal location of single TCSC and dual TCSC. Originality/value In addition to this, the proposed algorithm is compared with various other existing approaches, and the results thus obtained provide better performances than other techniques.

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Liu Le-Zhu, Zhang Ji-Qian, Xu Gui-Xia, Liang Li-Si, and Huang Shou-Fang. "A modified chaotic ant swarm optimization algorithm." Acta Physica Sinica 62, no. 17 (2013): 170501. http://dx.doi.org/10.7498/aps.62.170501.

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Michael Mahesh K. "Workflow Scheduling using Improved Moth Swarm Optimization Algorithm in Cloud Computing." Multimedia Research 3, no. 3 (2020): 36–43. http://dx.doi.org/10.46253/j.mr.v3i3.a5.

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Luo, Qifang, Xiao Yang, and Yongquan Zhou. "Nature-inspired approach: An enhanced moth swarm algorithm for global optimization." Mathematics and Computers in Simulation 159 (May 2019): 57–92. http://dx.doi.org/10.1016/j.matcom.2018.10.011.

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Abderazek, Hammoudi, Ferhat Hamza, Ali Riza Yildiz, and Sadiq M. Sait. "Comparative investigation of the moth-flame algorithm and whale optimization algorithm for optimal spur gear design." Materials Testing 63, no. 3 (2021): 266–71. http://dx.doi.org/10.1515/mt-2020-0039.

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Abstract In this study, two recent algorithms, the whale optimization algorithm and moth-flame optimization, are used to optimize spur gear design. The objective function is the minimization of the total weight of the spur gear pair. Moreover, the optimization problem is subjected to constraints on the main kinematic and geometric conditions as well as to the resistance of the material of the gear system. The comparison between moth-flame optimization (MFO), the whale optimization algorithm (WOA), and previous studies indicate that the final results obtained from both algorithms lead to a reduction in gear weight by 1.05 %. MFO and the WOA are compared with four additional swarm algorithms. The experimental results indicate that the algorithms introduced here, in particular MFO, outperform the four other methods when compared in terms of solution quality, robustness, and high success rate.

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Nadimi-Shahraki, Mohammad H., Ali Fatahi, Hoda Zamani, Seyedali Mirjalili, Laith Abualigah, and Mohamed Abd Elaziz. "Migration-Based Moth-Flame Optimization Algorithm." Processes 9, no. 12 (2021): 2276. http://dx.doi.org/10.3390/pr9122276.

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Moth–flame optimization (MFO) is a prominent swarm intelligence algorithm that demonstrates sufficient efficiency in tackling various optimization tasks. However, MFO cannot provide competitive results for complex optimization problems. The algorithm sinks into the local optimum due to the rapid dropping of population diversity and poor exploration. Hence, in this article, a migration-based moth–flame optimization (M-MFO) algorithm is proposed to address the mentioned issues. In M-MFO, the main focus is on improving the position of unlucky moths by migrating them stochastically in the early iterations using a random migration (RM) operator, maintaining the solution diversification by storing new qualified solutions separately in a guiding archive, and, finally, exploiting around the positions saved in the guiding archive using a guided migration (GM) operator. The dimensionally aware switch between these two operators guarantees the convergence of the population toward the promising zones. The proposed M-MFO was evaluated on the CEC 2018 benchmark suite on dimension 30 and compared against seven well-known variants of MFO, including LMFO, WCMFO, CMFO, CLSGMFO, LGCMFO, SMFO, and ODSFMFO. Then, the top four latest high-performing variants were considered for the main experiments with different dimensions, 30, 50, and 100. The experimental evaluations proved that the M-MFO provides sufficient exploration ability and population diversity maintenance by employing migration strategy and guiding archive. In addition, the statistical results analyzed by the Friedman test proved that the M-MFO demonstrates competitive performance compared to the contender algorithms used in the experiments.

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C., Shilaja, and Arunprasath T. "Optimal power flow using Moth Swarm Algorithm with Gravitational Search Algorithm considering wind power." Future Generation Computer Systems 98 (September 2019): 708–15. http://dx.doi.org/10.1016/j.future.2018.12.046.

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Cheruiyot, Fabian, and Davies Segera. "A Master-Slave Salp Swarm Algorithm Optimizer for Hybrid Energy Storage System Control Strategy in Electric Vehicles." Journal of Energy 2022 (September 14, 2022): 1–20. http://dx.doi.org/10.1155/2022/1648433.

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Pure electric vehicles provide an enticing ecofriendly alternative to traditional fossil fuel combustion engine locomotives. Batteries have primarily been used to store energy in electric vehicles; however, peak load demand and transient power leading to decreased battery lifespan have bred interest in hybrid energy storage systems in electric vehicles. Management of energy drawn from a hybrid energy storage system (HESS) in electric vehicles is a real-time multistage optimization problem aimed at minimizing energy consumption while aptly distributing energy drawn from the battery and capacitor to enhance the battery life cycle. This paper explores the feasibility of a master-slave salp swarm optimization algorithm (MSSSA) (metaheuristic algorithm) in a HESS control strategy for electric vehicles. Introducing a master-slave learning approach to the salp swarm algorithm (SSA) improves its performance by increasing its convergence rate while maintaining a balance between exploration and exploitation phases of the algorithm. A comparison of the MSSSA results with the SSA (salp swarm algorithm), DA (dynamic algorithm), WOA (whale optimization algorithm), MFO (moth flame optimization algorithm), GA (genetic algorithm), and PSO (particle swarm optimization algorithm) on benchmark test functions and dynamic program simulation of an electric vehicle’s HESS control strategy and shows preeminence of the MSSSA control strategy for HESS.

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Fountas, Nikolaos A., John D. Kechagias, and Nikolaos M. Vaxevanidis. "Swarm intelligence algorithms for optimising sliding wear of nanocomposites." Tribology and Materials 3, no. 1 (2024): 44–50. http://dx.doi.org/10.46793/tribomat.2024.004.

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This paper presents simulation results obtained by a set of modern algorithms adhering to swarm intelligence for minimising wear rate in the case of A356/Al2O3 nanocomposites produced using a compocasting process. Grey wolf optimisation (GWO) algorithm, moth-flame optimisation (MFO) algorithm, dragonfly algorithm (DA) and whale optimisation algorithm (WOA) were the algorithms under examination. A full quadratic regression equation that predicts wear rate, as the optimisation objective by considering reinforcement content, sliding speed, normal load and reinforcement size as the independent process parameters, was utilised as the objective function. Simulation results obtained by the selected algorithms were quite promising in terms of fast convergence and global optimum result arrival, thus prompting to further investigation of applying swarm intelligence to general problem-solving aspects related to tribology.

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Yang, Bin, and Qi Lin Zhang. "Parallelizing a Modified Particle Swarm Optimizer (PSO)." Advanced Materials Research 163-167 (December 2010): 2404–9. http://dx.doi.org/10.4028/www.scientific.net/amr.163-167.2404.

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Recently, a modified Particle Swarm Optimizer (MLPSO) has been succeeded in solving truss topological optimization problems and competitive results are obtained. Since this optimizer belongs to evolutionary algorithm and plagued by high computational cost as measured by execution time, in order to reduce its execution time for solving large complex optimization problem, a parallel version for this optimizer is studied in this paper. This paper first gives an overview of PSO algorithm as well as the modified PSO, and then a design and an implementation of parallel PSO is proposed. The performance of the proposed algorithm is tested by two examples and promising speed-up rate is obtained. Final part is conclusion and outlook.

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Alade, Oyekale Abel, Roselina Sallehuddin, and Nor Haizan Mohamed Radzi. "Enhancing extreme learning machines classification with mothflame optimization technique." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 2 (2022): 1027–35. https://doi.org/10.11591/ijeecs.v26.i2.pp1027-1035.

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Extreme learning machine (ELM) algorithm assigns the input weights and biases in a “one-time stamp” fashion, this method makes the algorithm to be ill-conditioned and reduces its classification accuracy. The contribution of this work is the enhancement of the performance of ELM with the moth-flame optimization (MFO) algorithm to improve classification accuracy. A hybrid of the Moth-flame optimization and extreme learning machine (MFO-ELM) algorithm is implemented in MATLAB. MFO ensures a concurrent simulation of exploration and exploitation of the search space to select an optimum candidate solution. The candidate solution is reshaped into input weights and biases for ELM classification. The hybrid algorithm is validated on five lifeselected datasets. The performance improvement of MFO-ELM is compared with ELM-optimized particle swarm optimization (PSO-ELM) and competitive swarm optimization (CSO-ELM) algorithms. The improvement rates are qualitatively and quantitatively evaluated to show the improvement of MFO-ELM on ELM and the other meta-heuristic algorithms. MFO-ELM improved the accuracies of the basic ELM in all 100% of the simulations and performed better than the other meta-heuristic algorithms in 80% of the simulations. The performance of MFO-ELM is more competitive, and it is recommended for solving classification problems.

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Yang, Bin, and Qi Lin Zhang. "Applying a Modified Particle Swarm Optimizer to Section Optimization of Steel Framed Structures." Advanced Materials Research 383-390 (November 2011): 1071–76. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.1071.

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As a new paradigm of Swarm Intelligence which is inspired by concepts from ’Social Psychology’ and ’Artificial Life’, the Particle Swarm Optimization (PSO), it is widely applied to various kinds of optimization problems especially of nonlinear, non-differentiable or non-convex types. In this paper, a modified guaranteed converged particle swarm algorithm (MGCPSO) is proposed in this paper, which is inspired by guaranteed converged particle swarm algorithm (GCPSO) proposed by von den Bergh. The section sizing optimization problem of steel framed structure subjected to various constraints based on Chinese Design Code are selected to illustrate the performance of the presented optimization algorithm.

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Wu, Xueyang, Yinghao Shan, and Kexin Fan. "A Modified Particle Swarm Algorithm for the Multi-Objective Optimization of Wind/Photovoltaic/Diesel/Storage Microgrids." Sustainability 16, no. 3 (2024): 1065. http://dx.doi.org/10.3390/su16031065.

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Microgrids have been widely used due to their advantages, such as flexibility and cleanliness. This study adopts the hierarchical control method for microgrids containing multiple energy sources, i.e., photovoltaic (PV), wind, diesel, and storage, and carries out multi-objective optimization in the tertiary control, i.e., optimizing the economic cost, environmental cost, and the degree of energy utilization of microgrids. As the traditional multi-objective particle swarm algorithm is prone to local convergence, this study introduces variable inertia weight and learning factors to obtain a modified particle swarm algorithm, which is more advantageous in multi-objective optimization. Compared to the traditional particle swarm algorithm, the modified particle swarm algorithm increased the photovoltaic absorbed rate from 0.7724 to 0.8683 and the wind energy absorbed rate from 0.6064 to 0.7158 in one day, which resulted in an increase in energy utilization by 14.89%, and a reduction in financial environmental costs from RMB 135,870 to RMB 132,230. The simulation of the optimization effect of the conventional particle swarm algorithm and the modified particle swarm algorithm on the microgrid were carried out, respectively, in MATLAB, which verifies the advantage of the modified particle swarm algorithm on the optimization of microgrids. Then, the optimization results, i.e., the data of the power scheduling process of the four power sources, were made into a table and imported into the microgrid model in Simulink. The simulation results indicated that the microgrid was able to output stable voltage, current, and frequency. Finally, the changes in microgrids affected by the external environment were further investigated from the aspects of the market environment and natural environment. Moreover, we verified the presence of a contradiction between the optimization of the microgrid economy and environmental protection. Thus, microgrids need to adjust their optimization focus according to the natural conditions in which they are located.

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Dong, Ming Gang, Xiao Hui Cheng, and Qin Zhou Niu. "A Constrained Particle Swarm Optimization Algorithm with Oracle Penalty Method." Applied Mechanics and Materials 303-306 (February 2013): 1519–23. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.1519.

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To solve constrained optimization problems, an Oracle penalty method-based comprehensive learning particle swarm optimization (OBCLPSO) algorithm was proposed. First, original Oracle penalty was modified. Secondly, the modified Oracle penalty method was combine with comprehensive learning particle swarm optimization algorithm. Finally, experimental results and comparisons were given to demonstrate the optimization performances of OBCLPSO. The results show that the proposed algorithm is a very competitive approach for constrained optimization problems.

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

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

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Khalid, Qazi Salman, Shakir Azim, Muhammad Abas, Abdur Rehman Babar, and Imran Ahmad. "Modified particle swarm algorithm for scheduling agricultural products." Engineering Science and Technology, an International Journal 24, no. 3 (2021): 818–28. http://dx.doi.org/10.1016/j.jestch.2020.12.019.

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Sharmila D , A. V. Pra.bu, N. Selvaganesh,. "AUTHORSHIP VERIFICATION USING MODIFIED PARTICLE SWARM OPTIMIZATION ALGORITHM." Psychology and Education Journal 58, no. 1 (2021): 4262–66. http://dx.doi.org/10.17762/pae.v58i1.1492.

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Digital forensics is the study of recovery and investigation of the materials found in digital devices, mainly in computers. Forensic authorship analysis is a branch of digital forensics. It includes tasks such as authorship attribution, authorship verification, and author profiling. In Authorship verification, with a given a set of sample documents D written by an author A and an unknown document d, the task is to find whether document d is written by A or not. Authorship verification has been previously done using genetic algorithms, SVM classifiers, etc. The existing system creates an ensemble model by combining the features based on the similarity scores, and the parameter optimization was done using a grid search. The accuracy of verification using the grid search method is 62.14%. The time complexity is high as the system tries all possible combinations of the features during the ensemble model's construction. In the proposed work, Modified Particle Swarm Optimization (MPSO) is used to construct the classification model in the training phase, instead of the ensemble model. In addition to the combination of linguistic and character features, Average Sentence Length is used to improve the verification task accuracy. The accuracy of verification has been improved to 63.38%.

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He, Guang, and Nan-jing Huang. "A modified particle swarm optimization algorithm with applications." Applied Mathematics and Computation 219, no. 3 (2012): 1053–60. http://dx.doi.org/10.1016/j.amc.2012.07.010.

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Journal articles on the topic 'Modified moth swarm algorithm'

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