Relevant bibliographies by topics / Modified moth swarm algorithm

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Modified moth swarm algorithm'

Author: Grafiati
Published: 4 June 2025
Last updated: 6 July 2025

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Journal articles on the topic "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&#39;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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Dissertations / Theses on the topic "Modified moth swarm algorithm"

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Wei, Xing. "Optimization of Strongly Nonlinear Dynamical Systems Using a Modified Genetic Algorithm With Micro-Movement (MGAM)." DigitalCommons@USU, 2009. https://digitalcommons.usu.edu/etd/450.

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The genetic algorithm (GA) is a popular random search and optimization method inspired by the concepts of crossover, random mutation, and natural selection from evolutionary biology. The real-valued genetic algorithm (RGA) is an improved version of the genetic algorithm designed for direct operation on real-valued variables. In this work, a modified version of a genetic algorithm is introduced, which is called a modified genetic algorithm with micro-movement (MGAM). It implements a particle swarm optimization(PSO)-inspired micro-movement phase that helps to improve the convergence rate, while employing the e'cient GA mechanism for maintaining population diversity. In order to test the capability of the MGAM, we firrst implement it on five generally used test functions. Then we test the MGAM on two typical nonlinear dynamical systems. The performance of the MGAM is compared to a basic RGA on all these applications. Finally, we implement the MGAM on the most important application, which is the plasma physics-based model of the solar wind-driven magnetosphere-ionosphere system (WINDMI). In order to use this model for real-time prediction of geomagnetic activity, the model parameters require up-dating every 6-8 hours. We use the MGAM to train the parameters of the model in order to achieve the lowest mean square error (MSE) against the measured auroral electrojet (AL) and Dst indices. The performance of the MGAM is compared to the RGA on historical geomagnetic storm datasets. While the MGAM performs substantially better than the RGA when evaluating standard test functions, the improvement is about 6-12 percent when used on the 20D nonlinear dynamical WINDMI model.
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Zemzami, Maria. "Variations sur PSO : approches parallèles, jeux de voisinages et applications Application d’un modèle parallèle de la méthode PSO au problème de transport d’électricité A modified Particle Swarm Optimization algorithm linking dynamic neighborhood topology to parallel computation An evolutionary hybrid algorithm for complex optimization problems Interoperability optimization using a modified PSO algorithm A comparative study of three new parallel models based on the PSO algorithm Optimization in collaborative information systems for an enhanced interoperability network." Thesis, Normandie, 2019. http://www.theses.fr/2019NORMIR11.

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Reconnue depuis de nombreuses années comme une méthode efficace pour la résolution de problèmes difficiles, la méta-heuristique d’optimisation par essaim de particules PSO (Particle Swarm Optimization) présente toutefois des inconvénients dont les plus étudiés sont le temps de calcul élevé et la convergence prématurée. Cette thèse met en exergue quelques variantes de la méthode PSO visant à échapper à ces deux inconvénients de la méthode. Ces variantes combinent deux approches : la parallélisation de la méthode de calcul et l’organisation de voisinages appropriés pour les particules. L’évaluation de la performance des modèles proposés a été effectuée sur la base d'une expérimentation sur une série de fonctions tests. A la lumière de l’analyse des résultats expérimentaux obtenus, nous observons que les différents modèles proposés donnent des résultats meilleurs que ceux du PSO classique en termes de qualité de la solution et du temps de calcul. Un modèle basé PSO a été retenu et développé en vue d'une expérimentation sur le problème du transport d’électricité. Une variante hybride de ce modèle avec la méthode du recuit simulé SA (Simulated Annealing) a été considérée et expérimentée sur la problématique des réseaux de collaboration<br>Known for many years as a stochastic metaheuristic effective in the resolution of difficult optimization problems, the Particle Swarm Optimization (PSO) method, however, shows some drawbacks, the most studied: high running time and premature convergence. In this thesis we consider some variants of the PSO method to escape these two disadvantages. These variants combine two approaches: the parallelization of the calculation and the organization of appropriate neighborhoods for the particles. To prove the performance of the proposed models, we performed an experiment on a series of test functions. By analyzing the obtained experimental results, we observe that the proposed models based on the PSO algorithm performed much better than basic PSO in terms of computing time and solution quality. A model based on the PSO algorithm was selected and developed for an experiment on the problem of electricity transmission. A hybrid variant of this model with Simulated Annealing (SA) algorithm has been considered and tested on the problem of collaborative networks
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Wang, Ya-shyan, and 王雅賢. "Research on a Modified Particle Swarm Optimization Algorithm." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/62188914837148750779.

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碩士<br>中原大學<br>資訊管理研究所<br>94<br>Particle Swarm Optimization (PSO), an algorithm with the concept of swarm intelligence, also a new branch in evolutionary computing, possesses the merits of fast converging, as well as the simplification in parameter setting. Nevertheless, PSO has the demerit of the inclination to trap into local optima because when particles move, they merely follow pbest and gbest .Although the standard PSO algorithm and other modified algorithms has attempted to enhance the efficiency in utilizing a swarm to search global best, they still fail in avoiding particles falling into local optima. This research has proposed a framework based on clustered Particle Swarm Optimization algorithm. This proposal firstly divides initially generated particles into different search clusters with K-means algorithm, and improves the local search ability by getting smaller Vmax settings with experimental methods. Then, the proposal gets global optima through the compare of gkbest obtained in different clusters. This is so called KPSO. Additionally, in order to ensure the convergence of the algorithm, this research has brought the concept of Belief Space in Culture Algorithm into KPSO, leading particles in population to search in good solution areas. This is the CKPSO. In short, this proposal purposes to increase the accuracy of mean best fitness by these two modified algorithms. The result of this research shows that KPSO and CKPSO need smaller Vmax to increase local searching capability, and clustered search has enhanced mean best fitness value. When the number of particles is small, however, KPSO might be unable to converge in handing complicated dimension problems. After adding the concept of Belief Space, CKPSO is capable of dealing with intricate dimension problems even though it has no absolute advantage compared with KPSO in terms of searching ability. Generally speaking, the performance of KPSO and CKPSO in test functions outshines that of standard PSO, HPSO and FPSO.
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Lin, Yung-Tso, and 林泳佐. "A Modified Cat Swarm Optimization Algorithm for Feature Selection of SVM." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/4rg8z8.

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碩士<br>國立中興大學<br>資訊管理學系所<br>101<br>Cat Swarm Optimization (CSO) is a novel meta-heuristic for an optimization evolutionary algorithm which based on swarm intelligence. CSO is originated by imitating the behavior of cats which includes two sub-modes: seeking and tracing. Prior studies display that the outcome of CSO algorithm is better than that of the well-known meta-heuristic algorithms, such as Genetic Algorithm and Particle Swarm Optimization. This study illustrates a modified version of cat swarm optimization (MCSO) algorithm to improve the search efficiency of problem space. A local search procedure is integrated into the basic CSO algorithm. And in this study, the MCSO algorithm is used to solve the feature selection of support vector machine (SVM). Experimental results show that the proposed MCSO algorithm spends less time but obtains better performance than basic CSO algorithm.
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Chuang, Cheng-Hsueh, and 莊承學. "The Resolution of Overlapping Autofluorescence Spectrum Using A Modified Particle Swarm Optimization Algorithm." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/76022545424235903050.

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碩士<br>國立高雄大學<br>電機工程學系碩士班<br>99<br>In this thesis, we take an improved particle swarm optimization algorithm for the separation of overlapping spectra where the speed update is the major improvement. Taking advantage of the concepts of inertia factor and constriction coefficient in the proposed algorithm, the convergence rate and the accuracy are significant improved. In the experiments, we used an optical Y-type fiber to conduct excitation light from the 337-nm nitrogen laser (VSL-337ND-S, LSI) to irradiate on the skin surface so that we could get an excitation fluorescence signal with the wavelength ranging from 400 nm to 600 nm. We collected the data from the skin to the spectrometer (SP-150, Princeton Instrument). We designed three experiments and compared the results with other algorithms. For the fitness function values, this algorithm achieves a higher fitness value about 1. For the convergence rate, this algorithm can successfully separate two fluorescent wrist skin materials in the signal of human skin autofluorescence experiment with 67% of individuals (particles) and 15 times of speed as compared with the genetic algorithm. The experimental results show that the content ratios of reduced-nicotinamide-adenine-dinucleotide and flavin-adenine-dinucleotide are 96.3533% and 3.6467%, respectively.
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Yu, Hong-Kuan, and 余泓寬. "Investigation of the Digital Image Stabilization Based on Modified Particle Swarm Optimization Algorithm." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/08971462378076036972.

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碩士<br>國立臺灣海洋大學<br>通訊與導航工程學系<br>101<br>General camera while shooting, because of manpower jitter, car driving Britain slope vibration, wind blowing, and other external interference leaving the shooting out of the image sequence will produce unwanted jitter. This thesis intends to propose a digital PI-based digital image stabilization technology to remove unwanted jitter effect such that the authenticity of the original image can be preserved. Proposed image stabilization technology includes the global mobile vector (the motion vector estimation unit) and the motion compensation vector (the motion compensation unit) which can prevent jitter and allow smooth movement of the image sequence. First the Sobel edge detection method will be applied to find the edges of objects in the entire image for the motion vector estimation, and the feature block will be established according to the most visible part of the object edges. Then the feature area within the block move vector can be obtained and the global motion vectors can be derived from the local motion vectors using the mode algorithm. The global motion vector should be the actual image motion vector. For the motion vector compensation, the smoothing index (SI) and moving error (ME) will be used to evaluate the performance of the image stabilization system. Meanwhile, the parameters of the digital PI controller will be adjusted according to the fitness value of the multi-objective particle swarm algorithm such that the effects of the jitter prevention and smooth movement for the image sequences can be achieved in different interference environments. The handheld experimental results show that the proposed digital image stabilization system not only can prevent the judder of the image sequence effectively but also eliminate the image delay due to the image translation.
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Mejia, Victor David Lopez, and 洪衛德. "A Modified Binary Particle Swarm Optimization Algorithm to Solve the Thermal Unit Commitment Problem." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/mrmzpc.

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碩士<br>國立中山大學<br>電機電力工程國際碩士學程<br>106<br>The last two decades have seen the advent of metaheuristic-based methods to solve both continuous and discrete optimization problems. While there are many paradigms available, the Particle Swarm Optimization (PSO) algorithm has had a profound impact in the mathematical optimization field. Extensive efforts, often with good results, have been made in the literature to improve the performance of the PSO algorithm by modifying the inertia weight, the position, and the velocity update equations. While these equations are still fundamental in its discrete counterpart, the Binary Particle Swarm Optimization (BPSO) algorithm, they are secondary to the velocity mapping procedure, which is essential in the discrete optimization process. Therefore, this thesis proposes a Modified Binary Particle Swarm Optimization algorithm (VS-BPSO) to solve the first stage of the forward day-ahead Thermal Unit Commitment Problem by considering two families of velocity mapping functions, namely the S-Shaped and V-shaped families. A novel pivot heuristic repair algorithm which effectively handles the conflict between system-wide and time-dependent constraints, is used to obtain a feasible Unit Commitment (UC) schedule. The Economic Dispatch (ED) subproblem is then solved for each time slot of the study period with an Enhanced Lambda Iteration Method (ELI). The viability of the proposed solution methodology is simulated in the MATLAB © environment and is verified with the use of two benchmark systems, namely the IEEE 10-unit and the IEEE 26-unit test case systems. The obtained numerical results demonstrate the enhanced performance of the VS-BPSO algorithm in terms of total cost and computational time when compared to other methods in the recent literature. Furthermore, three additional cases involving other constraints and objectives are included. Comprehensive discussion of the obtained results is provided.
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Chiu, Yi-Lin, and 邱奕霖. "Reactive Power and Voltage Control in Distorted Distribution Systems using Modified Cat Swarm Optimization Algorithm." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/27561486187167957108.

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碩士<br>國立雲林科技大學<br>電機工程系<br>104<br>The reactive power and voltage control problem in distribution systems is under study in this thesis. This problem is very important in a daily system operation. A proper control for the reactive power and voltage will improve the voltage profile, reduce system losses, and increase system efficiency, and that will improve the power supply quality and decrease power generation cost. First, a modified cat swarm optimization (MCSO) algorithm is proposed to solve the dispatch problem of under load tap changer (ULTC) and capacitors in a harmonic distorted distribution system. The main purpose is to find an optimum dispatch of ULTC and capacitors status, to reduce real power losses, and to reduce total harmonic distortion of bus voltage. To demonstrate the effectiveness of the proposed method, a 30-bus distorted distribution system is performed. And then, distributed generation is considered in the problem of reactive power and voltage control in distorted distribution systems. The purpose of the reactive power and voltage control problem is to find an optimum dispatching schedule of tap position for the main transformer ULTC and on/off status for the switched capacitors with considering uncertainties in a day. The constraints must be satisfied to minimize the purchase cost for buying electricity from the grid, operating cost of distributed generators and real power loss cost on feeders. To demonstrate the effectiveness of the proposed method for solving the dispatch problem of ULTC and capacitors in harmonic distorted distribution systems with distributed generation, the problem are performed on the 30-Bus and 123-Bus systems, and the results of the proposed method are compared with other algorithms.
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Cheng, Chiu-Chun, and 鄭秋君. "Application of Modified Particle Swarm Optimization Algorithm to Multi-user Detection in DS-UWB System." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/5775da.

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碩士<br>國立臺北科技大學<br>電機工程系所<br>95<br>For the multi-user application, the multiple access interference (MAI) due to imperfect orthogonality between spreading codes induced by UWB dense multi-path degrades system performance. Though optimum multi-user detector (OMD) can achieve remarkable performance, its computational complexity is too high to implement. Therefore, many sub-optimal multi-user detectors (MUDs) have been proposed to attain best trade-off between performance and complexity, such as Genetic algorithm-based multi-user detector (GA-MUD). However, BER performance of GA-MUD is not good enough and its convergence rate is slow. In order to increase the convergence rate and BER performance, we apply Particle Swarm Optimization algorithm (PSO) to replace GA to develop a new PSO-based MUD. This MUD employs rake receiver to generate initial solution for PSO-based optimization. Moreover, we combine mutation mechanism of GA to PSO for increasing diverseness of particles in searching space which is called PSO_Mutation-MUD. Experimental results show that the proposed PSO_Mutation-MUD attains highest gain in the CM4 environment. We also compare the performances between PSO_Mutation-MUD, PSO_CW-MUD, PSO_TCAV-MUD, and GA-MUD. The result reveals that our proposed PSO_Mutation-MUD yields the best performances over UWB channels.
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Wang, X., G. Zhang, J. Zhao, H. Rong, F. Ipate, and Raluca Lefticaru. "A modified membrane-inspired algorithm based on particle swarm optimization for mobile robot path planning." 2015. http://hdl.handle.net/10454/17606.

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Yes<br>To solve the multi-objective mobile robot path planning in a dangerous environment with dynamic obstacles, this paper proposes a modified membraneinspired algorithm based on particle swarm optimization (mMPSO), which combines membrane systems with particle swarm optimization. In mMPSO, a dynamic double one-level membrane structure is introduced to arrange the particles with various dimensions and perform the communications between particles in different membranes; a point repair algorithm is presented to change an infeasible path into a feasible path; a smoothness algorithm is proposed to remove the redundant information of a feasible path; inspired by the idea of tightening the fishing line, a moving direction adjustment for each node of a path is introduced to enhance the algorithm performance. Extensive experiments conducted in different environments with three kinds of grid models and five kinds of obstacles show the effectiveness and practicality of mMPSO.<br>National Natural Science Foundation of China (61170016, 61373047), the Program for New Century Excellent Talents in University (NCET-11-0715) and SWJTU supported project (SWJTU12CX008); grant of the Romanian National Authority for Scientific Research, CNCSUEFISCDI, project number PN-II-ID-PCE- 2011-3-0688.
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Book chapters on the topic "Modified moth swarm algorithm"

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Yang, Xiao, Qifang Luo, Jinzhong Zhang, Xiaopeng Wu, and Yongquan Zhou. "Moth Swarm Algorithm for Clustering Analysis." In Intelligent Computing Methodologies. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63315-2_44.

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Lebedev, Oleg B., Olga A. Purchina, and Dmitriy D. Fugarov. "Modified Adaptive Particle Swarm Algorithm." In Proceedings of the Seventh International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’23). Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43789-2_18.

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Strumberger, Ivana, Eva Tuba, Nebojsa Bacanin, and Milan Tuba. "Modified Moth Search Algorithm for Portfolio Optimization." In Smart Innovation, Systems and Technologies. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0077-0_45.

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Chakraborty, Sanjoy, Sukanta Nama, Apu Kumar Saha, and Seyedali Mirjalili. "A Modified Moth-Flame Optimization Algorithm for Image Segmentation." In Handbook of Moth-Flame Optimization Algorithm. CRC Press, 2022. http://dx.doi.org/10.1201/9781003205326-9.

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Awadallah, Mohammed A., Ahamad Tajudin Khader, Mohammed Azmi Al-Betar, and Asaju La’aro Bolaji. "Nurse Rostering Using Modified Harmony Search Algorithm." In Swarm, Evolutionary, and Memetic Computing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27242-4_4.

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Cuevas, Erik, Alberto Luque, Bernardo Morales Castañeda, and Beatriz Rivera. "Utilizing the Moth Swarm Algorithm to Improve Image Contrast." In Studies in Computational Intelligence. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63053-8_6.

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Kostrzewa, Daniel, and Henryk Josiński. "The Modified IWO Algorithm for Optimization of Numerical Functions." In Swarm and Evolutionary Computation. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29353-5_31.

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Sharma, Tarun Kumar, Millie Pant, and V. P. Singh. "Modified Onlooker Phase in Artificial Bee Colony Algorithm." In Swarm, Evolutionary, and Memetic Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35380-2_40.

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Cuevas, Erik, Daniel Zaldívar, and Marco Pérez-Cisneros. "Efficient Image Contrast Enhancement by Using the Moth Swarm Algorithm." In Intelligent Systems Reference Library. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45561-2_6.

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Wang, Ling, Mingde Zhang, Qun Niu, and Jun Yao. "A Modified Quantum-Inspired Particle Swarm Optimization Algorithm." In Artificial Intelligence and Computational Intelligence. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23896-3_51.

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Conference papers on the topic "Modified moth swarm algorithm"

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Wu, Chunyan, Ling Zhou, Fuqiang Peng, Pande Jiao, Dayong Zhou, and Qianlong Mei. "A Modified Particle Swarm Optimization Algorithm." In 2024 8th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE). IEEE, 2024. https://doi.org/10.1109/icemce64157.2024.10862446.

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Shi, Xiaojing, and Jie Lan. "English MOOC Data Analysis Model Based on Modified Particle Swarm Optimization (PSO) Algorithm." In 2024 3rd International Conference on Data Analytics, Computing and Artificial Intelligence (ICDACAI). IEEE, 2024. https://doi.org/10.1109/icdacai65086.2024.00034.

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Guvenc, Ugur, Serhat Duman, and Yunus Hinislioglu. "Chaotic Moth Swarm Algorithm." In 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA). IEEE, 2017. http://dx.doi.org/10.1109/inista.2017.8001138.

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Meng, Jiamin, Yuanyuan Sha, Lianwen Huang, Dan Li, Zan Yang, and Wei Nai. "Principal Component Analysis Based on t-Distribution Moth Swarm Algorithm." In 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC). IEEE, 2022. http://dx.doi.org/10.1109/itoec53115.2022.9734509.

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Bingi, Kishore, Rakshit Raghavendra Kulkarni, and Rhea Mantri. "Development of Hybrid Algorithm Using Moth-Flame and Particle Swarm Optimization." In 2021 IEEE Madras Section Conference (MASCON). IEEE, 2021. http://dx.doi.org/10.1109/mascon51689.2021.9563556.

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Guangyou, Yang. "A Modified Particle Swarm Optimizer Algorithm." In 2007 8th International Conference on Electronic Measurement and Instruments. IEEE, 2007. http://dx.doi.org/10.1109/icemi.2007.4350772.

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Yitong, Liu, Fu Mengyin, and Gao Hongbin. "A Modified Particle Swarm Optimization Algorithm." In 2007 Chinese Control Conference. IEEE, 2006. http://dx.doi.org/10.1109/chicc.2006.4347362.

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Liu, Enhai, Yongfeng Dong, Jie Song, Xiangdan Hou, and Nana Li. "A Modified Particle Swarm Optimization Algorithm." In 2008 International Workshop on Geoscience and Remote Sensing (ETT and GRS). IEEE, 2008. http://dx.doi.org/10.1109/ettandgrs.2008.355.

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Qian-Li Zhang, Xing Li, and Quang-Ahn Tran. "A modified particle swarm optimization algorithm." In Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527455.

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Jianmei Xiao, Xiaoming Zheng, Xihuai Wang, and Youfang Huang. "A Modified Artificial Fish-Swarm Algorithm." In 2006 6th World Congress on Intelligent Control and Automation. IEEE, 2006. http://dx.doi.org/10.1109/wcica.2006.1713010.

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