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13th International Conference on Intelligent Computing (ICIC)

作者： Yang, Xiao Luo, Qifang Zhang, Jinzhong Wu, Xiaopeng Zhou, Yongquan Guangxi Univ Nationalities Coll Informat Sci & Engn Nanning 530006 Peoples R China Guangxi High Sch Key Lab Complex Syst & Computat Intelligence Nanning 530006 Peoples R China

ISBN: (纸本)9783319633152;9783319633145

moth swarm algorithm (MSA) is a new swarm intelligent algorithm, it is inspired by the moth looking for food, phototaxis and celestial navigation in the dark environment, proposed a moth search algorithm. Because the algorithm has good convergence speed and high convergence precision, it is applied in many fields. Cluster analysis, as an effective tool in data mining, has attracted widespread attention and has been developed rapidly and has been successfully applied in recent years. Among the many clustering algorithms, the K-means clustering algorithm is easy to implement, so it is widely used. However, the K-means algorithm also has the disadvantages of large computational complexity and clustering effect depending on the selection of the initial clustering center, which seriously affects the clustering effect, and the algorithm is easy to fall into the local optimum. To solve these problems, The MSA is applied to cluster analysis, the results show that the MSA not only achieves superior accuracy, but also exhibits a higher level of stability.

关键词： Cluster analysis moth swarm algorithm K-means algorithm swarm intelligent algorithm

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moth swarm algorithm for Reactive Power Dispatch Considering Stochastic Nature of Renewable Energy Generation and Load 21

Moth Swarm Algorithm for Reactive Power Dispatch Considering...

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21st International Middle East Power Systems Conference (MEPCON)

作者： Abdel-Fatah, Said Ebeed, Mohamed Kamel, Salah Nasrat, Loai Egyptian Ferro Silicon Alloys Co Aswan Egypt Sohag Univ Fac Engn Sohag 82524 Egypt Aswan Univ Dept Elect Engn Aswan Egypt

ISBN: (纸本)9781728152899

The main task of the optimal reactive power dispatch (ORPD) problem solution is assigning the best operating condition of the reactive power sources in system to get the more stable and secure condition in electric systems. This paper offers a new application of moth swarm algorithm (MSA) to solve the ORPD. Owing to the stochastic nature of renewable energy resources (RER) and load demand (LD), the uncertainty of RER and LD must be considered for efficient management in power systems. In this paper the ORPD is solved with taken into consideration the uncertainties of load demand, solar and wind powers. The proposed technique is applied for ORPD solution on 30-bus test system to reduce the system power losses and the total voltage deviations. The capture results reveal to that the MSA is superior and it can solve the ORPD efficiently.

关键词： Optimal Reactive Power Dispatch uncertainty moth swarm algorithm

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Meta-heuristic moth swarm algorithm for multilevel thresholding image segmentation

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MULTIMEDIA TOOLS AND APPLICATIONS 2018年第18期77卷 23699-23727页

作者： Zhou, Yongquan Yang, Xiao Ling, Ying Zhang, Jinzhong Guangxi Univ Nationalities Coll Informat Sci & Engn Nanning 530006 Peoples R China Key Labs Guangxi High Sch Complex Syst & Computat Nanning 530006 Guangxi Peoples R China

Multilevel thresholding is a very important image processing technique in the field of image segmentation. However, the computational complexity of determining the optimal threshold grows exponentially with increasing thresholds. To overcome this drawback, in this paper, we propose a multi-threshold image segmentation method based on the moth swarm algorithm. The meta-heuristic algorithm uses Kapur's entropy method to optimize the thresholds for eight standard test images. When compared with other state-of-the-art evolutionary algorithms, the proposed method proved to be robust and effective according to numerical experimental results and image segmentation results. This indicates the high performance of the method for the segmentation of digital images.

关键词： Multilevel thresholding moth swarm algorithm Image segmentation Metaheuristic Kapur's entropy

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A context sensitive Masi entropy for multilevel image segmentation using moth swarm algorithm

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INFRARED PHYSICS & TECHNOLOGY 2019年第0期98卷 132-154页

作者： Bhandari, Ashish Kumar Rahul, Kusuma Natl Inst Technol Patna Dept Elect & Commun Engn Patna 800005 Bihar India

In this paper, a novel context sensitive energy curve based Masi entropy for image segmentation using moth swarm algorithm (MSA) has been proposed. Although Masi entropy deals with complete probability distribution for image segmentation but the performance is not satisfactory. However, better results can be obtained using the concept of energy curve for Masi entropy, but it consumes more time and also the complexity level for selecting suitable thresholds is high. MSA is a newly developed stochastic meta-heuristic optimization algorithm introduced after observing, mimicking and modeling the life cycle of moth swarm. It is used to simplify the problem of extensive exploration for finding the optimum threshold values and to increase the quality of the images. Experiments on standard daily-life color images are showed to establish the usefulness of the presented approach. The Energy-Masi-MSA technique is examined intensively regarding visual quality and quantitative matrices are considered to evaluate the results of the Energy-Masi-MSA scheme compared to existing methods. Unlike other meta-heuristic algorithms used for thresholding operations, MSA provides a higher performance regarding threshold quality and low computational cost. Experimental data boosts the use of MSA for energy curve based thresholding with Masi entropy.

关键词： Color image Masi entropy Energy curve moth swarm algorithm Multi-level image segmentation

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A Modified moth swarm algorithm Based on an Arithmetic Crossover for Constrained Optimization and Optimal Power Flow Problems

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IEEE ACCESS 2018年 6卷 45394-45416页

作者： Duman, Serhat Duzce Univ Technol Fac Dept Elect & Elect Engn TR-81620 Duzce Turkey

The moth swarm algorithm (MSA) is a new meta-heuristic optimization technique inspired by the navigational style of moths in nature. This paper represents a novel modified MSA with an arithmetic crossover (MSA-AC) with the aim of improving the search for a global optimum, the convergence speed to an optimal solution, and the performance of the traditional MSA. The proposed MSA-AC method was applied in 23 standard benchmark test functions and used in six CEC 2005 composite benchmark test functions. Furthermore, in order to verify the success of the optimal solution, the MSA-AC approach was used to solve the optimal power flow problem in the two-terminal high-voltage direct current systems of the modified New England 39-bus and the modified WSCC 9-bus test systems. The numerical results obtained from the MSA-AC were compared with both the traditional MSA method and with various optimization algorithms presented in the literature. The outcomes obtained from the comparative results indicate the potential of the proposed approach in finding the global optimum and the convergence to an optimal solution.

关键词： moth swarm algorithm arithmetic crossover benchmark test functions optimal power flow HVDC

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Optimal Load Shedding of Power System Including Optimal TCSC Allocation Using moth swarm algorithm

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2020年第2期44卷 741-765页

作者： Sayed, Fatma Kamel, Salah Yu, Juan Jurado, Francisco Aswan Univ Fac Engn Dept Elect Engn Aswan 81542 Egypt Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing Peoples R China Univ Jaen Dept Elect Engn Jaen 23700 Linares Spain

Voltage collapse, blackout and voltage instability are considered main problems faced by the power systems which are due to the lack of reactive power in the system. One of the most important sources for injecting reactive power to the system is the flexible AC transmission systems (FACTS) devices. Thyristor-controlled series capacitor (TCSC) is one of the modern FACTS devices that can be used for reducing the overloading in line flow, allowing power system to operate in secure state and improving power system stability. In this paper, optimal steady-state load shedding and TCSC allocation in power system problems are comprehensively solved using a new robust and effective technique, called moth swarm algorithm. This algorithm is based on the moth's orientation toward moonlight considering a new adaptive crossover and Levy flight mutation. Optimal load shedding and TCSC allocation are achieved simultaneously to remove or mitigate congestion and emergency situations. To prove the capability of the proposed algorithm for minimizing the amount of load shedding and active power loss, improving voltage profile and voltage stability, standard IEEE 30-bus test system is used under normal and contingency operations at different single- and multiobjective functions. The results obtained by the proposed algorithm are compared with those obtained by other well-known optimization techniques: TLBO, PSO, GWO, WOA, MFO. These results demonstrate the effectiveness and robustness of the proposed algorithm.

关键词： Optimal load shedding FACTS TCSC moth swarm algorithm Voltage stability Voltage profile

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Network Reconfiguration for Minimizing Power Loss by moth swarm algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第7期11卷 33-39页

作者： Thuan Thanh Nguyen Duong Thanh Long Ind Univ Ho Chi Minh City Fac Elect Engn Technol Ho Chi Minh City Vietnam

This paper presents a network reconfiguration approach for minimizing power loss of the distribution system based on moth swarm algorithm (MSA). The MSA is a recent metaheuristic inspired from the navigational technique of moths in the dark for finding food sources. For searching optimal solution, MSA used three different mechanisms of generating new solutions consisting of Levy-flights, Gaussian walks and spiral flight. The effectiveness of MSA is validated on two distribution systems consisting of the 33-nodes and 69-nodes. The simulation results are compared to particle swarm optimization and other available approaches in the literature. The calculated results on the test systems show that MSA can be an effective and reliable tool for the NR problems.

关键词： Network reconfiguration moth swarm algorithm power loss particle swarm optimization distribution system

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Dimension Synthesis and Optimized FOPID Control of the Delta Robot with moth swarm algorithm

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023年第5期48卷 6889-6902页

作者： Yigit, Tevfik Celik, Hakan Firat Univ Mech Engn TR-23200 Elazig Turkiye Nigde Omer Halisdemir Univ Mech Engn TR-51240 Nigde Turkiye

In this study, Delta robot that one of the most studied and used robot and has distinctive advantages and parallel structure, has been optimized in point of dimensions. 3D solid model of the optimized robot model transferred to MATLAB/Simulink environment. Dimensions of the Delta robot have been obtained to ensure desired workspace with minimum dimensions with moth swarm algorithm (MSA) optimization and particle swarm optimization (PSO). Widely known PSO algorithm has been used for the measure to the performance of the MSA algorithm that is new to the literature. Joints of the robot actuated with the brushless DC (BLDC) motors. System has been controlled fractional-order PID (FOPID) controller. Also integer-order PID controller (IOPID) has been used to clearly see the performance of the FOPID with the aim of comparison. Parameters of both of these controllers are optimized with MSA optimization in equal conditions. When the optimization cost results and the performance test results are examined, it is seen that the FOPID controller performed better than IOPID controller. Designed Delta robot system has been tested with different scenarios, and robotic system has been achieve that this scenarios with high performance.

关键词： Delta robot Dimension synthesis Fractional-order PID moth swarm algorithm

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moth swarm algorithm for Image Contrast Enhancement

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KNOWLEDGE-BASED SYSTEMS 2021年 212卷 106607-106607页

作者： Luque-Chang, Alberto Cuevas, Erik Perez-Cisneros, Marco Fausto, Fernando Valdivia-Gonzalez, Arturo Sarkar, Ram Univ Guadalajara Dept Elect CUCEI Av Revoluc 1500 Guadalajara 44430 Jalisco Mexico Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India

Image Contrast Enhancement (ICE) is a crucial step in several image processing and computer vision applications. Its main objective is to improve the quality of the visual information contained in the processed images. The presence of noise and small sets of pixels in images are not only irrelevant for their visualization. It also negatively affects the improvement process of ICE schemes since the inclusion of irrelevant information avoids the appropriate distribution of significant pixel intensities in the enhanced image. As a consequence of this effect, most of the proposed ICE methods present different associated problems such as the production of undesirable artifacts, noise amplification, over saturation and bad human visual perception. In this paper, an Image Contrast Enhancement (ICE) method for grayscale and color images is presented. The proposed approach has the propriety of eliminating noisy and irrelevant information in order to improve the distribution capacity of significant pixel intensities in the enhanced image. Our method eliminates multiple groups of a very small number of pixels that, according to their characteristics, do not represents any object or important detail of the image. This process is done by the Mean-shift algorithm, which is used to replace such sets of irrelevant pixels in the original histogram by significant pixel densities represented by local maxima. Then, the moth swarm algorithm (MSA) is used to redistribute the pixel intensities of the reduced histogram so that the value from Kullback-Leibler entropy (KL-entropy) has been maximized. The proposed approach has been tested considering different public datasets commonly used in the literature. Its results are also compared with those produced by other well-known ICE techniques. Evaluation of the experimental results demonstrates that the proposed approach highlights the important details of the image also improving its human visual appearance. (C) 2020 Elsevier B.V. All rights

关键词： Image enhancement moth swarm algorithm Global optimization

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Chaotic moth swarm algorithm

Chaotic Moth Swarm Algorithm

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IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA)

作者： Guvenc, Ugur Duman, Serhat Hinislioglu, Yunus Duzce Univ Dept Elect & Elect Engn Duzce Turkey

ISBN: (纸本)9781509057955

moth swarm algorithm (MSA) is one of the newest developed nature-inspired heuristics for optimization problem. Nevertheless MSA has a drawback which is slow convergence. Chaos is incorporated into MSA to eliminate this drawback. In this paper, ten chaotic maps have been embedded into MSA to find the best numbers of prospectors for increase the exploitation of the best promising solutions. The proposed method is applied to solve the well-known seven benchmark test functions. Simulation results show that chaotic maps can improve the performance of the original MSA in terms of the convergence speed. At the same time, sinusoidal map is the best map for improving the performance of MSA significantly.

关键词： chaotic maps optimization moth swarm algorithm

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