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An improved moth flame optimization algorithm based on modified dynamic opposite learning strategy

ARTIFICIAL INTELLIGENCE REVIEW 2023年第4期56卷 2811-2869页

作者： Sahoo, Saroj Kumar Saha, Apu Kumar Nama, Sukanta Masdari, Mohammad Natl Inst Technol Agartala Dept Math Agartala 799046 Tripura India Gomati Dist Polytech Dept Sci & Humanities Udaipur Tripura India Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh Iran

moth flame optimization (MFO) algorithm is a relatively new nature-inspired optimization algorithm based on the moth's movement towards the moon. Premature convergence and convergence to local optima are the main demerits of the algorithm. To avoid these drawbacks, a modified dynamic opposite learning-based MFO algorithm (m-DMFO) is presented in this paper, incorporating a modified dynamic opposite learning (DOL) strategy. To validate the performance of the proposed m-DMFO algorithm, it is tested via twenty-three benchmark functions, IEEE CEC'2014 test functions and compared with a wide range of optimization algorithms. Moreover, Friedman rank test, Wilcoxon rank test, convergence analysis, and diversity measurement have been conducted to measure the robustness of the proposed m-DMFO algorithm. The numerical results show that, the proposed m-DMFO algorithm achieved superior results in more than 90% occasions. The proposed m-DMFO achieves the best rank in Friedman rank test and Wilcoxon rank test respectively. In addition, four engineering design problems have been solved by the suggested m-DMFO algorithm. According to the results, it achieves extremely impressive results, which also illustrates that the algorithm is qualified in solving real-world problems. Analyses of numerical results, diversity measure, statistical tests and convergence results ensure the enhanced performance of the proposed m-DMFO algorithm.

关键词： moth flame optimization algorithm Dynamic opposite learning strategy Simple quadratic interpolation Swarm intelligence Benchmark functions

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Hybridization of moth flame optimization algorithm and quantum computing for gene selection in microarray data

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第2期12卷 2731-2750页

作者： Dabba, Ali Tari, Abdelkamel Meftali, Samy Mohamed Boudiaf Univ Fac Math & Comp Sci Comp Sci Dept Msila Algeria Abderrahmane Mira Univ Fac Sci Comp Sci Dept Bejaia Algeria Med Comp Lab LIMED Bejaia Algeria Univ Lille Lille France Res Ctr Comp Sci Signal & Automat Control Lille C Lille France

Ever-increasing data in various fields like Bioinformatics field, which has led to the need to find a way to reduce the data dimensionality. Gene selection problem has a large number of genes (relevant, redundant or noise), which needs an effective method to help us in detecting diseases and cancer. In this problem, computational complexity is reduced by selecting a small number of genes, but it is necessary to choose the relevant genes to keep a high level of accuracy. Therefore, in order to find the optimal gene subset, it is essential to devise an effective exploration approach that can investigate a large number of possible gene subsets. In addition, it is required to use a powerful evaluation method to evaluate the relevance of these gene subsets. In this paper, we present a novel swarm intelligence algorithm for gene selection called quantum moth flame optimization algorithm (QMFOA), which based on hybridization between quantum computation and moth flame optimization (MFO) algorithm. The purpose of QMFOA is to identify a small gene subset that can be used to classify samples with high accuracy. The QMFOA has a simple two-phase approach, the first phase is a pre-processing that uses to address the difficulty of high-dimensional data, which measure the redundancy and the relevance of the gene, in order to obtain the relevant gene set. The second phase is a hybridization among MFOA, quantum computing, and support vector machine with leave-one-out cross-validation, etc., in order to solve the gene selection problem. We use quantum computing to guarantee a good trade-off between the exploration and the exploitation of the search space, while a new update moth operation using Hamming distance and Archimedes spiral allows an efficient exploration of all possible gene-subsets. The main objective of the second phase is to determine the best relevant gene subset of all genes obtained in the first phase. In order to assess the performance of the proposed QMFOA, we test Q

关键词： Gene expression Feature selection moth flame optimization algorithm Quantum computing Microarray data Cancer classification Bio-inspired algorithms Molecular biology optimization algorithms Evolutionary algorithms Swarm intelligence

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Mesh Router Nodes Placement for Wireless Mesh Networks Based on an Enhanced moth-flame optimization algorithm

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MOBILE NETWORKS & APPLICATIONS 2023年第2期28卷 518-541页

作者： Taleb, Sylia Mekhmoukh Meraihi, Yassine Mirjalili, Seyedali Acheli, Dalila Ramdane-Cherif, Amar Gabis, Asma Benmessaoud Univ MHamed Bougara Boumerdes LIST Lab Ave Independence Boumerdes 35000 Algeria Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Univ MHamed Bougara Boumerdes LAA Lab Ave Independence Boumerdes 35000 Algeria Univ Versailles St Quentin en Yvelines LISV Lab 10-12 Ave Europe F-78140 Velizy Villacoublay France Ecole Natl Super Informat Lab Methodes Concept Syst BP 68M Oued Smar 16309 Alger Algeria

This paper proposes an enhanced version of moth flame optimization (MFO) algorithm, called Enhanced Chaotic Levy Opposition-based MFO (ECLO-MFO) for solving the mesh router nodes placement problem in wireless mesh network (WMN-MRNP). The proposed ECLO-MFO incorporates three strategies including the chaotic map concept, the Levy flight strategy, and the Opposition-Based Learning (OBL) technique to enhance the optimization performance of MFO. Firstly, chaotic maps are used to increase the chaotic stochastic behavior of the MFO algorithm. Levy flight distribution is adopted to increase the population diversity of MFO. Finally, OBL is introduced to improve the convergence speed of MFO and to explore the search space effectively. The effectiveness of the proposed ECLO-MFO is tested based on various scenarios under different settings, considering network connectivity and client coverage metrics. The results of simulation obtained using MATLAB 2020a demonstrate the accuracy and superiority of ECLO-MFO in determining the optimal positions of mesh routers when compared with the original MFO and ten other optimization algorithms such as Genetic algorithm (GA), Simulated Annealing (SA), Harmony Search (HS), Particle Swarm optimization (PSO), Artificial Bee Colony (ABC), Cuckoo Search algorithm (CS), Bat algorithm (BA), Firefly optimization (FA), Grey Wolf Optimizer (GWO), and Whale optimization algorithm (WOA).

关键词： Mesh router nodes placement moth flame optimization algorithm Network design Wireless mesh network

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moth flame optimization algorithm Range-Based for Node Localization Challenge in Decentralized Wireless Sensor Network

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INTERNATIONAL JOURNAL OF DISTRIBUTED SYSTEMS AND TECHNOLOGIES 2019年第1期10卷 82-109页

作者： Miloud, Mihoubi Abdellatif, Rahmoun Lorenz, Pascal Univ Djillali Liabes Sidi Bel Abbes EEDIS Lab Sidi Bel Abbes Algeria Higher Sch Comp Sci LabRI SBA Lab Sidi Bel Abbes Algeria Univ Haute Alsace Mulhouse France

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 multidimensional 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.

关键词： Localization Error Localization Time Metaheuristic moth flame optimization algorithm Node Localization optimization Wireless Sensor Network

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Optimal operation of microgrid with multi-energy complementary based on moth flame optimization algorithm

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2020年第7期42卷 785-806页

作者： Wang, Yongli Li, Fang Yu, Haiyang Wang, Yudong Qi, Chengyuan Yang, Jiale Song, Fuhao North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China State Grid Beijing Elect Power Co Beijing Peoples R China

Recently, hybrid distributed generation system has become a popular energy supply mode. It is obvious that the integrated system could improve energy efficiency and reduce costs. However, the system scheduling is a problem that would determine the operation cost. In this paper, a hybrid energy system including wind power, photovoltaics, gas turbines, and energy storage was introduced. In order to obtain the minimum operation cost, an operation optimization model was built. The schedule plan of each unit was optimized by moth flame optimization algorithm. Finally, through empirical research on a microgrid project, the optimization results in three configuration case of wind power, photovoltaics, and storage indicated that the operation optimization model in this paper could effectively reduce system operation cost, and the optimal output plan of each unit was obtained. And it is proved that the model proposed in this paper has a certain guiding role on economically dispatch of hybrid energy system.

关键词： Multi-energy system microgrid operating optimization renewable energy moth flame optimization algorithm

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moth flame optimization algorithm based Optimal Strategic Bidding in Deregulated Electricity Market

Moth Flame Optimization Algorithm based Optimal Strategic Bi...

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IEEE Region 10 Conference on Technology, Knowledge, and Society (TENCON)

作者： Sahoo, Abhilipsa Hota, Prakash Kumar VSSUT Dept Elect Engn Burla Odisha India

ISBN: (纸本)9781728118956

The restructuring of the electric power industries creates competitiveness among the market players. At present suppliers of the electricity market are following profit maximization policy. This goal can be achieved by the process of Strategic Bidding. In this article an endeavor has been made using moth flame optimization (MFO) to find out the optimal strategic bidding. In addition, different constraints have been dissected to make the market model. The efficacy of the suggested strategy has been verified with the system of IEEE 30 bus consisting of six generating utilities and two numbers of loads. The proposed strategy yields better results in carrying out the required job in comparison to the other adopted methods to achieve fair competition among the utilities.

关键词： Bidding Strategy Deregulated Power System moth flame optimization algorithm Market Clearing Price

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Parameter Identification of Single-Phase Inverter Based on Improved moth flame optimization algorithm

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2019年第4-5期47卷 456-469页

作者： Wu, Zhongqiang Shen, Dandan Shang, Mengyao Qi, Songqi Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao Hebei Peoples R China

By identifying the parameters of electronic circuit, parametric fault diagnosis of power electronic circuits can be realized. Many intelligent optimization algorithms are used to identify the parameters of electronic circuit, but most of them have the defects of slow convergence rate and easy to fall into local minimum. moth flame optimization algorithm is a novel swarm intelligence bionic algorithm based on the intelligence behavior of moth positioning, which also has the above drawbacks. In order to improve the performance of algorithm, when updating the moth position, moth firstly moves in a straight line to the optimal position, then Levy flight is added. The improved algorithm improves the global optimization ability and accelerates the convergence speed. The improved moth flame optimization algorithm is applied for the parameter identification of single-phase inverter. The identification result is compared with the results of the other optimization techniques. The effectiveness and superiority of the improved algorithm are verified.

关键词： parameter identification fault diagnosis optimization algorithm single-phase inverter transfer function model moth flame optimization algorithm particle swarm optimization genetic algorithm Fast Fourier transform Levy flight

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An integrated approach of class testing using firefly and moth flame optimization algorithm

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JOURNAL OF INFORMATION & optimization SCIENCES 2020年第2期41卷 599-612页

作者： Sharma, Rashmi Saha, Anju Guru Gobind Singh Indraprastha Univ Univ Sch Informat Sect 16 C New Delhi 110078 India

Path testing is an extensively used approach of software testing. Generation of test paths is the core concept of path testing. The recent approaches of software test paths generation are based on the nature-inspired metaheuristic algorithms. An amalgamated approach constructed on the glaring arrangement of fireflies together with the attractive conduct of moths with respect to a flame has been presented in this article. The proposed algorithm combines the individual aspects of two metaheuristics, namely, moth flame optimization algorithm (MFO) and Firefly algorithm (FA) for test paths generation. The hybrid algorithm selects a starting node, traverses the connected path and iteratively evolves the complete test path after applying a series of operations. The enactment of the proposed algorithm is verified on the five object-oriented benchmark applications. The proposed algorithm is compared with both MFO and FA. Results confirm full coverage of the path which is the main motive of path testing. Also, reduced and less redundant test paths have been generated via proposed hybrid algorithm as compared to MFO and FA.

关键词： Software testing Metaheuristic Firefly algorithm moth flame optimization algorithm Object-Oriented Path Testing State Machine

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Solution of Economic Load Dispatch Problems Through moth flame optimization algorithm

Solution of Economic Load Dispatch Problems Through Moth Fla...

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International Conference on Communication, Devices and Networking (ICCDN)

作者： Tripati, Princi Tomar, Upendra Singh, Vinod Kumar Bhoi, Akash Kumar Vikrant Inst Technol & Management Dept Elect Engn Gwalior India SR Grp Inst Dept Elect Engn Jhansi Uttar Pradesh India Sikkim Manipal Inst Technol Dept Elect & Elect Engn Rangpo 737136 Sikkim India

ISBN: (纸本)9789811079016;9789811079009

This paper presents a significantly efficient nature-motivated moth flame optimization (MFO) algorithm to solve the convex economic load dispatch (ELD) problems of the power system. The ELD focuses on the effective scheduling of the power-generating units so as to fulfil the total load demand and to satisfy the various constraints of the generating units as well as power network limitations. The aim of the proposed work is to reduce the quadratic cost function of the generating unit and hence obtain the minimum cost of generation so as to maintain the economy of the generation plant. The obtained better positions of moths around the flames describe about the best solutions obtained as so far for the proposed work of the ELD problems. This paper performs test on convex cost function of 18 unit system so as to validate the efficiency, reliability and robustness of the proposed methodology.

关键词： Economic load dispatch moth flame optimization algorithm

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A Node Deployment optimization algorithm of WSNs Based on Improved moth flame Search

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IEEE SENSORS JOURNAL 2022年第10期22卷 10018-10030页

作者： Yao, Yindi Hu, Shanshan Li, Ying Wen, Qin Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian 710121 Peoples R China Shaanxi Key Lab Informat Commun Network & Secur Xian 710121 Peoples R China Univ Posts & Telecommun Xian 710121 Peoples R China

Wireless sensor networks, one of the basic technologies of remote environmental monitoring, can provide efficient sensing and communication services under limited energy supply. Coverage control is an important method to ensure efficient communication and reliable data trans- mission. Given the complex physical environments, which impede the energy supplement and recovery of sensor nodes, the motivation of our research is to repair the coverage holes and reduce the energy consumption during the deployment of sensor nodes. Firstly, the variable spiral position update and the adaptive inertia weight strategy are adopted to improve local development and global search ability of the moth flame algorithm. Secondly, we analyze the virtual force of nodes, including the attractive force of uncovered grid points, the virtual force between adjacent sensor nodes and the repulsive force of boundary. The node resultant force is used as the disturbance factor of moth position updating to optimize the path, which effectively avoids the "premature" problem of the algorithm and accelerates global convergence. Finally, moth search is used to guide nodes to move to the area with coverage holes to achieve coverage optimization. In addition, we limit the random walking range of moths to reduce the moving distance. The simulation results show that compared with VFPSO, VFA and MFO algorithms, the coverage rate of VF-IMFO algorithm is increased by 7.16%, 3.85% and 22.2%, and the average moving distance of nodes is reduced by 9.01 m, 0.51 m and 32.46 m respectively. Moreover, under different deployment environments, the VF-IMFO algorithm still maintains remarkable performance advantages.

关键词： Coverage optimization disturbance factor moth flame optimization algorithm wireless sensor networks

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