Moth–flame optimization algorithm: variants and applications

This paper thoroughly presents a comprehensive review of the so-called moth–flame optimization (MFO) and analyzes its main characteristics. MFO is considered one of the promising metaheuristic algorithms and successfully applied in various optimization problems in a wide range of fields, such as power and energy systems, economic dispatch, engineering design, image processing and medical applications. This manuscript describes the available literature on MFO, including its variants and hybridization, the growth of MFO publications, MFO application areas, theoretical analysis and comparisons of MFO with other algorithms. Conclusions focus on the current work on MFO, highlight its weaknesses, and suggest possible future research directions. Researchers and practitioners of MFO belonging to different fields, like the domains of optimization, medical, engineering, clustering and data mining, among others will benefit from this study.

[1]  Xiangyang Wang,et al.  Feature selection based on rough sets and particle swarm optimization , 2007, Pattern Recognit. Lett..

[2]  Shailesh Tiwari,et al.  Advances in Computer and Computational Sciences , 2017 .

[3]  Laith Mohammad Abualigah,et al.  Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering , 2018, Studies in Computational Intelligence.

[4]  Mohammad Shehab,et al.  Hybridising cuckoo search algorithm for extracting the ODF maxima in spherical harmonic representation , 2019 .

[5]  Mohammad Shehab,et al.  Enhancing Cuckoo Search Algorithm by using Reinforcement Learning for Constrained Engineering optimization Problems , 2019, 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT).

[6]  Erik Valdemar Cuevas Jiménez,et al.  A global optimization algorithm inspired in the behavior of selfish herds , 2017, Biosyst..

[7]  Jianzhou Wang,et al.  A novel hybrid model for short-term wind power forecasting , 2019, Appl. Soft Comput..

[8]  Laith Mohammad Abualigah,et al.  A hybrid strategy for krill herd algorithm with harmony search algorithm to improve the data clustering , 2018, Intell. Decis. Technol..

[9]  Laith Mohammad Abualigah,et al.  A combination of objective functions and hybrid Krill herd algorithm for text document clustering analysis , 2018, Eng. Appl. Artif. Intell..

[10]  Hossam Faris,et al.  Chapter 28 – Evolving Radial Basis Function Networks Using Moth–Flame Optimizer , 2017 .

[11]  John Holland,et al.  Adaptation in Natural and Artificial Sys-tems: An Introductory Analysis with Applications to Biology , 1975 .

[12]  Jeng-Shyang Pan,et al.  Alzheimer's Disease Diagnosis Based on Moth Flame Optimization , 2016, ICGEC.

[13]  Ahamad Tajudin Abdul Khader,et al.  Hybridizing cuckoo search algorithm with bat algorithm for global numerical optimization , 2018, The Journal of Supercomputing.

[14]  S. Mini,et al.  Optimized Relay Nodes Positioning to Achieve Full Connectivity in Wireless Sensor Networks , 2018, Wireless Personal Communications.

[15]  Slawomir Koziel,et al.  Computational Optimization, Methods and Algorithms , 2016, Computational Optimization, Methods and Algorithms.

[16]  H. Ishibuchi,et al.  Multi-objective genetic algorithm and its applications to flowshop scheduling , 1996 .

[17]  Hui Huang,et al.  Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses , 2017, Neurocomputing.

[18]  Boumeddane Boussad,et al.  CFD Analysis of the Volute Geometry Effect on the Turbulent Air Flow through the Turbocharger Compressor , 2013 .

[19]  Xiaodong Li,et al.  Swarm Intelligence in Optimization , 2008, Swarm Intelligence.

[20]  Muazzam Maqsood,et al.  CAMONET: Moth-Flame Optimization (MFO) Based Clustering Algorithm for VANETs , 2018, IEEE Access.

[21]  Ahmed A. Zaki Diab,et al.  Optimal Sizing and Placement of Capacitors in Radial Distribution Systems Based on Grey Wolf, Dragonfly and Moth–Flame Optimization Algorithms , 2018, Iranian Journal of Science and Technology, Transactions of Electrical Engineering.

[22]  Al-Attar Ali Mohamed,et al.  Optimal power flow using moth swarm algorithm , 2017 .

[23]  Dalia Yousri,et al.  Parameters extraction of the three diode model for the multi-crystalline solar cell/module using Moth-Flame Optimization Algorithm , 2016 .

[24]  Ali Rıza Yıldız,et al.  Moth-flame optimization algorithm to determine optimal machining parameters in manufacturing processes , 2017 .

[25]  Bo Yang,et al.  Optimal power tracking of doubly fed induction generator-based wind turbine using swarm moth–flame optimizer , 2019, Trans. Inst. Meas. Control.

[26]  Almoataz Y. Abdelaziz,et al.  Optimal Multi-Criteria Design of Hybrid Power Generation Systems: A New Contribution , 2015 .

[27]  Bijaya K. Panigrahi,et al.  Solution to unit commitment in power system operation planning using binary coded modified moth flame optimization algorithm (BMMFOA): A flame selection based computational technique , 2017, J. Comput. Sci..

[28]  Mohammad Shehab,et al.  A HYBRID METHOD BASED ON CUCKOO SEARCH ALGORITHM FOR GLOBAL OPTIMIZATION PROBLEMS , 2018, Journal of Information and Communication Technology.

[29]  S. Pasandideh,et al.  Multi-item EOQ model with nonlinear unit holding cost and partial backordering: moth-flame optimization algorithm , 2017 .

[30]  P. Anbarasan,et al.  Optimal Reactive Power Dispatch Using Moth-Flame Optimization Algorithm , 2017 .

[31]  Hongbin Zhang,et al.  Feature selection using tabu search method , 2002, Pattern Recognit..

[32]  Bachir Bentouati,et al.  Optimal Power Flow using the Moth Flam Optimizer: A case study of the Algerian power system , 2016 .

[33]  Mohamed Abdel-Nasser,et al.  Performance evaluation of metaheuristic optimization methods with mutation operators for combined economic and emission dispatch , 2017, 2017 Nineteenth International Middle East Power Systems Conference (MEPCON).

[34]  Chao Lu,et al.  Multi-objective cellular particle swarm optimization for wellbore trajectory design , 2019, Appl. Soft Comput..

[35]  Vimal J. Savsani,et al.  Non-dominated sorting moth flame optimization (NS-MFO) for multi-objective problems , 2017, Eng. Appl. Artif. Intell..

[36]  Moh'd Khaled Yousef Shambour Adaptive multi-crossover evolutionary algorithm for real-world optimisation problems , 2018 .

[37]  Mohammad Shehab,et al.  Modified Cuckoo Search Algorithm for Solving Global Optimization Problems , 2017 .

[38]  Bhola Jha,et al.  Moth-Flame Optimization-Based Fuzzy-PID Controller for Optimal Control of Active Magnetic Bearing System , 2018, Iranian Journal of Science and Technology, Transactions of Electrical Engineering.

[39]  Xuehua Zhao,et al.  An improved grasshopper optimization algorithm with application to financial stress prediction , 2018, Applied Mathematical Modelling.

[40]  Qinghai Bai,et al.  Analysis of Particle Swarm Optimization Algorithm , 2010, Comput. Inf. Sci..

[41]  Yongquan Zhou,et al.  Lévy-Flight Moth-Flame Algorithm for Function Optimization and Engineering Design Problems , 2016 .

[42]  Vikash Kumar Gupta,et al.  Moth-flame optimization based algorithm for FACTS devices allocation in a power system , 2017, 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS).

[43]  Gai-Ge Wang,et al.  An Effective Hybrid Firefly Algorithm with Harmony Search for Global Numerical Optimization , 2013, TheScientificWorldJournal.

[44]  F. Glover HEURISTICS FOR INTEGER PROGRAMMING USING SURROGATE CONSTRAINTS , 1977 .

[45]  Mohammed Azmi Al-Betar,et al.  Hybridizing cuckoo search algorithm with hill climbing for numerical optimization problems , 2017, 2017 8th International Conference on Information Technology (ICIT).

[46]  Salah Kamel,et al.  Optimal Installation of Multiple DG using Chaotic Moth-flame Algorithm and Real Power Loss Sensitivity Factor in Distribution System , 2018, 2018 International Conference on Smart Energy Systems and Technologies (SEST).

[47]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[48]  R. H. Bhesdadiya,et al.  Moth-Flame Optimizer Method for Solving Constrained Engineering Optimization Problems , 2018 .

[49]  Sanjeevikumar Padmanaban,et al.  A Hybrid Moth-Flame Fuzzy Logic Controller Based Integrated Cuk Converter Fed Brushless DC Motor for Power Factor Correction , 2018, Electronics.

[50]  Haoran Zhao,et al.  Using GM (1,1) Optimized by MFO with Rolling Mechanism to Forecast the Electricity Consumption of Inner Mongolia , 2016 .

[51]  Banaja Mohanty,et al.  Performance analysis of moth flame optimization algorithm for AGC system , 2019 .

[52]  Ernst Bonek,et al.  Space Division Multiple Access (SDMA): An Editorial Introduction , 1999, Wirel. Pers. Commun..

[53]  Aboul Ella Hassanien,et al.  Moth-flame Optimization Based Segmentation for MRI Liver Images , 2017, AISI.

[54]  Indrajit N. Trivedi,et al.  Moth-Flame optimization Algorithm for solving real challenging constrained engineering optimization problems , 2016, 2016 IEEE Students' Conference on Electrical, Electronics and Computer Science (SCEECS).

[55]  Ali Asghar Heidari,et al.  Estimating Origin-Destination Matrices Using AN Efficient Moth Flame-Based Spatial Clustering Approach , 2017 .

[56]  Indrajit N. Trivedi,et al.  Optimal power flow with voltage stability improvement and loss reduction in power system using Moth-Flame Optimizer , 2016, Neural Computing and Applications.

[57]  Mohammed Azmi Al-Betar,et al.  New Selection Schemes for Particle Swarm Optimization , 2015, ICIT 2015.

[58]  Ashraf M. Hemeida,et al.  Moth-flame algorithm and loss sensitivity factor for optimal allocation of shunt capacitor banks in radial distribution systems , 2017, 2017 Nineteenth International Middle East Power Systems Conference (MEPCON).

[59]  Ender Hazir,et al.  Optimization of CNC cutting parameters using design of experiment (DOE) and desirability function , 2018, Journal of Forestry Research.

[60]  Ugur Güvenc,et al.  Chaotic Moth Swarm Algorithm , 2017, 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA).

[61]  Prakash Kumar Hota,et al.  Moth‐flame optimization algorithm optimized dual‐mode controller for multiarea hybrid sources AGC system , 2018 .

[62]  Li Li,et al.  Optimization of Water Resources Utilization by Multi-Objective Moth-Flame Algorithm , 2018, Water Resources Management.

[63]  Ahmed A. Ewees,et al.  A Bio-inspired Moth-Flame Optimization Algorithm for Arabic Handwritten Letter Recognition , 2017, 2017 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO).

[64]  Rui Wang,et al.  Research and Application of a Novel Hybrid Model Based on Data Selection and Artificial Intelligence Algorithm for Short Term Load Forecasting , 2017, Entropy.

[65]  O. Ceylan Harmonic elimination of multilevel inverters by moth-flame optimization algorithm , 2016, 2016 International Symposium on Industrial Electronics (INDEL).

[66]  A. Darwish Bio-inspired computing: Algorithms review, deep analysis, and the scope of applications , 2018, Future Computing and Informatics Journal.

[67]  Lavika Goel,et al.  Hybridization of moth flame optimization and gravitational search algorithm and its application to detection of food quality , 2017, 2017 Intelligent Systems Conference (IntelliSys).

[68]  Huseyin Ceylan,et al.  Harmony Search Algorithm for Transport Energy Demand Modeling , 2009 .

[69]  Rafael Asenjo,et al.  Correction to: Simultaneous multiprocessing in a software-defined heterogeneous FPGA , 2018, The Journal of Supercomputing.

[70]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[71]  Saeed Gholizadeh,et al.  Design of steel frames by an enhanced moth-flame optimization algorithm , 2017 .

[72]  A. E. Eiben,et al.  Parameter tuning for configuring and analyzing evolutionary algorithms , 2011, Swarm Evol. Comput..

[73]  Utkarsh Singh,et al.  A new optimal feature selection scheme for classification of power quality disturbances based on ant colony framework , 2019, Appl. Soft Comput..

[74]  Mohammed Azmi Al-Betar,et al.  A survey on applications and variants of the cuckoo search algorithm , 2017, Appl. Soft Comput..

[75]  Ayat Ali Saleh,et al.  Comparison of different optimization techniques for optimal allocation of multiple distribution generation , 2018, 2018 International Conference on Innovative Trends in Computer Engineering (ITCE).

[76]  Akash Saxena,et al.  An opposition theory enabled moth flame optimizer for strategic bidding in uniform spot energy market , 2019, Engineering Science and Technology, an International Journal.

[77]  Xiaoting Li,et al.  An Improved Bat Algorithm Based on Lévy Flights and Adjustment Factors , 2019, Symmetry.

[78]  Milad Ahangaran,et al.  Harmony Search Algorithm: Strengths and Weaknesses , 2013 .

[79]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[80]  Soheyl Khalilpourazari,et al.  An efficient hybrid algorithm based on Water Cycle and Moth-Flame Optimization algorithms for solving numerical and constrained engineering optimization problems , 2017, Soft Computing.

[81]  Jonathan Bennie,et al.  The ecological impacts of nighttime light pollution: a mechanistic appraisal , 2013, Biological reviews of the Cambridge Philosophical Society.

[82]  Vikas,et al.  Multi-objective Moth Flame Optimization , 2016, 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[83]  Rahul Dhiman Moth-Flame Optimization Technique for Optimal Coordination of Directional Overcurrent Relay System , 2018 .

[84]  Kun-Huang Chen,et al.  An improved particle swarm optimization for feature selection , 2011, Intell. Data Anal..

[85]  Nebojsa Bacanin,et al.  Moth Search Algorithm for Drone Placement Problem , 2018 .

[86]  Eid Emary,et al.  Feature selection approach based on moth-flame optimization algorithm , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[87]  Lalit Chandra Saikia,et al.  Automatic generation control in competitive market conditions with moth-flame optimization based cascade controller , 2016, 2016 IEEE Region 10 Conference (TENCON).

[88]  Seyed Mohammad Mirjalili,et al.  Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm , 2015, Knowl. Based Syst..

[89]  Aboul Ella Hassanien,et al.  A hybrid SA-MFO algorithm for function optimization and engineering design problems , 2018 .

[90]  Ragab A. El-Sehiemy,et al.  Economic Power Dispatch with Emission Constraint and Valve Point Loading Effect Using Moth Flame Optimization Algorithm , 2018, Advanced Engineering Forum.

[91]  Aboul Ella Hassanien,et al.  Bio-inspired Swarm Techniques for Thermogram Breast Cancer Detection , 2016 .

[92]  Pradeep Jangir,et al.  Optimal Power Flow using a Hybrid Particle Swarm Optimizer with Moth Flame Optimizer , 2017 .

[93]  David E. Goldberg,et al.  Time Complexity of genetic algorithms on exponentially scaled problems , 2000, GECCO.

[94]  Abhipsa Sahu,et al.  Performance comparison of 2-DOF PID controller based on Moth-flame optimization technique for load frequency control of diverse energy source interconnected power system , 2018, 2018 Technologies for Smart-City Energy Security and Power (ICSESP).

[95]  Pradeep Jangir,et al.  Economic Load Dispatch problem with ramp rate limits and prohibited operating zones solve using Levy flight Moth-Flame optimizer , 2016, 2016 International Conference on Energy Efficient Technologies for Sustainability (ICEETS).

[96]  Saeid Homayouni,et al.  Object-based classification of hyperspectral data using Random Forest algorithm , 2018, Geo spatial Inf. Sci..

[97]  Sakti Prasad Ghoshal,et al.  Concentric circular antenna array synthesis for side lobe suppression using moth flame optimization , 2018 .

[98]  Panos M. Pardalos,et al.  An improved adaptive binary Harmony Search algorithm , 2013, Inf. Sci..

[99]  Alice E. Smith,et al.  Efficiently Solving the Redundancy Allocation Problem Using Tabu Search , 2003 .

[100]  Salah Kamel,et al.  Enhancing security of power systems including SSSC using moth-flame optimization algorithm , 2016, 2016 Eighteenth International Middle East Power Systems Conference (MEPCON).

[101]  Tarek H. M. Abou-El-Enien,et al.  Modified Moth-Flame Optimization Algorithms for Terrorism Prediction , 2016 .

[102]  Prakash Kotecha,et al.  Single level production planning in petrochemical industries using Moth-flame optimization , 2016, 2016 IEEE Region 10 Conference (TENCON).

[103]  Aboul Ella Hassanien,et al.  An improved moth flame optimization algorithm based on rough sets for tomato diseases detection , 2017, Comput. Electron. Agric..

[104]  Sumit Paudyal,et al.  Optimal capacitor placement and sizing considering load profile variations using moth-flame optimization algorithm , 2017, 2017 International Conference on Modern Power Systems (MPS).

[105]  Indrajit N. Trivedi,et al.  Moth flame optimization to solve optimal power flow with non-parametric statistical evaluation validation , 2017 .

[106]  Hamdan Daniyal,et al.  Application of Moth-Flame Optimizer and Ant Lion Optimizer to Solve Optimal Reactive Power Dispatch Problems , 2018 .

[107]  Aboul Ella Hassanien,et al.  Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation , 2017, Expert Syst. Appl..

[108]  Sriparna Saha,et al.  New cuckoo search algorithms with enhanced exploration and exploitation properties , 2018, Expert Syst. Appl..

[109]  Jinzhong Zhang,et al.  Meta-heuristic moth swarm algorithm for multilevel thresholding image segmentation , 2018, Multimedia Tools and Applications.

[110]  Prakash Kumar Hota,et al.  Comparative Performance Analysis of PID Controller with Filter for Automatic Generation Control with Moth-Flame Optimization Algorithm , 2018, Advances in Intelligent Systems and Computing.

[111]  Hossam Faris,et al.  Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems , 2017, Adv. Eng. Softw..

[112]  Essam Said Hanandeh,et al.  A novel hybridization strategy for krill herd algorithm applied to clustering techniques , 2017, Appl. Soft Comput..

[113]  Dalia Yousri,et al.  Biological Inspired Optimization Algorithms for Cole-Impedance Parameters Identification , 2017 .

[114]  Almoataz Y. Abdelaziz,et al.  Dynamic performance enhancement for wind energy conversion system using Moth-Flame Optimization based blade pitch controller , 2018, Sustainable Energy Technologies and Assessments.

[115]  Arup Kumar Goswami,et al.  Profit maximization with integration of wind farm in contingency constraint deregulated power market using Moth Flame Optimization algorithm , 2016, 2016 IEEE Region 10 Conference (TENCON).

[116]  R. H. Bhesdadiya,et al.  A Novel Hybrid Approach Particle Swarm Optimizer with Moth-Flame Optimizer Algorithm , 2017 .

[117]  Jinzhong Zhang,et al.  An improved sine cosine water wave optimization algorithm for global optimization , 2018, J. Intell. Fuzzy Syst..

[118]  Sirapat Chiewchanwattana,et al.  Multilevel thresholding for satellite image segmentation with moth-flame based optimization , 2016, 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE).

[119]  N. M. Endersby,et al.  Parasitoids associated with the diamondback moth, Plutella xylostella (L.), in the Eastern Cape, South Africa , 2001 .

[120]  Manisha Sharma,et al.  A Solution to Non-convex/Convex and Dynamic Economic Load Dispatch Problem Using Moth Flame Optimizer , 2018 .

[121]  Hamdan Daniyal,et al.  Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique , 2017, Appl. Soft Comput..

[122]  Maxine Metz,et al.  Strengths and Weaknesses , 2012 .

[123]  Aboul Ella Hassanien,et al.  Moth-flame optimization for training Multi-Layer Perceptrons , 2015, 2015 11th International Computer Engineering Conference (ICENCO).

[124]  Christian Callegari,et al.  Advances in Computing, Communications and Informatics (ICACCI) , 2015 .

[125]  Hamdan Daniyal,et al.  Application of moth-flame optimization algorithm for solving optimal reactive power dispatch problem , 2016 .

[126]  Ragab A. El-Sehiemy,et al.  An enhanced moth-flame optimizer for solving non-smooth economic dispatch problems with emissions , 2018, Energy.

[127]  Colin R. Reeves,et al.  Improving the Efficiency of Tabu Search for Machine Sequencing Problems , 1993 .

[128]  Almoataz Y. Abdelaziz,et al.  LVCI approach for optimal allocation of distributed generations and capacitor banks in distribution grids based on moth–flame optimization algorithm , 2018 .

[129]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[130]  Arup Kumar Goswami,et al.  Moth Flame Optimization based optimal bidding strategy under transmission congestion in deregulated power market , 2016, 2016 IEEE Region 10 Conference (TENCON).

[131]  Laith Mohammad Abualigah,et al.  Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering , 2017, The Journal of Supercomputing.

[132]  Cunbin Li,et al.  A least squares support vector machine model optimized by moth-flame optimization algorithm for annual power load forecasting , 2016, Applied Intelligence.

[133]  T. Pulliam,et al.  A comparative evaluation of genetic and gradient-based algorithms applied to aerodynamic optimization , 2008 .

[134]  A. Sleit,et al.  Moth Flame Optimization Based on Golden Section Search and its Application for Link Prediction Problem , 2018, Modern Applied Science.

[135]  Yuejiu Zheng,et al.  A Fuzzy State-of-Charge Estimation Algorithm Combining Ampere-Hour and an Extended Kalman Filter for Li-Ion Batteries Based on Multi-Model Global Identification , 2018, Applied Sciences.

[136]  MirjaliliSeyedali Moth-flame optimization algorithm , 2015 .

[137]  Puja Singh,et al.  Optical network unit placement in Fiber-Wireless (FiWi) access network by Moth-Flame optimization algorithm , 2017 .

[138]  Munish Rattan,et al.  Performance Optimization of Broadwell-Y Shaped Transistor Using Artificial Neural Network and Moth-Flame Optimization Technique , 2018 .

[139]  Salah Kamel,et al.  Optimal allocation of renewable dg sources in distribution networks considering load growth , 2017, 2017 Nineteenth International Middle East Power Systems Conference (MEPCON).

[140]  Xin-She Yang,et al.  Discrete cuckoo search algorithm for the travelling salesman problem , 2014, Neural Computing and Applications.

[141]  Jinzhong Zhang,et al.  Moth Swarm Algorithm for Clustering Analysis , 2017, ICIC.

[142]  Sérgio Moro,et al.  Unfolding the relations between companies and technologies under the Big Data umbrella , 2018, Comput. Ind..

[143]  Alden H. Wright,et al.  Genetic Algorithms for Real Parameter Optimization , 1990, FOGA.