Multi-verse optimizer algorithm: a comprehensive survey of its results, variants, and applications

This review paper presents a comprehensive and full review of the so-called optimization algorithm, multi-verse optimizer algorithm (MOA), and reviews its main characteristics and procedures. This optimizer is a kind of the most recent powerful nature-inspired meta-heuristic algorithms, where it has been successfully implemented and utilized in several optimization problems in a variety of several fields, which are covered in this context, such as benchmark test functions, machine learning applications, engineering applications, network applications, parameters control, and other applications of MOA. This paper covers all the available publications that have been used MOA in its application, which are published in the literature including the variants of MOA such as binary, modifications, hybridizations, chaotic, and multi-objective. Followed by its applications, the assessment and evaluation, and finally the conclusions, which interested in the current works on the optimization algorithm, recommend potential future research directions.

[1]  G. Di Caro,et al.  Ant colony optimization: a new meta-heuristic , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[2]  Hardian Gunardi.  Penerapan Multi-Verse Optimizer untuk menyelesaikan Asymmetric Travelling Salesman Problem , 2018 .

[3]  Nadeem Javaid,et al.  A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid , 2017 .

[4]  Diego Oliva,et al.  Multi-level thresholding-based grey scale image segmentation using multi-objective multi-verse optimizer , 2019, Expert Syst. Appl..

[5]  R. H. Bhesdadiya,et al.  A novel hybrid Particle Swarm Optimizer with multi verse optimizer for global numerical optimization and Optimal Reactive Power Dispatch problem , 2017 .

[6]  Hossam Faris,et al.  Training feedforward neural networks using multi-verse optimizer for binary classification problems , 2016, Applied Intelligence.

[7]  Chuanpei Xu,et al.  A Multi-Verse Optimizer with Levy Flights for Numerical Optimization and Its Application in Test Scheduling for Network-on-Chip , 2016, PloS one.

[8]  Fred W. Glover,et al.  Tabu Search - Part I , 1989, INFORMS J. Comput..

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

[10]  Aboul Ella Hassanien,et al.  Designing Multilayer Feedforward Neural Networks Using Multi-Verse Optimizer , 2017 .

[11]  Guozhu Liu,et al.  Network intrusion detection based on Chaotic Multi-Verse Optimizer , 2018 .

[12]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.

[13]  D. Werner,et al.  Wind Driven Optimization (WDO): A novel nature-inspired optimization algorithm and its application to electromagnetics , 2010, 2010 IEEE Antennas and Propagation Society International Symposium.

[14]  Seyedali Mirjalili,et al.  SCA: A Sine Cosine Algorithm for solving optimization problems , 2016, Knowl. Based Syst..

[15]  Huiru Zhao,et al.  DGM (1, 1) model optimized by MVO (multi-verse optimizer) for annual peak load forecasting , 2016, Neural Computing and Applications.

[16]  Nithin V. George,et al.  Swarm and evolutionary computing algorithms for system identification and filter design: A comprehensive review , 2017, Swarm Evol. Comput..

[17]  H. SulaimanM.,et al.  An Application of Multi-Verse Optimizer for Optimal Reactive Power Dispatch Problem , 2016 .

[18]  Nassima Dif,et al.  A Multi-Verse Optimizer Approach for Instance Selection and Optimizing 1-NN Algorithm , 2018, Int. J. Strateg. Inf. Technol. Appl..

[19]  Mohammad Alshinwan,et al.  Salp swarm algorithm: a comprehensive survey , 2019, Neural Computing and Applications.

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

[21]  Ragab A. El-Sehiemy,et al.  Application of multi-verse optimizer for transmission network expansion planning in power systems , 2019, 2019 International Conference on Innovative Trends in Computer Engineering (ITCE).

[22]  Laith Mohammad Abualigah,et al.  APPLYING GENETIC ALGORITHMS TO INFORMATION RETRIEVAL USING VECTOR SPACE MODEL , 2015 .

[23]  Indrajit N. Trivedi,et al.  Voltage stability enhancement and voltage deviation minimization using multi-verse optimizer algorithm , 2016, 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT).

[24]  Li Ran,et al.  An Improved Data Fusion Method IICKPAD for Privacy Protection in Wireless Sensor Networks , 2017 .

[25]  Hossam Faris,et al.  A binary multi-verse optimizer for 0-1 multidimensional knapsack problems with application in interactive multimedia systems , 2019, Comput. Ind. Eng..

[26]  Darrell Whitley,et al.  A genetic algorithm tutorial , 1994, Statistics and Computing.

[27]  Xin-She Yang,et al.  Flower Pollination Algorithm for Global Optimization , 2012, UCNC.

[28]  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.

[29]  Nathan Seiberg,et al.  From big crunch to big bang , 2002 .

[30]  Rajeev R. Raje,et al.  On the application of search-based techniques for software engineering predictive modeling: A systematic review and future directions , 2017, Swarm Evol. Comput..

[31]  Seyed Mohammad Mirjalili,et al.  Multi-Verse Optimizer: a nature-inspired algorithm for global optimization , 2015, Neural Computing and Applications.

[32]  Harish Garg,et al.  A hybrid PSO-GA algorithm for constrained optimization problems , 2016, Appl. Math. Comput..

[33]  Jianghua Cao,et al.  Design optimization of a SRM motor by a nature-inspired algorithm: Multi-verse optimizer , 2018, 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA).

[34]  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..

[35]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[36]  Ibrahim Berkan Aydilek.  A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems , 2018, Appl. Soft Comput..

[37]  Li Cheng,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010 .

[38]  Ashraf Darwish,et al.  A new chaotic multi-verse optimization algorithm for solving engineering optimization problems , 2018, J. Exp. Theor. Artif. Intell..

[39]  Mohamed Abdel-Basset,et al.  Grid quorum-based spatial coverage for IoT smart agriculture monitoring using enhanced multi-verse optimizer , 2018, Neural Computing and Applications.

[40]  Ramin Rajabioun,et al.  Cuckoo Optimization Algorithm , 2011, Appl. Soft Comput..

[41]  Ali Diabat,et al.  A comprehensive survey of the Grasshopper optimization algorithm: results, variants, and applications , 2020, Neural Computing and Applications.

[42]  Dervis Karaboga,et al.  A comparative study of Artificial Bee Colony algorithm , 2009, Appl. Math. Comput..

[43]  Hossam Faris,et al.  A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture , 2017, Neural Computing and Applications.

[44]  Bo Xing,et al.  Gravitational Search Algorithm , 2014 .

[45]  Hossam Faris,et al.  Evolutionary static and dynamic clustering algorithms based on multi-verse optimizer , 2018, Eng. Appl. Artif. Intell..

[46]  Aboul Ella Hassanien,et al.  Chaotic multi-verse optimizer-based feature selection , 2017, Neural Computing and Applications.

[47]  Mohammed Azmi Al-Betar,et al.  comprehensive review : Krill Herd algorithm ( KH ) and its pplications saju , 2016 .

[48]  Mohamed Abdel-Basset,et al.  A hybrid whale optimization algorithm based on local search strategy for the permutation flow shop scheduling problem , 2018, Future Gener. Comput. Syst..

[49]  Mohammad Alshinwan,et al.  Moth–flame optimization algorithm: variants and applications , 2019, Neural Computing and Applications.

[50]  A. B. Inamdar,et al.  Inherent optical properties retrieval from deep waters using Multi Verse Optimizer , 2018, Remote Sensing.

[51]  Laith Mohammad Abualigah,et al.  A Novel Weighting Scheme Applied to Improve the Text Document Clustering Techniques , 2018 .

[52]  Jingxin Liu,et al.  An Mutational Multi-Verse Optimizer with Lévy Flight , 2018, ICIC.

[53]  Fred Glover,et al.  Tabu Search - Part II , 1989, INFORMS J. Comput..

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

[55]  Bachir,et al.  A solution to the optimal power flow using multiverse optimizer , 2016 .

[56]  Dan Simon,et al.  Biogeography-Based Optimization , 2022 .

[57]  Hossam Faris,et al.  Multi-verse Optimizer: Theory, Literature Review, and Application in Data Clustering , 2019, Nature-Inspired Optimizers.

[58]  Ahmed Fathy,et al.  Multi-Verse Optimizer for Identifying the Optimal Parameters of PEMFC Model , 2018 .

[59]  Xu Zhao,et al.  Estimates of energy consumption in China using a self-adaptive multi-verse optimizer-based support vector machine with rolling cross-validation , 2018, Energy.

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

[61]  Aboul Ella Hassanien,et al.  Handbook of Research on Machine Learning Innovations and Trends , 2017 .

[62]  Mohammed Azmi Al-Betar,et al.  A krill herd algorithm for efficient text documents clustering , 2016, 2016 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE).

[63]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[64]  Luis Lopez,et al.  A binary multi-verse optimizer algorithm applied to the set covering problem , 2017, 2017 4th International Conference on Systems and Informatics (ICSAI).

[65]  Xin-She Yang,et al.  Firefly algorithm, stochastic test functions and design optimisation , 2010, Int. J. Bio Inspired Comput..

[66]  Pratyusha Rakshit,et al.  Noisy evolutionary optimization algorithms - A comprehensive survey , 2017, Swarm Evol. Comput..

[67]  Hamed Shah-Hosseini,et al.  Problem solving by intelligent water drops , 2007, 2007 IEEE Congress on Evolutionary Computation.

[68]  Yongqian Liu,et al.  An improved SVM classifier based on multi-verse optimizer for fault diagnosis of autopilot , 2018, 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC).

[69]  Hong Wang,et al.  Bacterial Colony Optimization , 2012 .

[70]  Chetna,et al.  Development of Multi-verse Optimizer (MVO) for LabVIEW , 2018 .

[71]  Laith Mohammad Abualigah,et al.  A new feature selection method to improve the document clustering using particle swarm optimization algorithm , 2017, J. Comput. Sci..

[72]  John R. Koza,et al.  Evolution of Subsumption Using Genetic Programming , 1993 .

[73]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[74]  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..

[75]  Mohammad Masoud Javidi,et al.  Estimates of residential building energy consumption using a multi-verse optimizer-based support vector machine with k-fold cross-validation , 2019, Evol. Syst..