A Comprehensive Survey on the Recent Variants and Applications of Membrane-Inspired Evolutionary Algorithms

Archives of Computational Methods in Engineering - Tập 29 - Trang 3041-3057 - 2022
Bisan Alsalibi1, Seyedali Mirjalili2,3, Laith Abualigah4, Rafaa Ismael yahya5, Amir H. Gandomi6
1School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia
2Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Adelaide, Australia
3Yonsei Frontier Lab, Yonsei University, Seoul, South Korea
4Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, Jordan
5Department of Computer, College of Science, Mustansiriyah University, Baghdad, Iraq
6Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, Australia

Tóm tắt

In the last decade, the application of membrane-inspired evolutionary algorithms in real-life problems has attracted much attention due to their flexibility and parallelizability. Almost seven years have passed since the first membrane algorithms survey paper was published in 2014. Considering the importance and ongoing research on such algorithms and their applications in various disciplines, this paper presents a comprehensive review of the published literature and suggests future directions. This review aims to summarize and analyze membrane algorithms based on the used nature-inspired algorithm, membrane structure, membrane rules, and their merits and demerits. Furthermore, an extensive bibliography about their real-world applications is presented.

Tài liệu tham khảo

Alsalibi B, Venkat I, Subramanian K, Lutfi SL, Wilde PD (2015) The impact of bio-inspired approaches toward the advancement of face recognition. ACM Comput Surv 48(1):1–33. https://doi.org/10.1145/2791121 de Castro LN (2007) Fundamentals of natural computing: an overview. Phys Life Rev 4(1):1–36 AlSalibi BA, Jelodar MB, Venkat I (2013) A comparative study between the nearest neighbor and genetic algorithms : A revisit to the traveling salesman problem Yahya RI, Hasan S, George LE, Alsalibi B (2015) Membrane computing for 2d image segmentation Eiben A, Schoenauer M (2002) Evolutionary computing evolutionary Computation, Information Processing Letters 82 (1) 1 – 6, Simon D (2013) Evolutionary optimization algorithms: biologically-inspired and population-based approaches to computer intelligence Xin Yao, Yong Liu, Guangming Lin (1999) Evolutionary programming made faster. IEEE Trans Evolut Comput 3(2):82–102 Beyer H, Schwefel H (2004) Evolution strategies–a comprehensive introduction. Nat Comput 1:3–52 Cheng J, Zhang G, Wang T (2015) A membrane-inspired evolutionary algorithm based on population p systems and differential evolution for multi-objective optimization. J Comput Theor Nanosci 12(7):1150–1160 Holland J (1973) Genetic algorithms and the optimal allocation of trials. SIAM J Comput 2:88–105 Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge, MA, USA Kennedy J, Eberhart R (1995) Particle swarm optimization, in: Proceedings of ICNN’95 - International Conference on Neural Networks, Vol. 4, pp. 1942–1948 Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theor Comput Sci 344(2):243–278 https://doi.org/10.1016/j.tcs.2005.05.020, http://www.sciencedirect.com/science/article/pii/S0304397505003798 Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial Bee colony (abc) algorithm. J Global Optim 39:459–471 Yang X, Deb S (2013) Cuckoo search: recent advances and applications. Neural Comput Appl 24:169–174 Yang X-S (2010) A new metaheuristic bat-inspired algorithm. In: González J, Pelta D, Cruz C, Terrazas G, Krasnogor N (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), vol 284. Studies in Computational Intelligence. Springer, Berlin Heidelberg, pp 65–74 Gandomi AH, Alavi AH (2012) Krill herd: A new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845. https://doi.org/10.1016/j.cnsns.2012.05.010 Mirjalili S, Mirjalili SM, Lewis A (2014) Grey Wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007 Abualigah L, Diabat A, Mirjalili S, Abd Elaziz M, Gandomi AH (2021) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609 Paun G (2000) Computing with membranes. J Comput Syst Sci 61:108–143 Paun G, Perez-Jimenez MJ, Riscos-Nunez A (2004) Tissue p systems with cell division. Sevilla, Report RGNC, Second Brainstorming Week on Membrane Computing, pp 380–386 Alsalibi B, Venkat I, Subramanian K, Christinal HA (2014) A bio-inspired software for homology groups of 2d digital images. Asian Conf Membr Comput ACMC 2014:1–4. https://doi.org/10.1109/ACMC.2014.7065800 Nishida TY (2006) Membrane algorithms: approximate algorithms for NP-complete optimization problems. Springer, Berlin Heidelberg, pp 303–314 Gheorghe M, Zhang G, Pan L, Perez-Jimenez M (2014) Evolutionary membrane computing: A comprehensive survey and new results. Inf Sci 279:528–551 Zhang G, Prez-Jimnez MJ, Gheorghe M (2017) Real-Life applications with membrane computing, 1st edn. Springer Publishing Company,Incorporated Păun G (2003) Membrane computing, in: International Symposium on Fundamentals of Computation Theory, Springer, pp. 284–295 Song B, Li K, Orellana-Martín D, Pérez-Jiménez MJ, PéRez-Hurtado I (2021) A survey of nature-inspired computing: Membrane computing. ACM Comput Surv 54(1):1–31. https://doi.org/10.1145/3431234 Nishida TY, (2007) Membrane algorithm with Brownian subalgorithm and genetic subalgorithm. Int J Found Comput Sci 18(06):1353–1360. https://doi.org/10.1142/S012905410700539X Wang J, Hu J, Peng H, Pérez-Jiménez MJ, Riscos-Núñez A (2015) Decision tree models induced by membrane systems, Romanian. J Inf Sci Technol 18:228–239 Cui Y, Han Y, Geng Z, Zhu Q, Fan J (2019) Production optimization and energy saving of complex chemical processes using novel competing evolutionary membrane algorithm: Emphasis on ethylene cracking. Energy Convers Manag 196:311–319 https://doi.org/10.1016/j.enconman.2019.05.101 Andreu-Guzmán JA, Valencia-Cabrera L (2020) A novel solution for GCP based on an olms membrane algorithm with dynamic operators. J Membr Comput 2:1–13 Zhang X, Li J, Zhang L (2016) A multi-objective membrane algorithm guided by the skin membrane. Nat Comput 15(4):597–610. https://doi.org/10.1007/s11047-016-9572-3 Li Z, Zhang L, Su Y, Li J, Wang X (2018) A skin membrane-driven membrane algorithm for many-objective optimization. Neural Comput Appl 30(1):141–152. https://doi.org/10.1007/s00521-016-2675-z Orozco-Rosas U, Montiel O, Sepúlveda R (2019) Mobile robot path planning using membrane evolutionary artificial potential field. Appl Soft Comput 77:236–251. https://doi.org/10.1016/j.asoc.2019.01.036 Niu Y, He J, Wang Z, Xiao J (2014) A p-based hybrid evolutionary algorithm for vehicle routing problem with time windows. Math Probl Eng 2014:1–11 Solomon M (1987) Algorithms for the vehicle routing and scheduling problems with time window constraints. Oper Res 35:254–265 He J, Xiao J, Liu X, Wu T, Song T (2015) A novel membrane-inspired algorithm for optimizing solid waste transportation. Optik 126(23):3883–3888 https://doi.org/10.1016/j.ijleo.2015.07.152 Peng H, Wang J, Pérez-Jiménez MJ, Riscos-Núñez A (2015) An unsupervised learning algorithm for membrane computing. Inf Sci 304:80–91. https://doi.org/10.1016/j.ins.2015.01.019 Peng H, Shi P, Wang J, Riscos-Núñez A, Pérez-Jiménez MJ (2017) Multiobjective fuzzy clustering approach based on tissue-like membrane systems. Knowl-Based Syst 125:74–82. https://doi.org/10.1016/j.knosys.2017.03.024 Liu C, Fan L, Liu Z, Dai X, Xu J, Chang B (2018) Community detection in complex networks by using membrane algorithm. International Journal of Modern Physics C 29(01):1850003. https://doi.org/10.1142/S0129183118500031, arXiv:https://doi.org/10.1142/S0129183118500031 Wang H, Chen S, Luo L (2020) A diffusion algorithm based on p systems for continuous global optimization. J Comput Sci 44:101112. https://doi.org/10.1016/j.jocs.2020.101112 Liu C, Fan L (2016) A hybrid evolutionary algorithm based on tissue membrane systems and CMA-ES for solving numerical optimization problems. Knowl-Based Syst 105:38–47. https://doi.org/10.1016/j.knosys.2016.04.025 Zhang G, Rong H, Cheng J, Qin Y (2014) A population-membrane-system-inspired evolutionary algorithm for distribution network reconfiguration. Ch J Electron 437–441 Zhang G, Cheng J, Gheorghe M, Ipate F, Wang X (2015) Qeam: An approximate algorithm using p systems with active membranes. Int J Comput Commun Control 10:263–279 Vent W (1975) Rechenberg, ingo, evolutionsstrategie — optimierung technischer systeme nach prinzipien der biologischen evolution. 170 s. mit 36 abb. frommann-holzboog-verlag. stuttgart 1973. broschiert, Feddes Repertorium 86 (5): 337–337. https://doi.org/10.1002/fedr.19750860506. arXiv: https://onlinelibrary.wiley.com/doi/pdf/10.1002/fedr.19750860506 Hu J, Peng H, Wang J, Yu W (2020) knn-p: A knn classifier optimized by p systems. Theor Comput Sci 817:55–65 Peng H, Wang J, Shi P, Riscos-Núñez A, Pérez-Jiménez MJ (2015) An automatic clustering algorithm inspired by membrane computing. Pattern Recognit Lett 68:34–40. https://doi.org/10.1016/j.patrec.2015.08.008 Wang L, Liu X, Sun M, Qu J (2020) An extended clustering membrane system based on particle swarm optimization and cell-like p system with active membranes. Math Probl Eng 2010:18 Zhang Z, Xinzhong Peng H (2014) A novel framework of tissue membrane systems for image fusion. Biomed Mater Eng 24:1–24 Peng H, Wang J, Pérez-Jiménez MJ (2015) Optimal multi-level thresholding with membrane computing. Digital Signal Process 37:53–64 https://doi.org/10.1016/j.dsp.2014.10.006 Guo D, Zhang G, Zhou Y, Yuan J, Paul P, Fu K, Zhu M (2020) Image thresholding using a membrane algorithm based on enhanced particle swarm optimization with hyperparameter. Int J Unconv Comput 15:83–106 Gao T, Liu X, Wang L (2018) An improved pso-based clustering algorithm inspired by tissue-like P system, in: Y. Tan, Y. Shi, Q. Tang (Eds.), Data Mining and Big Data-Third International Conference, DMBD 2018, Shanghai, China, June 17-22, Proceedings, Vol. 10943 of Lecture Notes in Computer Science, Springer, 2018, pp. 325–335. https://doi.org/10.1007/978-3-319-93803-5_31 Singh G, Deep K (2017) Effectiveness of new multiple-PSO based membrane optimization algorithms on CEC 2014 benchmarks and iris classification. Nat Comput: Int J 16(3):473–496. https://doi.org/10.1007/s11047-016-9573-2 Singh G, Deep K (2016) A new membrane algorithm using the rules of particle swarm optimization incorporated within the framework of cell-like p-systems to solve sudoku. Appl Soft Comput 45:27–39 https://doi.org/10.1016/j.asoc.2016.03.020 Xiao J, Huang Y, Cheng Z, He J, Niu Y (2014) A hybrid membrane evolutionary algorithm for solving constrained optimization problems. Optik 125(2):897–902 https://doi.org/10.1016/j.ijleo.2013.08.032 Wang X, Zhang G, Zhao J, Rong H, Ipate F, Lefticaru R (2015) A modified membrane-inspired algorithm based on particle swarm optimization for mobile robot path planning. Int J Comput Commun Control 10:732–745 Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theor Comput Sci 344(2–3):243–278 Luo Y, Guo P, Zhang M (2019) A framework of ant colony p system. IEEE Access 7:157655–157666. https://doi.org/10.1109/ACCESS.2019.2949952 Niu Y, Wang S, He J, Xiao J (2015) A novel membrane algorithm for capacitated vehicle routing problem. Soft Comput 19(2):471–482. https://doi.org/10.1007/s00500-014-1266-0 Peng H, Wang J (2017) A hybrid approach based on tissue p systems and artificial bee colony for IIR system identification. Neural Comput Appl 28(9):2675–2685. https://doi.org/10.1007/s00521-016-2201-3 Yang X (2010) A new metaheuristic bat-inspired algorithm, ArXiv abs/1004.4170 Alsalibi B, Venkat I, Al-Betar MA (2017) A membrane-inspired bat algorithm to recognize faces in unconstrained scenarios. Eng Appl Artif Intell 64:242–260. https://doi.org/10.1016/j.engappai.2017.06.018 Huang G, Mattar M, Berg T, Learned-Miller E (2008) Labeled faces in the wild: A database forstudying face recognition in unconstrained environments Phillips P, Beveridge J, Draper B, Givens G, OToole A, Bolme D, Dunlop J, Lui YM, Sahibzada H, Weimer S (2012) The good, the bad and the ugly face challenge problem. Image Vis Comput 30:177–185 Martínez A (1998) The ar face database Alsalibi B, Abualigah L, Khader AT, A novel bat algorithm with dynamic membrane structure for optimization problems, Appl Intell Abualigah L, Alsalibi B, Shehab M, Alshinwan M, Khasawneh A, Alabool H (2020) A parallel hybrid krill herd algorithm for feature selection. Int J Mach Learn Cybern 1–24 Maroosi A (2021) A cuckoo search algorithm inspired from membrane systems Zhu X, Wang N (2017) Cuckoo search algorithm with membrane communication mechanism for modeling overhead crane systems using RBF neural networks. Appl Soft Comput 56:458–471 Maroosi A, Muniyandi RC, Sundararajan E, Zin AM (2016) A parallel membrane inspired harmony search for optimization problems: A case study based on a flexible job shop scheduling problem. Appl Soft Comput 120–136 Dong W, Zhou K, Qi H, He C, Zhang J (2018) A tissue p system based evolutionary algorithm for multi-objective VRPTW. Swarm Evol Comput 39:310–322. https://doi.org/10.1016/j.swevo.2017.11.001 Liu C, Du Y, Lei J (2019) A SOM-based membrane optimization algorithm for community detection. Entropy 21:533 Xiao J, He J, Chen P, Niu Y (2016) An improved dynamic membrane evolutionary algorithm for constrained engineering design problems. Nat Comput 15:579–589 Guo W, Xiang L, Liu X (2019) An advanced membrane evolutionary algorithm for constrained engineering design problems, in: International Conference on Human Centered Computing HCC 2019: Human Centered Computing, Vol. 11956, pp. 123–132| Niu Y, Zhang Y, Cao Z, Gao K, Xiao J, Song W, Zhang F (2021) Mimoa: A membrane-inspired multi-objective algorithm for green vehicle routing problem with stochastic demands. Swarm Evol Comput 60:100767. https://doi.org/10.1016/j.swevo.2020.100767 Liu C, Du Y, Li A, Lei J (2020) Evolutionary multi-objective membrane algorithm. IEEE Access 8:6020–6031. https://doi.org/10.1109/ACCESS.2019.2939217 Liu C, Du Y (2019) A membrane algorithm based on chemical reaction optimization for many-objective optimization problems. Knowl-Based Syst 165:306–320. https://doi.org/10.1016/j.knosys.2018.12.001 Chen T, Yu Y, Zhao K, Yu Z (2017) A membrane-genetics algorithm for multi-objective optimization problems, in: 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp. 1–6. https://doi.org/10.1109/CISP-BMEI.2017.8302326 Guo P, Quan C, Chen H (2019) Meamvc: A membrane evolutionary algorithm for solving minimum vertex cover problem. IEEE Access 7:60774–60784. https://doi.org/10.1109/ACCESS.2019.2915550 Guo P, Wang X, Zeng Y, Chen H (2019) Meamcp: A membrane evolutionary algorithm for solving maximum clique problem. IEEE Access 7:108360–108370. https://doi.org/10.1109/ACCESS.2019.2933383 He J, Xiao J, Shao Z (2014) An adaptive membrane algorithm for solving combinatorial optimization problems. Acta Mathematica Scientia 34(5):1377–1394. https://doi.org/10.1016/S0252-9602(14)60090-4 Guo P, Hou M, Ye L (2020) Meatsp: A membrane evolutionary algorithm for solving TSP. IEEE Access 8:199081–199096. https://doi.org/10.1109/ACCESS.2020.3035058 Yang X, Xiang L, Liu X (2019) A multi-population genetic algorithm based on dynamic p system for solving constrained optimization problems, in: 2019 10th International Conference on Information Technology in Medicine and Education (ITME), pp. 592–596. https://doi.org/10.1109/ITME.2019.00138 Han M, Liu C, Xing J (2014) An evolutionary membrane algorithm for global numerical optimization problems. Inf Sci 276:219–241. https://doi.org/10.1016/j.ins.2014.02.057 Liu C, Fan L (2016) Evolutionary algorithm based on dynamical structure of membrane systems in uncertain environments. Int J Biomath 09(02):1650017. https://doi.org/10.1142/S1793524516500170 Han M, Liu C (2014) Endpoint prediction model for basic oxygen furnace steel-making based on membrane algorithm evolving extreme learning machine. Appl Soft Comput 19:430–437. https://doi.org/10.1016/j.asoc.2013.09.012 Yu C, Lian Q, Zhang D, Wu C (2018) Pame: Evolutionary membrane computing for virtual network embedding. J Parallel Distrib Comput 111:136–151. https://doi.org/10.1016/j.jpdc.2017.08.005 Mehta V, Bawa S, Singh J (2020) Analytical review of clustering techniques and proximity measures. Artificial Intelligence Review 1–29