Abstract
A large number of intelligent algorithms based on social intelligent behavior have been extensively researched in the past few decades, through the study of natural creatures, and applied to various optimization fields. The learning-based intelligent optimization algorithm (LIOA) refers to an intelligent optimization algorithm with a certain learning ability. This is how the traditional intelligent optimization algorithm combines learning operators or specific learning mechanisms to give itself some learning ability, thereby achieving better optimization behavior. We conduct a comprehensive survey of LIOAs in this paper. The research includes the following sections: Statistical analysis about LIOAs, classification of LIOA learning method, application of LIOAs in complex optimization scenarios, and LIOAs in engineering applications. The future insights and development direction of LIOAs are also discussed.
Similar content being viewed by others
References
Holland JH (1992) Genetic algorithms. Sci Am 267(1):66–72
Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern B-Cybern 26(1):29–41
StornR, Price K (1996) Minimizing the real functions of the ICEC'96 contest by differential evolution. In: Proceedings of IEEE international conference on evolutionary computation, IEEE, pp 842–844
KennedyJ, Eberhart R (1995) Particle swarm optimization (PSO). In: Proceedings of IEEE international conference on neural networks, Perth, Australia, pp 1942–1948
Passino KM (2012) Bacterial foraging optimization. IGI Global, Innovations and Developments of Swarm Intelligence Applications, pp 219–234
Eusuff M, Lansey K, Pasha F (2006) Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng Optim 38(2):129–154
Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471
Yang X-S (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio-Inspired Comput 2(2):78–84
Mehrabian AR, Lucas C (2006) A novel numerical optimization algorithm inspired from weed colonization. Ecol Inform 1(4):355–366
Yang X-S, Deb S, Cuckoo search via Lévy flights, (2009) World Congress on Nature & Biologically Inspired Computing (NaBIC). IEEE 2009:210–214
Yang X-S (2010) A new metaheuristic bat-inspired algorithm, Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Springer 2010:65–74
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61
Faramarzi A, Heidarinejad M, Mirjalili S, Gandomi AH (2020) Marine predators algorithm: a nature-inspired metaheuristic, expert systems with applications 113377
Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191
Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27(4):1053–1073
Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98
Gandomi AH (2014) Interior search algorithm (ISA): a novel approach for global optimization. ISA Trans 53(4):1168–1183
Wang G-G, Deb S, Cui Z (2019) Monarch butterfly optimization. Neural Comput Appl 31(7):1995–2014
Wang G-G, Deb S, Coelho LdS (2015) Elephant herding optimization. In: 2015 3rd international symposium on computational and business intelligence (ISCBI). IEEE, pp 1–5
Wang G-G, Deb S, Gao X-Z, Coelho LDS (2016) A new metaheuristic optimisation algorithm motivated by elephant herding behaviour. Int J Bio-Inspired Comput 8(6):394–409
Li J, Ying S, Alavi AH, Wang G-G (2020) Elephant herding optimization: variants, hybrids, and applications. Mathematics 8(9):1415
Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845
Wang G-G, Guo L, Gandomi AH, Hao G-S, Wang H (2014) Chaotic krill herd algorithm. Inform Sci 274:17–34
Wang H, Yi J-H (2018) An improved optimization method based on krill herd and artificial bee colony with information exchange. Memetic Comput 10(2):177–198
Wang G-G, Gandomi AH, Alavi AH (2014) An effective krill herd algorithm with migration operator in biogeography-based optimization. Appl Math Model 38(9–10):2454–2462
Wang G-G, Deb S, Gandomi AH, Alavi AH (2016) Opposition-based krill herd algorithm with Cauchy mutation and position clamping. Neurocomputing 177:147–157
Li B, Fan ZT, Zhang XL, Huang D-S (2019) Robust dimensionality reduction via feature space to feature space distance metric learning. Neural Netw 112:1–14
Liang X, Wu D, Huang D-S (2019) Image Co-Segmentation via Locally Biased Discriminative Clustering. IEEE Trans Knowl Data Eng 31(11):2228–2233
Liang X, Zhu L, Huang D-S (2017) Multi-task ranking SVM for image cosegmentation. Neurocomputing 247:126–136
Wu D, Yang H-W, Huang D-S, Yuan C-A, Qin X, Zhao Y, Zhao X-Y, Sun J-H (2019) Omnidirectional feature learning for person re-identification. IEEE Access 7:28402–28411
Wu D, Zheng S-J, Bao W-Z, Zhang X-P, Yuan C-A, Huang D-S (2019) A novel deep model with multi-loss and efficient training for person re-identification. Neurocomputing 324:69–75
Sang H-Y, Pan Q-K, Duan P-Y, Li J-Q (2015) An effective discrete invasive weed optimization algorithm for lot-streaming flowshop scheduling problems. J Intell Manuf 29(6):1337–1349
Sang H-Y, Pan Q-K, Li J-Q, Wang P, Han Y-Y, Gao K-Z, Duan P (2019) Effective invasive weed optimization algorithms for distributed assembly permutation flowshop problem with total flowtime criterion. Swarm Evol Comput 44:64–73
Pan Q-K, Sang H-Y, Duan J-H, Gao L (2014) An improved fruit fly optimization algorithm for continuous function optimization problems. Knowl-Based Syst 62:69–83
Gao D, Wang G-G, Pedrycz W (2020) Solving fuzzy job-shop scheduling problem using DE algorithm improved by a selection mechanism. IEEE Trans Fuzzy Syst 28:3265
Gao D, Wang G-G, Pedrycz W (2020) Solving fuzzy job-shop scheduling problem using DE algorithm improved by a selection mechanism. IEEE Trans Fuzzy Syst 28(12):3265–3275
Rizk-Allah RM, El-Sehiemy RA, Wang G-G (2018) A novel parallel hurricane optimization algorithm for secure emission/economic load dispatch solution. Appl Soft Comput 63:206–222
Li M, Xiao D, Zhang Y, Nan H (2015) Reversible data hiding in encrypted images using cross division and additive homomorphism. Signal Process: Image Commun 39:234–248
Li M, Guo Y, Huang J, Li Y (2018) Cryptanalysis of a chaotic image encryption scheme based on permutation-diffusion structure. Signal Process: Image Commun 62:164–172
Fan H, Li M, Liu D, Zhang E (2018) Cryptanalysis of a colour image encryption using chaotic APFM nonlinear adaptive filter. Signal Process 143:28–41
Zhang Y, Gong D, Hu Y, Zhang W (2015) Feature selection algorithm based on bare bones particle swarm optimization. Neurocomputing 148:150–157
Zhang Y, Song X-F, Gong D-W (2017) A return-cost-based binary firefly algorithm for feature selection. Inform Sci 418–419:561–574
Mao W, He J, Tang J, Li Y (2018) Predicting remaining useful life of rolling bearings based on deep feature representation and long short-term memory neural network. Adv Mech Eng 10(12):168781401881718
Jian M, Lam K-M, Dong J (2014) Facial-feature detection and localization based on a hierarchical scheme. Inform Sci 262:1–14
Fan L, Xu S, Liu D, Ru Y (2018) Semi-supervised community detection based on distance dynamics. IEEE Access 6:37261–37271
Wang G-G, Chu HE, Mirjalili S (2016) Three-dimensional path planning for UCAV using an improved bat algorithm. Aerosp Sci Technol 49:231–238
Wang G, Guo L, Duan H, Liu L, Wang H, Shao M (2012) Path planning for uninhabited combat aerial vehicle using hybrid meta-heuristic DE/BBO algorithm. Adv Sci Eng Med 4(6):550–564
Wang G-G, Cai X, Cui Z, Min G, Chen J (2020) High performance computing for cyber physical social systems by using evolutionary multi-objective optimization algorithm. IEEE Trans Emerg Top Comput 8(1):20–30
Cui Z, Sun B, Wang G-G, Xue Y, Chen J (2017) A novel oriented cuckoo search algorithm to improve DV-Hop performance for cyber-physical systems. J Parallel Distr Com 103:42–52
Jian M, Lam K-M, Dong J (2014) Illumination-insensitive texture discrimination based on illumination compensation and enhancement. Inform Sci 269:60–72
Wang G-G, Guo L, Duan H, Liu L, Wang H (2012) The model and algorithm for the target threat assessment based on Elman_AdaBoost strong predictor. Acta Electronica Sinica 40(5):901–906
Jian M, Lam KM, Dong J, Shen L (2015) Visual-patch-attention-aware saliency detection. IEEE Trans Cybern 45(8):1575–1586
Wang G-G, Lu M, Dong Y-Q, Zhao X-J (2016) Self-adaptive extreme learning machine. Neural Comput Appl 27(2):291–303
Mao W, Zheng Y, Mu X, Zhao J (2013) Uncertainty evaluation and model selection of extreme learning machine based on Riemannian metric. Neural Comput Appl 24(7–8):1613–1625
Liu G, Zou J (2018) Level set evolution with sparsity constraint for object extraction. IET Image Proc 12(8):1413–1422
Liu K, Gong D, Meng F, Chen H, Wang G-G (2017) Gesture segmentation based on a two-phase estimation of distribution algorithm. Inf Sci 394–395:88–105
Rizk-Allah RM, El-Sehiemy RA, Deb S, Wang G-G (2017) A novel fruit fly framework for multi-objective shape design of tubular linear synchronous motor. J Supercomput 73(3):1235–1256
Yi J-H, Xing L-N, Wang G-G, Dong J, Vasilakos AV, Alavi AH, Wang L (2020) Behavior of crossover operators in NSGA-III for large-scale optimization problems. Inf Sci 509:470–487
Yi J-H, Deb S, Dong J, Alavi AH, Wang G-G (2018) An improved NSGA-III Algorithm with adaptive mutation operator for big data optimization problems. Future Gener Comput Syst 88:571–585
Wang G-G, Tan Y (2019) Improving metaheuristic algorithms with information feedback models. IEEE Trans Cybern 49(2):542–555
Gu Z-M, Wang G-G (2020) Improving NSGA-III algorithms with information feedback models for large-scale many-objective optimization. Future Gener Comput Syst 107:49–69
Zhang Y, Wang G-G, Li K, Yeh W-C, Jian M, Dong J (2020) Enhancing MOEA/D with information feedback models for large-scale many-objective optimization. Inform Sci 522:1–16
Liu G, Deng M (2018) Parametric active contour based on sparse decomposition for multi-objects extraction. Signal Process 148:314–321
Sun J, Miao Z, Gong D, Zeng X-J, Li J, Wang G-G (2020) Interval multi-objective optimization with memetic algorithms. IEEE Trans Cybern 50(8):3444–3457
Srikanth K, Panwar LK, Panigrahi BK, Herrera-Viedma E, Sangaiah AK, Wang G-G (2018) Meta-heuristic framework: quantum inspired binary grey wolf optimizer for unit commitment problem. Comput Electr Eng 70:243–260
Chen S, Chen R, Wang G-G, Gao J, Sangaiah AK (2018), An adaptive large neighborhood search heuristic for dynamic vehicle routing problems. Comput Electr Eng
Feng Y, Wang G-G (2018) Binary moth search algorithm for discounted 0–1 knapsack problem. IEEE Access 6:10708–10719
Feng Y, Wang G-G, Wang L (2018) Solving randomized time-varying knapsack problems by a novel global firefly algorithm. Eng Comput-Germany 34(3):621–635
Abdel-Basset M, Zhou Y (2018) An elite opposition-flower pollination algorithm for a 0–1 knapsack problem. Int J Bio-Inspired Comput 11(1):46–53
Yi J-H, Wang J, Wang G-G (2016) Improved probabilistic neural networks with self-adaptive strategies for transformer fault diagnosis problem. Adv Mech Eng 8(1):1–13
Mao W, He J, Li Y, Yan Y (2016) Bearing fault diagnosis with auto-encoder extreme learning machine: a comparative study. Proc Inst Mech Eng C J Mech Eng Sci 231(8):1560–1578
Mao W, Feng W, Liang X (2019) A novel deep output kernel learning method for bearing fault structural diagnosis. Mech Syst Signal Process 117:293–318
Feng H-M (2006) Self-generation RBFNs using evolutional PSO learning. Neurocomputing 70(1–3):241–251
Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295
Wang H, Wu Z, Rahnamayan S, Liu Y, Ventresca M (2011) Enhancing particle swarm optimization using generalized opposition-based learning. Inform Sci 181(20):4699–4714
Xia X, Liu J, Hu Z (2014) An improved particle swarm optimizer based on tabu detecting and local learning strategy in a shrunk search space. Appl Soft Comput 23:76–90
Lim WH, Mat Isa NA (2014) Teaching and peer-learning particle swarm optimization. Appl Soft Comput 18:39–58
Lim WH, Mat Isa NA (2014) Bidirectional teaching and peer-learning particle swarm optimization. Inform Sci 280:111–134
Hu Z, Bao Y, Xiong T (2014) Comprehensive learning particle swarm optimization based memetic algorithm for model selection in short-term load forecasting using support vector regression. Appl Soft Comput 25:15–25
Chen Z-Y, Kuo RJ (2015) Immunological algorithm-based neural network learning for sales forecasting. Appl Artif Intell 29(9):904–922
Chen D, Zou F, Lu R, Wang P (2016) Learning backtracking search optimisation algorithm and its application. Inform Sci 376:71–94
Gong YJ, Li JJ, Zhou Y, Li Y, Chung HSH, Shi YH, Zhang J (2016) Genetic learning particle swarm optimization. IEEE Trans Cybern 46(10):2277–2290
Chen G, Douch CIJ, Zhang M (2016) Accuracy-based learning classifier systems for multistep reinforcement learning: a fuzzy logic approach to handling continuous inputs and learning continuous actions. IEEE Trans Evol Comput 20(6):953–971
Song Z, Peng J, Li C, Liu PX (2017) A simple brain storm optimization algorithm with a periodic quantum learning strategy. IEEE Access 6:19968–19983
Mei Y, Tan G, Liu Z (2017) An improved brain-inspired emotional learning algorithm for fast classification. Algorithms 10(2):70
Yu X, Estevez C (2018) Adaptive, multiswarm comprehensive learning particle swarm optimization. Information 9(7):173
Li X-L, He X-D (2014) A hybrid particle swarm optimization method for structure learning of probabilistic relational models. Inform Sci 283:258–266
Gheisari S, Meybodi MR (2016) BNC-PSO: structure learning of Bayesian networks by Particle Swarm Optimization. Inform Sci 348:272–289
Contaldi C, Vafaee F, Nelson PC (2018) Bayesian network hybrid learning using an elite-guided genetic algorithm. Artif Intell Rev 52(1):245–272
Alonso JI, de la Ossa L, Gámez JA, Puerta JM (2018) On the use of local search heuristics to improve GES-based Bayesian network learning. Appl Soft Comput 64:366–376
Shen X-N, Minku LL, Marturi N, Guo Y-N, Han Y (2017) A Q-learning-based memetic algorithm for multi-objective dynamic software project scheduling. Inform Sci 428:1–29
Samma H, Mohamad-Saleh J, Suandi SA, Lahasan B (2020) Q-learning-based simulated annealing algorithm for constrained engineering design problems. Neural Comput Appl 32(9):5147–5161
Sadhu AK, Konar A, Bhattacharjee T, Das S (2018) Synergism of firefly algorithm and Q-learning for robot arm path planning. Swarm Evol Comput 43:50–68
Marandi F, FatemiGhomi SMT (2019) Network configuration multi-factory scheduling with batch delivery: a learning-oriented simulated annealing approach. Comput Ind Eng 132:293–310
Liu J, Wang Q, He C, Jaffrès-Runser K, Xu Y, Li Z, Xu Y (2020) QMR:Q-learning based multi-objective optimization Routing protocol for flying ad hoc networks. Comput Commun 150:304–316
Li J, Xiao D-D, Lei H, Zhang T, Tian T (2020) Using cuckoo search algorithm with Q-learning and genetic operation to solve the problem of logistics distribution center location. Mathematics 8(2):149
Jiang Z, Gu J, Fan W, Liu W, Zhu B (2018) Q-learning approach to coordinated optimization of passenger inflow control with train skip-stopping on a urban rail transit line. Comput Ind Eng 127:1131–1142
Hsieh Y-Z, Su M-C (2016) A Q-learning-based swarm optimization algorithm for economic dispatch problem. Neural Comput Appl 27(8):2333–2350
Ding D, Fan X, Zhao Y, Kang K, Yin Q, Zeng J (2020) Q-learning based dynamic task scheduling for energy-efficient cloud computing. Future Gener Comput Syst 108:361–371
Das PK, Behera HS, Panigrahi BK (2016) Intelligent-based multi-robot path planning inspired by improved classical Q-learning and improved particle swarm optimization with perturbed velocity. Eng Sci Technol Int J 19(1):651–669
Arin A, Rabadi G (2017) Integrating estimation of distribution algorithms versus Q-learning into meta-RaPS for solving the 0–1 multidimensional knapsack problem. Comput Ind Eng 112:706–720
Ahmadi E, Goldengorin B, Süer GA, Mosadegh H (2018) A hybrid method of 2-TSP and novel learning-based GA for job sequencing and tool switching problem. Appl Soft Comput 65:214–229
Xie J, Chen W, Dai H, Liu S, Ai W (2019) A distributed cooperative learning algorithm based on zero-gradient-sum strategy using radial basis function network. Neurocomputing 323:244–255
Maitra M, Chatterjee A (2008) A hybrid cooperative–comprehensive learning based PSO algorithm for image segmentation using multilevel thresholding. Expert Syst Appl 34(2):1341–1350
Ma K, Liu X, Li G, Hu S, Yang J, Guan X (2019) Resource allocation for smart grid communication based on a multi-swarm artificial bee colony algorithm with cooperative learning. Eng Appl Artif Intell 81:29–36
Ding G, Dong F, Zou H (2019) Fruit fly optimization algorithm based on a hybrid adaptive-cooperative learning and its application in multilevel image thresholding. Appl Soft Comput 84:105704
Boryczka U, Kozak J (2015) Enhancing the effectiveness of Ant Colony Decision Tree algorithms by co-learning. Appl Soft Comput 30:166–178
Alexandridis A, Chondrodima E, Sarimveis H (2016) Cooperative learning for radial basis function networks using particle swarm optimization. Appl Soft Comput 49:485–497
Shahrabi J, Adibi MA, Mahootchi M (2017) A reinforcement learning approach to parameter estimation in dynamic job shop scheduling. Comput Ind Eng 110:75–82
Hou Y, Ong Y-S, Feng L, Zurada JM (2017) An evolutionary transfer reinforcement learning framework for multiagent systems. IEEE Trans Evol Comput 21(4):601–615
Emary E, Zawbaa HM, Grosan C (2017) Experienced gray wolf optimization through reinforcement learning and neural networks. IEEE Trans Neural Netw Learn Syst 29(3):681–694
Chen C-H, Liu C-B (2017) Reinforcement learning-based differential evolution with cooperative coevolution for a compensatory neuro-fuzzy controller. IEEE Trans Neural Netw Learn Syst 29(10):4719–4729
CardosoBora T, CoccoMariani V, dos SantosCoelho L (2019) Multi-objective optimization of the environmental-economic dispatch with reinforcement learning based on non-dominated sorting genetic algorithm. Appl Therm Eng 146:688–700
Cao Z, Lin C, Zhou M, Huang R (2019) Scheduling semiconductor testing facility by using cuckoo search algorithm with reinforcement learning and surrogate modeling. IEEE Trans Autom Sci Eng 16(2):825–837
Bora TC, Lebensztajn L, Coelho LDS (2012) Non-dominated sorting genetic algorithm based on reinforcement learning to optimization of broad-band reflector antennas satellite. IEEE Trans Magn 48(2):767–770
Almahdi S, Yang SY (2019) A constrained portfolio trading system using particle swarm algorithm and recurrent reinforcement learning. Expert Syst Appl 130:145–156
Abed-alguni BH (2017) Action-selection method for reinforcement learning based on cuckoo search algorithm. Arab J Sci Eng 43(12):6771–6785
Wu QH, Liao HL (2013) Function optimisation by learning automata. Inform Sci 220:379–398
Hashemi AB, Meybodi MR (2011) A note on the learning automata based algorithms for adaptive parameter selection in PSO. Appl Soft Comput 11(1):689–705
Dai C, Wang Y, Ye M, Xue X, Liu H (2015) An orthogonal evolutionary algorithm with learning automata for multiobjective optimization. IEEE Trans Cybern 46(12):3306–3319
Balusu N, Pabboju S, Narsimha G (2019) An Intelligent channel assignment approach for minimum interference in wireless mesh networks using learning automata and genetic algorithms. Wirel Pers Commun 106(3):1293–1307
Anari B, Akbari Torkestani J, Rahmani AM (2018) A learning automata-based clustering algorithm using ant swarm intelligence. Expert Systems 35(6):e12310
Zuo X, Zhang G, Tan W (2014) Self-adaptive learning PSO-based deadline constrained task scheduling for hybrid iaaS cloud. IEEE Trans Autom Sci Eng 11(2):564–573
Xue Y, Zhuang Y, Ni T, Ni S, Wen X (2014) Self-adaptive learning based discrete differential evolution algorithm for solving CJWTA problem. J Syst Eng Electron 25(1):59–68
Xiao-li L, Li L-H, Zhang B-L, Guo Q-J (2013) Hybrid self-adaptive learning based particle swarm optimization and support vector regression model for grade estimation. Neurocomputing 118:179–190
Wang Y, Li B, Weise T, Wang J, Yuan B, Tian Q (2011) Self-adaptive learning based particle swarm optimization. Inform Sci 181(20):4515–4538
Wang S, Zhang H, Zhang Y, Zhou A (2020) Adaptive population structure learning in evolutionary multi-objective optimization. Soft Comput 24:10025–10042
Wang F, Zhang H, Li K, Lin Z, Yang J, Shen X (2018) A hybrid particle swarm optimization algorithm using adaptive learning strategy. Inform Sci 436:162–177
Sun J, Zhang H, Zhou A, Zhang Q, Zhang K (2019) A new learning-based adaptive multi-objective evolutionary algorithm. Swarm Evol Comput 44:304–319
Li C, Yang S, Nguyen TT (2011) A self-learning particle swarm optimizer for global optimization problems. IEEE Trans Syst Man Cybern B Cybern 42(3):627–646
Gu Q, Hao X (2018) Adaptive iterative learning control based on particle swarm optimization. J Supercomput 76(5):3615–3622
Birjali M, Beni-Hssane A, Erritali M (2018) A novel adaptive e-learning model based on Big Data by using competence-based knowledge and social learner activities. Appl Soft Comput 69:14–32
Bahmani-Firouzi B, Farjah E, Azizipanah-Abarghooee R (2013) An efficient scenario-based and fuzzy self-adaptive learning particle swarm optimization approach for dynamic economic emission dispatch considering load and wind power uncertainties. Energy 50:232–244
Wei W, Zhou J, Chen F, Yuan H (2016) Constrained differential evolution using generalized opposition-based learning. Soft Comput 20(11):4413–4437
Sun L, Chen S, Xu J, Tian Y (2019) Improved monarch butterfly optimization algorithm based on opposition-based learning and random local perturbation. Complexity 2019:4182148
Shekhawat S, Saxena A (2019) Development and applications of an intelligent crow search algorithm based on opposition based learning. ISA Trans. https://doi.org/10.1016/j.isatra.2019.1009.1004
Sharma TK, Pant M (2017) Opposition based learning ingrained shuffled frog-leaping algorithm. J Comput Sci 21:307–315
Park S-Y, Kim Y-J, Kim J-J, Lee J-J (2014) Speeded-up cuckoo search using opposition-based learning. In: 2014 14th international conference on control, automation and systems (ICCAS 2014). IEEE, pp 535–539
Oliva D, AbdElaziz M (2020) An improved brainstorm optimization using chaotic opposite-based learning with disruption operator for global optimization and feature selection. Soft Comput 24:14051
Mahdavi S, Rahnamayan S, Deb K (2018) Opposition based learning: a literature review. Swarm Evol Comput 39:1–23
Ma X, Liu F, Qi Y, Gong M, Yin M, Li L, Jiao L, Wu J (2014) MOEA/D with opposition-based learning for multiobjective optimization problem. Neurocomputing 146:48–64
Liu H, Xu G, Ding G, Li D (2014) Integrating opposition-based learning into the evolution equation of bare-bones particle swarm optimization. Soft Comput 19(10):2813–2836
Gupta S, Deep K (2019) An efficient grey wolf optimizer with opposition-based learning and chaotic local search for integer and mixed-integer optimization problems. Arab J Sci Eng 44(8):7277–7296
Gupta S, Deep K (2019) A hybrid self-adaptive sine cosine algorithm with opposition based learning. Expert Syst Appl 119:210–230
Guo Z, Wang S, Yue X, Yang H (2017) Global harmony search with generalized opposition-based learning. Soft Comput 21(8):2129–2137
Gao XZ, Wang X, Ovaska SJ, Zenger K (2012) A hybrid optimization method of harmony search and opposition-based learning. Eng Optim 44(8):895–914
Feng Y, Wang G-G, Dong J, Wang L (2018) Opposition-based learning monarch butterfly optimization with Gaussian perturbation for large-scale 0–1 knapsack problem. Comput Electr Eng 67:454–468
Ahandani MA, Alavi-Rad H (2015) Opposition-based learning in shuffled frog leaping: an application for parameter identification. Inform Sci 291:19–42
Ahandani MA (2016) Opposition-based learning in the shuffled bidirectional differential evolution algorithm. Swarm Evol Comput 26:64–85
Ewees AA, Abd Elaziz M, Houssein EH (2018) Improved grasshopper optimization algorithm using opposition-based learning. Expert Syst Appl 112:156–172
Zhang X, Wang X, Kang Q, Cheng J (2019) Differential mutation and novel social learning particle swarm optimization algorithm. Inform Sci 480:109–129
Liu Z-Z, Chu D-H, Song C, Xue X, Lu B-Y (2016) Social learning optimization (SLO) algorithm paradigm and its application in QoS-aware cloud service composition. Inform Sci 326:315–333
Cheng R, Jin Y (2015) A social learning particle swarm optimization algorithm for scalable optimization. Inform Sci 291:43–60
Cai Y, Liao J, Wang T, Chen Y, Tian H (2016) Social learning differential evolution. Inform Sci 433:464–509
Zhan Z-H, Zhang J, Li Y, Shi Y-H (2011) Orthogonal learning particle swarm optimization. IEEE Trans Evol Comput 15(6):832–847
Xiong G, Shi D (2018) Orthogonal learning competitive swarm optimizer for economic dispatch problems. Appl Soft Comput 66:134–148
Peng Y, Lu B-L (2015) Hybrid learning clonal selection algorithm. Inform Sci 296:128–146
Liu R, Wang L, Ma W, Mu C, Jiao L (2013) Quadratic interpolation based orthogonal learning particle swarm optimization algorithm. Nat Comput 13(1):17–37
Li X-T, Yin M-H (2012) Parameter estimation for chaotic systems using the cuckoo search algorithm with an orthogonal learning method. Chin Phys B 21(5):050507
Li X, Wang J, Yin M (2013) Enhancing the performance of cuckoo search algorithm using orthogonal learning method. Neural Comput Appl 24(6):1233–1247
Lei Y-X, Gou J, Wang C, Cai Y-Q, Luo W (2017) Improved differential evolution with a modified orthogonal learning strategy. IEEE Access 5:9699–9716
Darwish A, Ezzat D, Hassanien AE (2020) An optimized model based on convolutional neural networks and orthogonal learning particle swarm optimization algorithm for plant diseases diagnosis. Swarm Evol Comput 52:100616
Bai W, Eke I, Lee KY (2017) An improved artificial bee colony optimization algorithm based on orthogonal learning for optimal power flow problem. Control Eng Pract 61:163–172
Ferri C, Flach P, Hernández-Orallo J (2004) Delegating classifiers. In: Proceedings of 21th international conference on machine leaning (ICML-2004). Omnipress, Alberta, pp 106–110
Thathachar MA, Sastry PS (2002) Varieties of learning automata: an overview . IEEE Trans Syst Man Cybern B (Cybern) 32(6):711–722
Kaelbling LP, Littman ML, Moore AW (1996) Reinforcement learning: A survey. J Artif Intell Res 4:237–285
Cao Z, Wang L (2019) An active learning brain storm optimization algorithm with a dynamically changing cluster cycle for global optimization. Cluster Comput 22(4):1413–1429
Gao WF, Huang LL, Liu SY, Dai C (2015) Artificial bee colony algorithm based on information learning. IEEE Trans Cybern 45(12):2827–2839
Lin A, Sun W, Yu H, Wu G, Tang H (2018) Global genetic learning particle swarm optimization with diversity enhancement by ring topology. Swarm Evol Comput 44:571–583
Deb S, Gao X-Z, Tammi K, Kalita K, Mahanta P (2020) A new teaching–learning-based chicken swarm optimization algorithm. Soft Comput 24(7):5313–5331
Das SP, Padhy S (2015) A novel hybrid model using teaching–learning-based optimization and a support vector machine for commodity futures index forecasting. Int J Mach Learn Cybern 9(1):97–111
Cheng T, Chen M, Fleming PJ, Yang Z, Gan S (2016) A novel hybrid teaching learning based multi-objective particle swarm optimization. Neurocomputing 222:11–25
Chen X, Tianfield H, Mei C, Du W, Liu G (2017) Biogeography-based learning particle swarm optimization. Soft Comput 21(24):7519–7541
Chen D, Zou F, Wang J, Yuan W (2015) SAMCCTLBO: a multi-class cooperative teaching–learning-based optimization algorithm with simulated annealing. Soft Comput 20(5):1921–1943
Bhandari AK, Kumar IV (2019) A context sensitive energy thresholding based 3D Otsu function for image segmentation using human learning optimization. Appl Soft Comput 82:105570
Li W, Wang G-G (2021) Elephant herding optimization using dynamic topology and biogeography-based optimization based on learning for numerical optimization. Eng Comput (in press)
Qin Q, Cheng S, Zhang Q, Li L, Shi Y (2015) Particle swarm optimization with interswarm interactive learning strategy. IEEE Trans Cybern 46(10):2238–2251
Kai Z, Jinchun S, Ke N, Song L (2016) Lagrange interpolation learning particle swarm optimization. PLoS ONE 11(4):e0154191
Heidari AA, Aljarah I, Faris H, Chen H, Luo J, Mirjalili S (2020) An enhanced associative learning-based exploratory whale optimizer for global optimization. Neural Comput Appl 32:5185–5211
Li J, Li Y-x, Tian S-s, Xia J-l (2020) An improved cuckoo search algorithm with self-adaptive knowledge learning. Neural Comput Appl 32:11967–11997
Chen D, Zou F, Lu R, Li S (2018) Backtracking search optimization algorithm based on knowledge learning. Inform Sci 473:202–226
Vafashoar R, Meybodi MR, Momeni Azandaryani AH (2012) CLA-DE: a hybrid model based on cellular learning automata for numerical optimization. Appl Intell 36(3):735–748
Vafashoar R, Meybodi MR (2019) A multi-population differential evolution algorithm based on cellular learning automata and evolutionary context information for optimization in dynamic environments. Appl Soft Comput 88:106009
Vafashoar R, Meybodi MR (2018) Cellular learning automata based bare bones PSO with maximum likelihood rotated mutations. Swarm Evol Comput 44:680–694
Branke J, Greco S, Slowinski R, Zielniewicz P (2015) Learning value functions in interactive evolutionary multiobjective optimization. IEEE Trans Evol Comput 19(1):88–102
Zhong Y, Lin J, Wang L, Zhang H (2018) Discrete comprehensive learning particle swarm optimization algorithm with Metropolis acceptance criterion for traveling salesman problem. Swarm Evol Comput 42:77–88
Yu X, Zhang X (2017) Multiswarm comprehensive learning particle swarm optimization for solving multiobjective optimization problems. PLoS ONE 12(2):e0172033
Yu X, Zhang X (2014) Enhanced comprehensive learning particle swarm optimization. Appl Math Comput 242:265–276
Mahadevan K, Kannan PS (2010) Comprehensive learning particle swarm optimization for reactive power dispatch. Appl Soft Comput 10(2):641–652
Lynn N, Suganthan PN (2015) Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation. Swarm Evol Comput 24:11–24
Lin A, Sun W, Yu H, Wu G, Tang H (2019) Adaptive comprehensive learning particle swarm optimization with cooperative archive. Appl Soft Comput 77:533–546
Lin A, Sun W (2018) Multi-leader comprehensive learning particle swarm optimization with adaptive mutation for economic load dispatch problems. Energies 12(1):116
Lei Z, Gao S, Gupta S, Cheng J, Yang G (2020) An aggregative learning gravitational search algorithm with self-adaptive gravitational constants. Expert Syst Appl 152:113396
Gülcü Ş, Kodaz H (2015) A novel parallel multi-swarm algorithm based on comprehensive learning particle swarm optimization. Eng Appl Artif Intell 45:33–45
Al-Obeidat F, Belacel N, Carretero JA, Mahanti P (2010) Differential evolution for learning the classification method PROAFTN. Knowl-Based Syst 23(5):418–426
Wang Y, Li H-X, Huang T, Li L (2014) Differential evolution based on covariance matrix learning and bimodal distribution parameter setting. Appl Soft Comput 18:232–247
Jiang Q, Wang L, Cheng J, Zhu X, Li W, Lin Y, Yu G, Hei X, Zhao J, Lu X (2017) Multi-objective differential evolution with dynamic covariance matrix learning for multi-objective optimization problems with variable linkages. Knowl-Based Syst 121:111–128
Almaraashi M, John R, Hopgood A, Ahmadi S (2016) Learning of interval and general type-2 fuzzy logic systems using simulated annealing: theory and practice. Inform Sci 360:21–42
Xu G, Cui Q, Shi X, Ge H, Zhan Z-H, Lee HP, Liang Y, Tai R, Wu C (2019) Particle swarm optimization based on dimensional learning strategy. Swarm Evol Comput 45:33–51
Xiao S, Wang W, Wang H, Tan D, Wang Y, Yu X, Wu R (2019) An improved artificial bee colony algorithm based on elite strategy and dimension learning. Mathematics 7(3):289
Hafiz F, Swain A, Patel N, Naik C (2018) A two-dimensional (2-D) learning framework for Particle Swarm based feature selection. Pattern Recogn 76:416–433
Yu K, Liang JJ, Qu BY, Cheng Z, Wang H (2018) Multiple learning backtracking search algorithm for estimating parameters of photovoltaic models. Appl Energy 226:408–422
Xu G, Liu B, Song J, Xiao S, Wu A (2019) Multiobjective sorting-based learning particle swarm optimization for continuous optimization. Nat Comput 18(2):313–331
Duan M, Yang H, Liu H, Chen J (2018) A differential evolution algorithm with dual preferred learning mutation. Appl Intell 49(2):605–627
Zhang A, Sun G, Ren J, Li X, Wang Z, Jia X (2016) A dynamic neighborhood learning-based gravitational search algorithm. IEEE Trans Cybern 48(1):436–447
Cao L, Xu L, Goodman ED (2018) A neighbor-based learning particle swarm optimizer with short-term and long-term memory for dynamic optimization problems. Inform Sci 453:463–485
Ye W, Feng W, Fan S (2017) A novel multi-swarm particle swarm optimization with dynamic learning strategy. Appl Soft Comput 61:832–843
Cai Z, Gu J, Luo J, Zhang Q, Chen H, Pan Z, Li Y, Li C (2019) Evolving an optimal kernel extreme learning machine by using an enhanced grey wolf optimization strategy. Expert Syst Appl 138:112814
Xia X, Tang Y, Wei B, Gui L (2019) Dynamic multi-swarm particle swarm optimization based on elite learning. IEEE Access 7:184849–184865
Lv L, Zhao J, Wang J, Fan T (2018) Multi-objective firefly algorithm based on compensation factor and elite learning. Future Gener Comput Syst 91:37–47
Lim WH, Mat Isa NA (2014) An adaptive two-layer particle swarm optimization with elitist learning strategy. Inform Sciences 273:49–72
Chang R-I, Lin S-Y, Hung Y (2012) Particle swarm optimization with query-based learning for multi-objective power contract problem. Expert Syst Appl 39(3):3116–3126
Chang R-I, Hsu H-M, Lin S-Y, Chang C-C, Ho J-M (2017) Query-based learning for dynamic particle swarm optimization. IEEE Access 5:7648–7658
Huang H, Qin H, Hao Z, Lim A (2012) Example-based learning particle swarm optimization for continuous optimization. Inform Sci 182(1):125–138
Wang Q, Zhou Y, Zhang W, Tang Z, Chen X (2020) Adaptive sampling using self-paced learning for imbalanced cancer data pre-diagnosis. Expert Syst Appl 152:113334
Li H, Gong M, Wang C, Miao Q (2019) Pareto self-paced learning based on differential evolution. IEEE Trans Cybern https://doi.org/10.1109/TCYB.2019.2935762
Gong M, Li H, Meng D, Miao Q, Liu J (2018) Decomposition-based evolutionary multi-objective optimization to self-paced learning. IEEE Trans Evol Comput 23(2):288–302
Chen C, Wang P, Dong H, Wang X (2019) Hierarchical learning water cycle algorithm. Appl Soft Comput 86:105935
Zhu T, Hao Y, Luo W, Ning H (2017) Learning enhanced differential evolution for tracking optimal decisions in dynamic power systems. Appl Soft Comput 67:812–821
Zhang Q, Liu L (2019) Whale optimization algorithm based on lamarckian learning for global optimization problems. IEEE Access 7:36642–36666
Sun Y, Gao Y (2019) A multi-objective particle swarm optimization algorithm based on gaussian mutation and an improved learning strategy. Mathematics 7(2):148
Peng B, Zhang Y, Lü Z, Cheng T, Glover F (2020) A learning-based memetic algorithm for the multiple vehicle pickup and delivery problem with LIFO loading. Comput Ind Eng 142:106241
Nitisiri K, Gen M, Ohwada H (2019) A parallel multi-objective genetic algorithm with learning based mutation for railway scheduling. Comput Ind Eng 130:381–394
Li W, Wang G-G, Alavi AH (2020) Learning-based elephant herding optimization algorithm for solving numerical optimization problems. Knowl-Based Syst 195:105675
Gong YJ, Zhang J, Zhou Y (2017) Learning multimodal parameters: a bare-bones niching differential evolution approach. IEEE Trans Neural Netw Learn Syst 29(7):2944–2959
Dora S, Sundaram S, Sundararajan N (2018) An interclass margin maximization learning algorithm for evolving spiking neural network. IEEE Trans Cybern 49(3):989–999
Chu X, Wu T, Weir JD, Shi Y, Niu B, Li L (2018) Learning–interaction–diversification framework for swarm intelligence optimizers: a unified perspective. Neural Comput Appl 32:1789–1809
Cheng TCE, Kuo W-H, Yang D-L (2013) Scheduling with a position-weighted learning effect based on sum-of-logarithm-processing-times and job position. Inform Sci 221:490–500
Chen X, Chau V, Xie P, Sterna M, Błażewicz J (2017) Complexity of late work minimization in flow shop systems and a particle swarm optimization algorithm for learning effect. Comput Ind Eng 111:176–182
Antonelli M, Ducange P, Marcelloni F (2014) A fast and efficient multi-objective evolutionary learning scheme for fuzzy rule-based classifiers. Inform Sci 283:36–54
Korte B, Vygen J, Korte B, Vygen J (2012) Combinatorial optimization. Springer, New York
Bertsekas DP (1982) Constrained optimization and Lagrange multiplier methods. Academic Press, London
Nguyen TT, Yang S, Branke J (2012) Evolutionary dynamic optimization: a survey of the state of the art. Swarm Evol Comput 6:1–24
Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 41576011, No. U1706218, No. 41706010, and No. 61503165).
Funding
The authors confirm that there is no source of funding for this study.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Li, W., Wang, GG. & Gandomi, A.H. A Survey of Learning-Based Intelligent Optimization Algorithms. Arch Computat Methods Eng 28, 3781–3799 (2021). https://doi.org/10.1007/s11831-021-09562-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11831-021-09562-1