Skip to main content

Advertisement

SpringerLink
Log in

From ants to whales: metaheuristics for all tastes

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

Nature-inspired metaheuristics comprise a compelling family of optimization techniques. These algorithms are designed with the idea of emulating some kind natural phenomena (such as the theory of evolution, the collective behavior of groups of animals, the laws of physics or the behavior and lifestyle of human beings) and applying them to solve complex problems. Nature-inspired methods have taken the area of mathematical optimization by storm. Only in the last few years, literature related to the development of this kind of techniques and their applications has experienced an unprecedented increase, with hundreds of new papers being published every single year. In this paper, we analyze some of the most popular nature-inspired optimization methods currently reported on the literature, while also discussing their applications for solving real-world problems and their impact on the current literature. Furthermore, we open discussion on several research gaps and areas of opportunity that are yet to be explored within this promising area of science.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Abualigah LM, Khader AT (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Supercomput 73(11):4773–4795

    Article  Google Scholar 

  • Abualigah LM, Khader AT, Al-Betar MA, Awadallah MA (2016) A krill herd algorithm for efficient text documents clustering. In: ISCAIE 2016—2016 IEEE symposium on computer applications & industrial electronics, pp 67–72

  • Abualigah LM, Khader AT, Hanandeh ES, Gandomi AH (2017a) A novel hybridization strategy for krill herd algorithm applied to clustering techniques. Appl Soft Comput J. 60:423–435

    Article  Google Scholar 

  • Abualigah LM, Khader AT, Al-Betar MA, Alomari OA (2017b) Text feature selection with a robust weight scheme and dynamic dimension reduction to text document clustering. Expert Syst Appl 84:24–36

    Article  Google Scholar 

  • Al-Betar MA, Awadallah MA, Abu Doush I, Alsukhni E, ALkhraisat H (2018) A non-convex economic dispatch problem with valve loading effect using a new modified β-Hill Climbing Local Search Algorithm. Arab J Sci Eng 43:7439–7456

    Article  Google Scholar 

  • Alia OM, Al-Ajouri A (2017) maximizing wireless sensor network coverage with minimum cost using Harmony Search Algorithm. IEEE Sens J 17(3):882–896

    Article  Google Scholar 

  • Alomari OA, Khader AT (2017) MA Al Betar, and LM Abualigah (2017) Gene selection for cancer classification by combining minimum redundancy maximum relevancy and bat-inspired algorithm. Int J Data Min Bioinform 19(1):32

    Article  Google Scholar 

  • Alshamlan H, Badr G, Alohali Y (2015) MRMR-ABC: a hybrid gene selection algorithm for cancer classification using microarray gene expression profiling. Biomed Res. Int. 2015:1–15

    Article  Google Scholar 

  • Alswaitti M, Albughdadi M, Isa NAM (2018) Density-based particle swarm optimization algorithm for data clustering. Expert Syst Appl 91:170–186

    Article  Google Scholar 

  • Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: Crow Search Algorithm. Comput Struct 169:1–12

    Article  Google Scholar 

  • Askarzadeh A, Rezazadeh A (2012) Parameter identification for solar cell models using harmony search-based algorithms. Sol Energy 86(11):3241–3249

    Article  Google Scholar 

  • Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: 2007 IEEE congress on evolutionary computation, CEC 2007, pp 4661–4667

  • Auger A, Schoenauer M, Vanhaecke N (2004) {LS-CMA-ES}: a second-order algorithm for covariance matrix adaptation. Parallel Probl Solving Nat PPSN VIII 3242(1):182–191

    Google Scholar 

  • Avigad J, Donnelly K (2004) Formalizing O notation in Isabelle/HOL. Springer, Berlin, pp 357–371

    MATH  Google Scholar 

  • Babu TS, Ram JP, Dragicevic T, Miyatake M, Blaabjerg F, Rajasekar N (2017) Particle Swarm Optimization based solar PV array reconfiguration of the maximum power extraction under partial shading conditions. IEEE Trans Sustain Energy 9:74–85

    Article  Google Scholar 

  • Back T, Hoffmeister F, Schwefel HP (1991) A survey of evolution strategies. In: Proceedings of the fourth international conference on genetic algorithms, vol 9, p 8

  • Bäck T, Foussette C, Krause P (2013) Contemporary evolution strategies, vol 47. Springer, Berlin

    Book  MATH  Google Scholar 

  • Basseur M, Lemesre J, Dhaenens C, Talbi EG (2004) Cooperation between branch and bound and evolutionary approaches to solve a bi-objective flow shop problem, vol 2632. Springer, Berlin

    Google Scholar 

  • Behnck LP, Doering D, Pereira CE, Rettberg A (2015) A modified simulated annealing algorithm for SUAVs path planning. IFAC-PapersOnLine 28(10):63–68

    Article  Google Scholar 

  • Bekdaş G, Nigdeli SM, Yang XS (2015) Sizing optimization of truss structures using flower pollination algorithm. Appl Soft Comput J 37:322–331

    Article  Google Scholar 

  • Benkhoud K, Bouallègue S (2017) Dynamics modeling and advanced metaheuristics based LQG controller design for a Quad Tilt Wing UAV. Int J Dyn Control 6(2):630–651

    Article  MathSciNet  Google Scholar 

  • Beyer HG, Sendhoff B (2008) Covariance matrix adaptation revisited—the CMSA evolution strategy. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol 5199, LNCS, pp 123–132

  • Bhardwaj T, Sharma TK, Pandit MR (2014) Social engineering prevention by detecting malicious URLs using Artificial Bee Colony Algorithm. In: 3rd international conference on soft computing for problem solving, advances in intelligent systems, pp 355–363

  • Binitha S, Sathya SS (2012) A survey of bio inspired optimization algorithms. Int J Soft Comput Eng 2(2):137–151

    Google Scholar 

  • Birbil ŞI, Fang SC (2003) An electromagnetism-like mechanism for global optimization. J Glob Optim 25(3):263–282

    Article  MathSciNet  MATH  Google Scholar 

  • Boussaïd I, Lepagnot J, Siarry P (2013) A survey on optimization metaheuristics. Inf Sci (Ny) 237:82–117

    Article  MathSciNet  MATH  Google Scholar 

  • Burke EK et al (2013) Hyper-heuristics: a survey of the state of the art. J Oper Res Soc 64(12):1695–1724

    Article  Google Scholar 

  • Camarena O, Cuevas E, Pérez-cisneros M, Fausto F, González A, Valdivia A (2018) Ls-II: an improved locust search algorithm for solving constrained optimization problems

  • Cao S, Wang J, Gu X (2012) A wireless sensor network location algorithm based on Firefly Algorithm. Asia Simul Conf 2012:18–26

    Google Scholar 

  • Cavazzuti M (2013) Optimization methods: from theory to design. Springer, Berlin

    Book  MATH  Google Scholar 

  • Chen C (2017) Image segmentation for lung lesions using ant colony optimization classifier in chest CT. In: Advances in intelligent information hiding and multimedia signal processing, pp 283–289

    Google Scholar 

  • Cheng S, Shi Y, Qin Q, Ting TO, Bai R (2014) Maintaining population diversity in brain storm optimization algorithm. In: Proceedings 2014 IEEE congress on evolutionary computation CEC 2014, pp 3230–3237

  • Contreras-Cruz MA, Lopez-Perez JJ, Ayala-Ramirez V (2017) Distributed path planning for multi-robot teams based on artificial bee colony. In: Proceeding on IEEE congress on evolutionary computation CEC 2017 pp 541–548

  • Črepiňsek M, Liu S-H, Mernik M (2013) Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput Surv 45(33):1–33

    Article  MATH  Google Scholar 

  • Cuevas E, Cienfuegos M, Zaldívar D, Pérez-cisneros M (2013a) A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Syst Appl 40(16):6374–6384

    Article  Google Scholar 

  • Cuevas E, Echavarría A, Ramírez-Ortegón MA (2013b) An optimization algorithm inspired by the states of matter that improves the balance between exploration and exploitation. Appl Intell 40(2):256–272

    Article  Google Scholar 

  • Cuevas E, González A, Fausto F, Zaldívar D, Pérez-Cisneros M (2015a) An optimisation algorithm based on the behaviour of locust swarms. Int. Bio Inspir Comput 7(6):402

    Article  Google Scholar 

  • Cuevas E, González A, Fausto F, Zaldívar D, Pérez-Cisneros M (2015b) Multithreshold segmentation by using an algorithm based on the behavior of locust swarms. Math Probl Eng 2015:26

    Google Scholar 

  • Cuevas E, Díaz Cortés MA, Oliva Navarro DA (2016) Advances of evolutionary computation: methods and operators, 1st edn. Springer, Berlin

    Book  Google Scholar 

  • Cuevas E, Osuna V, Oliva D (2017a) Evolutionary computation techniques: a comparative perspective, vol 686. Springer, Berlin

    Book  Google Scholar 

  • Cuevas E, Gálvez J, Avalos O (2017b) Parameter estimation for chaotic fractional systems by using the locust search algorithm. Comput y Sist 21(2):369–380

    Google Scholar 

  • Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):4–31

    Article  Google Scholar 

  • Das S, Mullick SS, Suganthan PN (2016) Recent advances in differential evolution—an updated survey. Swarm Evol Comput 27:1–30

    Article  Google Scholar 

  • Deif DS, Member S, Gadallah Y, Member S (2017) “An Ant Colony Optimization approach for the deployment of reliable wireless sensor networks. IEEE Access 5:10744–10756

    Article  Google Scholar 

  • Díaz-Cortés M-A, Cuevas E, Rojas R (2017) Engineering applications of soft computing. Springer, Berlin

    Book  Google Scholar 

  • Din M, Pal SK, Muttoo SK, Jain A (2016) Applying Cuckoo Search for analysis of LFSR based cryptosystem. Perspect Sci 8:435–439

    Article  Google Scholar 

  • Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theor Comput Sci 344(2–3):243–278

    Article  MathSciNet  MATH  Google Scholar 

  • Dorigo M, Stützle T (2004) Ant colony optimization. Springer, Berlin

    Book  MATH  Google Scholar 

  • Du H, Wang Z, Zhan WEI (2018) Elitism and distance strategy for selection of evolutionary algorithms. IEEE Access 6:44531–44541

    Article  Google Scholar 

  • El Aziz MA, Ewees AA, Hassanien AE (2017) Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation. Expert Syst Appl 83:242–256

    Article  Google Scholar 

  • Emary E, Zawbaa HM, Hassanien AE (2016) Binary grey wolf optimization approaches for feature selection. Neurocomputing 172:371–381

    Article  Google Scholar 

  • Eskandar H, Sadollah A, Bahreininejad A, Hamdi M (2012) Water cycle algorithm—a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 110–111:151–166

    Article  Google Scholar 

  • Feng Y, Wang GG, Deb S, Lu M, Zhao XJ (2017) Solving 0–1 knapsack problem by a novel binary monarch butterfly optimization. Neural Comput Appl 28(7):1619–1634

    Article  Google Scholar 

  • Galinier P, Hamiez JP, Hao JK, Porumbel D (2013) Handbook of optimization, vol 38. Springer, Berlin

    Book  Google Scholar 

  • Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845

    Article  MathSciNet  MATH  Google Scholar 

  • Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68

    Article  Google Scholar 

  • Gerules G, Janikow C (2016) A survey of modularity in genetic programming. In: 2016 IEEE congress on evolutionary computation CEC 2016, pp 5034–5043

  • Ghazali R, Deris MM, Nawi NM, Abawajy JH (2018) Recent advances on soft computing and data mining, vol 700. Springer, Berlin

    Book  Google Scholar 

  • Gomes AM, Oliveira JF (2006) Solving Irregular Strip Packing problems by hybridising simulated annealing and linear programming. Eur J Oper Res 171(3):811–829

    Article  MATH  Google Scholar 

  • González A, Cuevas E, Fausto F, Valdivia A, Rojas R (2017) A template matching approach based on the behavior of swarms of locust. Appl Intell 47(4):1087–1098

    Article  Google Scholar 

  • Goudos SK (2017) Antenna design using binary differential evolution. In: IEEE antennas and propagation magazine

  • Goyal S, Patterh MS (2015) Performance of BAT algorithm on localization of wireless sensor network. Wirel Pers Commun 6(3):351–358

    Google Scholar 

  • Guerrero M, Montoya FG, Baños R, Alcayde A, Gil C (2017) Adaptive community detection in complex networks using genetic algorithms. Neurocomputing 266:101–113

    Article  Google Scholar 

  • Guha D, Roy PK, Banerjee S (2016) Load frequency control of interconnected power system using grey Wolf Optimization. Swarm Evol Comput 27:97–115

    Article  Google Scholar 

  • Gutin G, Punnen AP (2007) The traveling salesman problem and its variations. Springer, US

    Book  MATH  Google Scholar 

  • Han W, Wang H, Chen L (2014) Parameters identification for photovoltaic module based on an Improved Artificial Fish Swarm Algorithm

  • Han X, Quan L, Xiong X, Almeter M, Xiang J, Lan Y (2017) A novel data clustering algorithm based on modified gravitational search algorithm. Eng Appl Artif Intell 61:1–7

    Article  Google Scholar 

  • Harman M, Langdon WB, Weimer W (2013) Genetic programming for reverse engineering. In: 20th working conference on reverse engineering, WCRE 2013, pp 1–10

  • He L, Huang S (2017) Modified firefly algorithm based multilevel thresholding for color image segmentation. Neurocomputing 240:152–174

    Article  Google Scholar 

  • Hinojosa S, Oliva D, Cuevas E, Pajares G, Avalos O, Gálvez J (2018) Improving multi-criterion optimization with chaos: a novel Multi-Objective Chaotic Crow Search Algorithm. Neural Comput Appl 29(8):319–335

    Article  Google Scholar 

  • Horng M-H, Jiang T-W (2010) Multilevel image thresholding selection using the Artificial Bee Colony Algorithm. Artif Intell Comput Intell 6320:318–325

    Article  Google Scholar 

  • Huang T, Jia XD, Yuan HQ, Jiang JQ (2017) Niching community based differential evolution for multimodal optimization problems. In: IEEE, Piscataway

  • Ibrahim E, Birchell S, Elfayoumy S (2012) Automatic heart volume measurement from CMR images using ant colony optimization with iterative salient isolated thresholding. J Cardiovasc Magn Reson 14(1):1–2

    Article  Google Scholar 

  • Idris I et al (2015) A combined negative selection algorithm-Particle Swarm Optimization for an email spam detection system. Eng Appl Artif Intell 39:33–44

    Article  Google Scholar 

  • Jadhav AN, Gomathi N (2016) WGC: hybridization of exponential grey wolf optimizer with whale optimization for data clustering. Alex Eng J 57:1569–1584

    Article  Google Scholar 

  • Johny DC, Assistant AJS (2017) Negative selection algorithm : a survey. Int J Sci Eng Technol Res 6(4):711–715

    Google Scholar 

  • Jourdan L, Basseur M, Talbi EG (2009) Hybridizing exact methods and metaheuristics: a taxonomy. Eur J Oper Res 199(3):620–629

    Article  MathSciNet  MATH  Google Scholar 

  • 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

    Article  MathSciNet  MATH  Google Scholar 

  • Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput J 8(1):687–697

    Article  Google Scholar 

  • Kaveh A, Khayatazad M (2012) A new meta-heuristic method: ray optimization. Comput Struct 112–113:283–294

    Article  Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle Swarm Optimization. IEEE Int Conf Neural Netw 4:1942–1948

    Google Scholar 

  • Keshtegar B, Hao P, Wang Y, Li Y (2017) Optimum design of aircraft panels based on adaptive dynamic harmony search. Thin-Walled Struct 118(May):37–45

    Article  Google Scholar 

  • Khairuzzaman AKM, Chadhury S (2017) Moth-Flame Optimization Algorithm based multilevel thresholding for image segmentation. Int J Appl Metaheuristic Comput 8(4):58–83

    Article  Google Scholar 

  • Khairuzzaman AKM, Chaudhury S (2017) Multilevel thresholding using grey wolf optimizer for image segmentation. Expert Syst Appl 86:64–76

    Article  Google Scholar 

  • Khatibinia M, Yazdani H (2017) Accelerated multi-gravitational search algorithm for size optimization of truss structures. Swarm Evol Comput 1:1. https://doi.org/10.1016/j.swevo.2017.07.001

    Article  Google Scholar 

  • Khu ST, Liong SY, Babovic V, Madsen H, Muttil N (2001) Genetic programming and its application in real-time runoff forecasting. J Am Water Resour Assoc 37(2):439–451

    Article  Google Scholar 

  • Kiranyaz S, Uhlmann S, Ince T, Gabbouj M (2015) Perceptual dominant color extraction by multidimensional Particle Swarm Optimization. EURASIP J Adv Signal Process 2009:451638

    Article  MATH  Google Scholar 

  • Kirkpatrick S, Gelatt CD, Vecch MP (2007) Optimization by simulated annealing. Science 220(4598):671–680

    Article  MathSciNet  MATH  Google Scholar 

  • Kora P, Kalva SR (2015) Improved Bat algorithm for the detection of myocardial infarction. Springerplus 4(1):666

    Article  Google Scholar 

  • Laguna M, Martí R (2003) Scatter Search, Methodology and Implementations in C. Springer, New York

    Book  MATH  Google Scholar 

  • Li P, Duan H (2012) Path planning of unmanned aerial vehicle based on improved gravitational search algorithm. Sci China Technol Sci 55(10):2712–2719

    Article  Google Scholar 

  • Lin M, Tsai J, Yu C (2012) A review of deterministic optimization methods in engineering and management. Math Probl Eng 2012:1–15

    MathSciNet  MATH  Google Scholar 

  • Liu B, Koziel S, Zhang Q (2016) A multi-fidelity surrogate-model-assisted evolutionary algorithm for computationally expensive optimization problems. J Comput Sci 12:28–37

    Article  MathSciNet  Google Scholar 

  • Ma J, Ting TO, Man KL, Zhang N, Guan SU, Wong PWH (2013) Parameter estimation of photovoltaic models via cuckoo search. J Appl Math 2013:10–12

    MathSciNet  Google Scholar 

  • Mafarja MM, Mirjalili S (2016) Hybrid Whale Optimization Algorithm with simulated annealing for feature selection. Neurocomputing 260:302–312

    Article  Google Scholar 

  • Mann PS, Singh S (2017) Improved metaheuristic based energy-efficient clustering protocol for wireless sensor networks. Eng Appl Artif Intell 57(2016):142–152

    Article  Google Scholar 

  • Marinaki M, Marinakis Y (2016) A Glowworm Swarm Optimization algorithm for the vehicle routing problem with stochastic demands. Expert Syst Appl 46(4):145–163

    Article  Google Scholar 

  • Massan SUR, Wagan AI, Shaikh MM, Abro R (2015) Wind turbine micrositing by using the firefly algorithm. Appl Soft Comput J 27:450–456

    Article  Google Scholar 

  • McCall J (2005) Genetic algorithms for modelling and optimisation. J Comput Appl Math 184(1):205–222

    Article  MathSciNet  MATH  Google Scholar 

  • Mesbahi T, Rizoug N, Bartholomeus P, Sadoun R, Khenfri F, Lemoigne P (2017) Optimal energy management for a Li-ion battery/supercapacitor hybrid energy storage system based on Particle Swarm Optimization incorporating Nelder-Mead simplex approach. IEEE Trans Intell Veh 2(2):1

    Article  Google Scholar 

  • Mesejo P, Ibáñez Ó, Cordón Ó, Cagnoni S (2016) A survey on image segmentation using metaheuristic-based deformable models: state of the art and critical analysis. Appl Soft Comput J 44:1–29

    Article  Google Scholar 

  • Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249

    Article  Google Scholar 

  • Mirjalili S (2016) SCA : a sine cosine algorithm for solving optimization problems. Knowl Based Syst 96:120–133

    Article  Google Scholar 

  • Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  • Mirjalili S, Mirjalili SM, Lewis A (2014) Grey Wolf Optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  • Mitchell M (1995) Genetic algorithms: an overview. Complexity 1(1):31–39

    Article  MATH  Google Scholar 

  • Mitchell M (1996) An introduction to genetic algorithms. The MIT Press, Cambridge

    MATH  Google Scholar 

  • Moayedikia A, Ong K-L, Boo YL, Yeoh WG, Jensen R (2017) Feature selection for high dimensional imbalanced class data using harmony search. Eng Appl Artif Intell 57(2016):38–49

    Article  Google Scholar 

  • Mohamed AW, Sabry HZ, Khorshid M (2012) An alternative differential evolution algorithm for global optimization. J Adv Res 3(2):149–165

    Article  Google Scholar 

  • Mohammad L, Abualigah Q, Hanandeh ES (2015) Applying Genetic Algorithms to information retrieval using vector space model. Int J Comput Sci Eng Appl 5(1):19–28

    Google Scholar 

  • Nagpal S, Arora S, Dey S, Shreya (2017) Feature selection using Gravitational Search Algorithm for biomedical data. Procedia Comput Sci 115:258–265

    Article  Google Scholar 

  • Neumann F, Witt C (2010) Bioinspired computation in combinatorial optimization—algorithms and their computational complexity. Springer, Berlin

    MATH  Google Scholar 

  • Olague G, Trujillo L (2012) Interest point detection through multiobjective genetic programming. Appl Soft Comput J 12(8):2566–2582

    Article  Google Scholar 

  • Oliva D, Cuevas E, Pajares G (2014) Parameter identification of solar cells using artificial bee colony optimization. Energy 72:93–102

    Article  Google Scholar 

  • Oliva D, Hinojosa S, Cuevas E, Pajares G, Avalos O, Gálvez J (2017) Cross entropy based thresholding for magnetic resonance brain images using Crow Search Algorithm. Expert Syst Appl 79:164–180

    Article  Google Scholar 

  • Opara KR, Arabas J (2018) Differential evolution: a survey of theoretical analyses. Swarm Evol Comput. https://doi.org/10.1016/j.swevo.2018.06.010

    Article  Google Scholar 

  • Osman IH, Laporte G (1996) Metaheuristics: a bibliography. Ann Oper Res 63(5):511–623

    Article  MATH  Google Scholar 

  • Ouaddah A, Boughaci D (2016) Harmony search algorithm for image reconstruction from projections. App Soft Comput J 46:924–935

    Article  Google Scholar 

  • Ouadfel S, Taleb-Ahmed A (2016) Social spiders optimization and flower pollination algorithm for multilevel image thresholding: a performance study. Expert Syst Appl 55:566–584

    Article  Google Scholar 

  • Oz I, Topcuoglu HR, Ermis M (2013) A meta-heuristic based three-dimensional path planning environment for unmanned aerial vehicles. Simulation 89(8):903–920

    Article  Google Scholar 

  • Padhye N, Mittal P, Deb K (2013) Differential evolution: performances and analyses. In: 2013 IEEE congress on evolutionary computation (CEC), pp 1960–1967

  • Pardalos PM, Du D-Z, Graham RL (2013) Handbook of combinatorial optimization. Springer, US

    Book  MATH  Google Scholar 

  • Pereira FB, Tavares J (2009) Bio-inspired algorithms for the vehicle routing problem. Springer, US

    Book  Google Scholar 

  • Pereira DR et al (2016) Social-Spider Optimization-based support vector machines applied for energy theft detection. Comput Electr Eng 49:25–38

    Article  Google Scholar 

  • Pham DT, Huynh TTB, Bui TL (2013) A survey on hybridizing genetic algorithm with dynamic programming for solving the traveling salesman problem. In: 2013 international conference soft computer pattern recognition, SoCPaR 2013, pp 66–71

  • Piotrowski AP (2017) Review of differential evolution population size. Swarm Evol Comput 32:1–24

    Article  Google Scholar 

  • Piotrowski AP, Napiorkowski JJ (2016) Searching for structural bias in Particle Swarm Optimization and differential evolution algorithms. Swarm Intell 10(4):307–353

    Article  Google Scholar 

  • Piotrowski AP, Napiorkowski JJ (2018) Some metaheuristics should be simplified. Inf Sci (NY) 427:32–62

    Article  MathSciNet  Google Scholar 

  • Plateau A, Tachat D, Tolla P (2002) A hybrid search combining interior point methods and metaheuristics for 0–1 programming. Int Trans Oper Res 9(6):731–746

    Article  MathSciNet  MATH  Google Scholar 

  • Poli R, Kennedy J, Blackwell T (2007a) Particle Swarm Optimization. Swarm Intell 1(1):33–57

    Article  Google Scholar 

  • Poli R, Langdon WB, McPhee NF, Koza JR (2007b) Genetic programming an introductory tutorial and a survey of techniques and applications. Technical report CES475, vol 18, Oct 2007, pp 1–112

  • Portmann MC, Vignier A, Dardilhac D, Dezalay D (1998) Branch and bound crossed with GA to solve hybrid flowshops. Eur J Oper Res 107(2):389–400

    Article  MATH  Google Scholar 

  • Potvin JY (2009) A review of bio-inspired algorithms for vehicle routing. Stud Comput Intell 161(July):1–34

    Google Scholar 

  • Prakash DB, Lakshminarayana C (2016) Optimal siting of capacitors in radial distribution network using Whale Optimization Algorithm. Alex Eng J 56:499–509

    Article  Google Scholar 

  • Prasad D, Mukherjee A, Mukherjee V (2017) Application of chaotic krill herd algorithm for optimal power flow with direct current link placement problem. Chaos Solitons Fractals 103:90–100

    Article  MathSciNet  Google Scholar 

  • Puchinger J, Raidl GR (2005) Combining metaheuristics and exact algorithms in combinatorial optimization: a survey and classification. In: Mira J, Álvarez JR (eds) Artificial intelligence and knowledge engineering applications: a bioinspired approach. IWINAC 2005. Lecture notes in computer science, vol 3562. Springer, Berlin.

    Google Scholar 

  • Rahimi S, Abdollahpouri A, Moradi P (2018) A multi-objective Particle Swarm Optimization algorithm for community detection in complex networks. Swarm Evol Comput 39:297–309

    Article  Google Scholar 

  • Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci (NY) 179(13):2232–2248

    Article  MATH  Google Scholar 

  • Regis RG (2013) Particle swarm with radial basis function surrogates for expensive black-box optimization. J Comput Sci 5(1):1–12

    MathSciNet  Google Scholar 

  • Rere LMR, Fanany MI, Arymurthy AM (2015) Simulated annealing algorithm for deep learning. Procedia Comput Sci 72:137–144

    Article  Google Scholar 

  • Reyna A, Fausto F (2017) AISearch. Nature-inspired Metaheuristic Optimization Algorithms. https://aisearch.github.io. Accessed 01 Jan 2017

  • Rutenbar RA (1989) Simulated annealing algorithms: an overview. IEEE Circuits Dev Mag 5(1):19–26

    Article  Google Scholar 

  • Sahlol AT, Ewees AA, Hemdan AM, Hassanien AE (2009) Training of feedforward neural networks using sine-cosine algorithm to improve the prediction of liver enzymes on FIsh farmed on nano-selenite. In: 2016 12th international conference computer engineering conference (ICENCO), pp 35–40

  • Sahoo A, Chandra S (2017) Multi-objective Grey Wolf Optimizer for improved cervix lesion classification. Appl Soft Comput J 52:64–80

    Article  Google Scholar 

  • Saji Y, Riffi ME (2016) A novel discrete bat algorithm for solving the travelling salesman problem. Neural Comput Appl 27(7):1853–1866

    Article  Google Scholar 

  • Salimans T, Ho J, Chen X, Sidor S, Sutskever I (2017) Evolution strategies as a scalable alternative to reinforcement learning, pp 1–13. arXiv:1703.03864v2

  • Sapra D, Sharma R, Agarwal AP (2017) Comparative study of metaheuristic algorithms using Knapsack Problem. In: 7th International conference on cloud computing, data science & engineering, pp 134–137

  • Sarjila R, Ravi K, Edward JB, Kumar KS, Prasad A (2016) Parameter extraction of solar photovoltaic modules using Gravitational Search Algorithm

  • Sayed GI, Hassanien AE, Nassef TM (2017) Genetic and evolutionary computing, vol 536. Springer, Berlin

    Google Scholar 

  • Schneider JJ, Kirkpatrick S (2006) Stochastic optimization. Springer, Berlin

    MATH  Google Scholar 

  • Sette S, Boullart L (2001) Genetic programming: principles and applications. Eng Appl Artif Intell 14(6):727–736

    Article  Google Scholar 

  • Shukla UP, Nanda SJ (2016) Parallel social spider clustering algorithm for high dimensional datasets. Eng Appl Artif Intell 56:75–90

    Article  Google Scholar 

  • Shukla R, Singh D (2016) Selection of parameters for advanced machining processes using firefly algorithm. Eng Sci Technol Int J 20(1):1–10

    Google Scholar 

  • Siddique N, Adeli H (2016) Simulated annealing, its variants and engineering applications. Int J Artif Intell Tools 25(06):1630001

    Article  Google Scholar 

  • Silva P, Santos CP, Matos V, Costa L (2014) Automatic generation of biped locomotion controllers using genetic programming. Rob Auton Syst 62(10):1531–1548

    Article  Google Scholar 

  • Sipper M, Fu W, Ahuja K, Moore JH (2018) “Investigating the parameter space of evolutionary algorithms. BioData Min 11(1):2

    Article  Google Scholar 

  • Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  MathSciNet  MATH  Google Scholar 

  • Tan Y, Zhu Y (2010) Fireworks algorithm for optimization. In: ICSI 2010—proceedings first international conference, part I, 2010, June, pp 355–364

  • Tolabi HB, Ayob SM (2014) New technique for global solar radiation forecasting by simulated annealing and genetic algorithms using. Appl Sol Energy 50(3):202–206

    Article  Google Scholar 

  • Tsai P, Nguyen T, Dao T (2016) Genetic and evolutionary robot path planning optimization based on multiobjective grey wolf optimizer. In: Genetic and evolutionary computing proceedings of the tenth international conference on genetic and evolutionary computing, pp 166–173

  • Valdivia-Gonzalez A, Zaldívar D, Fausto F, Camarena O, Cuevas E, Perez-Cisneros M (2017a) A states of matter search-based approach for solving the problem of intelligent power allocation in plug-in hybrid electric vehicles. Energies 10(1):92

    Article  Google Scholar 

  • Valdivia-Gonzalez A, Zaldívar D, Fausto F, Camarena O, Cuevas E, Perez-Cisneros M (2017b) A states of matter search-based approach for solving the problem of intelligent power allocation in plug-in hybrid electric vehicles. Energies 10(1):92

    Article  Google Scholar 

  • Van Sickel JH, Lee KY, Heo JS (2007) Differential evolution and its applications to power plant control. In: 14th international conference on intelligent systems applications to power systems, no 2, pp 560–565

  • Vanneschi L, Castelli M, Silva S (2014) A survey of semantic methods in genetic programming. Genet Program Evolv Mach 15(2):195–214

    Article  Google Scholar 

  • Vocking B et al (2011) Algorithms unplugged. Springer, Berlin Heidelberg

    Book  MATH  Google Scholar 

  • Wang KJ, Adrian AM, Chen KH, Wang KM (2015) An improved electromagnetism-like mechanism algorithm and its application to the prediction of diabetes mellitus. J Biomed Inform 54:220–229

    Article  Google Scholar 

  • Wild SM, Regis RG, Shoemaker CA (2008) ORBIT: optimization by radial basis function interpolation in trust-regions. SIAM J Sci Comput 30(6):3197–3219

    Article  MathSciNet  MATH  Google Scholar 

  • Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Article  Google Scholar 

  • Wu J, Qiu T, Wang L, Huang H (2011) An approach to feature selection based on Ant Colony Optimization and Rough Set, pp. 466–471

    Google Scholar 

  • Xiang T (2016) Vehicle routing problem based on particle Swarm Optimization Algorithm with gauss mutation. Am J Softw Eng Appl 5(1):1

    Google Scholar 

  • Xie C, Zheng H (2016) Application of improved cuckoo search algorithm to path planning unmanned aerial vehicles. In: 12th international conference intelligent computing theories and application, ICIC 2016, pp 722–729

  • Xu H, Pu P, Duan F (2018) Dynamic vehicle routing problems with enhanced ant colony optimization. Discret Dyn Nat Soc 2018:1–13

    MATH  Google Scholar 

  • Wei L, Zhang Z, Zhang D, Leung SCH (2017) A simulated annealing algorithm for the capacitated vehicle routing problem with two-dimensional loading constraints. Eur. J. Oper. Res. https://doi.org/10.1016/j.ejor.2017.08.035

    Article  MATH  Google Scholar 

  • Yadav PK, Prajapati NL (2012) An overview of genetic algorithm and modeling. Int J Sci Res Publ 2(9):1–4

    Google Scholar 

  • Yan L, Yujuan Q, Zujian W, Wang L, Yan J (2015) A hybrid method combining genetic algorithm and Hooke–Jeeves method for 4PLRP. In: 2014 IEEE/CIC international conference on communication China—Work. CIC/ICCC 2014, vol 10, no. 4, pp 36–40

  • Yang X (2008) Nature-inspired metaheuristic algorithms, 2nd edn. Luniver Press, Beckington

    Google Scholar 

  • Yang X (2010a) Firefly algorithm, Lévy flights and global optimization. Springer, Berlin

    Book  Google Scholar 

  • Yang X-S (2010b) A new metaheuristic bat-inspired algorithm. Stud Comput Intell 284:65–74

    MATH  Google Scholar 

  • Yang XS (2011) Metaheuristic optimization: algorithm analysis and open problems. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence lecture notes in bioinformatics), vol 6630 LNCS, pp 21–32

    Chapter  Google Scholar 

  • Yang XS (2012) Flower pollination algorithm for global optimization. In: Lecture notes in computer science, vol 7445, LNCS, pp 240–249

  • Yang XS, He X (2013) Firefly algorithm: recent advances and applications. Int J Swarm Intell 1(1):1–14

    Article  Google Scholar 

  • Yang XS (2015) Nature-inspired algorithms: success and challenges. Comput Methods Appl Sci 38:129–143

    Article  Google Scholar 

  • Yang X-S (2018) Swarm-based metaheuristic algorithms and no-free-lunch theorems. Intech Open 2:64

    Google Scholar 

  • Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: Proceedings 2009 world congress on nature and biologically inspired computing, NABIC 2009, pp 210–214

  • Yang XS, Deb S, Hanne T, He X (2015) Attraction and diffusion in nature-inspired optimization algorithms. Neural Comput Appl 19:1–8

    Google Scholar 

  • You I, Yim K, Barolli L (2017) A Social Spider Optimization based home energy management system. In: Advances in network-based information systems, 20th international conference on network-based information systems, pp 771–778

  • Yurtkuran A, Emel E (2010) A new hybrid electromagnetism-like algorithm for capacitated vehicle routing problems. Expert Syst Appl 37(4):3427–3433

    Article  Google Scholar 

  • Zelinka I (2015) A survey on evolutionary algorithms dynamics and its complexity—mutual relations, past, present and future. Swarm Evol Comput 25:2–14

    Article  Google Scholar 

  • Zhang SZ, Lee CKM (2016) An improved artificial bee colony algorithm for the capacitated vehicle routing problem. In: Proceedings—2015 IEEE international conference on systems, man, and cybernetics SMC 2015, pp 2124–2128

  • Zhang S, Zhou Y (2017) Template matching using grey wolf optimizer with lateral inhibition. Opt-Int J Light Electron Opt 130:1229–1243

    Article  Google Scholar 

  • Zhou Y, Zhao R, Luo Q, Wen C (2017a) “Sensor deployment scheme based on Social Spider Optimization Algorithm for wireless sensor networks. Neural Process Lett 48:71–94

    Article  Google Scholar 

  • Zhou Y, Wang R, Zhao C, Luo Q, Metwally MA (2017b) Discrete greedy flower pollination algorithm for spherical traveling salesman problem. Neural Comput Appl. https://doi.org/10.1007/s00521-017-3176-4

    Article  Google Scholar 

  • Zou Y, Chakrabarty K (2003) Sensor deployment and target localization based on virtual forces. In: Twenty-second annual joint conference of the IEEE computer and communications, vol 2, no. C, pp 1293–1303

Download references

Author information

Authors and Affiliations

  1. Departamento de Electrónica, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, 44430, Guadalajara, Mexico

    Fernando Fausto, Erik Cuevas & Marco Perez-Cisneros

  2. Facultad de Ingenierías, Universidad Autónoma de Baja California, Blvd. Benito Juárez, 21280, Mexicali, Mexico

    Adolfo Reyna-Orta & Ángel G. Andrade

Authors
  1. Fernando Fausto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Adolfo Reyna-Orta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Erik Cuevas
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ángel G. Andrade
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Marco Perez-Cisneros
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Fernando Fausto or Erik Cuevas.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

In Table 3, we present a list comprised of several nature-inspired metaheuristics currently proposed on the literature. A total of 168 different algorithms, along with their respective abbreviations, authors and its year of proposal, have been documented for this table (note that some of the algorithms abbreviations might be repeated). Further information about these methods may be found in (Reyna et al. 2017).

Table 3 List of nature-inspired metaheuristics
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fausto, F., Reyna-Orta, A., Cuevas, E. et al. From ants to whales: metaheuristics for all tastes. Artif Intell Rev 53, 753–810 (2020). https://doi.org/10.1007/s10462-018-09676-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-018-09676-2

Keywords

Search

Navigation