Abstract
Metaheuristic approaches receive a great interest in the area of optimization, especially when exact methods are missing, or the cost is extremely high. Besides the possibility to report good solutions in reasonable time, metaheuristic techniques are widely applicable. There are diverse categories of techniques that differ in number of search agents (or solutions), solution representation, and movement mechanism in search space. Just mentioned ingredients are determined according to the motivation or inspiration philosophy behind the technique. Nature-inspired optimization category is very popular and has proven high efficiency in many problems. It contains famous subclasses like evolutionary algorithms, swarm intelligence, and single-based techniques. Famous and classical examples of each subclass are genetic algorithm, particle swarm, and ant colony optimization, and simulated annealing, respectively. Nature-inspired optimization family grows so fast, and many members have joined it recently, for example, emperor penguin colony (2019), seagull optimization algorithm (2019), sailfish optimizer (2019), pity beetle algorithm (2018), emperor penguin optimizer (2018), multi-objective artificial sheep algorithm (2018), salp swarm algorithm (2017), electromagnetic field optimization (2016), sine cosine algorithm (2016), moth-flame optimization (2015), grey wolf optimizer (2014), flower pollination algorithm (2012), bat algorithm (2010), cuckoo search algorithm (2009), firefly algorithm (2008), and many others. There are many proposed hybridization and cooperation methods between techniques to produce improved versions of original ones. Nature-inspired techniques have been used in many application areas like theoretical computer science, engineering and control, forecasting, medical field, finance, management, operation research, and others. Also, new scientific disciplines like renewable energy, robotics, and navigation are feasible areas to make use of nature-inspired techniques. This chapter sheds light on six so recently new techniques that belong to nature-inspired optimization class.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dey N (2018) Advancements in applied metaheuristic computing. IGI Global, Hershey, PA, 978-1
Khosravy M, Gupta N, Patel N, Senjyu T, Duque CA (2020) Particle swarm optimization of morphological filters for electrocardiogram baseline drift estimation. In: Dey N, Ashour AS, Bhattacharyya S (eds) Applied nature-inspired computing: algorithms and case studies. Springer, Singapore, pp 1–21
Moraes CA, De Oliveira, EJ, Khosravy, M, Oliveira, LW, Honório, LM, Pinto MF (2020) A hybrid bat-inspired algorithm for power transmission expansion planning on a practical Brazilian network. In: Dey N, Ashour AS, Bhattacharyya S (eds) Applied nature-inspired computing: algorithms and case studies. Springer, Singapore, pp 71–95
Sedaaghi MH, Khosravi M (2003) Morphological ECG signal preprocessing with more efficient baseline drift removal. In: Proceedings of the 7th IASTED international conference, ASC, pp 205–209
Khosravi M, Sedaaghi MH (2004) Impulsive noise suppression of electrocardiogram signals with mediated morphological filters. In: The 11th Iranian conference on biomedical engineering, Tehran, Iran, pp 207–212
Khosravy M, Asharif MR, Sedaaghi MH (2008) Medical image noise suppression: using mediated morphology. IEICE technical report, IEICE, pp 265–270
Khosravy M, Gupta N, Marina N, Sethi IK, Asharif MR (2017) Perceptual adaptation of image based on Chevreul-Mach bands visual phenomenon. IEEE Signal Process Lett 24(5):594–598
Khosravy M, Gupta N, Marina N, Sethi IK, Asharif MR (2017) Brain action inspired morphological image enhancement. Nature-inspired computing and optimization. Springer, Cham, pp 381–407
Gutierrez CE, Alsharif MR, Cuiwei H, Khosravy M, Villa R, Yamashita K, Miyagi H (2013) Uncover news dynamic by principal component analysis. ICIC Express Lett 7(4):1245–1250
Gutierrez CE, Alsharif MR, Khosravy M, Yamashita K, Miyagi H, Villa R (2014) Main large data set features detection by a linear predictor model. In: AIP conference proceedings, vol 1618, no 1, pp 733–737
Gutierrez CE, Alsharif MR, Yamashita K, Khosravy M (2014) A tweets mining approach to detection of critical events characteristics using random forest. Int J Next-Gener Comput 5(2):167–176
Khosravy M, Alsharif MR, Guo B, Lin H, Yamashita K (2009) A robust and precise solution to permutation indeterminacy and complex scaling ambiguity in BSS-based blind MIMO-OFDM receiver. In: International conference on independent component analysis and signal separation. Springer, Berlin, pp 670–677
Asharif F, Tamaki S, Alsharif MR, Ryu HG (2013) Performance improvement of constant modulus algorithm blind equalizer for 16 QAM modulation. Int J Innovative Comput Inf Control 7(4):1377–1384
Khosravy M, Alsharif MR, Yamashita K (2009) An efficient ICA based approach to multiuser detection in MIMO OFDM systems. Multi-carrier systems & solutions. Springer, Dordrecht, pp 47–56
Khosravy M, Alsharif MR, Khosravi M, Yamashita K (2010) An optimum pre-filter for ICA based multi-input multi-output OFDM system. In: 2010 2nd international conference on education technology and computer, vol 5. IEEE, pp V5-129
Khosravy M, Patel N, Gupta N, Sethi IK (2019) Image quality assessment: a review to full reference indexes. Recent trends in communication, computing, and electronics. Springer, Singapore, pp 279–288
Khosravy M, Asharif MR, Sedaaghi MH (2008) Morphological adult and fetal ECG preprocessing: employing mediated morphology (医用画像). 電子情報通信学会技術研究報告. MI, 医用画像 107(461):363–369
Sedaaghi MH, Daj R, Khosravi M (2001) Mediated morphological filters. In: Proceedings 2001 international conference on image processing (Cat. No. 01CH37205), vol 3. IEEE, pp 692–695
Khosravy M, Gupta N, Marina N, Sethi IK, Asharif MR (2017) Morphological filters: an inspiration from natural geometrical erosion and dilation. Nature-inspired computing and optimization. Springer, Cham, pp 349–379
Khosravy M, Asharif MR, Yamashita K (2009) A PDF-matched short-term linear predictability approach to blind source separation. Int J Innovative Comput Inf Control (IJICIC) 5(11):3677–3690
Khosravy M, Alsharif MR, Yamashita K (2009) A PDF-matched modification to stone’s measure of predictability for blind source separation. In: International symposium on neural networks. Springer, Berlin, pp 219–228
Khosravy M, Asharif MR, Yamashita K (2011) A theoretical discussion on the foundation of Stone’s blind source separation. Signal Image Video Process 5(3):379–388
Khosravy M, Asharif M, Yamashita K (2008) A probabilistic short-length linear predictability approach to blind source separation. In: 23rd international technical conference on circuits/systems, computers and communications (ITC-CSCC 2008), Yamaguchi, Japan, pp 381–384
Khosravy M, Kakazu S, Alsharif MR, Yamashita K (2010) Multiuser data separation for short message service using ICA (信号処理). 電子情報通信学会技術研究報告. SIP, 信号処理: IEICE Tech Rep 109(435):113–117
Khosravy M, Gupta N, Marina N, Asharif MR, Asharif F, Sethi IK (2015) Blind components processing a novel approach to array signal processing: a research orientation. In: 2015 international conference on intelligent informatics and biomedical sciences (ICIIBMS), IEEE, pp 20–26
Khosravy M, Punkoska N, Asharif F, Asharif MR (2014) Acoustic OFDM data embedding by reversible Walsh-Hadamard transform. In: AIP conference proceedings, vol 1618, no 1, pp 720–723
Holland J (1975) Adaptation in natural and artificial systems. University of Michigan Press. Ann Arbor, MI.
Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley
Beyer HG, Schwefel HP (2002) Evolution strategies—a comprehensive introduction. Nat Comput 1(1):3–52
Gupta N, Patel N, Tiwari BN, Khosravy M (2018) Genetic algorithm based on enhanced selection and log-scaled mutation technique. In: Proceedings of the future technologies conference. Springer, Cham, pp 730–748
Singh G, Gupta N, Khosravy M (2015) New crossover operators for real coded genetic algorithm (RCGA). In: 2015 international conference on intelligent informatics and biomedical sciences (ICIIBMS), IEEE, pp 135–140
Gupta N, Khosravy M, Patel N, Senjyu T (2018) A bi-level evolutionary optimization for coordinated transmission expansion planning. IEEE Access 6:48455–48477
Gupta N, Khosravy M, Patel N, Sethi IK (2018) Evolutionary optimization based on biological evolution in plants. Procedia Comput Sci 126:146–155
Gupta N, Khosravy M, Mahela OP, Patel N (2020) Plants biology inspired genetics algorithm: superior efficiency to firefly optimizer. In: Applications of firefly algorithm and its variants, from springer tracts in nature-inspired computing (STNIC). Springer International Publishing, in press
Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv (CSUR) 35(3):268–308
Fleischer M (2003) The measure of Pareto optima applications to multi-objective metaheuristics. In: International conference on evolutionary multi-criterion optimization. Springer, Berlin, pp 519–533
Talbi EG (2009) Metaheuristics: from design to implementation, vol 74. Wiley, Hoboken
Blum C, Roli A (2008) Hybrid metaheuristics: an introduction. Hybrid metaheuristics. Springer, Berlin, pp 1–30
Dixit A, Kumar S, Pant M, Bansal R (2018) Hybrid nature-inspired algorithms: methodologies, architecture, and reviews. In: International proceedings on advances in soft computing, intelligent systems and applications. Springer, Singapore, pp 299–306
Yang XS (2014) Nature-inspired optimization algorithms. Elsevier, Amsterdam
Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680
Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359
Dorigo M, Caro GD, Gambardella LM (1999) Ant algorithms for discrete optimization. Artif Life 5(2):137–172
Haddad OB, Afshar A, Mariño MA (2008) Honey-bee mating optimization (HBMO) algorithm in deriving optimal operation rules for reservoirs. J Hydroinformatics 10(3):257–264
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-tr06, vol 200. Erciyes University, Engineering Faculty, Computer Engineering Department, pp 1–10
Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, Piscataway, NJ, USA, pp 1942–1948
Noller C, Smith VR (1987) Ultraviolet selection pressure on earliest organisms. In: Kingston H, Fulling CP (eds) Natural environment background analysis. Oxford University Press, Oxford, pp 211–219
Yang XS (2008) Nature-inspired metaheuristic algorithms, 1st edn. Luniver Press, Bristol
Yang XS (2012) Bat algorithm for multi-objective optimisation. arXiv preprint arXiv:1203.6571
Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68
Yang XS (2012) Flower pollination algorithm for global optimization. In: International conference on unconventional computing and natural computation. Springer, Berlin, pp 240–249
Lindfield G, Penny J (2017) Introduction to nature-inspired optimization. Academic Press, Cambridge
Dorigo M, Stützle T (2004) Ant colony optimization. MIT Press, Cambridge
Passino KM (2010) Bacterial foraging optimization. Int J Swarm Intell Res (IJSIR) 1(1):1–16
Biswas A, Mishra KK, Tiwari S, Misra AK (2013) Physics-inspired optimization algorithms: a survey. J Optim
Bozorg-Haddad O (ed) (2018) Advanced optimization by nature-inspired algorithms. Springer, Singapore
Chu SC, Tsai PW (2007) Computational intelligence based on the behavior of cats. Int J Innovative Comput Inf Control 3(1):163–173
Kashan AH (2009) League championship algorithm: a new algorithm for numerical function optimization. In: 2009 international conference of soft computing and pattern recognition, IEEE, pp 43–48
Ahmadi-Javid A (2011) Anarchic society optimization: a human-inspired method. In: 2011 IEEE Congress of Evolutionary Computation (CEC), IEEE, pp 2586–2592
Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315
Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61
Abedinia O, Amjady N, Ghasemi A (2016) A new metaheuristic algorithm based on shark smell optimization. Complexity 21(5):97–116
Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98
Kuo RJ, Zulvia FE (2015) The gradient evolution algorithm: a new metaheuristic. Inf Sci 316:246–265
Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249
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
Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12
Yang XS (ed) (2017) Nature-inspired algorithms and applied optimization, vol 744. Springer, Berlin
Harifi S, Khalilian M, Mohammadzadeh J, Ebrahimnejad S (2019) Emperor penguins colony: a new metaheuristic algorithm for optimization. Evol Intel 12(2):211–226
Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: 2007 IEEE congress on evolutionary computation, IEEE, pp 4661–4667
Sang HY, Duan PY, Li JQ (2018) An effective invasive weed optimization algorithm for scheduling semiconductor final testing problem. Swarm Evol Comput 38:42–53
Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18
Mendenhall W, Beaver RJ, Barbara MB (2012) Introduction to probability and statistics. Cengage Learning, Boston
Dhiman G, Kumar V (2019) Seagull optimization algorithm: theory and its applications for large-scale industrial engineering problems. Knowl-Based Syst 165:169–196
Liang JJ, Suganthan PN, Deb K (2005) Novel composition test functions for numerical global optimization. In: Proceedings 2005 IEEE swarm intelligence symposium, SIS 2005, IEEE, pp 68–75
Chen Q, Liu B, Zhang Q, Liang J (2015) Evaluation criteria for CEC special session and competition on bound constrained single-objective computationally expensive numerical optimization. In: CEC
Dhiman G, Kumar V (2017) Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Adv Eng Softw 114:48–70
Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513
Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133
Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248
Wilcoxon F (1992) Individual comparisons by ranking methods. Breakthroughs in statistics. Springer, New York, NY, pp 196–202
Mann HB, Whitney DR (1947) On a test of whether one of two random variables is stochastically larger than the other. Ann Math Stat 50–60
Shadravan S, Naji HR, Bardsiri VK (2019) The sailfish optimizer: a novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Eng Appl Artif Intell 80:20–34
Moosavi SHS, Bardsiri VK (2017) Satin bowerbird optimizer: a new optimization algorithm to optimize ANFIS for software development effort estimation. Eng Appl Artif Intell 60:1–15
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
Wang GG (2003) Adaptive response surface method using inherited latin hypercube design points. J Mech Des 125(2):210–220
Liu J, Li Y (2012) An improved adaptive response surface method for structural reliability analysis. J Central South Univ 19:1148–1154
Cheng MY, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112
Siddall JN (1972) Analytical decision-making in engineering design. Prentice Hall, USA
Coello CAC (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Methods Appl Mech Eng 191(11–12):1245–1287
Ragsdell K, Phillips D (1976) Optimal design of a class of welded structures using geometric programming. ASME J Eng Ind 98:1021–1025
He Q, Wang L (2007) An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell 20(1):89–99
Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2013) Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems. Appl Soft Comput 13(5):2592–2612
Liu H, Cai Z, Wang Y (2010) Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl Soft Comput 10(2):629–640
Ray T, Saini P (2001) Engineering design optimization using a swarm with an intelligent information sharing among individuals. Eng Optim 33(6):735–748
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 110:151–166
Kallioras NA, Lagaros ND, Avtzis DN (2018) Pity beetle algorithm—a new metaheuristic inspired by the behavior of bark beetles. Adv Eng Softw 121:147–166
Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: 1998 IEEE international conference on evolutionary computation proceedings. IEEE world congress on computational intelligence (Cat. No. 98TH8360), IEEE, pp 69–73
Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73
Kennedy J, Mendes R (2002) Population structure and particle swarm performance. In: Proceedings of the 2002 Congress on Evolutionary Computation CEC’02 (Cat. No. 02TH8600), vol 2, IEEE, pp 1671–1676
Parsopoulos KE (2004) UPSO: a unified particle swarm optimization scheme. Lecture series on computer and computational science, vol 1, pp 868–873
Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evol Comput 8(3):204–210
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
Chen WN, Zhang J, Lin Y, Chen N, Zhan ZH, Chung HSH, Shi YH (2012) Particle swarm optimization with an aging leader and challengers. IEEE Trans Evol Comput 17(2):241–258
Chen D, Wang J, Zou F, Hou W, Zhao C (2012) An improved group search optimizer with operation of quantum-behaved swarm and its application. Appl Soft Comput 12(2):712–725
Loshchilov I, Stuetzle T, Liao T (2013) Ranking results of CEC’13 special session & competition on real-parameter single objective optimization. In: 2013 IEEE Congress on Evolutionary Computation (CEC), June, pp 20–23
Liang JJ, Qu BY, Suganthan PN (2013) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore, 635
Preux P, Munos R, Valko M (2014) Bandits attack function optimization. In: 2014 IEEE Congress on Evolutionary Computation (CEC), IEEE, pp 2245–2252
Yu C, Kelley L, Zheng S, Tan Y (2014) Fireworks algorithm with differential mutation for solving the CEC 2014 competition problems. In: 2014 IEEE Congress on Evolutionary Computation (CEC), IEEE, pp 3238–3245
Hu Z, Bao Y, Xiong T (2014) Partial opposition-based adaptive differential evolution algorithms: evaluation on the CEC 2014 benchmark set for real-parameter optimization. In: 2014 IEEE Congress on Evolutionary Computation (CEC), IEEE, pp 2259–2265
Tanabe R, Fukunaga AS (2014) Improving the search performance of SHADE using linear population size reduction. In: 2014 IEEE Congress on Evolutionary Computation (CEC), IEEE, pp 1658–1665
Dhiman G, Kumar V (2018) Emperor penguin optimizer: a bio-inspired algorithm for engineering problems. Knowl-Based Syst 159:20–50
Chen Q, Liu B, Zhang Q, Liang J, Suganthan P, Qu B (2014) Problem definitions and evaluation criteria for CEC 2015 special session on bound constrained single-objective computationally expensive numerical optimization. Technical report, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Technical Report, Nanyang Technological University
Lai X, Li C, Zhang N, Zhou J (2018) A multi-objective artificial sheep algorithm. Neural Comput Appl 1–35
Wang W, Li C, Liao X, Qin H (2017) Study on unit commitment problem considering pumped storage and renewable energy via a novel binary artificial sheep algorithm. Appl Energy 187:612–626
Mirjalili S, Saremi S, Mirjalili SM, Coelho LDS (2016) Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Syst Appl 47:106–119
Coello CC, Lechuga MS (2002) MOPSO: A proposal for multiple objective particle swarm optimization. In: Proceedings of the 2002 Congress on Evolutionary Computation, CEC’02 (Cat. No. 02TH8600), vol 2. IEEE, pp 1051–1056
Deb K, Pratap A, Agarwal S, Meyarivan TAMT (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197
Zhang Q, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731
Zhang Q, Zhou A, Zhao S, Suganthan PN, Liu W, Tiwari S (2009) Multi-objective optimization test instances for the CEC 2009 special session and competition. University of Essex
Sierra MR, Coello CAC (2005) Improving PSO-based multi-objective optimization using crowding, mutation and ∈-dominance. In: Evolutionary multi-criterion optimization. Springer, Berlin
Su YX, Chi R (2017) Multi-objective particle swarm-differential evolution algorithm. Neural Comput Appl 28(2):1–12
Zitzler E, Deb K, Thiele L (2000) Comparison of multiobjective evolutionary algorithms: empirical results. Evol Comput 8(2):173–195
Basu M (2004) An interactive fuzzy satisfying method based on evolutionary programming technique for multiobjective short-term hydrothermal scheduling. Electr Power Syst Res 69(2–3):277–285
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Helmi, A.M., Lotfy, M.E. (2020). Recent Advances of Nature-Inspired Metaheuristic Optimization. In: Khosravy, M., Gupta, N., Patel, N., Senjyu, T. (eds) Frontier Applications of Nature Inspired Computation. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-15-2133-1_1
Download citation
DOI: https://doi.org/10.1007/978-981-15-2133-1_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-2132-4
Online ISBN: 978-981-15-2133-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)